






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			:	str								='▁'

_UpperCamelCase			:	Dict								={'vocab_file': 'spiece.model'}

_UpperCamelCase			:	Tuple								={
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

_UpperCamelCase			:	Dict								={
    'google/pegasus-xsum': 512,
}


_UpperCamelCase			:	int								=logging.get_logger(__name__)






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Tuple     =   VOCAB_FILES_NAMES

		__snake_case      :       Optional[Any]     =   VOCAB_FILES_NAMES
		__snake_case      :       Union[str, Any]     =   PRETRAINED_VOCAB_FILES_MAP
		__snake_case      :       Any     =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__snake_case      :       int     =   ["input_ids", "attention_mask"]







		def __init__(					self       ,A__       ,A__="<pad>"       ,A__="</s>"       ,A__="<unk>"       ,A__="<mask_2>"       ,A__="<mask_1>"       ,A__=None       ,A__=103       ,A__ = None       ,**A__       ,):
						_A	:				Optional[int]   =      offset
						if additional_special_tokens is not None:
										if not isinstance(A__       ,A__		):
														raise TypeError(
														    f"""additional_special_tokens should be of type {type(A__		)}, but is"""
														    f""" {type(A__		)}"""		)

										_A	:				str   =      (
										    ([mask_token_sent] + additional_special_tokens)
										    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
										    else additional_special_tokens
										)
										# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
										additional_special_tokens_extended += [
										    f"""<unk_{i}>""" for i in range(len(A__		)       ,self.offset - 1		)
										]

										if len(set(A__		)		) != len(A__		):
														raise ValueError(
														    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
														    f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""		)
										_A	:				Optional[Any]   =      additional_special_tokens_extended
						else:
										_A	:				Dict   =      [mask_token_sent] if mask_token_sent is not None else []
										additional_special_tokens += [f"""<unk_{i}>""" for i in range(2       ,self.offset		)]

						_A	:				Tuple   =      {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    eos_token=A__       ,unk_token=A__       ,mask_token=A__       ,pad_token=A__       ,mask_token_sent=A__       ,offset=A__       ,additional_special_tokens=A__       ,sp_model_kwargs=self.sp_model_kwargs       ,**A__       ,)
						_A	:				Dict   =      mask_token_sent
						_A	:				int   =      vocab_file
						_A	:				Optional[int]   =      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.Load(A__		)

						# add special tokens to encoder dict
						_A	:				Dict[int, str]   =      {
						    0: self.pad_token,
						    1: self.eos_token,
						}

						if self.mask_token_sent is not None:
										self.encoder.update(
										    {
										        2: self.mask_token_sent,
										        3: self.mask_token,
										    }		)

						if self.offset > 0:
										# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
										# mask_token_sent is already added to list -> so start at 1
										self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1       ,self.offset - 1		)}		)

						_A	:				Dict[str, int]   =      {v: k for k, v in self.encoder.items()}







		@property
		def  A__      (					self		):
						return len(self.sp_model		) + self.offset







		def  A__      (					self		):
						_A	:				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		):
						_A	:				str   =      self.__dict__.copy()
						_A	:				int   =      None
						return state







		def __setstate__(					self       ,A__		):
						_A	:				Union[str, Any]   =      d

						# for backward compatibility
						if not hasattr(self       ,'''sp_model_kwargs'''		):
										_A	:				int   =      {}

						_A	:				List[str]   =      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.Load(self.vocab_file		)







		def  A__      (					self       ,A__		):
						return self.sp_model.encode(A__       ,out_type=A__		)







		def  A__      (					self       ,A__		):
						if token in self.decoder:
										return self.decoder[token]
						elif token in self.added_tokens_decoder:
										return self.added_tokens_decoder[token]
						_A	:				Tuple   =      self.sp_model.piece_to_id(A__		)
						return sp_id + self.offset







		def  A__      (					self       ,A__		):
						if index in self.encoder:
										return self.encoder[index]
						elif index in self.added_tokens_encoder:
										return self.added_tokens_encoder[index]
						else:
										_A	:				Dict   =      self.sp_model.IdToPiece(index - self.offset		)
						return token







