





'''simple docstring'''




import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
  # expert layer
  __UpperCAmelCase					=   flax_key_tuple[:-1] + ('weight',)
  __UpperCAmelCase					=   torch.permute(_lowerCAmelCase       ,    (0, 2, 1)    )
 elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase    ):
  # linear layer
  __UpperCAmelCase					=   flax_key_tuple[:-1] + ('weight',)
  __UpperCAmelCase					=   flax_tensor.T
 elif flax_key_tuple[-1] in ["scale", "embedding"]:
  __UpperCAmelCase					=   flax_key_tuple[:-1] + ('weight',)

 return flax_key_tuple, flax_tensor







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[str]:
 if "metadata" in layer:
  __UpperCAmelCase					=   layer.split('metadata'    )
  __UpperCAmelCase					=   ''.join(split_layer[0]    )[:-1]
  __UpperCAmelCase					=   [tuple(('metadata' + split_layer[1]).split('/'    )    )]
 elif "kvstore" in layer:
  __UpperCAmelCase					=   layer.split('kvstore'    )
  __UpperCAmelCase					=   ''.join(split_layer[0]    )[:-1]
  __UpperCAmelCase					=   [tuple(('kvstore' + split_layer[1]).split('/'    )    )]

 else:
  __UpperCAmelCase					=   layer.split('/'    )
  __UpperCAmelCase					=   '/'.join(split_layer[:-1]    )
  __UpperCAmelCase					=   (split_layer[-1],)

 if "kvstore/path" in layer:
  __UpperCAmelCase					=   F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
 elif "kvstore/driver" in layer:
  __UpperCAmelCase					=   'file'
 else:
  __UpperCAmelCase					=   checkpoint_info[layer]

 return curr_real_layer_name, split_layer, content







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   rename_keys(_lowerCAmelCase    )
 __UpperCAmelCase					=   {}
 for k, v in current_block.items():
  __UpperCAmelCase					=   v
 __UpperCAmelCase					=   new_current_block
 torch.save(_lowerCAmelCase       ,    _lowerCAmelCase    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase = WEIGHTS_NAME    )->     str:
 __UpperCAmelCase					=   convert_file_size_to_int(_lowerCAmelCase    )
 __UpperCAmelCase					=   []
 __UpperCAmelCase					=   {}
 __UpperCAmelCase					=   0
 __UpperCAmelCase					=   0

 os.makedirs(_lowerCAmelCase       ,    exist_ok=_lowerCAmelCase    )
 with gfile.GFile(switch_checkpoint_path + '/checkpoint'       ,    'rb'    ) as fp:
  __UpperCAmelCase					=   serialization.msgpack_restore(fp.read()    )['optimizer']['target']
  __UpperCAmelCase					=   flatten_dict(_lowerCAmelCase       ,    sep='/'    )

 __UpperCAmelCase					=   {}
 for layer in checkpoint_info.keys():
  __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   get_key_and_tensorstore_dict(
      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )
  if curr_real_layer_name in all_layers:
   __UpperCAmelCase					=   content
  else:
   __UpperCAmelCase					=   {split_layer[-1]: content}

 for key in all_layers.keys():
  # open tensorstore file
  __UpperCAmelCase					=   ts.open(unflatten_dict(all_layers[key]    )    ).result().read().result()
  __UpperCAmelCase					=   torch.tensor(_lowerCAmelCase    )
  __UpperCAmelCase					=   raw_weights.numel() * dtype_byte_size(raw_weights.dtype    )

  # use the renaming pattern from the small conversion scripts
  __UpperCAmelCase					,    __UpperCAmelCase					=   rename_base_flax_keys(tuple(key.split('/'    )    )       ,    _lowerCAmelCase    )
  __UpperCAmelCase					=   '/'.join(_lowerCAmelCase    )

  # If this weight is going to tip up over the maximal size, we split.
  if current_block_size + weight_size > max_shard_size:
   __UpperCAmelCase					=   os.path.join(
       _lowerCAmelCase       ,    weights_name.replace('.bin'       ,    F'-{len(_lowerCAmelCase    )+1:05d}-of-???.bin'    )    )
   rename_and_save_block(_lowerCAmelCase       ,    _lowerCAmelCase    )
   sharded_state_dicts.append(current_block.keys()    )
   del current_block
   __UpperCAmelCase					=   {}
   __UpperCAmelCase					=   0

