

'''simple docstring'''

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 import BertTokenizer


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase					 =	{
    "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"
        ),
    },
}
UpperCAmelCase					 =	{
    "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"
        ),
    },
}
UpperCAmelCase					 =	{
    "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"
        ),
    },
}

UpperCAmelCase					 =	{
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
UpperCAmelCase					 =	{
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
UpperCAmelCase					 =	{
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


UpperCAmelCase					 =	{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase					 =	{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase					 =	{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}


class 	lowerCAmelCase  (   lowercase_ ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class 	lowerCAmelCase  (   lowercase_ ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






UpperCAmelCase					 =	collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

UpperCAmelCase					 =	collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


UpperCAmelCase					 =	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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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 	lowerCAmelCase  :







       def __call__(				self      :			Optional[int]    ,   __lowercase      :			List[str]    ,   __lowercase      :			List[str] = None    ,   __lowercase      :			Optional[int] = None    ,   __lowercase      :			Optional[Any] = False    ,   __lowercase      :			Tuple = False    ,   __lowercase      :			List[str] = None    ,   __lowercase      :			List[str] = None    ,   __lowercase      :			int = None    ,   **__lowercase      :			int    ,   ):







            """simple docstring"""







            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:
                 __lowercase      		=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    ,   )
            __lowercase      		=titles if not isinstance(_lowercase    ,   _lowercase   ) else [titles]
            __lowercase      		=texts if not isinstance(_lowercase    ,   _lowercase   ) else [texts]
            __lowercase      		=len(_lowercase   )
            __lowercase      		=questions if not isinstance(_lowercase    ,   _lowercase   ) else [questions] * n_passages
            if len(_lowercase   ) != len(_lowercase   ):
                 raise ValueError(
                     f'''There should be as many titles than texts but got {len(_lowercase   )} titles and {len(_lowercase   )} texts.'''   )
            __lowercase      		=super().__call__(_lowercase    ,   _lowercase    ,   padding=_lowercase    ,   truncation=_lowercase   )['input_ids']
            __lowercase      		=super().__call__(_lowercase    ,   add_special_tokens=_lowercase    ,   padding=_lowercase    ,   truncation=_lowercase   )['input_ids']
            __lowercase      		={
                '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:
                 __lowercase      		=[]
                 for input_ids in encoded_inputs["input_ids"]:
                      attention_mask.append([int(input_id != self.pad_token_id   ) for input_id in input_ids]   )
                 __lowercase      		=attention_mask
            return self.pad(_lowercase    ,   padding=_lowercase    ,   max_length=_lowercase    ,   return_tensors=_lowercase   )







       def       snake_case (				self      :			Union[str, Any]    ,   __lowercase      :			Union[str, Any]    ,   __lowercase      :			Any    ,   __lowercase      :			int = 16    ,   __lowercase      :			Any = 64    ,   __lowercase      :			Dict = 4    ,   ):







            """simple docstring"""







            __lowercase      		=reader_input['input_ids']
            __lowercase		,  __lowercase		,  __lowercase      		=reader_output[:3]
            __lowercase      		=len(_lowercase   )
            __lowercase      		=sorted(range(_lowercase   )    ,   reverse=_lowercase    ,   key=relevance_logits.__getitem__   )
            __lowercase      		=[]
            for doc_id in sorted_docs:
                 __lowercase      		=list(input_ids[doc_id]   )
                 # assuming question & title information is at the beginning of the sequence
                 __lowercase      		=sequence_ids.index(self.sep_token_id    ,   2   ) + 1  # second sep id
                 if sequence_ids[-1] == self.pad_token_id:
                      __lowercase      		=sequence_ids.index(self.pad_token_id   )
                 else:
                      __lowercase      		=len(_lowercase   )

                 __lowercase      		=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       snake_case (				self      :			List[str]    ,   __lowercase      :			Union[str, Any]    ,   __lowercase      :			Tuple    ,   __lowercase      :			int    ,   __lowercase      :			List[Any]    ,   ):







            """simple docstring"""







            __lowercase      		=[]
            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)   )
            __lowercase      		=sorted(_lowercase    ,   key=lambda __lowercase   : x[1]    ,   reverse=_lowercase   )
            __lowercase      		=[]
            for (start_index, end_index), score in scores:
                 if start_index > end_index:
                      raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''   )
                 __lowercase      		=end_index - start_index + 1
                 if length > max_answer_length:
                      raise ValueError(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 	lowerCAmelCase  (   lowercase_ ,      lowercase_ ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			READER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			READER_PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase_												=			["input_ids", "attention_mask"]





'''simple docstring'''

import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
							from cookiecutter.main import cookiecutter

							SCREAMING_SNAKE_CASE							:				Dict     	=			True
except ImportError:
							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			False

