






import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


a		:       Tuple			     = logging.get_logger(__name__)

a		:       Optional[Any]			     = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

a		:       Any			     = {
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
a		:       List[Any]			     = {
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
a		:       Optional[int]			     = {
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

a		:       str			     = {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
a		:       Any			     = {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
a		:       Dict			     = {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


a		:       Union[str, Any]			     = {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
a		:       Optional[Any]			     = {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
a		:       Optional[Any]			     = {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : Any							       =	VOCAB_FILES_NAMES
      a  : Tuple							       =	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      a  : List[str]							       =	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a  : Union[str, Any]							       =	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      a  : Tuple							       =	DPRContextEncoderTokenizer


class     a    ( lowercase__   ):



      """simple docstring"""


      a  : Dict							       =	VOCAB_FILES_NAMES
      a  : List[Any]							       =	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      a  : Optional[Any]							       =	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a  : Optional[Any]							       =	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      a  : Union[str, Any]							       =	DPRQuestionEncoderTokenizer


a		:       Union[str, Any]			     = collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

a		:       Union[str, Any]			     = collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


a		:       Dict			     = r"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "
@add_start_docstrings(lowercase__   )
class     a    :



      """simple docstring"""


      def __call__(      self					:			Any		,      __lowercase					:			str		,      __lowercase					:			Optional[str] = None		,      __lowercase					:			Optional[str] = None		,      __lowercase					:			Union[bool, str] = False		,      __lowercase					:			Union[bool, str] = False		,      __lowercase					:			Optional[int] = None		,      __lowercase					:			Optional[Union[str, TensorType]] = None		,      __lowercase					:			Optional[bool] = None		,      **__lowercase					:			Union[str, Any]		,      )							->      BatchEncoding:
        if titles is None and texts is None:
          return super().__call__(
              __lowercase		,      padding=__lowercase		,      truncation=__lowercase		,      max_length=__lowercase		,      return_tensors=__lowercase		,      return_attention_mask=__lowercase		,      **__lowercase		,      )
        elif titles is None or texts is None:
          __UpperCAmelCase							:	Any           =  titles if texts is None else texts
          return super().__call__(
              __lowercase		,      __lowercase		,      padding=__lowercase		,      truncation=__lowercase		,      max_length=__lowercase		,      return_tensors=__lowercase		,      return_attention_mask=__lowercase		,      **__lowercase		,      )
        __UpperCAmelCase							:	Optional[int]           =  titles if not isinstance(__lowercase		,      __lowercase					) else [titles]
        __UpperCAmelCase							:	Optional[int]           =  texts if not isinstance(__lowercase		,      __lowercase					) else [texts]
        __UpperCAmelCase							:	List[str]           =  len(__lowercase					)
        __UpperCAmelCase							:	List[Any]           =  questions if not isinstance(__lowercase		,      __lowercase					) else [questions] * n_passages
        assert len(__lowercase					) == len(
            __lowercase					), f"""There should be as many titles than texts but got {len(__lowercase					)} titles and {len(__lowercase					)} texts."""
        __UpperCAmelCase							:	Union[str, Any]           =  super().__call__(__lowercase		,      __lowercase		,      padding=__lowercase		,      truncation=__lowercase					)["""input_ids"""]
        __UpperCAmelCase							:	Optional[int]           =  super().__call__(__lowercase		,      add_special_tokens=__lowercase		,      padding=__lowercase		,      truncation=__lowercase					)["""input_ids"""]
        __UpperCAmelCase							:	List[Any]           =  {
            """input_ids""": [
                (encoded_question_and_title + encoded_text)[:max_length]
                if max_length is not None and truncation
                else encoded_question_and_title + encoded_text
                for encoded_question_and_title, encoded_text in zip(__lowercase		,      __lowercase					)
            ]
        }
        if return_attention_mask is not False:
          __UpperCAmelCase							:	List[str]           =  []
          for input_ids in encoded_inputs["input_ids"]:
            attention_mask.append([int(input_id != self.pad_token_id					) for input_id in input_ids]					)
          __UpperCAmelCase							:	List[Any]           =  attention_mask
        return self.pad(__lowercase		,      padding=__lowercase		,      max_length=__lowercase		,      return_tensors=__lowercase					)
      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			BatchEncoding		,      __lowercase					:			DPRReaderOutput		,      __lowercase					:			int = 16		,      __lowercase					:			int = 64		,      __lowercase					:			int = 4		,      )							->      List[DPRSpanPrediction]:
        __UpperCAmelCase							:	Any           =  reader_input["""input_ids"""]
        __UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase							:	Any           =  reader_output[:3]
        __UpperCAmelCase							:	List[Any]           =  len(__lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  sorted(range(__lowercase					)		,      reverse=__lowercase		,      key=relevance_logits.__getitem__					)
        __UpperCAmelCase							:	List[DPRReaderOutput]           =  []
        for doc_id in sorted_docs:
          __UpperCAmelCase							:	Optional[int]           =  list(input_ids[doc_id]					)
          # assuming question & title information is at the beginning of the sequence
          __UpperCAmelCase							:	Union[str, Any]           =  sequence_ids.index(self.sep_token_id		,      2					) + 1  # second sep id
          if sequence_ids[-1] == self.pad_token_id:
            __UpperCAmelCase							:	Tuple           =  sequence_ids.index(self.pad_token_id					)
          else:
            __UpperCAmelCase							:	List[str]           =  len(__lowercase					)

