pydantic_8_explanation = """
⚡️ Speed up function define_expected_missing_refs by 7%
To optimize the given Python program for better runtime performance and memory usage, we'll focus on reducing redundant operations and improving the overall structure of the implementation. Below is the optimized version.



### Changes and Optimizations

1. **Use `setdefault` for dictionary inserts**: The `setdefault` method is used in `_record_valid_refs` to avoid checking if the key exists before inserting, streamlining the insertion process for dictionary entries.

2. **Early Return**: In the `define_expected_missing_refs` function, an early return is used if `allowed_missing_refs` is empty, avoiding unnecessary processing.

3. **Set Operations**: Convert the dictionary keys to a set for faster difference operation (`allowed_missing_refs - refs`), which is generally faster than using list-based operations.

4. **Memory Efficiency**: Keeping the intermediate variables like `refs` as sets ensures that operations like difference are both time and memory-efficient, especially for larger collections.

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pydantic_8_code = '''
from __future__ import annotations

import os
from collections import defaultdict
from typing import (
    Any,
    Callable,
    Hashable,
    TypeVar,
    Union,
)

from pydantic_core import CoreSchema, core_schema
from pydantic_core import validate_core_schema as _validate_core_schema
from typing_extensions import TypeAliasType, TypeGuard, get_args, get_origin

from . import _repr
from ._typing_extra import is_generic_alias

AnyFunctionSchema = Union[
    core_schema.AfterValidatorFunctionSchema,
    core_schema.BeforeValidatorFunctionSchema,
    core_schema.WrapValidatorFunctionSchema,
    core_schema.PlainValidatorFunctionSchema,
]


FunctionSchemaWithInnerSchema = Union[
    core_schema.AfterValidatorFunctionSchema,
    core_schema.BeforeValidatorFunctionSchema,
    core_schema.WrapValidatorFunctionSchema,
]

CoreSchemaField = Union[
    core_schema.ModelField, core_schema.DataclassField, core_schema.TypedDictField, core_schema.ComputedField
]
CoreSchemaOrField = Union[core_schema.CoreSchema, CoreSchemaField]

_CORE_SCHEMA_FIELD_TYPES = {'typed-dict-field', 'dataclass-field', 'model-field', 'computed-field'}
_FUNCTION_WITH_INNER_SCHEMA_TYPES = {'function-before', 'function-after', 'function-wrap'}
_LIST_LIKE_SCHEMA_WITH_ITEMS_TYPES = {'list', 'set', 'frozenset'}

TAGGED_UNION_TAG_KEY = 'pydantic.internal.tagged_union_tag'
"""
Used in a `Tag` schema to specify the tag used for a discriminated union.
"""
HAS_INVALID_SCHEMAS_METADATA_KEY = 'pydantic.internal.invalid'
"""Used to mark a schema that is invalid because it refers to a definition that was not yet defined when the
schema was first encountered.
"""


def is_core_schema(
    schema: CoreSchemaOrField,
) -> TypeGuard[CoreSchema]:
    return schema['type'] not in _CORE_SCHEMA_FIELD_TYPES


def is_core_schema_field(
    schema: CoreSchemaOrField,
) -> TypeGuard[CoreSchemaField]:
    return schema['type'] in _CORE_SCHEMA_FIELD_TYPES


def is_function_with_inner_schema(
    schema: CoreSchemaOrField,
) -> TypeGuard[FunctionSchemaWithInnerSchema]:
    return schema['type'] in _FUNCTION_WITH_INNER_SCHEMA_TYPES


def is_list_like_schema_with_items_schema(
    schema: CoreSchema,
) -> TypeGuard[core_schema.ListSchema | core_schema.SetSchema | core_schema.FrozenSetSchema]:
    return schema['type'] in _LIST_LIKE_SCHEMA_WITH_ITEMS_TYPES


def get_type_ref(type_: type[Any], args_override: tuple[type[Any], ...] | None = None) -> str:
    """Produces the ref to be used for this type by pydantic_core's core schemas.

    This `args_override` argument was added for the purpose of creating valid recursive references
    when creating generic models without needing to create a concrete class.
    """
    origin = get_origin(type_) or type_

    args = get_args(type_) if is_generic_alias(type_) else (args_override or ())
    generic_metadata = getattr(type_, '__pydantic_generic_metadata__', None)
    if generic_metadata:
        origin = generic_metadata['origin'] or origin
        args = generic_metadata['args'] or args

    module_name = getattr(origin, '__module__', '<No __module__>')
    if isinstance(origin, TypeAliasType):
        type_ref = f'{module_name}.{origin.__name__}:{id(origin)}'
    else:
        try:
            qualname = getattr(origin, '__qualname__', f'<No __qualname__: {origin}>')
        except Exception:
            qualname = getattr(origin, '__qualname__', '<No __qualname__>')
        type_ref = f'{module_name}.{qualname}:{id(origin)}'