SCREAMING_SNAKE_CASE							:				str     	=			logging.get_logger(__name__)  # pylint: disable=invalid-name




def 							_UpperCamelCase				(   lowerCAmelCase__:					Namespace   )      -> Optional[Any]:
							return AddNewModelCommand(args.testing		,args.testing_file		,path=args.path   )






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				@staticmethod
				def   a__					(    _lowercase       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				parser.add_parser('add-new-model'       )
											add_new_model_parser.add_argument('--testing',     action='store_true',     help='If in testing mode.'       )
											add_new_model_parser.add_argument('--testing_file',     type=_lowercase,     help='Configuration file on which to run.'       )
											add_new_model_parser.add_argument(
											    '--path',     type=_lowercase,     help='Path to cookiecutter. Should only be used for testing purposes.'       )
											add_new_model_parser.set_defaults(func=_lowercase       )



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase=None,     *_lowercase       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				testing
											SCREAMING_SNAKE_CASE_				=				testing_file
											SCREAMING_SNAKE_CASE_				=				path





				def   a__					(    self       )     ->							Optional[int]:
											warnings.warn(
											    'The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '
											    'It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '
											    'checks, you should use `transformers-cli add-new-model-like` instead.'       )
											if not _has_cookiecutter:
																		raise ImportError(
																		    'Model creation dependencies are required to use the `add_new_model` command. Install them by running '
																		    'the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n'       )
											# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
											SCREAMING_SNAKE_CASE_				=				[directory for directory in os.listdir() if 'cookiecutter-template-' == directory[:22]]
											if len(_lowercase       ) > 0:
																		raise ValueError(
																		    'Several directories starting with `cookiecutter-template-` in current working directory. '
																		    'Please clean your directory by removing all folders starting with `cookiecutter-template-` or '
																		    'change your working directory.'       )

											SCREAMING_SNAKE_CASE_				=				(
											    Path(_lowercase       ).parent.parent.parent.parent if self._path is None else Path(self._path       ).parent.parent
											)
											SCREAMING_SNAKE_CASE_				=				path_to_transformer_root / 'templates' / 'adding_a_new_model'

											# Execute cookiecutter
											if not self._testing:
																		cookiecutter(str(_lowercase       )       )
											else:
																		with open(self._testing_file,     'r'       ) as configuration_file:
																									SCREAMING_SNAKE_CASE_				=				json.load(_lowercase       )

																		cookiecutter(
																		    str(path_to_cookiecutter if self._path is None else self._path       ),     no_input=_lowercase,     extra_context=_lowercase,     )

											SCREAMING_SNAKE_CASE_				=				[directory for directory in os.listdir() if 'cookiecutter-template-' in directory[:22]][0]

											# Retrieve configuration
											with open(directory + '/configuration.json',     'r'       ) as configuration_file:
																		SCREAMING_SNAKE_CASE_				=				json.load(_lowercase       )

											SCREAMING_SNAKE_CASE_				=				configuration['lowercase_modelname']
											SCREAMING_SNAKE_CASE_				=				configuration['generate_tensorflow_pytorch_and_flax']
											os.remove(f"""{directory}/configuration.json"""       )

											SCREAMING_SNAKE_CASE_				=				'PyTorch' in generate_tensorflow_pytorch_and_flax
											SCREAMING_SNAKE_CASE_				=				'TensorFlow' in generate_tensorflow_pytorch_and_flax
											SCREAMING_SNAKE_CASE_				=				'Flax' in generate_tensorflow_pytorch_and_flax

											SCREAMING_SNAKE_CASE_				=				f"""{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"""
											os.makedirs(_lowercase,     exist_ok=_lowercase       )
											os.makedirs(f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}""",     exist_ok=_lowercase       )

											# Tests require submodules as they have parent imports
											with open(f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py""",     'w'       ):
																		pass

											shutil.move(
											    f"""{directory}/__init__.py""",     f"""{model_dir}/__init__.py""",     )
											shutil.move(
											    f"""{directory}/configuration_{lowercase_model_name}.py""",     f"""{model_dir}/configuration_{lowercase_model_name}.py""",     )

											def remove_copy_lines(_lowercase       ):
																		with open(_lowercase,     'r'       ) as f:
																									SCREAMING_SNAKE_CASE_				=				f.readlines()
																		with open(_lowercase,     'w'       ) as f:
																									for line in lines:
																																if "# Copied from transformers." not in line:
																																							f.write(_lowercase       )

											if output_pytorch:
																		if not self._testing:
																									remove_copy_lines(f"""{directory}/modeling_{lowercase_model_name}.py"""       )

																		shutil.move(
																		    f"""{directory}/modeling_{lowercase_model_name}.py""",     f"""{model_dir}/modeling_{lowercase_model_name}.py""",     )

