






"""simple docstring"""
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()
lowerCamelCase_							      = logging.get_logger('''transformers.models.speecht5''')

lowerCamelCase_							      = {
    '''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''',
}
lowerCamelCase_							      = {
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
lowerCamelCase_							      = {
    '''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''',
}
lowerCamelCase_							      = {
    '''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''',
}
lowerCamelCase_							      = {
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
lowerCamelCase_							      = {
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
lowerCamelCase_							      = {
    '''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''',
}
lowerCamelCase_							      = {
    '''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''',
}
lowerCamelCase_							      = {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowerCamelCase_							      = {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCamelCase_							      = {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCamelCase_							      = []
lowerCamelCase_							      = [
    '''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''',
]
lowerCamelCase_							      = IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
lowerCamelCase_							      = IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
lowerCamelCase_							      = IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]

def 							snake_case (  A__      ,A__      ,A__      ,A__      ,A__		):
			for attribute in key.split("."		):
						UpperCAmelCase_	:					Dict     	=   getattr(A__      ,A__		)

			if weight_type is not None:
						UpperCAmelCase_	:					str     	=   getattr(A__      ,A__		).shape
			else:
						UpperCAmelCase_	:					Any     	=   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":
						UpperCAmelCase_	:					Optional[Any]     	=   value
			elif weight_type == "weight_g":
						UpperCAmelCase_	:					int     	=   value
			elif weight_type == "weight_v":
						UpperCAmelCase_	:					List[Any]     	=   value
			elif weight_type == "bias":
						UpperCAmelCase_	:					Any     	=   value
			elif weight_type == "running_mean":
						UpperCAmelCase_	:					Tuple     	=   value
			elif weight_type == "running_var":
						UpperCAmelCase_	:					List[Any]     	=   value
			elif weight_type == "num_batches_tracked":
						UpperCAmelCase_	:					List[str]     	=   value
			else:
						UpperCAmelCase_	:					Optional[int]     	=   value

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

def 							snake_case (  A__      ,A__		):
			for key in ignore_keys:
						if key.endswith(".*"		):
									if name.startswith(key[:-1]		):
												return True
						elif ".*." in key:
									UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   key.split(".*."		)
									if prefix in name and suffix in name:
												return True
						elif key in name:
									return True
			return False

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					List[Any]     	=   []

			if task == "s2t":
						UpperCAmelCase_	:					List[str]     	=   hf_model.speechta.encoder.prenet.feature_encoder
						UpperCAmelCase_	:					Tuple     	=   MAPPING_S2T
						UpperCAmelCase_	:					Union[str, Any]     	=   IGNORE_KEYS_S2T
			elif task == "t2s":
						UpperCAmelCase_	:					Dict     	=   None
						UpperCAmelCase_	:					Optional[Any]     	=   MAPPING_T2S
						UpperCAmelCase_	:					List[str]     	=   IGNORE_KEYS_T2S
			elif task == "s2s":
						UpperCAmelCase_	:					Optional[Any]     	=   hf_model.speechta.encoder.prenet.feature_encoder
						UpperCAmelCase_	:					Tuple     	=   MAPPING_S2S
						UpperCAmelCase_	:					Dict     	=   IGNORE_KEYS_S2S
			else:
						raise ValueError(F"""Unsupported task: {task}"""		)