          __UpperCAmelCase							:	List[Any]           =  self._get_best_spans(
              start_logits=start_logits[doc_id][passage_offset:sequence_len]		,      end_logits=end_logits[doc_id][passage_offset:sequence_len]		,      max_answer_length=__lowercase		,      top_spans=__lowercase		,      )
          for start_index, end_index in best_spans:
            start_index += passage_offset
            end_index += passage_offset
            nbest_spans_predictions.append(
                DPRSpanPrediction(
                    span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]		,      relevance_score=relevance_logits[doc_id]		,      doc_id=__lowercase		,      start_index=__lowercase		,      end_index=__lowercase		,      text=self.decode(sequence_ids[start_index : end_index + 1]					)		,      )					)
          if len(__lowercase					) >= num_spans:
            break
        return nbest_spans_predictions[:num_spans]




      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			List[int]		,      __lowercase					:			List[int]		,      __lowercase					:			int		,      __lowercase					:			int		,      )							->      List[DPRSpanPrediction]:
        __UpperCAmelCase							:	int           =  []
        for start_index, start_score in enumerate(__lowercase					):
          for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]					):
            scores.append(((start_index, start_index + answer_length), start_score + end_score)					)
        __UpperCAmelCase							:	str           =  sorted(__lowercase		,      key=lambda __lowercase					: x[1]		,      reverse=__lowercase					)
        __UpperCAmelCase							:	Dict           =  []
        for (start_index, end_index), score in scores:
          assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
          __UpperCAmelCase							:	Any           =  end_index - start_index + 1
          assert length <= max_answer_length, f"""Span is too long: {length} > {max_answer_length}"""
          if any(
              start_index <= prev_start_index <= prev_end_index <= end_index
              or prev_start_index <= start_index <= end_index <= prev_end_index
              for (prev_start_index, prev_end_index) in chosen_span_intervals					):
            continue
          chosen_span_intervals.append((start_index, end_index)					)

          if len(__lowercase					) == top_spans:
            break
        return chosen_span_intervals


@add_end_docstrings(lowercase__   )
class     a    ( lowercase__							,       lowercase__   ):



      """simple docstring"""


      a  : Optional[Any]							       =	VOCAB_FILES_NAMES
      a  : Optional[Any]							       =	READER_PRETRAINED_VOCAB_FILES_MAP
      a  : List[Any]							       =	READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a  : List[str]							       =	READER_PRETRAINED_INIT_CONFIGURATION
      a  : str							       =	['input_ids', 'attention_mask']
      a  : int							       =	DPRReaderTokenizer










import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a		:       List[str]			     = "src/diffusers"
a		:       str			     = "."


# This is to make sure the diffusers module imported is the one in the repo.
a		:       Tuple			     = importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(DIFFUSERS_PATH, "__init__.py"),
    submodule_search_locations=[DIFFUSERS_PATH],
)
a		:       List[str]			     = spec.loader.load_module()






def 				lowerCamelCase__      (    __lowerCamelCase			:     Dict  ,		__lowerCamelCase			:     Tuple		):
  return line.startswith(__lowerCamelCase		) or len(__lowerCamelCase		) <= 1 or re.search(R"""^\s*\)(\s*->.*:|:)\s*$"""  ,		__lowerCamelCase		) is not None






def 				lowerCamelCase__      (    __lowerCamelCase			:     Any		):
  __UpperCAmelCase							:	Optional[int]           =  object_name.split("""."""		)
  __UpperCAmelCase							:	List[Any]           =  0