  __UpperCAmelCase					=   raw_weights.to(getattr(_lowerCAmelCase       ,    _lowerCAmelCase    )    )
  current_block_size += weight_size
  total_size += weight_size

 # Add the last block
 __UpperCAmelCase					=   os.path.join(_lowerCAmelCase       ,    weights_name.replace('.bin'       ,    F'-{len(_lowerCAmelCase    )+1:05d}-of-???.bin'    )    )
 rename_and_save_block(_lowerCAmelCase       ,    _lowerCAmelCase    )
 sharded_state_dicts.append(current_block.keys()    )

 # If we only have one shard, we return it
 if len(_lowerCAmelCase    ) == 1:
  return {weights_name: sharded_state_dicts[0]}, None

 # Otherwise, let's build the index
 __UpperCAmelCase					=   {}
 __UpperCAmelCase					=   {}
 for idx, shard in enumerate(_lowerCAmelCase    ):
  __UpperCAmelCase					=   weights_name.replace(
      '.bin'       ,    F'-{idx+1:05d}-of-{len(_lowerCAmelCase    ):05d}.bin'    )  # len(sharded_state_dicts):05d}
  __UpperCAmelCase					=   os.path.join(_lowerCAmelCase       ,    weights_name.replace('.bin'       ,    F'-{idx+1:05d}-of-???.bin'    )    )
  os.rename(_lowerCAmelCase       ,    os.path.join(_lowerCAmelCase       ,    _lowerCAmelCase    )    )
  __UpperCAmelCase					=   shard
  for key in shard:
   __UpperCAmelCase					=   shard_file

    # Add the metadata
 __UpperCAmelCase					=   {'total_size': total_size}
 __UpperCAmelCase					=   {'metadata': metadata, 'weight_map': weight_map}

 with open(os.path.join(_lowerCAmelCase       ,    _lowerCAmelCase    )       ,    'w'       ,    encoding='utf-8'    ) as f:
  __UpperCAmelCase					=   json.dumps(_lowerCAmelCase       ,    indent=2       ,    sort_keys=_lowerCAmelCase    ) + '\n'
  f.write(_lowerCAmelCase    )

 return metadata, index


if __name__ == "__main__":
    _A:					Tuple							=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--switch_t5x_checkpoint_path""",
        default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
        type=str,
        required=False,
        help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
    )
    parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
    parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
    parser.add_argument(
        """--pytorch_dump_folder_path""",
        default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
        type=str,
        required=False,
        help="""Path to the output pytorch model.""",
    )
    _A:					Union[str, Any]							=				parser.parse_args()
    shard_on_the_fly(
        args.switch_tax_checkpoint_path,
        args.pytorch_dump_folder_path,
        args.max_shard_size,
        args.dtype,
    )







def _lowerCAmelCase     (      )->     List[str]:
 from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

 __UpperCAmelCase					=   SwitchTransformersConfig.from_pretrained('google/switch-base-8'    )
 config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'    )
 __UpperCAmelCase					=   SwitchTransformersForConditionalGeneration.from_pretrained(
     '/home/arthur_huggingface_co/transformers/switch_converted'       ,    device_map='auto'    )

 __UpperCAmelCase					=   TaTokenizer.from_pretrained('t5-small'    )
 __UpperCAmelCase					=   'A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'

 __UpperCAmelCase					=   tokenizer(_lowerCAmelCase       ,    return_tensors='pt'    ).input_ids
 __UpperCAmelCase					=   model.generate(_lowerCAmelCase       ,    decoder_start_token_id=0    )
 print(tokenizer.decode(out[0]    )    )












'''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


_A:					List[str]							=				logging.get_logger(__name__)

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

_A:					int							=				{
    """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:					int							=				{
    """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:					Dict							=				{
    """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:					Any							=				{
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
_A:					Tuple							=				{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
_A:					int							=				{
    """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:					List[Any]							=				{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
_A:					Dict							=				{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Tuple						   =       VOCAB_FILES_NAMES
    _A :			Dict						   =       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    _A :			Any						   =       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			Optional[Any]						   =       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			List[str]						   =       VOCAB_FILES_NAMES
    _A :			str						   =       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    _A :			Union[str, Any]						   =       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			int						   =       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