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

						UpperCAmelCase_	:					Optional[Any]     	=   False
						if "conv_layers" in name:
									load_conv_layer(
									    A__      ,A__      ,A__      ,A__      ,hf_model.config.feat_extract_norm == "group"      ,)
									UpperCAmelCase_	:					Tuple     	=   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:
															UpperCAmelCase_	,      UpperCAmelCase_	:					Dict     	=   key.split(".*."		)
															if prefix in name and suffix in name:
																		UpperCAmelCase_	:					Optional[Any]     	=   suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
												if key in name:
															UpperCAmelCase_	:					List[Any]     	=   True
															if "*" in mapped_key:
																		UpperCAmelCase_	:					Optional[Any]     	=   name.split(A__		)[0].split("."		)[-2]
																		UpperCAmelCase_	:					str     	=   mapped_key.replace("*"      ,A__		)
															if "weight_g" in name:
																		UpperCAmelCase_	:					Any     	=   "weight_g"
															elif "weight_v" in name:
																		UpperCAmelCase_	:					Optional[int]     	=   "weight_v"
															elif "bias" in name:
																		UpperCAmelCase_	:					str     	=   "bias"
															elif "weight" in name:
																		UpperCAmelCase_	:					int     	=   "weight"
															elif "running_mean" in name:
																		UpperCAmelCase_	:					Optional[Any]     	=   "running_mean"
															elif "running_var" in name:
																		UpperCAmelCase_	:					Tuple     	=   "running_var"
															elif "num_batches_tracked" in name:
																		UpperCAmelCase_	:					Union[str, Any]     	=   "num_batches_tracked"
															else:
																		UpperCAmelCase_	:					List[str]     	=   None
															set_recursively(A__      ,A__      ,A__      ,A__      ,A__		)
												continue
						if not is_used:
									unused_weights.append(A__		)

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

def 							snake_case (  A__      ,A__      ,A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Tuple     	=   full_name.split("conv_layers."		)[-1]
			UpperCAmelCase_	:					Any     	=   name.split("."		)
			UpperCAmelCase_	:					List[Any]     	=   int(items[0]		)
			UpperCAmelCase_	:					Any     	=   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."""		)
									UpperCAmelCase_	:					Tuple     	=   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."""		)
									UpperCAmelCase_	:					Any     	=   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."""		)
									UpperCAmelCase_	:					str     	=   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."""		)
									UpperCAmelCase_	:					str     	=   value
									logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
			else:
						unused_weights.append(A__		)

@torch.no_grad()
def 							snake_case (  A__      ,A__      ,A__      ,A__=None      ,A__=None      ,A__=None      ,):
			if config_path is not None:
						UpperCAmelCase_	:					Optional[Any]     	=   SpeechTaConfig.from_pretrained(A__		)
			else:
						UpperCAmelCase_	:					Dict     	=   SpeechTaConfig()

			if task == "s2t":
						UpperCAmelCase_	:					Optional[int]     	=   config.max_text_positions
						UpperCAmelCase_	:					Tuple     	=   SpeechTaForSpeechToText(A__		)
			elif task == "t2s":
						UpperCAmelCase_	:					Union[str, Any]     	=   18_76
						UpperCAmelCase_	:					str     	=   6_00
						UpperCAmelCase_	:					Any     	=   config.max_speech_positions
						UpperCAmelCase_	:					Optional[Any]     	=   SpeechTaForTextToSpeech(A__		)
			elif task == "s2s":
						UpperCAmelCase_	:					Optional[Any]     	=   18_76
						UpperCAmelCase_	:					Optional[Any]     	=   config.max_speech_positions
						UpperCAmelCase_	:					Optional[Any]     	=   SpeechTaForSpeechToSpeech(A__		)
			else:
						raise ValueError(F"""Unknown task name: {task}"""		)

			if vocab_path:
						UpperCAmelCase_	:					Optional[int]     	=   SpeechTaTokenizer(A__      ,model_max_length=config.max_text_positions		)

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

			UpperCAmelCase_	:					List[Any]     	=   SpeechTaFeatureExtractor()
			UpperCAmelCase_	:					Tuple     	=   SpeechTaProcessor(tokenizer=A__      ,feature_extractor=A__		)
			processor.save_pretrained(A__		)

			UpperCAmelCase_	:					Union[str, Any]     	=   torch.load(A__		)
			recursively_load_weights(fairseq_checkpoint["model"]      ,A__      ,A__		)

			model.save_pretrained(A__		)

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


if __name__ == "__main__":
			lowerCamelCase_							      = 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.'''
			)

			lowerCamelCase_							      = 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,
			)











"""simple docstring"""
import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english