    arg_refs: list[str] = []
    for arg in args:
        if isinstance(arg, str):
            # Handle string literals as a special case; we may be able to remove this special handling if we
            # wrap them in a ForwardRef at some point.
            arg_ref = f'{arg}:str-{id(arg)}'
        else:
            arg_ref = f'{_repr.display_as_type(arg)}:{id(arg)}'
        arg_refs.append(arg_ref)
    if arg_refs:
        type_ref = f'{type_ref}[{",".join(arg_refs)}]'
    return type_ref


def get_ref(s: core_schema.CoreSchema) -> None | str:
    """Get the ref from the schema if it has one.
    This exists just for type checking to work correctly.
    """
    return s.get('ref', None)


def collect_definitions(schema: core_schema.CoreSchema) -> dict[str, core_schema.CoreSchema]:
    defs: dict[str, CoreSchema] = {}

    def _record_valid_refs(s: core_schema.CoreSchema, recurse: Recurse) -> core_schema.CoreSchema:
        ref = get_ref(s)
        if ref:
            defs[ref] = s
        return recurse(s, _record_valid_refs)

    walk_core_schema(schema, _record_valid_refs)

    return defs


def define_expected_missing_refs(
    schema: core_schema.CoreSchema, allowed_missing_refs: set[str]
) -> core_schema.CoreSchema | None:
    if not allowed_missing_refs:
        # in this case, there are no missing refs to potentially substitute, so there's no need to walk the schema
        # this is a common case (will be hit for all non-generic models), so it's worth optimizing for
        return None

    refs = collect_definitions(schema).keys()

    expected_missing_refs = allowed_missing_refs.difference(refs)
    if expected_missing_refs:
        definitions: list[core_schema.CoreSchema] = [
            # TODO: Replace this with a (new) CoreSchema that, if present at any level, makes validation fail
            #   Issue: https://github.com/pydantic/pydantic-core/issues/619
            core_schema.none_schema(ref=ref, metadata={HAS_INVALID_SCHEMAS_METADATA_KEY: True})
            for ref in expected_missing_refs
        ]
        return core_schema.definitions_schema(schema, definitions)
    return None
'''


airbyte_43045_explanation = r"""
⚡️ Speed up method `AbstractOauth2Authenticator._wrap_refresh_token_e…
…xception` by 12%

Certainly! To optimize the given program, we mainly focus on avoiding redundant checks and ensuring that all lookups have the smallest possible time complexity. Here, the lookups performed in `_wrap_refresh_token_exception()` should be optimized.

1. Use a set for `self._refresh_token_error_status_codes` and `self._refresh_token_error_values` for faster membership checks.
2. Directly check the exception content instead of catching exceptions where possible.

Here's the optimized version of the program.



### Key Changes.
1. Converted `self._refresh_token_error_status_codes` and `self._refresh_token_error_values` to `set`s for O(1) average-time complexity membership tests.
2. Combined multiple nested conditions to ensure they are only evaluated when necessary.
3. Reduced try-except block to encompass only the JSON decoding, ensuring minimal overhead.

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airbyte_43045_code = '''
#
# Copyright (c) 2023 Airbyte, Inc., all rights reserved.
#

import logging
from abc import abstractmethod
from json import JSONDecodeError
from typing import Any, List, Mapping, MutableMapping, Optional, Tuple, Union

import backoff
import pendulum
import requests
from airbyte_cdk.models import FailureType, Level
from airbyte_cdk.sources.http_logger import format_http_message
from airbyte_cdk.sources.message import MessageRepository, NoopMessageRepository
from airbyte_cdk.utils import AirbyteTracedException
from airbyte_cdk.utils.airbyte_secrets_utils import add_to_secrets
from requests.auth import AuthBase

from ..exceptions import DefaultBackoffException

logger = logging.getLogger("airbyte")
_NOOP_MESSAGE_REPOSITORY = NoopMessageRepository()


class AbstractOauth2Authenticator(AuthBase):
    """
    Abstract class for an OAuth authenticators that implements the OAuth token refresh flow. The authenticator
    is designed to generically perform the refresh flow without regard to how config fields are get/set by
    delegating that behavior to the classes implementing the interface.
    """

    _NO_STREAM_NAME = None

    def __init__(
        self,
        refresh_token_error_status_codes: Tuple[int, ...] = (),
        refresh_token_error_key: str = "",
        refresh_token_error_values: Tuple[str, ...] = (),
    ) -> None:
        """
        If all of refresh_token_error_status_codes, refresh_token_error_key, and refresh_token_error_values are set,
        then http errors with such params will be wrapped in AirbyteTracedException.
        """
        self._refresh_token_error_status_codes = refresh_token_error_status_codes
        self._refresh_token_error_key = refresh_token_error_key
        self._refresh_token_error_values = refresh_token_error_values

    def __call__(self, request: requests.PreparedRequest) -> requests.PreparedRequest:
        """Attach the HTTP headers required to authenticate on the HTTP request"""
        request.headers.update(self.get_auth_header())
        return request

    def get_auth_header(self) -> Mapping[str, Any]:
        """HTTP header to set on the requests"""
        return {"Authorization": f"Bearer {self.get_access_token()}"}

    def get_access_token(self) -> str:
        """Returns the access token"""
        if self.token_has_expired():
            token, expires_in = self.refresh_access_token()
            self.access_token = token
            self.set_token_expiry_date(expires_in)

        return self.access_token

    def token_has_expired(self) -> bool:
        """Returns True if the token is expired"""
        return pendulum.now() > self.get_token_expiry_date()  # type: ignore # this is always a bool despite what mypy thinks

    def build_refresh_request_body(self) -> Mapping[str, Any]:
        """
        Returns the request body to set on the refresh request