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

_A:					Tuple							=				collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_A:					Optional[Any]							=				r"""
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer's default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    """






@add_start_docstrings(UpperCAmelCase_							)
class UpperCAmelCase							:



    def __call__(  self     ,			__A     ,			__A = None     ,			__A = None     ,			__A = False     ,			__A = False     ,			__A = None     ,			__A = None     ,			__A = None     ,			**__A     ,			):
     if titles is None and texts is None:
      return super().__call__(
          __A     ,			padding=__A     ,			truncation=__A     ,			max_length=__A     ,			return_tensors=__A     ,			return_attention_mask=__A     ,			**__A     ,			)
     elif titles is None or texts is None:
      __UpperCAmelCase					=   titles if texts is None else texts
      return super().__call__(
          __A     ,			__A     ,			padding=__A     ,			truncation=__A     ,			max_length=__A     ,			return_tensors=__A     ,			return_attention_mask=__A     ,			**__A     ,			)
     __UpperCAmelCase					=   titles if not isinstance(__A     ,			__A					) else [titles]
     __UpperCAmelCase					=   texts if not isinstance(__A     ,			__A					) else [texts]
     __UpperCAmelCase					=   len(__A					)
     __UpperCAmelCase					=   questions if not isinstance(__A     ,			__A					) else [questions] * n_passages
     if len(__A					) != len(__A					):
      raise ValueError(
          f'There should be as many titles than texts but got {len(__A					)} titles and {len(__A					)} texts.'					)
     __UpperCAmelCase					=   super().__call__(__A     ,			__A     ,			padding=__A     ,			truncation=__A					)['input_ids']
     __UpperCAmelCase					=   super().__call__(__A     ,			add_special_tokens=__A     ,			padding=__A     ,			truncation=__A					)['input_ids']
     __UpperCAmelCase					=   {
         '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(__A     ,			__A					)
         ]
     }
     if return_attention_mask is not False:
      __UpperCAmelCase					=   []
      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					=   attention_mask
     return self.pad(__A     ,			padding=__A     ,			max_length=__A     ,			return_tensors=__A					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A = 16     ,			__A = 64     ,			__A = 4     ,			):
     __UpperCAmelCase					=   reader_input['input_ids']
     __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   reader_output[:3]
     __UpperCAmelCase					=   len(__A					)
     __UpperCAmelCase					=   sorted(range(__A					)     ,			reverse=__A     ,			key=relevance_logits.__getitem__					)
     __UpperCAmelCase					=   []
     for doc_id in sorted_docs:
      __UpperCAmelCase					=   list(input_ids[doc_id]					)
      # assuming question & title information is at the beginning of the sequence
      __UpperCAmelCase					=   sequence_ids.index(self.sep_token_id     ,			2					) + 1  # second sep id
      if sequence_ids[-1] == self.pad_token_id:
       __UpperCAmelCase					=   sequence_ids.index(self.pad_token_id					)
      else:
       __UpperCAmelCase					=   len(__A					)

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




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			):
     __UpperCAmelCase					=   []
     for start_index, start_score in enumerate(__A					):
      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					=   sorted(__A     ,			key=lambda __A					: x[1]     ,			reverse=__A					)
     __UpperCAmelCase					=   []
     for (start_index, end_index), score in scores:
      if start_index > end_index:
       raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]'					)
      __UpperCAmelCase					=   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(__A					) == top_spans:
       break
     return chosen_span_intervals






@add_end_docstrings(UpperCAmelCase_							)
class UpperCAmelCase							(		UpperCAmelCase_					,			UpperCAmelCase_							):
    _A :			Optional[int]						   =       VOCAB_FILES_NAMES
    _A :			Optional[int]						   =       READER_PRETRAINED_VOCAB_FILES_MAP
    _A :			Dict						   =       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			Union[str, Any]						   =       READER_PRETRAINED_INIT_CONFIGURATION
    _A :			List[Any]						   =       ["""input_ids""", """attention_mask"""]





