




import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


lowerCamelCase__ :   Any					=							logging.get_logger(__name__)

lowerCamelCase__ :   Any					=							{'vocab_file': 'vocab.txt'}

lowerCamelCase__ :   List[str]					=							{
    'vocab_file': {
        'facebook/esm2_t6_8M_UR50D': 'https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt',
        'facebook/esm2_t12_35M_UR50D': 'https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt',
    },
}

lowerCamelCase__ :   Tuple					=							{
    'facebook/esm2_t6_8M_UR50D': 1_024,
    'facebook/esm2_t12_35M_UR50D': 1_024,
}

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[Any]							)					-> Dict:
      with open(__UpperCAmelCase			,			'r'							) as f:
            SCREAMING_SNAKE_CASE_							=			f.read().splitlines()
            return [l.strip() for l in lines]





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    VOCAB_FILES_NAMES
 lowercase_				    =    PRETRAINED_VOCAB_FILES_MAP
 lowercase_				    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_				    =    ["input_ids", "attention_mask"]



 def __init__(							self  :		List[Any]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		int="<unk>"				,				_lowerCAmelCase  :		Tuple="<cls>"				,				_lowerCAmelCase  :		Union[str, Any]="<pad>"				,				_lowerCAmelCase  :		Union[str, Any]="<mask>"				,				_lowerCAmelCase  :		Dict="<eos>"				,				**_lowerCAmelCase  :		Dict				,				):
       super().__init__(**_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			load_vocab_file(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			dict(enumerate(self.all_tokens						)						)
       SCREAMING_SNAKE_CASE_							=			{tok: ind for ind, tok in enumerate(self.all_tokens						)}
       SCREAMING_SNAKE_CASE_							=			unk_token
       SCREAMING_SNAKE_CASE_							=			cls_token
       SCREAMING_SNAKE_CASE_							=			pad_token
       SCREAMING_SNAKE_CASE_							=			mask_token
       SCREAMING_SNAKE_CASE_							=			eos_token
       SCREAMING_SNAKE_CASE_							=			self.all_tokens
       self._create_trie(self.unique_no_split_tokens						)



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		int						):
       return self._id_to_token.get(_lowerCAmelCase				,				self.unk_token						)



 def       lowerCAmelCase_  (							self  :		Optional[Any]				,				_lowerCAmelCase  :		str						):
       return self._token_to_id.get(_lowerCAmelCase				,				self._token_to_id.get(self.unk_token						)						)



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		Tuple				,				**_lowerCAmelCase  :		Dict						):
       return text.split()



 def       lowerCAmelCase_  (							self  :		Optional[Any]				,				_lowerCAmelCase  :		int=False						):
       return len(self._id_to_token						)



 def       lowerCAmelCase_  (							self  :		int						):
       return {token: i for i, token in enumerate(self.all_tokens						)}



 def       lowerCAmelCase_  (							self  :		List[str]				,				_lowerCAmelCase  :		str						):
       return self._token_to_id.get(_lowerCAmelCase				,				self._token_to_id.get(self.unk_token						)						)



 def       lowerCAmelCase_  (							self  :		str				,				_lowerCAmelCase  :		int						):
       return self._id_to_token.get(_lowerCAmelCase				,				self.unk_token						)



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		List[int]				,				_lowerCAmelCase  :		Optional[List[int]] = None						):
       SCREAMING_SNAKE_CASE_							=			[self.cls_token_id]
       SCREAMING_SNAKE_CASE_							=			[self.eos_token_id]  # No sep token in ESM vocabulary
       if token_ids_a is None:
             if self.eos_token_id is None:
                   return cls + token_ids_a
             else:
                   return cls + token_ids_a + sep
       elif self.eos_token_id is None:
             raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!'						)
       return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token



 def       lowerCAmelCase_  (							self  :		List[Any]				,				_lowerCAmelCase  :		List				,				_lowerCAmelCase  :		Optional[List] = None				,				_lowerCAmelCase  :		bool = False						):
       if already_has_special_tokens:
             if token_ids_a is not None:
                   raise ValueError(
                       'You should not supply a second sequence if the provided sequence of '
                       'ids is already formatted with special tokens for the model.'						)

             return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
       SCREAMING_SNAKE_CASE_							=			[1] + ([0] * len(_lowerCAmelCase						)) + [1]
       if token_ids_a is not None:
             mask += [0] * len(_lowerCAmelCase						) + [1]
       return mask



 def       lowerCAmelCase_  (							self  :		List[Any]				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		int						):
       SCREAMING_SNAKE_CASE_							=			os.path.join(_lowerCAmelCase				,				(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt'						)
       with open(_lowerCAmelCase				,				'w'						) as f:
             f.write('\n'.join(self.all_tokens						)						)
       return (vocab_file,)



