


import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
SCREAMING_SNAKE_CASE       :      int												= "src/transformers"
SCREAMING_SNAKE_CASE       :      Any												= "docs/source/en"
SCREAMING_SNAKE_CASE       :      str												= "."



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						Optional[int]:
    with open(lowerCamelCase_							, 'r'							, encoding='utf-8'							, newline='\n' ) as f:
        _lowercase       :							Dict      		=		f.readlines()
    # Find the start prompt.
    _lowercase       :							int      		=		0
    while not lines[start_index].startswith(lowerCamelCase_ ):
        start_index += 1
    start_index += 1

    _lowercase       :							Any      		=		start_index
    while not lines[end_index].startswith(lowerCamelCase_ ):
        end_index += 1
    end_index -= 1

    while len(lines[start_index] ) <= 1:
        start_index += 1
    while len(lines[end_index] ) <= 1:
        end_index -= 1
    end_index += 1
    return "".join(lines[start_index:end_index] ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
SCREAMING_SNAKE_CASE       :      Dict												= "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE       :      Dict												= re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
SCREAMING_SNAKE_CASE       :      int												= re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
SCREAMING_SNAKE_CASE       :      List[Any]												= re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE       :      str												= direct_transformers_import(TRANSFORMERS_PATH)



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						Optional[Any]:
    _lowercase       :							List[Any]      		=		re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'							, lowerCamelCase_ )
    return [m.group(0 ) for m in matches]



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Optional[Any]:
    _lowercase       :							Optional[int]      		=		2 if text == '✅' or text == '❌' else len(lowerCamelCase_ )
    _lowercase       :							Union[str, Any]      		=		(width - text_length) // 2
    _lowercase       :							str      		=		width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent



def 				UpperCamelCase_(      )    ->						List[Any]:
    _lowercase       :							Any      		=		transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    _lowercase       :							str      		=		{
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    _lowercase       :							str      		=		{name: config.replace('Config'							, '' ) for name, config in model_name_to_config.items()}

    # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
    _lowercase       :							Optional[Any]      		=		collections.defaultdict(lowerCamelCase_ )
    _lowercase       :							Tuple      		=		collections.defaultdict(lowerCamelCase_ )
    _lowercase       :							int      		=		collections.defaultdict(lowerCamelCase_ )
    _lowercase       :							str      		=		collections.defaultdict(lowerCamelCase_ )
    _lowercase       :							str      		=		collections.defaultdict(lowerCamelCase_ )

    # Let's lookup through all transformers object (once).
    for attr_name in dir(lowerCamelCase_ ):
        _lowercase       :							Optional[int]      		=		None
        if attr_name.endswith('Tokenizer' ):
            _lowercase       :							str      		=		slow_tokenizers
            _lowercase       :							Any      		=		attr_name[:-9]
        elif attr_name.endswith('TokenizerFast' ):
            _lowercase       :							Union[str, Any]      		=		fast_tokenizers
            _lowercase       :							str      		=		attr_name[:-13]
        elif _re_tf_models.match(lowerCamelCase_ ) is not None:
            _lowercase       :							str      		=		tf_models
            _lowercase       :							Dict      		=		_re_tf_models.match(lowerCamelCase_ ).groups()[0]
        elif _re_flax_models.match(lowerCamelCase_ ) is not None:
            _lowercase       :							Union[str, Any]      		=		flax_models
            _lowercase       :							List[Any]      		=		_re_flax_models.match(lowerCamelCase_ ).groups()[0]
        elif _re_pt_models.match(lowerCamelCase_ ) is not None:
            _lowercase       :							Any      		=		pt_models
            _lowercase       :							Dict      		=		_re_pt_models.match(lowerCamelCase_ ).groups()[0]

        if lookup_dict is not None:
            while len(lowerCamelCase_ ) > 0:
                if attr_name in model_name_to_prefix.values():
                    _lowercase       :							Dict      		=		True
                    break
                # Try again after removing the last word in the name
                _lowercase       :							List[str]      		=		''.join(camel_case_split(lowerCamelCase_ )[:-1] )

    # Let's build that table!
    _lowercase       :							Optional[Any]      		=		list(model_name_to_config.keys() )
    model_names.sort(key=str.lower )
    _lowercase       :							Tuple      		=		['Model', 'Tokenizer slow', 'Tokenizer fast', 'PyTorch support', 'TensorFlow support', 'Flax Support']
    # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
    _lowercase       :							List[Any]      		=		[len(lowerCamelCase_ ) + 2 for c in columns]
    _lowercase       :							str      		=		max([len(lowerCamelCase_ ) for name in model_names] ) + 2

    # Build the table per se
    _lowercase       :							int      		=		'|' + '|'.join([_center_text(lowerCamelCase_							, lowerCamelCase_ ) for c, w in zip(lowerCamelCase_							, lowerCamelCase_ )] ) + '|\n'
    # Use ":-----:" format to center-aligned table cell texts
    table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths] ) + "|\n"

    _lowercase       :							List[str]      		=		{True: '✅', False: '❌'}
    for name in model_names:
        _lowercase       :							List[str]      		=		model_name_to_prefix[name]
        _lowercase       :							Dict      		=		[
            name,
            check[slow_tokenizers[prefix]],
            check[fast_tokenizers[prefix]],
            check[pt_models[prefix]],
            check[tf_models[prefix]],
            check[flax_models[prefix]],
        ]
        table += "|" + "|".join([_center_text(lowerCamelCase_							, lowerCamelCase_ ) for l, w in zip(lowerCamelCase_							, lowerCamelCase_ )] ) + "|\n"
    return table



def 				UpperCamelCase_(      lowerCamelCase_=False )    ->						List[Any]:
    _lowercase		,				_lowercase		,				_lowercase		,				_lowercase       :							Dict      		=		_find_text_in_file(
        filename=os.path.join(lowerCamelCase_							, 'index.md' )							, start_prompt='<!--This table is updated automatically from the auto modules'							, end_prompt='<!-- End table-->'							, )
    _lowercase       :							Union[str, Any]      		=		get_model_table_from_auto_modules()

