
import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
lowercase        =  logging.get_logger('''transformers.models.speecht5''')

lowercase        =  {
    '''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
    '''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
    '''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
    '''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
lowercase        =  {
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
lowercase        =  {
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
    '''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
    '''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
    '''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
lowercase        =  {
    '''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
    '''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
    '''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
    '''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
    '''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
    '''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
    '''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
    '''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
lowercase        =  {
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
lowercase        =  {
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
lowercase        =  {
    '''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
    '''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
    '''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
    '''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
    '''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
    '''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
    '''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
    '''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
    '''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
lowercase        =  {
    '''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
    '''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
    '''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
    '''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
    '''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
    '''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
    '''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
    '''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
    '''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
    '''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
    '''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
    '''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
    '''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
lowercase        =  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowercase        =  {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase        =  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase        =  []
lowercase        =  [
    '''encoder.version''',
    '''encoder.layers.*.norm_k.weight''',
    '''encoder.layers.*.norm_k.bias''',
    '''decoder.version''',
    '''decoder.layers.*.norm_k.weight''',
    '''decoder.layers.*.norm_k.bias''',
    '''decoder.pos_emb.pe_k''',
    '''speech_encoder_prenet.embed_positions._float_tensor''',
    '''text_decoder_prenet.embed_positions._float_tensor''',
]
lowercase        =  IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
lowercase        =  IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
lowercase        =  IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]



def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      List[Any]			,				UpperCAmelCase__				:      Optional[int]			,				UpperCAmelCase__				:      List[Any]       )  ->    List[Any]:
							for attribute in key.split("""."""       ):
														lowerCamelCase_  =					getattr(UpperCAmelCase__			,				UpperCAmelCase__       )

							if weight_type is not None:
														lowerCamelCase_  =					getattr(UpperCAmelCase__			,				UpperCAmelCase__       ).shape
							else:
														lowerCamelCase_  =					hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
														    F''' {value.shape} for {full_name}'''       )

							if weight_type == "weight":
														lowerCamelCase_  =					value
							elif weight_type == "weight_g":
														lowerCamelCase_  =					value
							elif weight_type == "weight_v":
														lowerCamelCase_  =					value
							elif weight_type == "bias":
														lowerCamelCase_  =					value
							elif weight_type == "running_mean":
														lowerCamelCase_  =					value
							elif weight_type == "running_var":
														lowerCamelCase_  =					value
							elif weight_type == "num_batches_tracked":
														lowerCamelCase_  =					value
							else:
														lowerCamelCase_  =					value

							logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.'''       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]			,				UpperCAmelCase__				:      str       )  ->    Union[str, Any]:
							for key in ignore_keys:
														if key.endswith(""".*"""       ):
																					if name.startswith(key[:-1]       ):
																												return True
														elif ".*." in key:
																					lowerCamelCase_  ,   lowerCamelCase_  =					key.split(""".*."""       )
																					if prefix in name and suffix in name:
																												return True
														elif key in name:
																					return True
							return False



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]			,				UpperCAmelCase__				:      List[Any]			,				UpperCAmelCase__				:      str       )  ->    Union[str, Any]:
							lowerCamelCase_  =					[]

							if task == "s2t":
														lowerCamelCase_  =					hf_model.speechta.encoder.prenet.feature_encoder
														lowerCamelCase_  =					MAPPING_S2T
														lowerCamelCase_  =					IGNORE_KEYS_S2T
							elif task == "t2s":
														lowerCamelCase_  =					None
														lowerCamelCase_  =					MAPPING_T2S
														lowerCamelCase_  =					IGNORE_KEYS_T2S
							elif task == "s2s":
														lowerCamelCase_  =					hf_model.speechta.encoder.prenet.feature_encoder
														lowerCamelCase_  =					MAPPING_S2S
														lowerCamelCase_  =					IGNORE_KEYS_S2S
							else:
														raise ValueError(F'''Unsupported task: {task}'''       )

							for name, value in fairseq_dict.items():
														if should_ignore(UpperCAmelCase__			,				UpperCAmelCase__       ):
																					logger.info(F'''{name} was ignored'''       )
																					continue

														lowerCamelCase_  =					False
														if "conv_layers" in name:
																					load_conv_layer(
																					    UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				hf_model.config.feat_extract_norm == """group"""			,				)
																					lowerCamelCase_  =					True
														else:
																					for key, mapped_key in MAPPING.items():
																												# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