@require_tokenizers
class      UpperCamelCase_			(__A							,		unittest.TestCase	):
	__magic_name__       =							BertTokenizer
	__magic_name__       =							BertTokenizerFast
	__magic_name__       =							True
	__magic_name__       =							True
	__magic_name__       =							filter_non_english





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Optional[Any]:
				super().setUp()

				UpperCAmelCase_	:					Optional[int]     	=   [
				    "[UNK]",
				    "[CLS]",
				    "[SEP]",
				    "[PAD]",
				    "[MASK]",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				UpperCAmelCase_	:					Optional[Any]     	=   os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["vocab_file"]		)
				with open(self.vocab_file      ,      "w"      ,      encoding="utf-8"		) as vocab_writer:
							vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					List[str]		)		->     List[str]:
				UpperCAmelCase_	:					str     	=   "UNwant\u00E9d,running"
				UpperCAmelCase_	:					Optional[int]     	=   "unwanted, running"
				return input_text, output_text





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Any:
				UpperCAmelCase_	:					Optional[int]     	=   self.tokenizer_class(self.vocab_file		)

				UpperCAmelCase_	:					int     	=   tokenizer.tokenize("UNwant\u00E9d,running"		)
				self.assertListEqual(lowerCAmelCase_      ,      ["un", "##want", "##ed", ",", "runn", "##ing"]		)
				self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_		)      ,      [9, 6, 7, 12, 10, 11]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     List[Any]:
				if not self.test_rust_tokenizer:
							return

				UpperCAmelCase_	:					List[Any]     	=   self.get_tokenizer()
				UpperCAmelCase_	:					str     	=   self.get_rust_tokenizer()

				UpperCAmelCase_	:					str     	=   "UNwant\u00E9d,running"

				UpperCAmelCase_	:					Any     	=   tokenizer.tokenize(lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   rust_tokenizer.tokenize(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   tokenizer.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   rust_tokenizer.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Dict     	=   self.get_rust_tokenizer()
				UpperCAmelCase_	:					int     	=   tokenizer.encode(lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[Any]     	=   rust_tokenizer.encode(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				# With lower casing
				UpperCAmelCase_	:					int     	=   self.get_tokenizer(do_lower_case=lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   self.get_rust_tokenizer(do_lower_case=lowerCAmelCase_		)

				UpperCAmelCase_	:					Any     	=   "UNwant\u00E9d,running"

				UpperCAmelCase_	:					Dict     	=   tokenizer.tokenize(lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   rust_tokenizer.tokenize(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Optional[int]     	=   tokenizer.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
				UpperCAmelCase_	:					Any     	=   rust_tokenizer.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   self.get_rust_tokenizer()
				UpperCAmelCase_	:					Dict     	=   tokenizer.encode(lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   rust_tokenizer.encode(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     List[str]:
				UpperCAmelCase_	:					Union[str, Any]     	=   BasicTokenizer()

				self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"		)      ,      ["ah", "\u535A", "\u63A8", "zz"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[int]:
				UpperCAmelCase_	:					Union[str, Any]     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "		)      ,      ["hello", "!", "how", "are", "you", "?"]		)
				self.assertListEqual(tokenizer.tokenize("H\u00E9llo"		)      ,      ["hello"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Optional[Any]:
				UpperCAmelCase_	:					Dict     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_      ,      strip_accents=lowerCAmelCase_		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "		)      ,      ["hällo", "!", "how", "are", "you", "?"]		)
				self.assertListEqual(tokenizer.tokenize("H\u00E9llo"		)      ,      ["h\u00E9llo"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Any:
				UpperCAmelCase_	:					str     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_      ,      strip_accents=lowerCAmelCase_		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "		)      ,      ["hallo", "!", "how", "are", "you", "?"]		)
				self.assertListEqual(tokenizer.tokenize("H\u00E9llo"		)      ,      ["hello"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Optional[Any]:
				UpperCAmelCase_	:					List[str]     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "		)      ,      ["hallo", "!", "how", "are", "you", "?"]		)
				self.assertListEqual(tokenizer.tokenize("H\u00E9llo"		)      ,      ["hello"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Optional[Any]:
				UpperCAmelCase_	:					List[Any]     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "		)      ,      ["HeLLo", "!", "how", "Are", "yoU", "?"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					str     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_      ,      strip_accents=lowerCAmelCase_		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "		)      ,      ["HäLLo", "!", "how", "Are", "yoU", "?"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     str:
				UpperCAmelCase_	:					Any     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_      ,      strip_accents=lowerCAmelCase_		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "		)      ,      ["HaLLo", "!", "how", "Are", "yoU", "?"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Dict:
				UpperCAmelCase_	:					Dict     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_      ,      never_split=["[UNK]"]		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"		)      ,      ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Optional[int]     	=   BasicTokenizer()
				UpperCAmelCase_	:					int     	=   "a\n'll !!to?'d of, can't."
				UpperCAmelCase_	:					Union[str, Any]     	=   ["a", "'", "ll", "!", "!", "to", "?", "'", "d", "of", ",", "can", "'", "t", "."]
				self.assertListEqual(tokenizer.tokenize(lowerCAmelCase_		)      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Dict:
				UpperCAmelCase_	:					List[str]     	=   ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