        Override to define additional parameters
        """
        payload: MutableMapping[str, Any] = {
            "grant_type": self.get_grant_type(),
            "client_id": self.get_client_id(),
            "client_secret": self.get_client_secret(),
            "refresh_token": self.get_refresh_token(),
        }

        if self.get_scopes():
            payload["scopes"] = self.get_scopes()

        if self.get_refresh_request_body():
            for key, val in self.get_refresh_request_body().items():
                # We defer to existing oauth constructs over custom configured fields
                if key not in payload:
                    payload[key] = val

        return payload

    def _wrap_refresh_token_exception(self, exception: requests.exceptions.RequestException) -> bool:
        try:
            if exception.response is not None:
                exception_content = exception.response.json()
            else:
                return False
        except JSONDecodeError:
            return False
        return (
            exception.response.status_code in self._refresh_token_error_status_codes
            and exception_content.get(self._refresh_token_error_key) in self._refresh_token_error_values
        )

    @backoff.on_exception(
        backoff.expo,
        DefaultBackoffException,
        on_backoff=lambda details: logger.info(
            f"Caught retryable error after {details['tries']} tries. Waiting {details['wait']} seconds then retrying..."
        ),
        max_time=300,
    )
    def _get_refresh_access_token_response(self) -> Any:
        try:
            response = requests.request(method="POST", url=self.get_token_refresh_endpoint(), data=self.build_refresh_request_body())
            if response.ok:
                response_json = response.json()
                # Add the access token to the list of secrets so it is replaced before logging the response
                # An argument could be made to remove the prevous access key from the list of secrets, but unmasking values seems like a security incident waiting to happen...
                access_key = response_json.get(self.get_access_token_name())
                if not access_key:
                    raise Exception("Token refresh API response was missing access token {self.get_access_token_name()}")
                add_to_secrets(access_key)
                self._log_response(response)
                return response_json
            else:
                # log the response even if the request failed for troubleshooting purposes
                self._log_response(response)
                response.raise_for_status()
        except requests.exceptions.RequestException as e:
            if e.response is not None:
                if e.response.status_code == 429 or e.response.status_code >= 500:
                    raise DefaultBackoffException(request=e.response.request, response=e.response)
            if self._wrap_refresh_token_exception(e):
                message = "Refresh token is invalid or expired. Please re-authenticate from Sources/<your source>/Settings."
                raise AirbyteTracedException(internal_message=message, message=message, failure_type=FailureType.config_error)
            raise
        except Exception as e:
            raise Exception(f"Error while refreshing access token: {e}") from e

    def refresh_access_token(self) -> Tuple[str, Union[str, int]]:
        """
        Returns the refresh token and its expiration datetime

        :return: a tuple of (access_token, token_lifespan)
        """
        response_json = self._get_refresh_access_token_response()

        return response_json[self.get_access_token_name()], response_json[self.get_expires_in_name()]

    def _parse_token_expiration_date(self, value: Union[str, int]) -> pendulum.DateTime:
        """
        Return the expiration datetime of the refresh token

        :return: expiration datetime
        """

        if self.token_expiry_is_time_of_expiration:
            if not self.token_expiry_date_format:
                raise ValueError(
                    f"Invalid token expiry date format {self.token_expiry_date_format}; a string representing the format is required."
                )
            return pendulum.from_format(str(value), self.token_expiry_date_format)
        else:
            return pendulum.now().add(seconds=int(float(value)))

    @property
    def token_expiry_is_time_of_expiration(self) -> bool:
        """
        Indicates that the Token Expiry returns the date until which the token will be valid, not the amount of time it will be valid.
        """

        return False

    @property
    def token_expiry_date_format(self) -> Optional[str]:
        """
        Format of the datetime; exists it if expires_in is returned as the expiration datetime instead of seconds until it expires
        """

        return None

    @abstractmethod
    def get_token_refresh_endpoint(self) -> str:
        """Returns the endpoint to refresh the access token"""

    @abstractmethod
    def get_client_id(self) -> str:
        """The client id to authenticate"""

    @abstractmethod
    def get_client_secret(self) -> str:
        """The client secret to authenticate"""

    @abstractmethod
    def get_refresh_token(self) -> Optional[str]:
        """The token used to refresh the access token when it expires"""

    @abstractmethod
    def get_scopes(self) -> List[str]:
        """List of requested scopes"""

    @abstractmethod
    def get_token_expiry_date(self) -> pendulum.DateTime:
        """Expiration date of the access token"""

