

import functools
import operator

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


_lowerCAmelCase			:						str  					=     logging.get_logger(__name__)

_lowerCAmelCase			:						Tuple  					=     {
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class      lowerCAmelCase					(     __UpperCamelCase					):


       '''simple docstring'''







       snake_case     				=						'unispeech-sat'

       def __init__(						self       :	List[Any]    ,						__snake_case       :	int=32    ,						__snake_case       :	str=768    ,						__snake_case       :	Optional[Any]=12    ,						__snake_case       :	List[Any]=12    ,						__snake_case       :	Tuple=3072    ,						__snake_case       :	Union[str, Any]="gelu"    ,						__snake_case       :	str=0.1    ,						__snake_case       :	List[str]=0.1    ,						__snake_case       :	Tuple=0.1    ,						__snake_case       :	Tuple=0.0    ,						__snake_case       :	List[Any]=0.0    ,						__snake_case       :	List[Any]=0.1    ,						__snake_case       :	str=0.1    ,						__snake_case       :	List[str]=0.02    ,						__snake_case       :	Dict=1e-5    ,						__snake_case       :	Any="group"    ,						__snake_case       :	Optional[int]="gelu"    ,						__snake_case       :	Optional[int]=(512, 512, 512, 512, 512, 512, 512)    ,						__snake_case       :	int=(5, 2, 2, 2, 2, 2, 2)    ,						__snake_case       :	Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)    ,						__snake_case       :	Optional[int]=False    ,						__snake_case       :	Optional[int]=128    ,						__snake_case       :	Union[str, Any]=16    ,						__snake_case       :	Optional[int]=False    ,						__snake_case       :	int=True    ,						__snake_case       :	Dict=0.05    ,						__snake_case       :	List[Any]=10    ,						__snake_case       :	Any=2    ,						__snake_case       :	Any=0.0    ,						__snake_case       :	Union[str, Any]=10    ,						__snake_case       :	List[Any]=0    ,						__snake_case       :	Any=320    ,						__snake_case       :	Optional[int]=2    ,						__snake_case       :	int=0.1    ,						__snake_case       :	Optional[int]=100    ,						__snake_case       :	int=256    ,						__snake_case       :	int=256    ,						__snake_case       :	str=0.1    ,						__snake_case       :	Optional[Any]="mean"    ,						__snake_case       :	int=False    ,						__snake_case       :	Tuple=False    ,						__snake_case       :	Tuple=256    ,						__snake_case       :	Optional[int]=(512, 512, 512, 512, 1500)    ,						__snake_case       :	int=(5, 3, 3, 1, 1)    ,						__snake_case       :	Tuple=(1, 2, 3, 1, 1)    ,						__snake_case       :	Optional[int]=512    ,						__snake_case       :	Any=0    ,						__snake_case       :	Any=1    ,						__snake_case       :	List[Any]=2    ,						__snake_case       :	Union[str, Any]=504    ,						**__snake_case       :	List[Any]    ,						) ->			Union[str, Any]:







        '''simple docstring'''

        super().__init__(**__snake_case    ,						pad_token_id=__snake_case    ,						bos_token_id=__snake_case    ,						eos_token_id=__snake_case  )
        lowerCamelCase       =     hidden_size
        lowerCamelCase       =     feat_extract_norm
        lowerCamelCase       =     feat_extract_activation
        lowerCamelCase       =     list(__snake_case  )
        lowerCamelCase       =     list(__snake_case  )
        lowerCamelCase       =     list(__snake_case  )
        lowerCamelCase       =     conv_bias
        lowerCamelCase       =     num_conv_pos_embeddings
        lowerCamelCase       =     num_conv_pos_embedding_groups
        lowerCamelCase       =     len(self.conv_dim  )
        lowerCamelCase       =     num_hidden_layers
        lowerCamelCase       =     intermediate_size
        lowerCamelCase       =     hidden_act
        lowerCamelCase       =     num_attention_heads
        lowerCamelCase       =     hidden_dropout
        lowerCamelCase       =     attention_dropout
        lowerCamelCase       =     activation_dropout
        lowerCamelCase       =     feat_proj_dropout
        lowerCamelCase       =     final_dropout
        lowerCamelCase       =     layerdrop
        lowerCamelCase       =     layer_norm_eps
        lowerCamelCase       =     initializer_range
        lowerCamelCase       =     vocab_size
        lowerCamelCase       =     num_clusters
        lowerCamelCase       =     do_stable_layer_norm
        lowerCamelCase       =     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
        lowerCamelCase       =     apply_spec_augment
        lowerCamelCase       =     mask_time_prob
        lowerCamelCase       =     mask_time_length
        lowerCamelCase       =     mask_time_min_masks
        lowerCamelCase       =     mask_feature_prob
        lowerCamelCase       =     mask_feature_length
        lowerCamelCase       =     mask_feature_min_masks

        # parameters for pretraining with codevector quantized representations
        lowerCamelCase       =     num_codevectors_per_group
        lowerCamelCase       =     num_codevector_groups
        lowerCamelCase       =     contrastive_logits_temperature
        lowerCamelCase       =     feat_quantizer_dropout
        lowerCamelCase       =     num_negatives
        lowerCamelCase       =     codevector_dim
        lowerCamelCase       =     proj_codevector_dim
        lowerCamelCase       =     diversity_loss_weight

        # ctc loss
        lowerCamelCase       =     ctc_loss_reduction
        lowerCamelCase       =     ctc_zero_infinity

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

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





       @property
       def 	lowerCamelCase__    (						self       :	Tuple  ) ->			Any:







        '''simple docstring'''

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










"""simple docstring"""






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer






class lowerCAmelCase__      (	tf.keras.layers.Layer		):






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase    , lowercase = None    , lowercase = None    ):
							super().__init__()
							_lowerCamelCase	:     Optional[Any]       =       pad_token_id
							_lowerCamelCase	:     Dict       =       max_length
							_lowerCamelCase	:     Tuple       =       vocab
							_lowerCamelCase	:     List[str]       =       merges
							_lowerCamelCase	:     int       =       BytePairTokenizer(lowercase    , lowercase    , sequence_length=lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , *lowercase    , **lowercase    ):
							_lowerCamelCase	:     str       =       [' '.join(lowercase    ) for m in tokenizer.bpe_ranks.keys()]
							_lowerCamelCase	:     Union[str, Any]       =       tokenizer.get_vocab()
							return cls(lowercase    , lowercase    , *lowercase    , **lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , *lowercase    , **lowercase    ):
							_lowerCamelCase	:     Optional[int]       =       GPTaTokenizer.from_pretrained(lowercase    , *lowercase    , **lowercase    )
							return cls.from_tokenizer(lowercase    , *lowercase    , **lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    ):
							return cls(**lowercase    )
			def    A_       (    self    ):
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}






			def    A_       (    self    , lowercase    , lowercase = None    ):
							_lowerCamelCase	:     int       =       self.tf_tokenizer(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       tf.ones_like(lowercase    )

							if self.pad_token_id is not None:
											# pad the tokens up to max length
											_lowerCamelCase	:     str       =       max_length if max_length is not None else self.max_length

											if max_length is not None:
															_lowerCamelCase,					_lowerCamelCase	:     Union[str, Any]       =       pad_model_inputs(
															    lowercase    , max_seq_length=lowercase    , pad_value=self.pad_token_id    )

							return {"attention_mask": attention_mask, "input_ids": input_ids}