				UpperCAmelCase_	:					int     	=   {}
				for i, token in enumerate(lowerCAmelCase_		):
							UpperCAmelCase_	:					Tuple     	=   i
				UpperCAmelCase_	:					Dict     	=   WordpieceTokenizer(vocab=lowerCAmelCase_      ,      unk_token="[UNK]"		)

				self.assertListEqual(tokenizer.tokenize(""		)      ,      []		)

				self.assertListEqual(tokenizer.tokenize("unwanted running"		)      ,      ["un", "##want", "##ed", "runn", "##ing"]		)

				self.assertListEqual(tokenizer.tokenize("unwantedX running"		)      ,      ["[UNK]", "runn", "##ing"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Any:
				self.assertTrue(_is_whitespace(" "		)		)
				self.assertTrue(_is_whitespace("\t"		)		)
				self.assertTrue(_is_whitespace("\r"		)		)
				self.assertTrue(_is_whitespace("\n"		)		)
				self.assertTrue(_is_whitespace("\u00A0"		)		)

				self.assertFalse(_is_whitespace("A"		)		)
				self.assertFalse(_is_whitespace("-"		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Any:
				self.assertTrue(_is_control("\u0005"		)		)

				self.assertFalse(_is_control("A"		)		)
				self.assertFalse(_is_control(" "		)		)
				self.assertFalse(_is_control("\t"		)		)
				self.assertFalse(_is_control("\r"		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     int:
				self.assertTrue(_is_punctuation("-"		)		)
				self.assertTrue(_is_punctuation("$"		)		)
				self.assertTrue(_is_punctuation("`"		)		)
				self.assertTrue(_is_punctuation("."		)		)

				self.assertFalse(_is_punctuation("A"		)		)
				self.assertFalse(_is_punctuation(" "		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     str:
				UpperCAmelCase_	:					List[Any]     	=   self.get_tokenizer()
				UpperCAmelCase_	:					List[Any]     	=   self.get_rust_tokenizer()

				# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
				self.assertListEqual([tokenizer.tokenize(lowerCAmelCase_		) for t in ["Test", "\xad", "test"]]      ,      [["[UNK]"], [], ["[UNK]"]]		)

				self.assertListEqual(
				    [rust_tokenizer.tokenize(lowerCAmelCase_		) for t in ["Test", "\xad", "test"]]      ,      [["[UNK]"], [], ["[UNK]"]]		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     List[Any]:
				UpperCAmelCase_	:					str     	=   self.tokenizer_class.from_pretrained("bert-base-uncased"		)

				UpperCAmelCase_	:					Optional[Any]     	=   tokenizer.encode("sequence builders"      ,      add_special_tokens=lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   tokenizer.encode("multi-sequence build"      ,      add_special_tokens=lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_      ,      lowerCAmelCase_		)

				assert encoded_sentence == [101] + text + [102]
				assert encoded_pair == [101] + text + [102] + text_a + [102]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     List[str]:
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
										UpperCAmelCase_	:					Optional[int]     	=   self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_      ,      **lowerCAmelCase_		)

