
"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


UpperCAmelCase__   :					str												=				logging.get_logger(__name__)

UpperCAmelCase__   :					Optional[int]												=				{'vocab_file': 'vocab.txt'}

UpperCAmelCase__   :					Optional[Any]												=				{
    'vocab_file': {
        'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
        'YituTech/conv-bert-medium-small': (
            'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
        ),
        'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
    }
}

UpperCAmelCase__   :					Union[str, Any]												=				{
    'YituTech/conv-bert-base': 5_1_2,
    'YituTech/conv-bert-medium-small': 5_1_2,
    'YituTech/conv-bert-small': 5_1_2,
}


UpperCAmelCase__   :					Dict												=				{
    'YituTech/conv-bert-base': {'do_lower_case': True},
    'YituTech/conv-bert-medium-small': {'do_lower_case': True},
    'YituTech/conv-bert-small': {'do_lower_case': True},
}



class 			lowerCAmelCase_		(a__						):


    """simple docstring"""



    __UpperCamelCase   :		Any       =     VOCAB_FILES_NAMES
    __UpperCamelCase   :		Tuple       =     PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase   :		Optional[int]       =     PRETRAINED_INIT_CONFIGURATION
    __UpperCamelCase   :		str       =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase   :		List[str]       =     ConvBertTokenizer



    def __init__(self     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__="[UNK]"     , SCREAMING_SNAKE_CASE__="[SEP]"     , SCREAMING_SNAKE_CASE__="[PAD]"     , SCREAMING_SNAKE_CASE__="[CLS]"     , SCREAMING_SNAKE_CASE__="[MASK]"     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=None     , **SCREAMING_SNAKE_CASE__     , )      ->	List[str]:



           """simple docstring"""




           super().__init__(
               SCREAMING_SNAKE_CASE__     , tokenizer_file=SCREAMING_SNAKE_CASE__     , do_lower_case=SCREAMING_SNAKE_CASE__     , unk_token=SCREAMING_SNAKE_CASE__     , sep_token=SCREAMING_SNAKE_CASE__     , pad_token=SCREAMING_SNAKE_CASE__     , cls_token=SCREAMING_SNAKE_CASE__     , mask_token=SCREAMING_SNAKE_CASE__     , tokenize_chinese_chars=SCREAMING_SNAKE_CASE__     , strip_accents=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__     , )

           SCREAMING_SNAKE_CASE__		: List[str]     =     json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
           if (
               normalizer_state.get("""lowercase"""     , SCREAMING_SNAKE_CASE__					) != do_lower_case
               or normalizer_state.get("""strip_accents"""     , SCREAMING_SNAKE_CASE__					) != strip_accents
               or normalizer_state.get("""handle_chinese_chars"""     , SCREAMING_SNAKE_CASE__					) != tokenize_chinese_chars
           ):
                  SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     getattr(SCREAMING_SNAKE_CASE__     , normalizer_state.pop("""type"""					)					)
                  SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     do_lower_case
                  SCREAMING_SNAKE_CASE__		: Tuple     =     strip_accents
                  SCREAMING_SNAKE_CASE__		: List[str]     =     tokenize_chinese_chars
                  SCREAMING_SNAKE_CASE__		: Tuple     =     normalizer_class(**SCREAMING_SNAKE_CASE__					)

           SCREAMING_SNAKE_CASE__		: Any     =     do_lower_case



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None					)      ->	Dict:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: Tuple     =     [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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None					)      ->	List[int]:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: Any     =     [self.sep_token_id]
           SCREAMING_SNAKE_CASE__		: int     =     [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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None					)      ->	Tuple[str]:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: Any     =     self._tokenizer.model.save(SCREAMING_SNAKE_CASE__     , name=SCREAMING_SNAKE_CASE__					)
           return tuple(SCREAMING_SNAKE_CASE__					)






"""simple docstring"""




import functools
import operator

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


UpperCAmelCase__   :					str												=				logging.get_logger(__name__)