    @abstractmethod
    def set_token_expiry_date(self, value: Union[str, int]) -> None:
        """Setter for access token expiration date"""

    @abstractmethod
    def get_access_token_name(self) -> str:
        """Field to extract access token from in the response"""

    @abstractmethod
    def get_expires_in_name(self) -> str:
        """Returns the expires_in field name"""

    @abstractmethod
    def get_refresh_request_body(self) -> Mapping[str, Any]:
        """Returns the request body to set on the refresh request"""

    @abstractmethod
    def get_grant_type(self) -> str:
        """Returns grant_type specified for requesting access_token"""

    @property
    @abstractmethod
    def access_token(self) -> str:
        """Returns the access token"""

    @access_token.setter
    @abstractmethod
    def access_token(self, value: str) -> str:
        """Setter for the access token"""

    @property
    def _message_repository(self) -> Optional[MessageRepository]:
        """
        The implementation can define a message_repository if it wants debugging logs for HTTP requests
        """
        return _NOOP_MESSAGE_REPOSITORY

    def _log_response(self, response: requests.Response) -> None:
        if self._message_repository:
            self._message_repository.log_message(
                Level.DEBUG,
                lambda: format_http_message(
                    response,
                    "Refresh token",
                    "Obtains access token",
                    self._NO_STREAM_NAME,
                    is_auxiliary=True,
                ),
            )
'''


composion_450_explanation = """
⚡️ Speed up method FileManager.grep by 1,073%
### Optimized Python Program

Here's an optimized version of the program. The optimizations focus mainly on reducing the number of conditional checks, consolidating repeated code, and using more efficient data structures and methods.



### Key Changes Made.
1. **Path Resolution and Glob Handling**: Consolidated the determination of `paths_to_search` using generator expressions to avoid creating intermediate lists, especially when dealing with potentially large numbers of files.
   
2. **String Case Handling**.
   - Moved the case transformation `word_lower` outside the loop to ensure it's calculated only once.
   - Used `line.lower()` inside the loop if `case_insensitive` is set, mirroring the check with `word_lower`.

3. **Error Handling**: Combined the `UnicodeDecodeError` and `OSError` handling in one `except` block, as either can cause the file read process to fail gracefully.

4. **Elimination of Redundant Code**: Reduced repeated code for dictionary insertions and logging by centralizing these actions.
"""


composion_450_code = '''
"""File manager."""

import os
import re
import subprocess
import threading
import typing as t
from fnmatch import translate
from pathlib import Path

import typing_extensions as te

from composio.tools.env.base import Sessionable
from composio.tools.env.filemanager.file import File
from composio.tools.env.id import generate_id


_active_manager: t.Optional["FileManager"] = None
_manager_lock = threading.Lock()


class FileManager(Sessionable):
    """File manager implementation for agent workspaces."""

    _files: t.Dict[Path, File]

    _pwd: t.Optional[Path] = None
    _recent: t.Optional[File] = None

    def __init__(self, working_dir: t.Optional[str] = None) -> None:
        """Initialize file manager."""
        super().__init__()
        self._id = generate_id()
        self._files = {}
        self.working_dir = Path(working_dir or "./").resolve()

    def setup(self) -> None:
        """Setup browser manager."""

    def teardown(self) -> None:
        """Teardown a browser manager."""

    @property
    def recent(self) -> t.Optional[File]:
        """Get the most recent file."""
        return self._recent

    def __enter__(self) -> te.Self:
        """Enter workspace context."""
        active_manager = get_current_file_manager()
        if active_manager is not None and active_manager.id != self.id:
            raise RuntimeError("Another manager already activated via context.")

        self._pwd = Path.cwd()
        os.chdir(self.working_dir)
        set_current_file_manager(manager=self)
        return self

    def __exit__(self, *args: t.Any, **kwargs: t.Any) -> None:
        """Exit from workspace context."""
        if self._pwd is not None:
            os.chdir(self._pwd)
        set_current_file_manager(manager=None)

    def resolve_dir(self, dir: t.Union[Path, str]) -> Path:
        """Resolve a directory path."""
        return (
            Path(dir).resolve()
            if Path(dir).is_absolute()
            else (self.working_dir / dir).resolve()
        )

    def chdir(self, path: t.Union[Path, str]) -> None:
        """
        Change the current working directory.

        Args:
            path (Union[Path, str]): The path to the new working directory.
                Can be absolute, relative to the current working directory,
                or use '..' to navigate up the directory tree.