										UpperCAmelCase_	:					int     	=   f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
										UpperCAmelCase_	:					Dict     	=   tokenizer_r.encode_plus(
										    lowerCAmelCase_      ,      return_attention_mask=lowerCAmelCase_      ,      return_token_type_ids=lowerCAmelCase_      ,      return_offsets_mapping=lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_      ,      )

										UpperCAmelCase_	:					int     	=   tokenizer_r.do_lower_case if hasattr(lowerCAmelCase_      ,      "do_lower_case"		) else False
										UpperCAmelCase_	:					Dict     	=   (
										    [
										        ((0, 0), tokenizer_r.cls_token),
										        ((0, 1), "A"),
										        ((1, 2), ","),
										        ((3, 5), "na"),
										        ((5, 6), "##ï"),
										        ((6, 8), "##ve"),
										        ((9, 15), tokenizer_r.mask_token),
										        ((16, 21), "Allen"),
										        ((21, 23), "##NL"),
										        ((23, 24), "##P"),
										        ((25, 33), "sentence"),
										        ((33, 34), "."),
										        ((0, 0), tokenizer_r.sep_token),
										    ]
										    if not do_lower_case
										    else [
										        ((0, 0), tokenizer_r.cls_token),
										        ((0, 1), "a"),
										        ((1, 2), ","),
										        ((3, 8), "naive"),
										        ((9, 15), tokenizer_r.mask_token),
										        ((16, 21), "allen"),
										        ((21, 23), "##nl"),
										        ((23, 24), "##p"),
										        ((25, 33), "sentence"),
										        ((33, 34), "."),
										        ((0, 0), tokenizer_r.sep_token),
										    ]
										)

										self.assertEqual(
										    [e[1] for e in expected_results]      ,      tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]		)		)
										self.assertEqual([e[0] for e in expected_results]      ,      tokens["offset_mapping"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     str:
				UpperCAmelCase_	:					Dict     	=   ["的", "人", "有"]
				UpperCAmelCase_	:					Union[str, Any]     	=   "".join(lowerCAmelCase_		)
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
										UpperCAmelCase_	:					Tuple     	=   True
										UpperCAmelCase_	:					Optional[Any]     	=   self.tokenizer_class.from_pretrained(lowerCAmelCase_      ,      **lowerCAmelCase_		)
										UpperCAmelCase_	:					Dict     	=   self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_      ,      **lowerCAmelCase_		)

										UpperCAmelCase_	:					Union[str, Any]     	=   tokenizer_p.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
										UpperCAmelCase_	:					Union[str, Any]     	=   tokenizer_r.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)

										UpperCAmelCase_	:					str     	=   tokenizer_r.convert_ids_to_tokens(lowerCAmelCase_		)
										UpperCAmelCase_	:					Optional[Any]     	=   tokenizer_p.convert_ids_to_tokens(lowerCAmelCase_		)

										# it is expected that each Chinese character is not preceded by "##"
										self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)
										self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

										UpperCAmelCase_	:					int     	=   False
										UpperCAmelCase_	:					str     	=   self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_      ,      **lowerCAmelCase_		)
										UpperCAmelCase_	:					List[str]     	=   self.tokenizer_class.from_pretrained(lowerCAmelCase_      ,      **lowerCAmelCase_		)

										UpperCAmelCase_	:					int     	=   tokenizer_r.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
										UpperCAmelCase_	:					int     	=   tokenizer_p.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)

										UpperCAmelCase_	:					Optional[Any]     	=   tokenizer_r.convert_ids_to_tokens(lowerCAmelCase_		)
										UpperCAmelCase_	:					List[str]     	=   tokenizer_p.convert_ids_to_tokens(lowerCAmelCase_		)

										# it is expected that only the first Chinese character is not preceded by "##".
										UpperCAmelCase_	:					Dict     	=   [
										    f"""##{token}""" if idx != 0 else token for idx, token in enumerate(lowerCAmelCase_		)
										]
										self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)
										self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)