																		shutil.move(
																		    f"""{directory}/test_modeling_{lowercase_model_name}.py""",     f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py""",     )
											else:
																		os.remove(f"""{directory}/modeling_{lowercase_model_name}.py"""       )
																		os.remove(f"""{directory}/test_modeling_{lowercase_model_name}.py"""       )

											if output_tensorflow:
																		if not self._testing:
																									remove_copy_lines(f"""{directory}/modeling_tf_{lowercase_model_name}.py"""       )

																		shutil.move(
																		    f"""{directory}/modeling_tf_{lowercase_model_name}.py""",     f"""{model_dir}/modeling_tf_{lowercase_model_name}.py""",     )

																		shutil.move(
																		    f"""{directory}/test_modeling_tf_{lowercase_model_name}.py""",     f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py""",     )
											else:
																		os.remove(f"""{directory}/modeling_tf_{lowercase_model_name}.py"""       )
																		os.remove(f"""{directory}/test_modeling_tf_{lowercase_model_name}.py"""       )

											if output_flax:
																		if not self._testing:
																									remove_copy_lines(f"""{directory}/modeling_flax_{lowercase_model_name}.py"""       )

																		shutil.move(
																		    f"""{directory}/modeling_flax_{lowercase_model_name}.py""",     f"""{model_dir}/modeling_flax_{lowercase_model_name}.py""",     )

																		shutil.move(
																		    f"""{directory}/test_modeling_flax_{lowercase_model_name}.py""",     f"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py""",     )
											else:
																		os.remove(f"""{directory}/modeling_flax_{lowercase_model_name}.py"""       )
																		os.remove(f"""{directory}/test_modeling_flax_{lowercase_model_name}.py"""       )

											shutil.move(
											    f"""{directory}/{lowercase_model_name}.md""",     f"""{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md""",     )

											shutil.move(
											    f"""{directory}/tokenization_{lowercase_model_name}.py""",     f"""{model_dir}/tokenization_{lowercase_model_name}.py""",     )

											shutil.move(
											    f"""{directory}/tokenization_fast_{lowercase_model_name}.py""",     f"""{model_dir}/tokenization_{lowercase_model_name}_fast.py""",     )

											from os import fdopen, remove
											from shutil import copymode, move
											from tempfile import mkstemp

											def replace(_lowercase,     _lowercase,     _lowercase       ):
																		# Create temp file
																		SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				mkstemp()
																		SCREAMING_SNAKE_CASE_				=				False
																		with fdopen(_lowercase,     'w'       ) as new_file:
																									with open(_lowercase       ) as old_file:
																																for line in old_file:
																																							new_file.write(_lowercase       )
																																							if line_to_copy_below in line:
																																														SCREAMING_SNAKE_CASE_				=				True
																																														for line_to_copy in lines_to_copy:
																																																					new_file.write(_lowercase       )

																		if not line_found:
																									raise ValueError(f"""Line {line_to_copy_below} was not found in file."""       )

																		# Copy the file permissions from the old file to the new file
																		copymode(_lowercase,     _lowercase       )
																		# Remove original file
																		remove(_lowercase       )
																		# Move new file
																		move(_lowercase,     _lowercase       )

											def skip_units(_lowercase       ):
																		return (
																		    ("generating PyTorch" in line and not output_pytorch)
																		    or ("generating TensorFlow" in line and not output_tensorflow)
																		    or ("generating Flax" in line and not output_flax)
																		)

											def replace_in_files(_lowercase       ):
																		with open(_lowercase       ) as datafile:
																									SCREAMING_SNAKE_CASE_				=				[]
																									SCREAMING_SNAKE_CASE_				=				False
																									SCREAMING_SNAKE_CASE_				=				False
																									for line in datafile:
																																if "# To replace in: " in line and "##" not in line:
																																							SCREAMING_SNAKE_CASE_				=				line.split('"'       )[1]
																																							SCREAMING_SNAKE_CASE_				=				skip_units(_lowercase       )
																																elif "# Below: " in line and "##" not in line:
																																							SCREAMING_SNAKE_CASE_				=				line.split('"'       )[1]
																																							SCREAMING_SNAKE_CASE_				=				skip_units(_lowercase       )
																																elif "# End." in line and "##" not in line:
																																							if not skip_file and not skip_snippet:
																																														replace(_lowercase,     _lowercase,     _lowercase       )

																																							SCREAMING_SNAKE_CASE_				=				[]
																																elif "# Replace with" in line and "##" not in line:
																																							SCREAMING_SNAKE_CASE_				=				[]
																																elif "##" not in line:
																																							lines_to_copy.append(_lowercase       )

																		remove(_lowercase       )

											replace_in_files(f"""{directory}/to_replace_{lowercase_model_name}.py"""       )
											os.rmdir(_lowercase       )





