
"""simple docstring"""






import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__SCREAMING_SNAKE_CASE							=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__SCREAMING_SNAKE_CASE							=     """    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""





class 				a__					(						unittest.TestCase       ):



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	=tempfile.mkdtemp()
      os.makedirs(os.path.join(self.transformer_dir      ,  'models/bert/'   )   )
      UpperCamelCase_     :					Optional[Any]   	=self.transformer_dir
      shutil.copy(
          os.path.join(snake_case__      ,  'src/transformers/models/bert/modeling_bert.py'   )      ,  os.path.join(self.transformer_dir      ,  'models/bert/modeling_bert.py'   )      ,  )



     def  lowerCamelCase_						(							self :Union[str, Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	="src/transformers"
      shutil.rmtree(self.transformer_dir   )



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :Any      ,  _lowerCamelCase :Optional[int]      ,  _lowerCamelCase :Any=None   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
      if overwrite_result is not None:
       UpperCamelCase_     :					Optional[int]   	=comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
      UpperCamelCase_     :					int   	=black.Mode(target_versions={black.TargetVersion.PYaa}      ,  line_length=119   )
      UpperCamelCase_     :					int   	=black.format_str(snake_case__      ,  mode=snake_case__   )
      UpperCamelCase_     :					Union[str, Any]   	=os.path.join(self.transformer_dir      ,  'new_code.py'   )
      with open(snake_case__      ,  'w'      ,  newline='\n'   ) as f:
       f.write(snake_case__   )
      if overwrite_result is None:
       self.assertTrue(len(check_copies.is_copy_consistent(snake_case__   )   ) == 0   )
      else:
       check_copies.is_copy_consistent(f.name      ,  overwrite=snake_case__   )
       with open(snake_case__      ,  'r'   ) as f:
        self.assertTrue(f.read()      ,  snake_case__   )



     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'   )
      self.assertEqual(snake_case__      ,  snake_case__   )



     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      self.check_copy_consistency(
          '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'      ,  'BertLMPredictionHead'      ,  REFERENCE_CODE + '\n'      ,  )

      # With no empty line at the end
      self.check_copy_consistency(
          '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'      ,  'BertLMPredictionHead'      ,  snake_case__      ,  )

      # Copy consistency with rename
      self.check_copy_consistency(
          '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'      ,  'TestModelLMPredictionHead'      ,  re.sub('Bert'      ,  'TestModel'      ,  snake_case__   )      ,  )

      # Copy consistency with a really long name
      UpperCamelCase_     :					Optional[int]   	="TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
      self.check_copy_consistency(
          f'''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'''      ,  f'''{long_class_name}LMPredictionHead'''      ,  re.sub('Bert'      ,  snake_case__      ,  snake_case__   )      ,  )

      # Copy consistency with overwrite
      self.check_copy_consistency(
          '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'      ,  'TestModelLMPredictionHead'      ,  snake_case__      ,  overwrite_result=re.sub('Bert'      ,  'TestModel'      ,  snake_case__   )      ,  )




     def  lowerCamelCase_						(							self :Union[str, Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Any   	=check_copies.LOCALIZED_READMES["README_zh-hans.md"]

      UpperCamelCase_     :					Any   	=(
          "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
          " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
          " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
          " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
          " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
          " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
          " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
          " method has been applied to compress GPT2 into"
          " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
          " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
          " Multilingual BERT into"
          " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
          " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
          " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
          " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
          " Luong, Quoc V. Le, Christopher D. Manning."
      )
      UpperCamelCase_     :					Tuple   	=(
          "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
          " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
          " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
          " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
      )
      UpperCamelCase_     :					str   	=(
          "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
          " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
          " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
          " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
          " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
          " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
          " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
          " method has been applied to compress GPT2 into"
          " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
          " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
          " Multilingual BERT into"
          " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
          " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
          " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
          " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
          " Christopher D. Manning 发布。\n"
      )