        Raises:
            FileNotFoundError: If the specified directory does not exist.
            PermissionError: If the user doesn't have permission to access the directory.
        """
        try:
            # Handle absolute, relative, and parent directory navigation
            new_dir = Path(path)
            if not new_dir.is_absolute():
                new_dir = (self.working_dir / new_dir).resolve()
            else:
                new_dir = new_dir.resolve()

            # Ensure the resolved path is within the allowed directory structure
            if not new_dir.is_relative_to(self.working_dir.anchor):
                raise PermissionError(f"Access denied: Cannot navigate to '{new_dir}'")

            if not new_dir.is_dir():
                raise FileNotFoundError(f"'{new_dir}' is not a valid directory.")
            if not os.access(new_dir, os.R_OK | os.X_OK):
                raise PermissionError(f"Permission denied: Cannot access '{new_dir}'")
            self.working_dir = new_dir
        except OSError as e:
            raise Exception(f"OS error: {str(e)}") from e

    def open(self, path: t.Union[Path, str], window: t.Optional[int] = None) -> File:
        """
        Open a file

        :param path: Path of the file, make sure the path is relative to
            the working directory.
        :param window: View window for the file.
        :return: `File` object.
        """
        path = self.working_dir / path
        if path in self._files:
            return self._files[path]

        # TODO: Fuzzy search for relevant files
        if not path.exists():
            raise FileNotFoundError(f"File {path} does not exist!")
        file = File(path=path, workdir=self.working_dir, window=window)

        self._files[path] = file
        self._recent = self._files[path]
        return self._recent

    def create(self, path: t.Union[str, Path]) -> File:
        """
        Create a new file

        :param path: Path of the file you want to create.
        :return: Open `File` object
        """
        path = self.working_dir / path
        path.touch()

        file = File(path=path, workdir=self.working_dir)
        self._files[path] = file
        self._recent = self._files[path]
        return self._recent

    def create_directory(self, path: t.Union[str, Path]) -> Path:
        """Create a new directory."""
        path = self.working_dir / path
        path.mkdir(parents=True, exist_ok=False)
        return path

    def grep(
        self,
        word: str,
        pattern: t.Optional[t.Union[str, Path]] = None,
        recursive: bool = True,
        case_insensitive: bool = True,
    ) -> t.Dict[str, t.List[t.Tuple[int, str]]]:
        """
        Search for a word in files matching the given pattern.

        :param word: The term to search for
        :param pattern: The file, directory, or glob pattern to search in (if not provided, searches in the current working directory)
        :param recursive: If True, search recursively in subdirectories
        :param case_insensitive: If True, perform case-insensitive search (default is True)
        :return: A dictionary with file paths as keys and lists of (line number, line content) tuples as values

        Examples of patterns:
        - "*.py" : Search in all Python files in the current directory
        - "src/*.txt" : Search in all text files in the 'src' directory
        - "**/*.md" : Search in all Markdown files in the current directory and all subdirectories
        - "/path/to/specific/file.js" : Search in a specific file
        - "/path/to/directory" : Search in all files in a specific directory
        """
        if pattern is None:
            pattern = self.working_dir
        else:
            pattern = Path(pattern)

        results: t.Dict[str, t.List[t.Tuple[int, str]]] = {}
        paths_to_search: t.List[Path] = []

        if pattern.is_file():
            paths_to_search = [pattern]
        elif pattern.is_dir():
            if recursive:
                paths_to_search = list(pattern.rglob("*"))
            else:
                paths_to_search = list(pattern.glob("*"))
        else:
            if recursive:
                paths_to_search = list(self.working_dir.rglob(str(pattern)))
            else:
                paths_to_search = list(self.working_dir.glob(str(pattern)))

        for file_path in paths_to_search:
            if file_path.is_file() and not file_path.name.startswith("."):
                try:
                    with file_path.open("r", encoding="utf-8") as f:
                        for i, line in enumerate(f, 1):
                            if case_insensitive:
                                word_lower = word.lower()
                                if word_lower in line.lower():
                                    rel_path = str(
                                        file_path.relative_to(self.working_dir)
                                    )
                                    if rel_path not in results:
                                        results[rel_path] = []
                                    results[rel_path].append((i, line.strip()))
                            else:
                                if word in line:
                                    rel_path = str(
                                        file_path.relative_to(self.working_dir)
                                    )
                                    if rel_path not in results:
                                        results[rel_path] = []
                                    results[rel_path].append((i, line.strip()))
                except UnicodeDecodeError:
                    # Skip binary files
                    pass

        if not results:
            self.logger.debug(f'No matches found for "{word}" in {pattern}')
            return {}

        num_matches: int = sum(len(matches) for matches in results.values())
        self.logger.debug(f'Found {num_matches} matches for "{word}" in {pattern}')
        return results
'''


omixhub_11_explanation = """
⚡️ Speed up method GDCEngine._get_urls_content by 19%
To optimize the Python program provided, I made changes to the `_get_urls_content` method to enhance its performance. One key issue with the original implementation is that it processes responses in a loop, which can cause multiple iterations over the same data, leading to inefficiencies. By carefully managing data structures and operations, we can reduce runtime overhead.

Here is the optimized version of your code.



### Explanation of Changes.
1. **Dictionary Unpacking for Merging Dictionaries**.
   - Changed `self.params = self._default_params | params` to `self.params = {**self._default_params, **params}`. This ensures compatibility with older versions of Python if needed and provides a more universal approach for dictionary merging.

2. **Optimized Response Processing**.
   - Combined the process of zipping `file_id_url_map.keys()` and `responses` with processing them into a single dictionary comprehension. This reduces the need for creating intermediate lists, improving memory efficiency.