																												if "*" in key:
																																			lowerCamelCase_  ,   lowerCamelCase_  =					key.split(""".*."""       )
																																			if prefix in name and suffix in name:
																																										lowerCamelCase_  =					suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																												if key in name:
																																			lowerCamelCase_  =					True
																																			if "*" in mapped_key:
																																										lowerCamelCase_  =					name.split(UpperCAmelCase__       )[0].split("""."""       )[-2]
																																										lowerCamelCase_  =					mapped_key.replace("""*"""			,				UpperCAmelCase__       )
																																			if "weight_g" in name:
																																										lowerCamelCase_  =					"""weight_g"""
																																			elif "weight_v" in name:
																																										lowerCamelCase_  =					"""weight_v"""
																																			elif "bias" in name:
																																										lowerCamelCase_  =					"""bias"""
																																			elif "weight" in name:
																																										lowerCamelCase_  =					"""weight"""
																																			elif "running_mean" in name:
																																										lowerCamelCase_  =					"""running_mean"""
																																			elif "running_var" in name:
																																										lowerCamelCase_  =					"""running_var"""
																																			elif "num_batches_tracked" in name:
																																										lowerCamelCase_  =					"""num_batches_tracked"""
																																			else:
																																										lowerCamelCase_  =					None
																																			set_recursively(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )
																												continue
														if not is_used:
																					unused_weights.append(UpperCAmelCase__       )

							logger.warning(F'''Unused weights: {unused_weights}'''       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      Union[str, Any]       )  ->    Union[str, Any]:
							lowerCamelCase_  =					full_name.split("""conv_layers."""       )[-1]
							lowerCamelCase_  =					name.split("""."""       )
							lowerCamelCase_  =					int(items[0]       )
							lowerCamelCase_  =					int(items[1]       )

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    F'''{full_name} has size {value.shape}, but'''
																												    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''       )
																					lowerCamelCase_  =					value
																					logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    F'''{full_name} has size {value.shape}, but'''
																												    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''       )
																					lowerCamelCase_  =					value
																					logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    F'''{full_name} has size {value.shape}, but'''
																												    F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''       )
																					lowerCamelCase_  =					value
																					logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    F'''{full_name} has size {value.shape}, but'''
																												    F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''       )
																					lowerCamelCase_  =					value
																					logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
							else:
														unused_weights.append(UpperCAmelCase__       )



@torch.no_grad()
def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]			,				UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      Tuple			,				UpperCAmelCase__				:      int=None			,				UpperCAmelCase__				:      str=None			,				UpperCAmelCase__				:      Tuple=None			,				)  ->    Dict:
							if config_path is not None:
														lowerCamelCase_  =					SpeechTaConfig.from_pretrained(UpperCAmelCase__       )
							else:
														lowerCamelCase_  =					SpeechTaConfig()

							if task == "s2t":
														lowerCamelCase_  =					config.max_text_positions
														lowerCamelCase_  =					SpeechTaForSpeechToText(UpperCAmelCase__       )
							elif task == "t2s":
														lowerCamelCase_  =					1_8_7_6
														lowerCamelCase_  =					6_0_0
														lowerCamelCase_  =					config.max_speech_positions
														lowerCamelCase_  =					SpeechTaForTextToSpeech(UpperCAmelCase__       )
							elif task == "s2s":
														lowerCamelCase_  =					1_8_7_6
														lowerCamelCase_  =					config.max_speech_positions
														lowerCamelCase_  =					SpeechTaForSpeechToSpeech(UpperCAmelCase__       )
							else:
														raise ValueError(F'''Unknown task name: {task}'''       )

							if vocab_path:
														lowerCamelCase_  =					SpeechTaTokenizer(UpperCAmelCase__			,				model_max_length=config.max_text_positions       )