		def  A__      (					self       ,A__		):
						_A	:				Optional[Any]   =      []
						_A	:				str   =      ''''''
						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	:				Optional[int]   =      []
										else:
														current_sub_tokens.append(A__		)
						out_string += self.sp_model.decode(A__		)
						return out_string.strip()







		def  A__      (					self       ,A__=False		):
						return 1







		def  A__      (					self       ,A__		):
						_A	:				int   =      set(self.all_special_ids		)  # call it once instead of inside list comp
						all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special

						return [1 if x in all_special_ids else 0 for x in seq]







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = False		):
						if already_has_special_tokens:
										return self._special_token_mask(A__		)
						elif token_ids_a is None:
										return self._special_token_mask(A__		) + [1]
						else:
										return self._special_token_mask(token_ids_a + token_ids_a		) + [1]







		def  A__      (					self       ,A__       ,A__=None		):
						if token_ids_a is None:
										return token_ids_a + [self.eos_token_id]
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return token_ids_a + token_ids_a + [self.eos_token_id]







		def  A__      (					self       ,A__       ,A__ = None		):
						if not os.path.isdir(A__		):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
										return
						_A	:				List[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	:				Tuple   =      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[Any]								=logging.get_logger(__name__)

_UpperCamelCase			:	Dict								={
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Tuple     =   "sew"







		def __init__(					self       ,A__=32       ,A__=768       ,A__=12       ,A__=12       ,A__=3072       ,A__=2       ,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__="group"       ,A__="gelu"       ,A__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)       ,A__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,A__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,A__=False       ,A__=128       ,A__=16       ,A__=True       ,A__=0.05       ,A__=10       ,A__=2       ,A__=0.0       ,A__=10       ,A__=0       ,A__="mean"       ,A__=False       ,A__=False       ,A__=256       ,A__=0       ,A__=1       ,A__=2       ,**A__       ,):
						super().__init__(**A__       ,pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__		)
						_A	:				str   =      hidden_size
						_A	:				Optional[Any]   =      feat_extract_norm
						_A	:				Any   =      feat_extract_activation
						_A	:				Optional[int]   =      list(A__		)
						_A	:				str   =      list(A__		)
						_A	:				Optional[Any]   =      list(A__		)
						_A	:				List[Any]   =      conv_bias
						_A	:				Optional[int]   =      num_conv_pos_embeddings
						_A	:				Any   =      num_conv_pos_embedding_groups
						_A	:				Union[str, Any]   =      len(self.conv_dim		)
						_A	:				List[Any]   =      num_hidden_layers
						_A	:				str   =      intermediate_size
						_A	:				List[Any]   =      squeeze_factor
						_A	:				Tuple   =      hidden_act
						_A	:				Union[str, Any]   =      num_attention_heads
						_A	:				Optional[int]   =      hidden_dropout
						_A	:				Optional[int]   =      attention_dropout
						_A	:				Optional[int]   =      activation_dropout
						_A	:				Tuple   =      feat_proj_dropout
						_A	:				Optional[Any]   =      final_dropout
						_A	:				str   =      layerdrop
						_A	:				int   =      layer_norm_eps
						_A	:				List[str]   =      initializer_range
						_A	:				List[Any]   =      vocab_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)`,'''
										    f"""but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"""
										    f"""= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

						# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
						_A	:				Union[str, Any]   =      apply_spec_augment
						_A	:				str   =      mask_time_prob
						_A	:				Any   =      mask_time_length
						_A	:				int   =      mask_time_min_masks
						_A	:				str   =      mask_feature_prob
						_A	:				Optional[Any]   =      mask_feature_length
						_A	:				Dict   =      mask_feature_min_masks

						# ctc loss
						_A	:				Optional[int]   =      ctc_loss_reduction
						_A	:				List[str]   =      ctc_zero_infinity

						# sequence classification
						_A	:				List[Any]   =      use_weighted_layer_sum
						_A	:				Optional[int]   =      classifier_proj_size







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