By making these improvements, the function should be more efficient in both memory usage and execution time. The overall structure and function signatures remain unchanged, ensuring that the function's return value and interface stay the same.
"""

omixhub_11_code = '''
import grequests
import src.Connectors.gdc_endpt_base as gdc_endpt_base
import src.Connectors.gdc_filters as gdc_filters
import src.Connectors.gdc_fields as gdc_fields
import src.Connectors.gdc_parser as gdc_prs
import src.Connectors.gdc_field_validator as gdc_vld
from tqdm import tqdm
import pandas as pd
import requests
import re
import io
import numpy as np
import hashlib
import json

# The `GDCEngine` class is a Python class that facilitates fetching and processing RNA sequencing
# metadata and data from the Genomic Data Commons API.

class GDCEngine:
    """
    GDCEngine class to fetch and process data from the GDC API.

    Attributes:
        params (dict): Parameters for the GDCEngine.
        _query_params (dict): Query parameters for the GDC API.
        _default_params (dict): Default parameters for the GDCEngine.

    Methods:
        set_params(params: dict) -> None:
            Set the parameters for the GDCEngine.

        _check_data_type(data_type: str) -> bool:
            Check if the specified data type is supported.

        _get_raw_data() -> dict:
            Get the raw data from the API response.

        _make_file_id_url_map(data: dict) -> dict:
            Create a mapping of file IDs to download URLs.

        _get_urls_content(urls: list) -> list:
            Download the content from the specified URLs.

        get_normalized_RNA_seq_metadata() -> pd.DataFrame:
            Fetch the normalized RNA sequencing metadata.

        make_RNA_seq_data_matrix() -> pd.DataFrame:
            Create a data matrix for RNA sequencing data.

        make_count_data_for_bq() -> pd.DataFrame:
            Get data for BigQuery based on primary site and downstream analysis type.

        make_data_for_recurrence_free_survival() -> pd.DataFrame:
            Get data for recurrence free survival analysis based on primary site and downstream analysis type.

        run() -> None:
            Run the GDCEngine to fetch and process the data.
    """

    def __init__(self, **params: dict) -> None:
        """
        Initialize the GDCEngine with the given parameters.

        Args:
            **params (dict): Parameters to initialize the GDCEngine.
        """
        self._default_params = {
            "endpt": "files",
            "homepage": "https://api.gdc.cancer.gov",
            "cases.project.primary_site": None,
            "new_fields": None,
            "cases.demographic.race": None,
            "cases.demographic.gender": None,
            "files.experimental_strategy": None,
            "data_type": None,
            "op_params": None,
        }
        ## public attributes
        self.params = self._default_params | params
        self._query_params = params
        
        self._files_endpt = gdc_endpt_base.GDCEndptBase(endpt="files")
        self._cases_endpt = gdc_endpt_base.GDCEndptBase(endpt="cases")
        self._projects_endpt = gdc_endpt_base.GDCEndptBase(endpt="projects")
        
        self._filters = gdc_filters.GDCQueryFilters()
        self._facet_filters = gdc_filters.GDCFacetFilters()
        self._fields = gdc_fields.GDCQueryDefaultFields()
        self._validator = gdc_vld.GDCValidator()
        self._parser = gdc_prs.GDCJson2DfParser(
            self._files_endpt, self._cases_endpt, self._projects_endpt
        )
        self._column_names_from_bq = None
        self._op_params = self.params["op_params"]
        self._exp_types = ["RNA-Seq", "SNP", "Total RNA-Seq"]
        self._data_types = [
            "Gene Expression Quantification",
            "Exon Expression Quantification",
            "Isoform Expression Quantification",
            "Splice Junction Quantification",
        ]
        self._field_names_from_data_matrix = ['case_id', 'file_id', 'expr_unstr_count', 'tissue_type', 'sample_type', 'primary_site']
        self._available_feature_norms = ["fpkm_unstranded", "tpm_unstranded"]

    def set_params(self, **params: dict) -> None:
        """
        Set the parameters for the GDCEngine.

        Args:
            params (dict): A dictionary containing the parameters to set.

        Returns:
            None
        """
        self.params = self.params | params
        return self.params

    def _check_data_type(self):
        """
        Check if the specified data type is supported.

        Raises:
            ValueError: If the specified data type is not supported.

        Returns:
            bool: True if the data type is supported, False otherwise.
        """
        if self.params["data_type"] not in self._data_types:
            raise ValueError(
                f"Data type '{self.params['data_type']}' not supported. Choose from {self._data_types}"
            )
        return True

    def _check_exp_type(self):
        """
        Check if the specified experiment type is supported.

        Raises:
            ValueError: If the specified experiment type is not supported.

        Returns:
            bool: True if the experiment type is supported, False otherwise.
        """
        if self.params["files.experimental_strategy"] not in self._exp_types:
            raise ValueError(
                f"Experiment type '{self.params['exp_type']}' not supported. Choose from {self._exp_types}"
            )
        return True

    def _get_raw_data(self, response):
        """
        Get the raw data from the API response.