 @property
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       return self.get_vocab_size(with_added_tokens=_lowerCAmelCase						)




 def       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		Union[List[str], List[AddedToken]]				,				_lowerCAmelCase  :		bool = False						):
       return super()._add_tokens(_lowerCAmelCase				,				special_tokens=_lowerCAmelCase						)





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


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

lowerCamelCase__ :   List[str]					=							{
    'facebook/s2t-small-librispeech-asr': (
        'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    "speech_to_text"
 lowercase_				    =    ["past_key_values"]
 lowercase_				    =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



 def __init__(							self  :		Tuple				,				_lowerCAmelCase  :		List[Any]=10_000				,				_lowerCAmelCase  :		List[Any]=12				,				_lowerCAmelCase  :		Union[str, Any]=2_048				,				_lowerCAmelCase  :		Optional[int]=4				,				_lowerCAmelCase  :		Union[str, Any]=6				,				_lowerCAmelCase  :		Optional[int]=2_048				,				_lowerCAmelCase  :		Optional[Any]=4				,				_lowerCAmelCase  :		Any=0.0				,				_lowerCAmelCase  :		Optional[Any]=0.0				,				_lowerCAmelCase  :		List[str]=True				,				_lowerCAmelCase  :		List[Any]=True				,				_lowerCAmelCase  :		int="relu"				,				_lowerCAmelCase  :		Union[str, Any]=256				,				_lowerCAmelCase  :		List[str]=0.1				,				_lowerCAmelCase  :		str=0.0				,				_lowerCAmelCase  :		Tuple=0.0				,				_lowerCAmelCase  :		List[str]=0.02				,				_lowerCAmelCase  :		List[str]=2				,				_lowerCAmelCase  :		Tuple=True				,				_lowerCAmelCase  :		List[str]=1				,				_lowerCAmelCase  :		str=0				,				_lowerCAmelCase  :		str=2				,				_lowerCAmelCase  :		Union[str, Any]=6_000				,				_lowerCAmelCase  :		List[str]=1_024				,				_lowerCAmelCase  :		str=2				,				_lowerCAmelCase  :		Optional[Any]=(5, 5)				,				_lowerCAmelCase  :		str=1_024				,				_lowerCAmelCase  :		str=80				,				_lowerCAmelCase  :		Tuple=1				,				**_lowerCAmelCase  :		Any				,				):
       SCREAMING_SNAKE_CASE_							=			vocab_size
       SCREAMING_SNAKE_CASE_							=			d_model
       SCREAMING_SNAKE_CASE_							=			encoder_ffn_dim
       SCREAMING_SNAKE_CASE_							=			encoder_layers
       SCREAMING_SNAKE_CASE_							=			encoder_attention_heads
       SCREAMING_SNAKE_CASE_							=			decoder_ffn_dim
       SCREAMING_SNAKE_CASE_							=			decoder_layers
       SCREAMING_SNAKE_CASE_							=			decoder_attention_heads
       SCREAMING_SNAKE_CASE_							=			dropout
       SCREAMING_SNAKE_CASE_							=			attention_dropout
       SCREAMING_SNAKE_CASE_							=			activation_dropout
       SCREAMING_SNAKE_CASE_							=			activation_function
       SCREAMING_SNAKE_CASE_							=			init_std
       SCREAMING_SNAKE_CASE_							=			encoder_layerdrop
       SCREAMING_SNAKE_CASE_							=			decoder_layerdrop
       SCREAMING_SNAKE_CASE_							=			use_cache
       SCREAMING_SNAKE_CASE_							=			encoder_layers
       SCREAMING_SNAKE_CASE_							=			scale_embedding  # scale factor will be sqrt(d_model) if True
       SCREAMING_SNAKE_CASE_							=			max_source_positions
       SCREAMING_SNAKE_CASE_							=			max_target_positions
       SCREAMING_SNAKE_CASE_							=			num_conv_layers
       SCREAMING_SNAKE_CASE_							=			list(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			conv_channels
       SCREAMING_SNAKE_CASE_							=			input_feat_per_channel
       SCREAMING_SNAKE_CASE_							=			input_channels

       if len(self.conv_kernel_sizes						) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                 F"but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes						)}`, "
                 F"`config.num_conv_layers = {self.num_conv_layers}`."						)

       super().__init__(
           pad_token_id=_lowerCAmelCase				,				bos_token_id=_lowerCAmelCase				,				eos_token_id=_lowerCAmelCase				,				is_encoder_decoder=_lowerCAmelCase				,				decoder_start_token_id=_lowerCAmelCase				,				**_lowerCAmelCase				,				)