														# Mask token behaves like a normal word, i.e. include the space before it
														lowerCamelCase_  =					AddedToken("""<mask>"""			,				lstrip=UpperCAmelCase__			,				rstrip=UpperCAmelCase__       )
														lowerCamelCase_  =					mask_token
														tokenizer.add_special_tokens({"""mask_token""": mask_token}       )
														tokenizer.add_tokens(["""<ctc_blank>"""]       )

							lowerCamelCase_  =					SpeechTaFeatureExtractor()
							lowerCamelCase_  =					SpeechTaProcessor(tokenizer=UpperCAmelCase__			,				feature_extractor=UpperCAmelCase__       )
							processor.save_pretrained(UpperCAmelCase__       )

							lowerCamelCase_  =					torch.load(UpperCAmelCase__       )
							recursively_load_weights(fairseq_checkpoint["""model"""]			,				UpperCAmelCase__			,				UpperCAmelCase__       )

							model.save_pretrained(UpperCAmelCase__       )

							if repo_id:
														print("""Pushing to the hub..."""       )
														processor.push_to_hub(UpperCAmelCase__       )
														model.push_to_hub(UpperCAmelCase__       )


if __name__ == "__main__":
		lowercase        =  argparse.ArgumentParser()
		parser.add_argument(
		    '''--task''',
		    default='''s2t''',
		    type=str,
		    help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
		)
		parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
		parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
		)

		lowercase        =  parser.parse_args()
		convert_speechta_checkpoint(
		    args.task,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.vocab_path,
		    args.push_to_hub,
		)







import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
		import jieba

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


lowercase        =  logging.get_logger(__name__)

lowercase        =  {'''vocab_file''': '''vocab.txt'''}

lowercase        =  {
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

lowercase        =  {
    '''openbmb/cpm-ant-10b''': 1_0_2_4,
}



def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[str]       )  ->    Dict:
							lowerCamelCase_							  =					collections.OrderedDict()
							with open(UpperCAmelCase__			,				"""r"""			,				encoding="""utf-8"""       ) as reader:
														lowerCamelCase_							  =					reader.readlines()
							for index, token in enumerate(UpperCAmelCase__       ):
														lowerCamelCase_							  =					token.rstrip("""\n"""       )
														lowerCamelCase_							  =					index
							return vocab





class       __A(				UpperCAmelCase       ):

					def __init__(	self					:     Optional[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     List[str]="<unk>"   ,					__UpperCamelCase					:     List[str]=2_0_0   ):
												lowerCamelCase_							  =					vocab
												lowerCamelCase_							  =					unk_token
												lowerCamelCase_							  =					max_input_chars_per_word



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					list(__UpperCamelCase   )
												if len(__UpperCamelCase   ) > self.max_input_chars_per_word:
																			return [self.unk_token]

												lowerCamelCase_							  =					0
												lowerCamelCase_							  =					[]
												while start < len(__UpperCamelCase   ):
																			lowerCamelCase_							  =					len(__UpperCamelCase   )
																			lowerCamelCase_							  =					None
																			while start < end:
																										lowerCamelCase_							  =					"""""".join(chars[start:end]   )
																										if substr in self.vocab:
																																	lowerCamelCase_							  =					substr
																																	break
																										end -= 1
																			if cur_substr is None:
																										sub_tokens.append(self.unk_token   )
																										start += 1
																			else:
																										sub_tokens.append(__UpperCamelCase   )
																										lowerCamelCase_							  =					end

												return sub_tokens





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE			    =    ['''input_ids''', '''attention_mask''']
					SCREAMING_SNAKE_CASE			    =    False

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Optional[int]="<d>"   ,					__UpperCamelCase					:     List[str]="</d>"   ,					__UpperCamelCase					:     Optional[Any]="<s>"   ,					__UpperCamelCase					:     Union[str, Any]="</s>"   ,					__UpperCamelCase					:     List[str]="<pad>"   ,					__UpperCamelCase					:     Union[str, Any]="<unk>"   ,					__UpperCamelCase					:     List[Any]="</n>"   ,					__UpperCamelCase					:     Tuple="</_>"   ,					__UpperCamelCase					:     Optional[Any]="left"   ,					**__UpperCamelCase					:     List[str]   ,					):
												requires_backends(self   ,					["""jieba"""]   )
												super().__init__(
												    bod_token=__UpperCamelCase   ,					eod_token=__UpperCamelCase   ,					bos_token=__UpperCamelCase   ,					eos_token=__UpperCamelCase   ,					pad_token=__UpperCamelCase   ,					unk_token=__UpperCamelCase   ,					line_token=__UpperCamelCase   ,					space_token=__UpperCamelCase   ,					padding_side=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
												lowerCamelCase_							  =					bod_token
												lowerCamelCase_							  =					eod_token
												lowerCamelCase_							  =					load_vocab(__UpperCamelCase   )
												lowerCamelCase_							  =					self.encoder[space_token]
												lowerCamelCase_							  =					self.encoder[line_token]

