





'''simple docstring'''

import math

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''data2vec-audio'''


    def __init__(		self ,		A=32 ,		A=768 ,		A=12 ,		A=12 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=0.1 ,		A=0.0 ,		A=0.1 ,		A=0.1 ,		A=0.02 ,		A=1e-5 ,		A="gelu" ,		A=(512, 512, 512, 512, 512, 512, 512) ,		A=(5, 2, 2, 2, 2, 2, 2) ,		A=(10, 3, 3, 3, 3, 2, 2) ,		A=False ,		A=16 ,		A=19 ,		A=5 ,		A=0.05 ,		A=10 ,		A=2 ,		A=0.0 ,		A=10 ,		A=0 ,		A="sum" ,		A=False ,		A=False ,		A=256 ,		A=(512, 512, 512, 512, 1500) ,		A=(5, 3, 3, 1, 1) ,		A=(1, 2, 3, 1, 1) ,		A=512 ,		A=0 ,		A=1 ,		A=2 ,		A=False ,		A=3 ,		A=2 ,		A=3 ,		A=None ,		**A ,		)		->   Dict:
        super().__init__(**A ,		pad_token_id=A ,		bos_token_id=A ,		eos_token_id=A       )
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			feat_extract_activation
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			conv_bias
        _SCREAMING_SNAKE_CASE									=			num_conv_pos_embeddings
        _SCREAMING_SNAKE_CASE									=			num_conv_pos_embedding_groups
        _SCREAMING_SNAKE_CASE									=			conv_pos_kernel_size
        _SCREAMING_SNAKE_CASE									=			len(self.conv_dim       )
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			hidden_dropout
        _SCREAMING_SNAKE_CASE									=			attention_dropout
        _SCREAMING_SNAKE_CASE									=			activation_dropout
        _SCREAMING_SNAKE_CASE									=			feat_proj_dropout
        _SCREAMING_SNAKE_CASE									=			final_dropout
        _SCREAMING_SNAKE_CASE									=			layerdrop
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			use_weighted_layer_sum

        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
        _SCREAMING_SNAKE_CASE									=			mask_time_prob
        _SCREAMING_SNAKE_CASE									=			mask_time_length
        _SCREAMING_SNAKE_CASE									=			mask_time_min_masks
        _SCREAMING_SNAKE_CASE									=			mask_feature_prob
        _SCREAMING_SNAKE_CASE									=			mask_feature_length
        _SCREAMING_SNAKE_CASE									=			mask_feature_min_masks

        # ctc loss
        _SCREAMING_SNAKE_CASE									=			ctc_loss_reduction
        _SCREAMING_SNAKE_CASE									=			ctc_zero_infinity

        # adapter
        _SCREAMING_SNAKE_CASE									=			add_adapter
        _SCREAMING_SNAKE_CASE									=			adapter_kernel_size
        _SCREAMING_SNAKE_CASE									=			adapter_stride
        _SCREAMING_SNAKE_CASE									=			num_adapter_layers
        _SCREAMING_SNAKE_CASE									=			output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        _SCREAMING_SNAKE_CASE									=			classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			list(A       )
        _SCREAMING_SNAKE_CASE									=			xvector_output_dim





    @property
    def 			snake_case_(		self       )		->   str:
        return math.prod(self.conv_stride       )









import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowerCamelCase__ :   Union[str, Any]					=							logging.get_logger(__name__)

lowerCamelCase__ :   Dict					=							{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__ :   Tuple					=							{
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

lowerCamelCase__ :   int					=							{
    'squeezebert/squeezebert-uncased': 512,
    'squeezebert/squeezebert-mnli': 512,
    'squeezebert/squeezebert-mnli-headless': 512,
}


lowerCamelCase__ :   str					=							{
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    VOCAB_FILES_NAMES
 lowercase_				    =    PRETRAINED_VOCAB_FILES_MAP
 lowercase_				    =    PRETRAINED_INIT_CONFIGURATION
 lowercase_				    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_				    =    SqueezeBertTokenizer



 def __init__(							self  :		Tuple				,				_lowerCAmelCase  :		Dict=None				,				_lowerCAmelCase  :		int=None				,				_lowerCAmelCase  :		List[str]=True				,				_lowerCAmelCase  :		str="[UNK]"				,				_lowerCAmelCase  :		Union[str, Any]="[SEP]"				,				_lowerCAmelCase  :		List[Any]="[PAD]"				,				_lowerCAmelCase  :		str="[CLS]"				,				_lowerCAmelCase  :		Dict="[MASK]"				,				_lowerCAmelCase  :		Optional[int]=True				,				_lowerCAmelCase  :		Dict=None				,				**_lowerCAmelCase  :		str				,				):
       super().__init__(
           _lowerCAmelCase				,				tokenizer_file=_lowerCAmelCase				,				do_lower_case=_lowerCAmelCase				,				unk_token=_lowerCAmelCase				,				sep_token=_lowerCAmelCase				,				pad_token=_lowerCAmelCase				,				cls_token=_lowerCAmelCase				,				mask_token=_lowerCAmelCase				,				tokenize_chinese_chars=_lowerCAmelCase				,				strip_accents=_lowerCAmelCase				,				**_lowerCAmelCase				,				)

       SCREAMING_SNAKE_CASE_							=			json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
       if (
           normalizer_state.get('lowercase'				,				_lowerCAmelCase						) != do_lower_case
           or normalizer_state.get('strip_accents'				,				_lowerCAmelCase						) != strip_accents
           or normalizer_state.get('handle_chinese_chars'				,				_lowerCAmelCase						) != tokenize_chinese_chars
       ):
             SCREAMING_SNAKE_CASE_							=			getattr(_lowerCAmelCase				,				normalizer_state.pop('type'						)						)
             SCREAMING_SNAKE_CASE_							=			do_lower_case
             SCREAMING_SNAKE_CASE_							=			strip_accents
             SCREAMING_SNAKE_CASE_							=			tokenize_chinese_chars
             SCREAMING_SNAKE_CASE_							=			normalizer_class(**_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			do_lower_case



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[int]=None						):
       SCREAMING_SNAKE_CASE_							=			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

       return output



 def       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		List[int]				,				_lowerCAmelCase  :		Optional[List[int]] = None						):
       SCREAMING_SNAKE_CASE_							=			[self.sep_token_id]
       SCREAMING_SNAKE_CASE_							=			[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						) * [0] + len(token_ids_a + sep						) * [1]




 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[str] = None						):
       SCREAMING_SNAKE_CASE_							=			self._tokenizer.model.save(_lowerCAmelCase				,				name=_lowerCAmelCase						)
       return tuple(_lowerCAmelCase						)