

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


UpperCAmelCase_							:					Optional[Any]		 =   logging.get_logger(__name__)


UpperCAmelCase_							:					str		 =   '''▁'''

UpperCAmelCase_							:					List[Any]		 =   {'''vocab_file''': '''spiece.model'''}

UpperCAmelCase_							:					Any		 =   {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

UpperCAmelCase_							:					int		 =   {
    '''google/reformer-crime-and-punishment''': 524_288,
}






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Union[str, Any]     					=				VOCAB_FILES_NAMES
	_lowercase		:   Dict     					=				PRETRAINED_VOCAB_FILES_MAP
	_lowercase		:   List[Any]     					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowercase		:   Optional[int]     					=				['''input_ids''', '''attention_mask''']




	def __init__(     self						:     List[Any]					,      __A						:     Tuple					,      __A						:     Dict="</s>"					,      __A						:     Any="<unk>"					,      __A						:     Any=[]					,      __A						:     Optional[Dict[str, Any]] = None					,      **__A						:     Tuple					,      ):
		__A :			Optional[Any]		    =			{} if sp_model_kwargs is None else sp_model_kwargs

		super().__init__(
		    eos_token=__A					,      unk_token=__A					,      additional_special_tokens=__A					,      sp_model_kwargs=self.sp_model_kwargs					,      **__A					,      )

		__A :			str		    =			vocab_file
		__A :			Tuple		    =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
		self.sp_model.Load(__A							)




	@property
	def        lowerCAmelCase_       (     self						:     Dict							):
		return self.sp_model.get_piece_size()




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Optional[int]		    =			{self.convert_ids_to_tokens(__A							): i for i in range(self.vocab_size							)}
		vocab.update(self.added_tokens_encoder							)
		return vocab




	def __getstate__(     self						:     Optional[int]							):
		__A :			Dict		    =			self.__dict__.copy()
		__A :			Tuple		    =			None
		return state




	def __setstate__(     self						:     int					,      __A						:     List[Any]							):
		__A :			str		    =			d

		# for backward compatibility
		if not hasattr(self					,      """sp_model_kwargs"""							):
			__A :			Union[str, Any]		    =			{}

		__A :			Union[str, Any]		    =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
		self.sp_model.Load(self.vocab_file							)




	def        lowerCAmelCase_       (     self						:     str					,      __A						:     str							):
		return self.sp_model.encode(__A					,      out_type=__A							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]					,      __A						:     str							):
		return self.sp_model.piece_to_id(__A							)




	def        lowerCAmelCase_       (     self						:     Any					,      __A						:     List[str]							):
		if index < self.sp_model.get_piece_size():
			__A :			Any		    =			self.sp_model.IdToPiece(__A							)
		return token




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Tuple							):
		__A :			Optional[Any]		    =			[]
		__A :			Optional[int]		    =			""""""
		for token in tokens:
			# make sure that special tokens are not decoded using sentencepiece model
			if token in self.all_special_tokens:
				out_string += self.sp_model.decode(__A							) + token
				__A :			Union[str, Any]		    =			[]
			else:
				current_sub_tokens.append(__A							)
		out_string += self.sp_model.decode(__A							)
		return out_string.strip()




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     str					,      __A						:     Optional[str] = None							):
		if not os.path.isdir(__A							):
			logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""							)
			return
		__A :			str		    =			os.path.join(
		    __A					,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

		if os.path.abspath(self.vocab_file							) != os.path.abspath(__A							) and os.path.isfile(self.vocab_file							):
			copyfile(self.vocab_file					,      __A							)
		elif not os.path.isfile(self.vocab_file							):
			with open(__A					,      """wb"""							) as fi:
				__A :			Any		    =			self.sp_model.serialized_model_proto()
				fi.write(__A							)

		return (out_vocab_file,)








import functools
import operator

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


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

UpperCAmelCase_							:					str		 =   {
    '''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_      (	_lowercase	):
	_lowercase		:   Dict     					=				'''unispeech'''




