






"""simple docstring"""





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

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


A__       :			Any=						logging.get_logger(__name__)

A__       :			Tuple=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

A__       :			Dict=						{
    """vocab_file""": {
        """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json""",
    },
    """merges_file""": {"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"""},
}

A__       :			int=						{
    """microsoft/biogpt""": 10_24,
}







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						Dict:
    """simple docstring"""

    UpperCamelCase__								=     set()
    UpperCamelCase__								=     word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)       )
        UpperCamelCase__								=     char
    return pairs





class  __lowerCamelCase     (	_a	):
   a   :					Optional[int]			    =VOCAB_FILES_NAMES
   a   :					Optional[Any]			    =PRETRAINED_VOCAB_FILES_MAP
   a   :					List[str]			    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a   :					List[str]			    =["""input_ids""", """attention_mask"""]

   def __init__(       self			,					snake_case_			,					snake_case_			,					snake_case_="<unk>"			,					snake_case_="<s>"			,					snake_case_="</s>"			,					snake_case_="</s>"			,					snake_case_="<pad>"			,					**snake_case_			,					)		->							Union[str, Any]:
       super().__init__(
           bos_token=snake_case_			,					eos_token=snake_case_			,					sep_token=snake_case_			,					unk_token=snake_case_			,					pad_token=snake_case_			,					**snake_case_			,					)

       try:
           import sacremoses
       except ImportError:
           raise ImportError(
               'You need to install sacremoses to use BioGptTokenizer. '
               'See https://pypi.org/project/sacremoses/ for installation.'   )

       UpperCamelCase__								=     'en'
       UpperCamelCase__								=     sacremoses
       # cache of sm.MosesTokenizer instance
       UpperCamelCase__								=     {}
       UpperCamelCase__								=     {}
       with open(snake_case_			,					encoding='utf-8'   ) as vocab_handle:
           UpperCamelCase__								=     json.load(snake_case_   )
       UpperCamelCase__								=     {v: k for k, v in self.encoder.items()}
       with open(snake_case_			,					encoding='utf-8'   ) as merges_handle:
           UpperCamelCase__								=     merges_handle.read().split('\n'   )[:-1]
       UpperCamelCase__								=     [tuple(merge.split()[:2]   ) for merge in merges]
       UpperCamelCase__								=     dict(zip(snake_case_			,					range(len(snake_case_   )   )   )   )
       UpperCamelCase__								=     {}

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[int]:
       return len(self.encoder   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Union[str, Any]:
       return dict(self.encoder			,					**self.added_tokens_encoder   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_   )		->							int:
       if lang not in self.cache_moses_tokenizer:
           UpperCamelCase__								=     self.sm.MosesTokenizer(lang=snake_case_   )
           UpperCamelCase__								=     moses_tokenizer
       return self.cache_moses_tokenizer[lang].tokenize(
           snake_case_			,					aggressive_dash_splits=snake_case_			,					return_str=snake_case_			,					escape=snake_case_   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_   )		->							Optional[int]:
       if lang not in self.cache_moses_detokenizer:
           UpperCamelCase__								=     self.sm.MosesDetokenizer(lang=snake_case_   )
           UpperCamelCase__								=     moses_detokenizer
       return self.cache_moses_detokenizer[lang].detokenize(snake_case_   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Optional[Any]:
       UpperCamelCase__								=     tuple(token[:-1]   ) + (token[-1] + '</w>',)
       if token in self.cache:
           return self.cache[token]
       UpperCamelCase__								=     get_pairs(snake_case_   )

       if not pairs:
           return token + "</w>"

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

               if word[i] == first and i < len(snake_case_   ) - 1 and word[i + 1] == second:
                   new_word.append(first + second   )
                   i += 2
               else:
                   new_word.append(word[i]   )
                   i += 1
           UpperCamelCase__								=     tuple(snake_case_   )
           UpperCamelCase__								=     new_word
           if len(snake_case_   ) == 1:
               break
           else:
               UpperCamelCase__								=     get_pairs(snake_case_   )
       UpperCamelCase__								=     ' '.join(snake_case_   )
       if word == "\n  </w>":
           UpperCamelCase__								=     '\n</w>'
       UpperCamelCase__								=     word
       return word

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_=False   )		->							Optional[int]:
       if bypass_tokenizer:
           UpperCamelCase__								=     text.split()
       else:
           UpperCamelCase__								=     self.moses_tokenize(snake_case_			,					self.lang   )

       UpperCamelCase__								=     []
       for token in text:
           if token:
               split_tokens.extend(list(self.bpe(snake_case_   ).split(' '   )   )   )

       return split_tokens

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Dict:
       return self.encoder.get(snake_case_			,					self.encoder.get(self.unk_token   )   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							List[str]:
       return self.decoder.get(snake_case_			,					self.unk_token   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Optional[int]:
       UpperCamelCase__								=     [t.replace(' '			,					''   ).replace('</w>'			,					' '   ) for t in tokens]
       UpperCamelCase__								=     ''.join(snake_case_   ).split()
       # detokenize
       UpperCamelCase__								=     self.moses_detokenize(snake_case_			,					self.lang   )
       return text

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_ = None   )		->							List[int]:
       if token_ids_a is None:
           return [self.sep_token_id] + token_ids_a
       UpperCamelCase__								=     [self.sep_token_id]
       return sep + token_ids_a + sep + token_ids_a

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_ = None			,					snake_case_ = False   )		->							List[int]:
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=snake_case_			,					token_ids_a=snake_case_			,					already_has_special_tokens=snake_case_   )
       # no bos used in fairseq
       if token_ids_a is not None:
           return [1] + ([0] * len(snake_case_   )) + [1] + ([0] * len(snake_case_   ))
       return [1] + ([0] * len(snake_case_   ))