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(lowerCamelCase_							, 'index.md' )							, 'w'							, encoding='utf-8'							, newline='\n' ) as f:
                f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
        else:
            raise ValueError(
                'The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.' )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE       :      Dict												= argparse.ArgumentParser()
 parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
 SCREAMING_SNAKE_CASE       :      Tuple												= parser.parse_args()

 check_model_table(args.fix_and_overwrite)






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


SCREAMING_SNAKE_CASE       :      Any												= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :      Union[str, Any]												= {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE       :      Union[str, 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"
        ),
    },
}
SCREAMING_SNAKE_CASE       :      Dict												= {
    "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"
        ),
    },
}
SCREAMING_SNAKE_CASE       :      str												= {
    "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"
        ),
    },
}

SCREAMING_SNAKE_CASE       :      Dict												= {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE       :      Optional[Any]												= {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE       :      Dict												= {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


SCREAMING_SNAKE_CASE       :      List[Any]												= {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
SCREAMING_SNAKE_CASE       :      Dict												= {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
SCREAMING_SNAKE_CASE       :      Dict												= {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}






class _lowerCamelCase(						_a   ):
       lowercase_ :	Any     =			VOCAB_FILES_NAMES
       lowercase_ :	Optional[int]     =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowercase_ :	str     =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_ :	str     =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class _lowerCamelCase(						_a   ):
       lowercase_ :	Optional[int]     =			VOCAB_FILES_NAMES
       lowercase_ :	Any     =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowercase_ :	str     =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_ :	Union[str, Any]     =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


SCREAMING_SNAKE_CASE       :      Optional[int]												= collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

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


SCREAMING_SNAKE_CASE       :      str												= 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(_a   )
class _lowerCamelCase:
       def __call__(					self,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = False,      lowerCamelCase = False,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   BatchEncoding:




           """simple docstring"""

           if titles is None and texts is None:
               return super().__call__(
                   lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase,      max_length=lowerCamelCase,      return_tensors=lowerCamelCase,      return_attention_mask=lowerCamelCase,      **lowerCamelCase,      )
           elif titles is None or texts is None:
               _lowercase       :							Dict      		=		titles if texts is None else texts
               return super().__call__(
                   lowerCamelCase,      lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase,      max_length=lowerCamelCase,      return_tensors=lowerCamelCase,      return_attention_mask=lowerCamelCase,      **lowerCamelCase,      )
           _lowercase       :							Union[str, Any]      		=		titles if not isinstance(lowerCamelCase,      lowerCamelCase) else [titles]
           _lowercase       :							Tuple      		=		texts if not isinstance(lowerCamelCase,      lowerCamelCase) else [texts]
           _lowercase       :							Optional[Any]      		=		len(lowerCamelCase)
           _lowercase       :							Any      		=		questions if not isinstance(lowerCamelCase,      lowerCamelCase) else [questions] * n_passages
           if len(lowerCamelCase) != len(lowerCamelCase):
               raise ValueError(
                   F'''There should be as many titles than texts but got {len(lowerCamelCase)} titles and {len(lowerCamelCase)} texts.''')
           _lowercase       :							Any      		=		super().__call__(lowerCamelCase,      lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase)['input_ids']
           _lowercase       :							Tuple      		=		super().__call__(lowerCamelCase,      add_special_tokens=lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase)['input_ids']
           _lowercase       :							int      		=		{
               '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(lowerCamelCase,      lowerCamelCase)
               ]
           }
           if return_attention_mask is not False:
               _lowercase       :							Optional[Any]      		=		[]
               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       :							Union[str, Any]      		=		attention_mask
           return self.pad(lowerCamelCase,      padding=lowerCamelCase,      max_length=lowerCamelCase,      return_tensors=lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = 16,      lowerCamelCase = 64,      lowerCamelCase = 4,      )       ->   List[DPRSpanPrediction]:




           """simple docstring"""

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

               _lowercase       :							Tuple      		=		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=lowerCamelCase,      top_spans=lowerCamelCase,      )
               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=lowerCamelCase,      start_index=lowerCamelCase,      end_index=lowerCamelCase,      text=self.decode(sequence_ids[start_index : end_index + 1]),      ))
               if len(lowerCamelCase) >= num_spans:
                   break
           return nbest_spans_predictions[:num_spans]




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )       ->   List[DPRSpanPrediction]:




           """simple docstring"""

           _lowercase       :							str      		=		[]
           for start_index, start_score in enumerate(lowerCamelCase):
               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       :							Dict      		=		sorted(lowerCamelCase,      key=lambda lowerCamelCase: x[1],      reverse=lowerCamelCase)
           _lowercase       :							List[str]      		=		[]
           for (start_index, end_index), score in scores:
               if start_index > end_index:
                   raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]''')
               _lowercase       :							Dict      		=		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(lowerCamelCase) == top_spans:
                   break
           return chosen_span_intervals






@add_end_docstrings(_a   )
class _lowerCamelCase(						_a, _a   ):
       lowercase_ :	Union[str, Any]     =			VOCAB_FILES_NAMES
       lowercase_ :	Any     =			READER_PRETRAINED_VOCAB_FILES_MAP
       lowercase_ :	Dict     =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_ :	Optional[int]     =			READER_PRETRAINED_INIT_CONFIGURATION
       lowercase_ :	str     =			["""input_ids""", """attention_mask"""]