  # First let's find the module where our object lives.
  __UpperCAmelCase							:	Optional[Any]           =  parts[i]
  while i < len(__lowerCamelCase		) and not os.path.isfile(os.path.join(__lowerCamelCase  ,		f"""{module}.py"""		)		):
    i += 1
    if i < len(__lowerCamelCase		):
      __UpperCAmelCase							:	List[str]           =  os.path.join(__lowerCamelCase  ,		parts[i]		)
  if i >= len(__lowerCamelCase		):
    raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}."""		)

  with open(os.path.join(__lowerCamelCase  ,		f"""{module}.py"""		)  ,		"""r"""  ,		encoding="""utf-8"""  ,		newline="""\n"""		) as f:
    __UpperCAmelCase							:	Optional[Any]           =  f.readlines()

  # Now let's find the class / func in the code!
  __UpperCAmelCase							:	List[str]           =  """"""
  __UpperCAmelCase							:	int           =  0
  for name in parts[i + 1 :]:
    while (
        line_index < len(__lowerCamelCase		) and re.search(Rf"""^{indent}(class|def)\s+{name}(\(|\:)"""  ,		lines[line_index]		) is None
    ):
      line_index += 1
    indent += "    "
    line_index += 1

  if line_index >= len(__lowerCamelCase		):
    raise ValueError(f""" {object_name} does not match any function or class in {module}."""		)

  # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
  __UpperCAmelCase							:	List[str]           =  line_index
  while line_index < len(__lowerCamelCase		) and _should_continue(lines[line_index]  ,		__lowerCamelCase		):
    line_index += 1
  # Clean up empty lines at the end (if any).
  while len(lines[line_index - 1]		) <= 1:
    line_index -= 1

  __UpperCAmelCase							:	Dict           =  lines[start_index:line_index]
  return "".join(__lowerCamelCase		)


a		:       Any			     = re.compile(r"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
a		:       Optional[int]			     = re.compile(r"^\s*(\S+)->(\S+)(\s+.*|$)")
a		:       Dict			     = re.compile(r"<FILL\s+[^>]*>")






def 				lowerCamelCase__      (    __lowerCamelCase			:     List[Any]		):
  __UpperCAmelCase							:	Optional[Any]           =  code.split("""\n"""		)
  __UpperCAmelCase							:	str           =  0
  while idx < len(__lowerCamelCase		) and len(lines[idx]		) == 0:
    idx += 1
  if idx < len(__lowerCamelCase		):
    return re.search(R"""^(\s*)\S"""  ,		lines[idx]		).groups()[0]
  return ""






def 				lowerCamelCase__      (    __lowerCamelCase			:     List[str]		):
  __UpperCAmelCase							:	Tuple           =  len(get_indent(__lowerCamelCase		)		) > 0
  if has_indent:
    __UpperCAmelCase							:	Optional[Any]           =  f"""class Bla:\n{code}"""
  __UpperCAmelCase							:	Dict           =  black.Mode(target_versions={black.TargetVersion.PYaa}  ,		line_length=119  ,		preview=__lowerCamelCase		)
  __UpperCAmelCase							:	Dict           =  black.format_str(__lowerCamelCase  ,		mode=__lowerCamelCase		)
  __UpperCAmelCase      , __UpperCAmelCase							:	Any           =  style_docstrings_in_code(__lowerCamelCase		)
  return result[len("""class Bla:\n"""		) :] if has_indent else result






def 				lowerCamelCase__      (    __lowerCamelCase			:     Dict  ,		__lowerCamelCase			:     Optional[Any]=False		):
  with open(__lowerCamelCase  ,		"""r"""  ,		encoding="""utf-8"""  ,		newline="""\n"""		) as f:
    __UpperCAmelCase							:	Optional[Any]           =  f.readlines()
  __UpperCAmelCase							:	Optional[int]           =  []
  __UpperCAmelCase							:	str           =  0
  # Not a for loop cause `lines` is going to change (if `overwrite=True`).
  while line_index < len(__lowerCamelCase		):
    __UpperCAmelCase							:	Dict           =  _re_copy_warning.search(lines[line_index]		)
    if search is None:
      line_index += 1
      continue

