




"""simple docstring"""






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		"""▁"""

lowercase__  		=		{"""vocab_file""": """sentencepiece.bpe.model"""}

lowercase__  		=		{
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowercase__  		=		{
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    VOCAB_FILES_NAMES
			lowerCamelCase__   =    PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   =    ["""input_ids""", """attention_mask"""]
			def __init__(						self						,   lowercase						,   lowercase="<s>"						,   lowercase="</s>"						,   lowercase="</s>"						,   lowercase="<s>"						,   lowercase="<unk>"						,   lowercase="<pad>"						,   lowercase="<mask>"						,   lowercase = None						,   **lowercase						,   ):
							# Mask token behave like a normal word, i.e. include the space before it
							_lowerCamelCase  :    str					=					AddedToken(lowercase						,   lstrip=lowercase						,   rstrip=lowercase						) if isinstance(lowercase						,   lowercase						) else mask_token

							_lowerCamelCase  :    Union[str, Any]					=					{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=lowercase						,   eos_token=lowercase						,   unk_token=lowercase						,   sep_token=lowercase						,   cls_token=lowercase						,   pad_token=lowercase						,   mask_token=lowercase						,   sp_model_kwargs=self.sp_model_kwargs						,   **lowercase						,   )

							_lowerCamelCase  :    Any					=					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.Load(str(lowercase						)						)
							_lowerCamelCase  :    str					=					vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							_lowerCamelCase  :    List[str]					=					{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							_lowerCamelCase  :    List[Any]					=					1

							_lowerCamelCase  :    List[Any]					=					len(self.sp_model						) + self.fairseq_offset
							_lowerCamelCase  :    int					=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
			def __getstate__(						self						):
							_lowerCamelCase  :    Tuple					=					self.__dict__.copy()
							_lowerCamelCase  :    Tuple					=					None
							_lowerCamelCase  :    Union[str, Any]					=					self.sp_model.serialized_model_proto()
							return state
			def __setstate__(						self						,   lowercase						):
							_lowerCamelCase  :    List[Any]					=					d

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

							_lowerCamelCase  :    List[str]					=					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)
			def       A_					(						self						,   lowercase						,   lowercase = None						):

							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_lowerCamelCase  :    Any					=					[self.cls_token_id]
							_lowerCamelCase  :    List[Any]					=					[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep
			def       A_					(						self						,   lowercase						,   lowercase = None						,   lowercase = False						):

							if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowercase						,   token_ids_a=lowercase						,   already_has_special_tokens=lowercase						)

							if token_ids_a is None:
											return [1] + ([0] * len(lowercase						)) + [1]
							return [1] + ([0] * len(lowercase						)) + [1, 1] + ([0] * len(lowercase						)) + [1]
			def       A_					(						self						,   lowercase						,   lowercase = None						):
							_lowerCamelCase  :    Tuple					=					[self.sep_token_id]
							_lowerCamelCase  :    Optional[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 + sep + token_ids_a + sep						) * [0]
			@property
			def       A_					(						self						):
							return len(self.sp_model						) + self.fairseq_offset + 1  # Add the <mask> token
			def       A_					(						self						):
							_lowerCamelCase  :    Dict					=					{self.convert_ids_to_tokens(lowercase						): i for i in range(self.vocab_size						)}
							vocab.update(self.added_tokens_encoder						)
							return vocab
			def       A_					(						self						,   lowercase						):
							return self.sp_model.encode(lowercase						,   out_type=lowercase						)
			def       A_					(						self						,   lowercase						):
							if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
							_lowerCamelCase  :    Union[str, Any]					=					self.sp_model.PieceToId(lowercase						)

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
			def       A_					(						self						,   lowercase						):
							if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset						)
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    List[Any]					=					''.join(lowercase						).replace(lowercase						,   ' '						).strip()
							return out_string






			def       A_					(						self						,   lowercase						,   lowercase = None						):
							if not os.path.isdir(lowercase						):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
											return
							_lowerCamelCase  :    int					=					os.path.join(
							    lowercase						,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

							if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase						) and os.path.isfile(self.vocab_file						):
											copyfile(self.vocab_file						,   lowercase						)
							elif not os.path.isfile(self.vocab_file						):
											with open(lowercase						,   'wb'						) as fi:
															_lowerCamelCase  :    str					=					self.sp_model.serialized_model_proto()
															fi.write(lowercase						)

							return (out_vocab_file,)