	def __init__(     self						:     str					,      __A						:     Tuple=32					,      __A						:     List[str]=768					,      __A						:     Dict=12					,      __A						:     Union[str, Any]=12					,      __A						:     Tuple=3072					,      __A						:     Any="gelu"					,      __A						:     int=0.1					,      __A						:     Optional[int]=0.1					,      __A						:     List[Any]=0.1					,      __A						:     Any=0.0					,      __A						:     List[str]=0.0					,      __A						:     int=0.1					,      __A						:     List[Any]=0.1					,      __A						:     List[str]=0.0_2					,      __A						:     List[str]=1e-5					,      __A						:     List[Any]="group"					,      __A						:     int="gelu"					,      __A						:     Any=(512, 512, 512, 512, 512, 512, 512)					,      __A						:     Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)					,      __A						:     Tuple=(10, 3, 3, 3, 3, 2, 2)					,      __A						:     Optional[int]=False					,      __A						:     Any=128					,      __A						:     Union[str, Any]=16					,      __A						:     Optional[Any]=False					,      __A						:     str=True					,      __A						:     Dict=0.0_5					,      __A						:     Optional[Any]=10					,      __A						:     Dict=2					,      __A						:     int=0.0					,      __A						:     List[str]=10					,      __A						:     str=0					,      __A						:     List[str]=320					,      __A						:     List[Any]=2					,      __A						:     Tuple=0.1					,      __A						:     Optional[int]=100					,      __A						:     Any=256					,      __A						:     Dict=256					,      __A						:     Tuple=0.1					,      __A						:     List[str]="mean"					,      __A						:     int=False					,      __A						:     List[str]=False					,      __A						:     List[Any]=256					,      __A						:     str=80					,      __A						:     Tuple=0					,      __A						:     Tuple=1					,      __A						:     int=2					,      __A						:     Dict=0.5					,      **__A						:     List[Any]					,      ):
		super().__init__(**__A					,      pad_token_id=__A					,      bos_token_id=__A					,      eos_token_id=__A							)
		__A :			Dict		    =			hidden_size
		__A :			Optional[Any]		    =			feat_extract_norm
		__A :			List[Any]		    =			feat_extract_activation
		__A :			str		    =			list(__A							)
		__A :			Optional[Any]		    =			list(__A							)
		__A :			Optional[int]		    =			list(__A							)
		__A :			List[Any]		    =			conv_bias
		__A :			Optional[int]		    =			num_conv_pos_embeddings
		__A :			List[Any]		    =			num_conv_pos_embedding_groups
		__A :			int		    =			len(self.conv_dim							)
		__A :			Optional[Any]		    =			num_hidden_layers
		__A :			List[str]		    =			intermediate_size
		__A :			Union[str, Any]		    =			hidden_act
		__A :			Optional[int]		    =			num_attention_heads
		__A :			Tuple		    =			hidden_dropout
		__A :			Optional[Any]		    =			attention_dropout
		__A :			Union[str, Any]		    =			activation_dropout
		__A :			Dict		    =			feat_proj_dropout
		__A :			Optional[int]		    =			final_dropout
		__A :			Dict		    =			layerdrop
		__A :			Optional[int]		    =			layer_norm_eps
		__A :			Optional[Any]		    =			initializer_range
		__A :			Optional[int]		    =			num_ctc_classes
		__A :			Dict		    =			vocab_size
		__A :			List[str]		    =			do_stable_layer_norm
		__A :			Tuple		    =			use_weighted_layer_sum
		__A :			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
		__A :			Tuple		    =			apply_spec_augment
		__A :			Union[str, Any]		    =			mask_time_prob
		__A :			Optional[Any]		    =			mask_time_length
		__A :			List[Any]		    =			mask_time_min_masks
		__A :			List[Any]		    =			mask_feature_prob
		__A :			Any		    =			mask_feature_length
		__A :			List[Any]		    =			mask_feature_min_masks

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

		# ctc loss
		__A :			List[str]		    =			ctc_loss_reduction
		__A :			Any		    =			ctc_zero_infinity

		# pretraining loss
		__A :			Union[str, Any]		    =			replace_prob




	@property
	def        lowerCAmelCase_       (     self						:     int							):
		return functools.reduce(operator.mul					,      self.conv_stride					,      1							)