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_ = None   )		->							List[int]:
       UpperCamelCase__								=     [self.sep_token_id]

       # no bos used in fairseq
       if token_ids_a is None:
           return len(token_ids_a + sep   ) * [0]
       return len(token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_ = None   )		->							Tuple[str]:
       if not os.path.isdir(snake_case_   ):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
           return
       UpperCamelCase__								=     os.path.join(
           snake_case_			,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )
       UpperCamelCase__								=     os.path.join(
           snake_case_			,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']   )

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

       UpperCamelCase__								=     0
       with open(snake_case_			,					'w'			,					encoding='utf-8'   ) as writer:
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,					key=lambda snake_case_   : kv[1]   ):
               if index != token_index:
                   logger.warning(
                       F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                       ' Please check that the tokenizer is not corrupted!'   )
                   UpperCamelCase__								=     token_index
               writer.write(' '.join(snake_case_   ) + '\n'   )
               index += 1

       return vocab_file, merge_file

   def __getstate__(       self   )		->							Any:
       UpperCamelCase__								=     self.__dict__.copy()
       UpperCamelCase__								=     None
       return state







   def __setstate__(       self			,					snake_case_   )		->							List[Any]:
       UpperCamelCase__								=     d

       try:
           import sacremoses
       except ImportError:
           raise ImportError(
               'You need to install sacremoses to use XLMTokenizer. '
               'See https://pypi.org/project/sacremoses/ for installation.'   )

       UpperCamelCase__								=     sacremoses












"""simple docstring"""





import functools
import operator

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


A__       :			int=						logging.get_logger(__name__)

A__       :			Optional[int]=						{
    """facebook/hubert-base-ls960""": """https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json""",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}
class  __lowerCamelCase     (	_a	):
   a   :					Dict			    ="""hubert"""

   def __init__(       self			,					snake_case_=32			,					snake_case_=768			,					snake_case_=12			,					snake_case_=12			,					snake_case_=3072			,					snake_case_="gelu"			,					snake_case_=0.1			,					snake_case_=0.1			,					snake_case_=0.1			,					snake_case_=True			,					snake_case_=0.0			,					snake_case_=0.1			,					snake_case_=0.1			,					snake_case_=0.02			,					snake_case_=1E-5			,					snake_case_="group"			,					snake_case_="gelu"			,					snake_case_=(512, 512, 512, 512, 512, 512, 512)			,					snake_case_=(5, 2, 2, 2, 2, 2, 2)			,					snake_case_=(10, 3, 3, 3, 3, 2, 2)			,					snake_case_=False			,					snake_case_=128			,					snake_case_=16			,					snake_case_=False			,					snake_case_=True			,					snake_case_=0.05			,					snake_case_=10			,					snake_case_=2			,					snake_case_=0.0			,					snake_case_=10			,					snake_case_=0			,					snake_case_="sum"			,					snake_case_=False			,					snake_case_=False			,					snake_case_=256			,					snake_case_=0			,					snake_case_=1			,					snake_case_=2			,					**snake_case_			,					)		->							Any:
       super().__init__(**snake_case_			,					pad_token_id=snake_case_			,					bos_token_id=snake_case_			,					eos_token_id=snake_case_   )
       UpperCamelCase__								=     hidden_size
       UpperCamelCase__								=     feat_extract_norm
       UpperCamelCase__								=     feat_extract_activation
       UpperCamelCase__								=     list(snake_case_   )
       UpperCamelCase__								=     list(snake_case_   )
       UpperCamelCase__								=     list(snake_case_   )
       UpperCamelCase__								=     conv_bias
       UpperCamelCase__								=     num_conv_pos_embeddings
       UpperCamelCase__								=     num_conv_pos_embedding_groups
       UpperCamelCase__								=     len(self.conv_dim   )
       UpperCamelCase__								=     num_hidden_layers
       UpperCamelCase__								=     intermediate_size
       UpperCamelCase__								=     hidden_act
       UpperCamelCase__								=     num_attention_heads
       UpperCamelCase__								=     hidden_dropout
       UpperCamelCase__								=     attention_dropout
       UpperCamelCase__								=     activation_dropout
       UpperCamelCase__								=     feat_proj_layer_norm
       UpperCamelCase__								=     feat_proj_dropout
       UpperCamelCase__								=     final_dropout
       UpperCamelCase__								=     layerdrop
       UpperCamelCase__								=     layer_norm_eps
       UpperCamelCase__								=     initializer_range
       UpperCamelCase__								=     vocab_size
       UpperCamelCase__								=     do_stable_layer_norm
       UpperCamelCase__								=     use_weighted_layer_sum
       UpperCamelCase__								=     classifier_proj_size

       if (
           (len(self.conv_stride   ) != self.num_feat_extract_layers)
           or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
           or (len(self.conv_dim   ) != self.num_feat_extract_layers)
       ):
           raise ValueError(
               'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
               ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
               F' {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,'
               F' `len(config.conv_kernel) = {len(self.conv_kernel   )}`.'   )

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       UpperCamelCase__								=     apply_spec_augment
       UpperCamelCase__								=     mask_time_prob
       UpperCamelCase__								=     mask_time_length
       UpperCamelCase__								=     mask_time_min_masks
       UpperCamelCase__								=     mask_feature_prob
       UpperCamelCase__								=     mask_feature_length
       UpperCamelCase__								=     mask_feature_min_masks

       # ctc loss
       UpperCamelCase__								=     ctc_loss_reduction
       UpperCamelCase__								=     ctc_zero_infinity







   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       return functools.reduce(operator.mul			,					self.conv_stride			,					1   )