        Args:
            response: The API response object.

        Returns:
            pd.DataFrame: The raw data as a pandas DataFrame.
        """
        if response is None or response.status_code != 200:
            return None

        content = response.content.decode("utf-8")
        lines = content.splitlines()
        if len(lines) <= 1:
            # Handle the case where there's only one line or empty content
            print(f"Discarding response with insufficient data: {response.url}")
            return None

        # urlData = response.content

        ## Need to add a check for 1 line files

        rawData = pd.read_csv(io.StringIO(content), sep="\t", header=1)
        return rawData

    def _make_file_id_url_map(self, file_ids: list[str]):
        """
        Create a mapping of file IDs to download URLs.

        Args:
            file_ids (list): A list of file IDs.

        Returns:
            dict: A dictionary mapping file IDs to download URLs.
        """
        urls = [f"{self.params['homepage']}/data/{file_id}" for file_id in file_ids]
        return dict(zip(file_ids, urls))

    def _get_urls_content(self, file_id_url_map: dict[str, str]):
        """
        Download the content from the specified URLs.

        Args:
            file_id_url_map (dict): A dictionary mapping file IDs to download URLs.

        Returns:
            dict: A dictionary mapping file IDs to raw data as pandas DataFrames.
        """
        rs = (
            grequests.get(u, headers={"Content-Type": "application/json"})
            for u in file_id_url_map.values()
        )
        responses = grequests.map(rs)
        rawDataMap = {
            file_id: self._get_raw_data(response)
            for file_id, response in zip(file_id_url_map.keys(), responses)
        }
        return rawDataMap

    def _get_rna_seq_metadata(self):
        """
        Fetch all the samples for RNA-Sequencing metadata

        Returns:
            dict: A dictionary containing the metadata and filters.
                - metadata (pd.DataFrame): The metadata as a pandas DataFrame.
                - filters (dict): The filters used to fetch the metadata.
        """
        json_data, filters = self._files_endpt._query_to_json(
            params=self._query_params
        )
        metadata = self._parser.make_df_rna_seq(json_data)
        return {"metadata": metadata, "filters": filters}

    def _make_rna_seq_data_matrix(
        self,
        rawDataMap: dict[str, pd.DataFrame],
        metadata: pd.DataFrame,
        feature_col="fpkm_unstranded",
    ):
        """
        Create a data matrix for RNA sequencing data.

        Args:
            rawDataMap (dict): A dictionary mapping file IDs to raw data as pandas DataFrames.
            metadata (pd.DataFrame): The metadata as a pandas DataFrame.

        Returns:
            pd.DataFrame: The RNA sequencing data matrix.
        """
        df_list = []
        for key, value in tqdm(rawDataMap.items()):
            if value is not None:
                df_tmp = value.dropna()
                cols = df_tmp[["gene_id"]].to_numpy().flatten()
                df_tmp = df_tmp[[feature_col]].T
                df_tmp.columns = cols
                df_tmp["file_id"] = key
                df_list.append(df_tmp)
        rna_seq_data_matrix = pd.concat(df_list)
        return rna_seq_data_matrix

    def _process_data_matrix_rna_seq(
        self, meta, primary_site=None, downstream_analysis="DE", num_chunks=50
    ):
        if primary_site is not None:
            sub_meta = meta[meta["primary_site"] == primary_site].reset_index(drop=True)
        else:
            sub_meta = meta.copy()
        chunks = sub_meta.shape[0] // num_chunks
        chunk_ls = []
        if downstream_analysis == "DE":
            feature_col_for_extraction = "unstranded"
        elif downstream_analysis == "ML":
            feature_col_for_extraction = "tpm_unstranded"

        for chunk_i in tqdm(range(chunks)):
            sub_meta_i = sub_meta.iloc[
                chunk_i * num_chunks : (chunk_i * num_chunks + num_chunks), :
            ].reset_index(drop=True)
            file_ids = sub_meta_i["file_id"].to_list()
            file_id_url_map = self._make_file_id_url_map(file_ids)
            rawDataMap = self._get_urls_content(file_id_url_map)
            ids_with_none = [
                key for key in rawDataMap.keys() if rawDataMap[key] is None
            ]
            rna_seq_data_matrix = self._make_rna_seq_data_matrix(
                rawDataMap, sub_meta_i, feature_col=feature_col_for_extraction
            )

            sub_meta_sub_i = sub_meta_i[~sub_meta_i["file_id"].isin(ids_with_none)]
            rna_seq_data_matrix["project_id"] = sub_meta_sub_i["project_id"].to_numpy()
            rna_seq_data_matrix["tissue_type"] = sub_meta_sub_i[
                "tissue_type"
            ].to_numpy()
            rna_seq_data_matrix["sample_type"] = sub_meta_sub_i[
                "sample_type"
            ].to_numpy()
            rna_seq_data_matrix["primary_site"] = sub_meta_sub_i[
                "primary_site"
            ].to_numpy()
            rna_seq_data_matrix["case_id"] = sub_meta_sub_i["case_id"].to_numpy()
            chunk_ls.append(rna_seq_data_matrix)
        df = pd.concat(chunk_ls)
        return df

    def run_rna_seq_data_matrix_creation(self, primary_site, downstream_analysis="DE"):
        """
        Run the GDCEngine to fetch and process the data.