      UpperCamelCase_     :					List[Any]   	=check_copies.convert_to_localized_md(
          snake_case__      ,  snake_case__      ,  localized_readme['format_model_list']   )

      self.assertFalse(snake_case__   )
      self.assertEqual(snake_case__      ,  snake_case__   )

      UpperCamelCase_     :					Dict   	=check_copies.convert_to_localized_md(
          snake_case__      ,  snake_case__      ,  localized_readme['format_model_list']   )

      # Check whether the number of models is equal to README.md after conversion.
      self.assertTrue(snake_case__   )

      UpperCamelCase_     :					Optional[int]   	=(
          "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
          " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
          " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
          " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
      )
      UpperCamelCase_     :					int   	=(
          "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
          " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
          " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
          " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
      )
      UpperCamelCase_     :					Optional[Any]   	=(
          "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
          " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
          " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
          " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
      )

      UpperCamelCase_     :					Optional[Any]   	=check_copies.convert_to_localized_md(
          snake_case__      ,  snake_case__      ,  localized_readme['format_model_list']   )

      # Check if the model link is synchronized.
      self.assertEqual(snake_case__      ,  snake_case__   )








"""simple docstring"""



import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


_lowerCAmelCase	:	str				=      logging.get_logger(__name__)


_lowerCAmelCase	:	int				=      {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase	:	List[Any]				=      {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

_lowerCAmelCase	:	List[str]				=      {
    """allenai/led-base-16384""": 16_384,
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    =VOCAB_FILES_NAMES
   SCREAMING_SNAKE_CASE_    =PRETRAINED_VOCAB_FILES_MAP
   SCREAMING_SNAKE_CASE_    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   SCREAMING_SNAKE_CASE_    =LEDTokenizer
   SCREAMING_SNAKE_CASE_    =['''input_ids''', '''attention_mask''']
   def __init__(		self      :	Optional[Any]							,			snake_case__      :	str=None							,			snake_case__      :	List[Any]=None							,			snake_case__      :	Dict=None							,			snake_case__      :	List[str]="replace"							,			snake_case__      :	Optional[int]="<s>"							,			snake_case__      :	List[str]="</s>"							,			snake_case__      :	Union[str, Any]="</s>"							,			snake_case__      :	Dict="<s>"							,			snake_case__      :	Tuple="<unk>"							,			snake_case__      :	Any="<pad>"							,			snake_case__      :	Dict="<mask>"							,			snake_case__      :	int=False							,			snake_case__      :	Optional[int]=True							,			**snake_case__      :	List[Any]							,			):


        '''simple docstring'''
        super().__init__(
            snake_case__							,			snake_case__							,			tokenizer_file=snake_case__							,			errors=snake_case__							,			bos_token=snake_case__							,			eos_token=snake_case__							,			sep_token=snake_case__							,			cls_token=snake_case__							,			unk_token=snake_case__							,			pad_token=snake_case__							,			mask_token=snake_case__							,			add_prefix_space=snake_case__							,			trim_offsets=snake_case__							,			**snake_case__							,			)

        UpperCAmelCase__						:    str							=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
        if pre_tok_state.get("add_prefix_space"							,			snake_case__							) != add_prefix_space:
             UpperCAmelCase__						:    Dict							=							getattr(snake_case__							,			pre_tok_state.pop("type"							)							)
             UpperCAmelCase__						:    str							=							add_prefix_space
             UpperCAmelCase__						:    Any							=							pre_tok_class(**snake_case__							)

        UpperCAmelCase__						:    Dict							=							add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        UpperCAmelCase__						:    List[str]							=							"post_processor"
        UpperCAmelCase__						:    List[Any]							=							getattr(self.backend_tokenizer							,			snake_case__							,			snake_case__							)
        if tokenizer_component_instance:
             UpperCAmelCase__						:    int							=							json.loads(tokenizer_component_instance.__getstate__()							)

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                  UpperCAmelCase__						:    Optional[Any]							=							tuple(state["sep"]							)
             if "cls" in state:
                  UpperCAmelCase__						:    Any							=							tuple(state["cls"]							)