UpperCAmelCase__   :					Dict												=				{
    'microsoft/unispeech-large-1500h-cv': (
        'https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		List[str]       =     '''unispeech'''



				def __init__(self     , SCREAMING_SNAKE_CASE__=32     , SCREAMING_SNAKE_CASE__=7_68     , SCREAMING_SNAKE_CASE__=12     , SCREAMING_SNAKE_CASE__=12     , SCREAMING_SNAKE_CASE__=30_72     , SCREAMING_SNAKE_CASE__="gelu"     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.02     , SCREAMING_SNAKE_CASE__=1E-5     , SCREAMING_SNAKE_CASE__="group"     , SCREAMING_SNAKE_CASE__="gelu"     , SCREAMING_SNAKE_CASE__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)     , SCREAMING_SNAKE_CASE__=(5, 2, 2, 2, 2, 2, 2)     , SCREAMING_SNAKE_CASE__=(10, 3, 3, 3, 3, 2, 2)     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=1_28     , SCREAMING_SNAKE_CASE__=16     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=0.05     , SCREAMING_SNAKE_CASE__=10     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=10     , SCREAMING_SNAKE_CASE__=0     , SCREAMING_SNAKE_CASE__=3_20     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=1_00     , SCREAMING_SNAKE_CASE__=2_56     , SCREAMING_SNAKE_CASE__=2_56     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__="mean"     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=2_56     , SCREAMING_SNAKE_CASE__=80     , SCREAMING_SNAKE_CASE__=0     , SCREAMING_SNAKE_CASE__=1     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.5     , **SCREAMING_SNAKE_CASE__     , )      ->	List[Any]:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__     , pad_token_id=SCREAMING_SNAKE_CASE__     , bos_token_id=SCREAMING_SNAKE_CASE__     , eos_token_id=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     hidden_size
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     feat_extract_norm
											SCREAMING_SNAKE_CASE__		: Any     =     feat_extract_activation
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     list(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: str     =     list(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     list(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     conv_bias
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     num_conv_pos_embeddings
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     num_conv_pos_embedding_groups
											SCREAMING_SNAKE_CASE__		: Dict     =     len(self.conv_dim					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     num_hidden_layers
											SCREAMING_SNAKE_CASE__		: str     =     intermediate_size
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     hidden_act
											SCREAMING_SNAKE_CASE__		: Any     =     num_attention_heads
											SCREAMING_SNAKE_CASE__		: Dict     =     hidden_dropout
											SCREAMING_SNAKE_CASE__		: List[str]     =     attention_dropout
											SCREAMING_SNAKE_CASE__		: Any     =     activation_dropout
											SCREAMING_SNAKE_CASE__		: int     =     feat_proj_dropout
											SCREAMING_SNAKE_CASE__		: List[Any]     =     final_dropout
											SCREAMING_SNAKE_CASE__		: str     =     layerdrop
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     layer_norm_eps
											SCREAMING_SNAKE_CASE__		: Dict     =     initializer_range
											SCREAMING_SNAKE_CASE__		: Dict     =     num_ctc_classes
											SCREAMING_SNAKE_CASE__		: List[Any]     =     vocab_size
											SCREAMING_SNAKE_CASE__		: str     =     do_stable_layer_norm
											SCREAMING_SNAKE_CASE__		: Dict     =     use_weighted_layer_sum
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     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
											SCREAMING_SNAKE_CASE__		: Tuple     =     apply_spec_augment
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     mask_time_prob
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     mask_time_length
											SCREAMING_SNAKE_CASE__		: List[str]     =     mask_time_min_masks
											SCREAMING_SNAKE_CASE__		: Tuple     =     mask_feature_prob
											SCREAMING_SNAKE_CASE__		: Any     =     mask_feature_length
											SCREAMING_SNAKE_CASE__		: int     =     mask_feature_min_masks

											# parameters for pretraining with codevector quantized representations
											SCREAMING_SNAKE_CASE__		: Dict     =     num_codevectors_per_group
											SCREAMING_SNAKE_CASE__		: Any     =     num_codevector_groups
											SCREAMING_SNAKE_CASE__		: str     =     contrastive_logits_temperature
											SCREAMING_SNAKE_CASE__		: str     =     feat_quantizer_dropout
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     num_negatives
											SCREAMING_SNAKE_CASE__		: Tuple     =     codevector_dim
											SCREAMING_SNAKE_CASE__		: List[Any]     =     proj_codevector_dim
											SCREAMING_SNAKE_CASE__		: Tuple     =     diversity_loss_weight

											# ctc loss
											SCREAMING_SNAKE_CASE__		: int     =     ctc_loss_reduction
											SCREAMING_SNAKE_CASE__		: str     =     ctc_zero_infinity

											# pretraining loss
											SCREAMING_SNAKE_CASE__		: List[str]     =     replace_prob



				@property
				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											return functools.reduce(operator.mul     , self.conv_stride     , 1					)




