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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__magic_name__  							=	logging.get_logger(__name__)


__magic_name__  							=	{
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

__magic_name__  							=	{
    '''vocab_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
        ),
    },
    '''tokenizer_config_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
        ),
    },
    '''merges_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
        ),
    },
}

__magic_name__  							=	'''</w>'''
__magic_name__  							=	'''@@ '''





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					set()
    snake_case__    =					word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)				)
        snake_case__    =					char
    return pairs


# Speech2Text2 has no max input length
__magic_name__  							=	{'''facebook/s2t-wav2vec2-large-en-de''': 1_024}





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Union[str, Any]     						=  VOCAB_FILES_NAMES
      _A    :		Tuple     						=  PRETRAINED_VOCAB_FILES_MAP
      _A    :		Optional[Any]     						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A    :		List[Any]     						=  ['input_ids', 'attention_mask']
      def __init__(      self		,						lowerCamelCase		,						lowerCamelCase="<s>"		,						lowerCamelCase="<pad>"		,						lowerCamelCase="</s>"		,						lowerCamelCase="<unk>"		,						lowerCamelCase=False		,						lowerCamelCase=None		,						**lowerCamelCase		,						):
          super().__init__(
              unk_token=lowerCamelCase		,						bos_token=lowerCamelCase		,						eos_token=lowerCamelCase		,						pad_token=lowerCamelCase		,						do_lower_case=lowerCamelCase		,						**lowerCamelCase		,						)

          snake_case__    =					do_lower_case

          with open(lowerCamelCase		,						encoding="utf-8"			) as vocab_handle:
              snake_case__    =					json.load(lowerCamelCase			)
          snake_case__    =					{v: k for k, v in self.encoder.items()}

          if merges_file is None:
              logger.info(F"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""			)

              snake_case__    =					None
              snake_case__    =					None
          else:
              with open(lowerCamelCase		,						encoding="utf-8"			) as merges_handle:
                  snake_case__    =					merges_handle.read().split("\n"			)[:-1]

              snake_case__    =					[tuple(merge.split()[:2]			) for merge in merges]
              snake_case__    =					dict(zip(lowerCamelCase		,						range(len(lowerCamelCase			)			)			)			)
              snake_case__    =					{}
      @property
      def A_      (      self			):
          return len(self.decoder			)
      def A_      (      self			):
          return dict(self.encoder		,						**self.added_tokens_encoder			)
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					tuple(token[:-1]			) + (token[-1] + BPE_TOKEN_MERGES,)
          if token in self.cache:
              return self.cache[token]
          snake_case__    =					get_pairs(lowerCamelCase			)

          if not pairs:
              return token

          while True:
              snake_case__    =					min(lowerCamelCase		,						key=lambda lowerCamelCase			: self.bpe_ranks.get(lowerCamelCase		,						float("inf"			)			)			)
              if bigram not in self.bpe_ranks:
                  break
              snake_case__    ,       snake_case__    =					bigram
              snake_case__    =					[]
              snake_case__    =					0
              while i < len(lowerCamelCase			):
                  try:
                      snake_case__    =					word.index(lowerCamelCase		,						lowerCamelCase			)
                  except ValueError:
                      new_word.extend(word[i:]			)
                      break
                  else:
                      new_word.extend(word[i:j]			)
                      snake_case__    =					j

                  if word[i] == first and i < len(lowerCamelCase			) - 1 and word[i + 1] == second:
                      new_word.append(first + second			)
                      i += 2
                  else:
                      new_word.append(word[i]			)
                      i += 1
              snake_case__    =					tuple(lowerCamelCase			)
              snake_case__    =					new_word
              if len(lowerCamelCase			) == 1:
                  break
              else:
                  snake_case__    =					get_pairs(lowerCamelCase			)
          snake_case__    =					" ".join(lowerCamelCase			)
          if word == "\n  " + BPE_TOKEN_MERGES:
              snake_case__    =					"\n" + BPE_TOKEN_MERGES

          if word.endswith(lowerCamelCase			):
              snake_case__    =					word.replace(lowerCamelCase		,						""			)

          snake_case__    =					word.replace(" "		,						lowerCamelCase			)
          snake_case__    =					word
          return word
      def A_      (      self		,						lowerCamelCase			):

          if self.bpe_ranks is None:
              raise ValueError(
                  "This tokenizer was instantiated without a `merges.txt` file, so"
                  " that it can only be used for decoding, not for encoding."
                  "Make sure to provide `merges.txt` file at instantiation to enable "
                  "encoding."			)

          if self.do_lower_case:
              snake_case__    =					text.lower()

          snake_case__    =					text.split()

          snake_case__    =					[]
          for token in text:
              if token:
                  split_tokens.extend(list(self.bpe(lowerCamelCase			).split(" "			)			)			)

          return split_tokens
      def A_      (      self		,						lowerCamelCase			):
          return self.encoder.get(lowerCamelCase		,						self.encoder.get(self.unk_token			)			)
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					self.decoder.get(lowerCamelCase		,						self.unk_token			)
          return result
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					" ".join(lowerCamelCase			)

          # make sure @@ tokens are concatenated
          snake_case__    =					"".join(string.split(lowerCamelCase			)			)

          return string



      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase = None			):
          if not os.path.isdir(lowerCamelCase			):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
              return
          snake_case__    =					os.path.join(
              lowerCamelCase		,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)
          snake_case__    =					os.path.join(
              lowerCamelCase		,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]			)

          with open(lowerCamelCase		,						"w"		,						encoding="utf-8"			) as f:
              f.write(json.dumps(self.encoder		,						indent=2		,						sort_keys=lowerCamelCase		,						ensure_ascii=lowerCamelCase			) + "\n"			)

          snake_case__    =					0
          if self.bpe_ranks is None:
              return (vocab_file,)

          with open(lowerCamelCase		,						"w"		,						encoding="utf-8"			) as writer:
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,						key=lambda lowerCamelCase			: kv[1]			):
                  if index != token_index:
                      logger.warning(
                          F"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
                          " Please check that the tokenizer is not corrupted!"			)
                      snake_case__    =					token_index
                  writer.write(" ".join(lowerCamelCase			) + "\n"			)
                  index += 1

          return (vocab_file, merges_file)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Dict     						=  'biogpt'
      def __init__(      self		,						lowerCamelCase=4_23_84		,						lowerCamelCase=10_24		,						lowerCamelCase=24		,						lowerCamelCase=16		,						lowerCamelCase=40_96		,						lowerCamelCase="gelu"		,						lowerCamelCase=0.1		,						lowerCamelCase=0.1		,						lowerCamelCase=10_24		,						lowerCamelCase=0.0_2		,						lowerCamelCase=1e-12		,						lowerCamelCase=True		,						lowerCamelCase=True		,						lowerCamelCase=0.0		,						lowerCamelCase=0.0		,						lowerCamelCase=1		,						lowerCamelCase=0		,						lowerCamelCase=2		,						**lowerCamelCase		,						):
          snake_case__    =					vocab_size
          snake_case__    =					max_position_embeddings
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					initializer_range
          snake_case__    =					layer_norm_eps
          snake_case__    =					scale_embedding
          snake_case__    =					use_cache
          snake_case__    =					layerdrop
          snake_case__    =					activation_dropout
          super().__init__(pad_token_id=lowerCamelCase		,						bos_token_id=lowerCamelCase		,						eos_token_id=lowerCamelCase		,						**lowerCamelCase			)