    # There is some copied code here, let's retrieve the original.
    __UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase							:	Union[str, Any]           =  search.groups()
    __UpperCAmelCase							:	Any           =  find_code_in_diffusers(__lowerCamelCase		)
    __UpperCAmelCase							:	Optional[int]           =  get_indent(__lowerCamelCase		)

    __UpperCAmelCase							:	Tuple           =  line_index + 1 if indent == theoretical_indent else line_index + 2
    __UpperCAmelCase							:	Any           =  theoretical_indent
    __UpperCAmelCase							:	Any           =  start_index

    # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
    __UpperCAmelCase							:	int           =  True
    while line_index < len(__lowerCamelCase		) and should_continue:
      line_index += 1
      if line_index >= len(__lowerCamelCase		):
        break
      __UpperCAmelCase							:	List[Any]           =  lines[line_index]
      __UpperCAmelCase							:	str           =  _should_continue(__lowerCamelCase  ,		__lowerCamelCase		) and re.search(f"""^{indent}# End copy"""  ,		__lowerCamelCase		) is None
    # Clean up empty lines at the end (if any).
    while len(lines[line_index - 1]		) <= 1:
      line_index -= 1

    __UpperCAmelCase							:	Optional[int]           =  lines[start_index:line_index]
    __UpperCAmelCase							:	int           =  """""".join(__lowerCamelCase		)

    # Remove any nested `Copied from` comments to avoid circular copies
    __UpperCAmelCase							:	Tuple           =  [line for line in theoretical_code.split("""\n"""		) if _re_copy_warning.search(__lowerCamelCase		) is None]
    __UpperCAmelCase							:	List[Any]           =  """\n""".join(__lowerCamelCase		)

    # Before comparing, use the `replace_pattern` on the original code.
    if len(__lowerCamelCase		) > 0:
      __UpperCAmelCase							:	List[str]           =  replace_pattern.replace("""with"""  ,		""""""		).split(""","""		)
      __UpperCAmelCase							:	Any           =  [_re_replace_pattern.search(__lowerCamelCase		) for p in patterns]
      for pattern in patterns:
        if pattern is None:
          continue
        __UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase							:	Union[str, Any]           =  pattern.groups()
        __UpperCAmelCase							:	List[str]           =  re.sub(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase		)
        if option.strip() == "all-casing":
          __UpperCAmelCase							:	List[Any]           =  re.sub(obja.lower()  ,		obja.lower()  ,		__lowerCamelCase		)
          __UpperCAmelCase							:	int           =  re.sub(obja.upper()  ,		obja.upper()  ,		__lowerCamelCase		)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
      __UpperCAmelCase							:	Union[str, Any]           =  blackify(lines[start_index - 1] + theoretical_code		)
      __UpperCAmelCase							:	Optional[Any]           =  theoretical_code[len(lines[start_index - 1]		) :]

    # Test for a diff and act accordingly.
    if observed_code != theoretical_code:
      diffs.append([object_name, start_index]		)
      if overwrite:
        __UpperCAmelCase							:	int           =  lines[:start_index] + [theoretical_code] + lines[line_index:]
        __UpperCAmelCase							:	Union[str, Any]           =  start_index + 1

  if overwrite and len(__lowerCamelCase		) > 0:
    # Warn the user a file has been modified.
    print(f"""Detected changes, rewriting {filename}."""		)
    with open(__lowerCamelCase  ,		"""w"""  ,		encoding="""utf-8"""  ,		newline="""\n"""		) as f:
      f.writelines(__lowerCamelCase		)
  return diffs






def 				lowerCamelCase__      (    __lowerCamelCase			:     bool = False		):
  __UpperCAmelCase							:	Tuple           =  glob.glob(os.path.join(__lowerCamelCase  ,		"""**/*.py"""		)  ,		recursive=__lowerCamelCase		)
  __UpperCAmelCase							:	Optional[int]           =  []
  for filename in all_files:
    __UpperCAmelCase							:	str           =  is_copy_consistent(__lowerCamelCase  ,		__lowerCamelCase		)
    diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
  if not overwrite and len(__lowerCamelCase		) > 0:
    __UpperCAmelCase							:	Union[str, Any]           =  """\n""".join(__lowerCamelCase		)
    raise Exception(
        """Found the following copy inconsistencies:\n"""
        + diff
        + """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."""		)


if __name__ == "__main__":
       a		:       Dict			     = argparse.ArgumentParser()
       parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
       a		:       Optional[int]			     = parser.parse_args()

       check_copies(args.fix_and_overwrite)