												del self.encoder[space_token]
												del self.encoder[line_token]

												lowerCamelCase_							  =					collections.OrderedDict(sorted(self.encoder.items()   ,					key=lambda __UpperCamelCase   : x[1]   )   )
												lowerCamelCase_							  =					{v: k for k, v in self.encoder.items()}

												lowerCamelCase_							  =					WordpieceTokenizer(vocab=self.encoder   ,					unk_token=self.unk_token   )

					@property
					def    lowercase__      (	self					:     Any   ):
												return self.encoder[self.bod_token]

					@property
					def    lowercase__      (	self					:     Any   ):
												return self.encoder[self.eod_token]

					@property
					def    lowercase__      (	self					:     int   ):
												return self.encoder["\n"]

					@property
					def    lowercase__      (	self					:     str   ):
												return len(self.encoder   )

					def    lowercase__      (	self					:     Any   ):
												return dict(self.encoder   ,					**self.added_tokens_encoder   )

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Optional[Any]   ):
												lowerCamelCase_							  =					[]
												for x in jieba.cut(__UpperCamelCase   ,					cut_all=__UpperCamelCase   ):
																			output_tokens.extend(self.wordpiece_tokenizer.tokenize(__UpperCamelCase   )   )
												return output_tokens

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     Dict   ,					**__UpperCamelCase					:     str   ):
												lowerCamelCase_							  =					[i for i in token_ids if i >= 0]
												lowerCamelCase_							  =					[
												    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
												]
												return super()._decode(__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Optional[Any]   ):
												return token in self.encoder

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     List[str]   ):
												return "".join(__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     int   ):
												return self.encoder.get(__UpperCamelCase   ,					self.encoder.get(self.unk_token   )   )

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Union[str, Any]   ):
												return self.decoder.get(__UpperCamelCase   ,					self.unk_token   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[str] = None   ):
												if os.path.isdir(__UpperCamelCase   ):
																			lowerCamelCase_							  =					os.path.join(
																			    __UpperCamelCase   ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
												else:
																			lowerCamelCase_							  =					(filename_prefix + """-""" if filename_prefix else """""") + save_directory
												lowerCamelCase_							  =					0
												if " " in self.encoder:
																			lowerCamelCase_							  =					self.encoder[""" """]
																			del self.encoder[" "]
												if "\n" in self.encoder:
																			lowerCamelCase_							  =					self.encoder["""\n"""]
																			del self.encoder["\n"]
												lowerCamelCase_							  =					collections.OrderedDict(sorted(self.encoder.items()   ,					key=lambda __UpperCamelCase   : x[1]   )   )
												with open(__UpperCamelCase   ,					"""w"""   ,					encoding="""utf-8"""   ) as writer:
																			for token, token_index in self.encoder.items():
																										if index != token_index:
																																	logger.warning(
																																	    F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
																																	    """ Please check that the vocabulary is not corrupted!"""   )
																																	lowerCamelCase_							  =					token_index
																										writer.write(token + """\n"""   )
																										index += 1
												return (vocab_file,)

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     List[int] = None   ):
												if token_ids_a is None:
																			return [self.bos_token_id] + token_ids_a
												return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     Optional[List[int]] = None   ,					__UpperCamelCase					:     bool = False   ):

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

												if token_ids_a is not None:
																			return [1] + ([0] * len(__UpperCamelCase   )) + [1] + ([0] * len(__UpperCamelCase   ))
												return [1] + ([0] * len(__UpperCamelCase   ))