"""simple docstring"""






import functools
import operator

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


lowercase__  		=		logging.get_logger(__name__)

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






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """sew-d"""
			def __init__(						self						,   lowercase=32						,   lowercase=768						,   lowercase=12						,   lowercase=12						,   lowercase=3072						,   lowercase=2						,   lowercase=512						,   lowercase=256						,   lowercase=True						,   lowercase=True						,   lowercase=("p2c", "c2p")						,   lowercase="layer_norm"						,   lowercase="gelu_python"						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=0.0						,   lowercase=0.1						,   lowercase=0.02						,   lowercase=1E-7						,   lowercase=1E-5						,   lowercase="group"						,   lowercase="gelu"						,   lowercase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)						,   lowercase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)						,   lowercase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)						,   lowercase=False						,   lowercase=128						,   lowercase=16						,   lowercase=True						,   lowercase=0.05						,   lowercase=10						,   lowercase=2						,   lowercase=0.0						,   lowercase=10						,   lowercase=0						,   lowercase="mean"						,   lowercase=False						,   lowercase=False						,   lowercase=256						,   lowercase=0						,   lowercase=1						,   lowercase=2						,   **lowercase						,   ):
							super().__init__(**lowercase						,   pad_token_id=lowercase						,   bos_token_id=lowercase						,   eos_token_id=lowercase						)
							_lowerCamelCase  :    List[str]					=					hidden_size
							_lowerCamelCase  :    int					=					feat_extract_norm
							_lowerCamelCase  :    Optional[Any]					=					feat_extract_activation
							_lowerCamelCase  :    Optional[Any]					=					list(lowercase						)
							_lowerCamelCase  :    Union[str, Any]					=					list(lowercase						)
							_lowerCamelCase  :    Optional[Any]					=					list(lowercase						)
							_lowerCamelCase  :    List[str]					=					conv_bias
							_lowerCamelCase  :    str					=					num_conv_pos_embeddings
							_lowerCamelCase  :    Optional[Any]					=					num_conv_pos_embedding_groups
							_lowerCamelCase  :    Tuple					=					len(self.conv_dim						)
							_lowerCamelCase  :    Optional[int]					=					num_hidden_layers
							_lowerCamelCase  :    List[str]					=					intermediate_size
							_lowerCamelCase  :    Dict					=					squeeze_factor
							_lowerCamelCase  :    Any					=					max_position_embeddings
							_lowerCamelCase  :    List[str]					=					position_buckets
							_lowerCamelCase  :    Dict					=					share_att_key
							_lowerCamelCase  :    List[str]					=					relative_attention
							_lowerCamelCase  :    Optional[Any]					=					norm_rel_ebd
							_lowerCamelCase  :    List[Any]					=					list(lowercase						)
							_lowerCamelCase  :    List[Any]					=					hidden_act
							_lowerCamelCase  :    Optional[Any]					=					num_attention_heads
							_lowerCamelCase  :    Optional[int]					=					hidden_dropout
							_lowerCamelCase  :    Optional[int]					=					attention_dropout
							_lowerCamelCase  :    Any					=					activation_dropout
							_lowerCamelCase  :    Tuple					=					feat_proj_dropout
							_lowerCamelCase  :    Any					=					final_dropout
							_lowerCamelCase  :    str					=					layer_norm_eps
							_lowerCamelCase  :    Tuple					=					feature_layer_norm_eps
							_lowerCamelCase  :    Any					=					initializer_range
							_lowerCamelCase  :    Dict					=					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
							_lowerCamelCase  :    Dict					=					apply_spec_augment
							_lowerCamelCase  :    int					=					mask_time_prob
							_lowerCamelCase  :    Union[str, Any]					=					mask_time_length
							_lowerCamelCase  :    Optional[Any]					=					mask_time_min_masks
							_lowerCamelCase  :    List[Any]					=					mask_feature_prob
							_lowerCamelCase  :    Union[str, Any]					=					mask_feature_length
							_lowerCamelCase  :    List[Any]					=					mask_feature_min_masks

							# ctc loss
							_lowerCamelCase  :    List[str]					=					ctc_loss_reduction
							_lowerCamelCase  :    List[str]					=					ctc_zero_infinity

							# sequence classification
							_lowerCamelCase  :    Dict					=					use_weighted_layer_sum
							_lowerCamelCase  :    List[Any]					=					classifier_proj_size






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