             UpperCAmelCase__						:    Any							=							False

             if state.get("add_prefix_space"							,			snake_case__							) != add_prefix_space:
                  UpperCAmelCase__						:    Union[str, Any]							=							add_prefix_space
                  UpperCAmelCase__						:    List[Any]							=							True

             if state.get("trim_offsets"							,			snake_case__							) != trim_offsets:
                  UpperCAmelCase__						:    Optional[int]							=							trim_offsets
                  UpperCAmelCase__						:    List[Any]							=							True

             if changes_to_apply:
                  UpperCAmelCase__						:    List[str]							=							getattr(snake_case__							,			state.pop("type"							)							)
                  UpperCAmelCase__						:    Optional[int]							=							component_class(**snake_case__							)
                  setattr(self.backend_tokenizer							,			snake_case__							,			snake_case__							)
   @property
   # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        if self._mask_token is None:
             if self.verbose:
                  logger.error("Using mask_token, but it is not set yet."							)
             return None
        return str(self._mask_token							)
   @mask_token.setter
   def       __a							(		self      :	Any							,			snake_case__      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__							) if isinstance(snake_case__							,			snake_case__							) else value
        UpperCAmelCase__						:    Dict							=							value
   def       __a							(		self      :	str							,			*snake_case__      :	Any							,			**snake_case__      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							kwargs.get("is_split_into_words"							,			snake_case__							)

        if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                 "to use it with pretokenized inputs."							)

        return super()._batch_encode_plus(*snake_case__							,			**snake_case__							)
   def       __a							(		self      :	List[str]							,			*snake_case__      :	Union[str, Any]							,			**snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							kwargs.get("is_split_into_words"							,			snake_case__							)

        if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                 "to use it with pretokenized inputs."							)

        return super()._encode_plus(*snake_case__							,			**snake_case__							)
   def       __a							(		self      :	Tuple							,			snake_case__      :	str							,			snake_case__      :	Optional[str] = None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							self._tokenizer.model.save(snake_case__							,			name=snake_case__							)
        return tuple(snake_case__							)
   def       __a							(		self      :	str							,			snake_case__      :	List[Any]							,			snake_case__      :	str=None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
             return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
   def       __a							(		self      :	str							,			snake_case__      :	List[int]							,			snake_case__      :	Optional[List[int]] = None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							[self.sep_token_id]
        UpperCAmelCase__						:    Optional[Any]							=							[self.cls_token_id]

        if token_ids_a is None:
             return len(cls + token_ids_a + sep							) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]




   def       __a							(		self      :	Any							,			snake_case__      :	Union[Dict[str, EncodedInput], BatchEncoding]							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	Optional[bool] = None							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							super()._pad(
            encoded_inputs=snake_case__							,			max_length=snake_case__							,			padding_strategy=snake_case__							,			pad_to_multiple_of=snake_case__							,			return_attention_mask=snake_case__							,			)

        # Load from model defaults
        if return_attention_mask is None:
             UpperCAmelCase__						:    Optional[int]							=							"attention_mask" in self.model_input_names

        if return_attention_mask and "global_attention_mask" in encoded_inputs:
             UpperCAmelCase__						:    List[Any]							=							encoded_inputs[self.model_input_names[0]]
             # `global_attention_mask` need to have the same length as other (sequential) inputs.
             UpperCAmelCase__						:    Any							=							len(encoded_inputs["global_attention_mask"]							) != len(snake_case__							)

             if needs_to_be_padded:
                  UpperCAmelCase__						:    List[str]							=							len(snake_case__							) - len(encoded_inputs["global_attention_mask"]							)

                  if self.padding_side == "right":
                       # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                       UpperCAmelCase__						:    Dict							=							(
                           encoded_inputs["global_attention_mask"] + [-1] * difference
                       )
                  elif self.padding_side == "left":
                       UpperCAmelCase__						:    Dict							=							[-1] * difference + encoded_inputs[
                           "global_attention_mask"
                       ]
                  else:
                       raise ValueError("Invalid padding strategy:" + str(self.padding_side							)							)

        return encoded_inputs