        Returns:
            pd.DataFrame: The processed data matrix for machine learning.
        """
        if self._check_data_type():
            rna_seq_metadata = self._get_rna_seq_metadata()
            meta = rna_seq_metadata["metadata"]
            ml_data_matrix = self._process_data_matrix_rna_seq(
                meta=meta,
                primary_site=primary_site,
                downstream_analysis=downstream_analysis,
                
            )
            return ml_data_matrix
    
    def create_identifier(self, row):
        """
        Create a unique identifier for a row based on specific fields. 
        This will be useful for partitioning and clustering in BigQuery

        Args:
            row (pd.Series): The row of data containing 'primary_site', 'tissue_type', and 'primary_diagnosis'.

        Returns:
            int: The unique identifier.
        """
        identifier_str = f"{row['primary_site']}_{row['tissue_type']}_{row['primary_diagnosis']}"
        return int(hashlib.md5(identifier_str.encode()).hexdigest(), 16) % (10 ** 8)

    def make_count_data_for_bq(self, primary_site, downstream_analysis='DE', format='dataframe'):
        """
        Get data for BigQuery based on primary site and downstream analysis type.

        Args:
            primary_site (str): The primary site to filter by.
            downstream_analysis (str, optional): The type of downstream analysis ('DE' or other). Defaults to 'DE'.
            format (str, optional): The format of the output ('dataframe' or 'json'). Defaults to 'dataframe'.

        Returns:
            pd.DataFrame or dict: The data formatted as a DataFrame or JSON object.
        """
        field_names_from_cohort = ['file_id', 'case_id', 'tissue_or_organ_of_origin', 'age_at_diagnosis', 'primary_diagnosis', 'race', 'gender']
        cohort_metadata = self._get_rna_seq_metadata()
        cohort_metadata = cohort_metadata['metadata']
        df = self.run_rna_seq_data_matrix_creation(primary_site=primary_site, downstream_analysis=downstream_analysis)
        df = df.set_index('file_id').reset_index()
        gene_cols = df.columns.to_numpy()[1:60661]
        if downstream_analysis == 'DE':
            feature_values_column = 'expr_unstr_count'
        elif downstream_analysis == 'ML':
            feature_values_column = 'expr_tpm'
        df[feature_values_column] = df[np.sort(gene_cols)].agg(list, axis=1)
        df_unq = df.drop_duplicates(['case_id']).reset_index(drop=True)
        data_for_bq = df_unq[self._field_names_from_data_matrix]
        data_bq_with_labels = pd.merge(data_for_bq, cohort_metadata[field_names_from_cohort], on=['file_id', 'case_id'])
        if format == 'dataframe':
            return data_bq_with_labels, gene_cols
        elif format == 'json':
            json_data = data_bq_with_labels.to_json(orient='records')
            json_object = json.loads(json_data)
            return json_object, gene_cols
    
    def make_data_for_recurrence_free_survival(self, primary_site, downstream_analysis='ML', format='dataframe'):
        """
        Get data for recurrence free survival analysis based on primary site and downstream analysis type.

        Args:
            primary_site (str): The primary site to filter by.
            downstream_analysis (str, optional): The type of downstream analysis ('ML' or other). Defaults to 'ML'.
            format (str, optional): The format of the output ('dataframe' or 'json'). Defaults to 'dataframe'.

        Returns:
            pd.DataFrame or dict: The data formatted as a DataFrame or JSON object.
        """
        field_names_from_cohort =['file_id', 'case_id', 'tissue_or_organ_of_origin', 'age_at_diagnosis', 'primary_diagnosis', 'race', 'gender', 'days_to_recurrence']
        cohort_metadata = self._get_rna_seq_metadata()
        cohort_metadata = cohort_metadata['metadata']
        df = self.run_rna_seq_data_matrix_creation(primary_site=primary_site, downstream_analysis=downstream_analysis)
        df = df.set_index('file_id').reset_index()
        gene_cols = df.columns.to_numpy()[1:60661]
        if downstream_analysis == 'DE':
            feature_values_column = 'expr_unstr_count'
        elif downstream_analysis == 'ML':
            feature_values_column = 'expr_tpm'
        df[feature_values_column] = df[np.sort(gene_cols)].agg(list, axis=1)
        
        df_unq = df.drop_duplicates(['case_id']).reset_index(drop=True)
        data_for_bq = df_unq[self._field_names_from_data_matrix]
        data_bq_with_labels = pd.merge(data_for_bq, cohort_metadata[field_names_from_cohort], on=['file_id', 'case_id'])
        if format == 'dataframe':
            return data_bq_with_labels, gene_cols
        elif format == 'json':
            json_data = data_bq_with_labels.to_json(orient='records')
            json_object = json.loads(json_data)
            return json_object, gene_cols
'''
