






'''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 		_UpperCAmelCase   (							__A							:   List[str]						,		__A							:   Optional[Any]						,		__A							:   Any						,		__A							:   Tuple						,		__A							:   Tuple      ):

				for attribute in key.split('''.'''      ):
								a_		:  List[Any]											=      getattr(snake_case__						,		snake_case__      )

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

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





def 		_UpperCAmelCase   (							__A							:   Any						,		__A							:   int      ):

				for key in ignore_keys:
								if key.endswith('''.*'''      ):
												if name.startswith(key[:-1]      ):
																return True
								elif ".*." in key:
												a_		:  int											=      key.split('''.*.'''      )
												if prefix in name and suffix in name:
																return True
								elif key in name:
												return True
				return False





def 		_UpperCAmelCase   (							__A							:   Dict						,		__A							:   int						,		__A							:   Any      ):
				a_		:  Tuple											=      []

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

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

								a_		:  Optional[Any]											=      False
								if "conv_layers" in name:
												load_conv_layer(
												    snake_case__						,		snake_case__						,		snake_case__						,		snake_case__						,		hf_model.config.feat_extract_norm == '''group'''						,		)
												a_		:  Union[str, Any]											=      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:
																				a_		:  Union[str, Any]											=      key.split('''.*.'''      )
																				if prefix in name and suffix in name:
																								a_		:  Optional[int]											=      suffix

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

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





def 		_UpperCAmelCase   (							__A							:   Union[str, Any]						,		__A							:   List[str]						,		__A							:   str						,		__A							:   Optional[int]						,		__A							:   Dict      ):
				a_		:  Dict											=      full_name.split('''conv_layers.'''      )[-1]
				a_		:  Dict											=      name.split('''.'''      )
				a_		:  Union[str, Any]											=      int(items[0]      )
				a_		:  List[str]											=      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.'      )
												a_		:  Optional[int]											=      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.'      )
												a_		:  str											=      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.'      )
												a_		:  List[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.'      )
												a_		:  Optional[Any]											=      value
												logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'      )
				else:
								unused_weights.append(snake_case__      )





@torch.no_grad()
def 		_UpperCAmelCase   (							__A							:   Tuple						,		__A							:   int						,		__A							:   Any						,		__A							:   List[Any]=None						,		__A							:   Tuple=None						,		__A							:   Dict=None						,		):

				if config_path is not None:
								a_		:  List[Any]											=      SpeechTaConfig.from_pretrained(snake_case__      )
				else:
								a_		:  List[Any]											=      SpeechTaConfig()

				if task == "s2t":
								a_		:  Union[str, Any]											=      config.max_text_positions
								a_		:  Optional[Any]											=      SpeechTaForSpeechToText(snake_case__      )
				elif task == "t2s":
								a_		:  List[Any]											=      18_76
								a_		:  Tuple											=      6_00
								a_		:  Union[str, Any]											=      config.max_speech_positions
								a_		:  Dict											=      SpeechTaForTextToSpeech(snake_case__      )
				elif task == "s2s":
								a_		:  List[str]											=      18_76
								a_		:  List[str]											=      config.max_speech_positions
								a_		:  Dict											=      SpeechTaForSpeechToSpeech(snake_case__      )
				else:
								raise ValueError(f'Unknown task name: {task}'      )

				if vocab_path:
								a_		:  str											=      SpeechTaTokenizer(snake_case__						,		model_max_length=config.max_text_positions      )

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

				a_		:  List[Any]											=      SpeechTaFeatureExtractor()
				a_		:  Optional[Any]											=      SpeechTaProcessor(tokenizer=snake_case__						,		feature_extractor=snake_case__      )
				processor.save_pretrained(snake_case__      )

				a_		:  Optional[Any]											=      torch.load(snake_case__      )
				recursively_load_weights(fairseq_checkpoint['''model''']						,		snake_case__						,		snake_case__      )

				model.save_pretrained(snake_case__      )

				if repo_id:
								print('''Pushing to the hub...'''      )
								processor.push_to_hub(snake_case__      )
								model.push_to_hub(snake_case__      )


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 itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				LongformerTokenizer
			snake_case__  =				True
			snake_case__  =				LongformerTokenizerFast
			snake_case__  =				True






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							a_		:  Tuple											=      [
							    '''l''',
							    '''o''',
							    '''w''',
							    '''e''',
							    '''r''',
							    '''s''',
							    '''t''',
							    '''i''',
							    '''d''',
							    '''n''',
							    '''\u0120''',
							    '''\u0120l''',
							    '''\u0120n''',
							    '''\u0120lo''',
							    '''\u0120low''',
							    '''er''',
							    '''\u0120lowest''',
							    '''\u0120newer''',
							    '''\u0120wider''',
							    '''<unk>''',
							]
							a_		:  Optional[Any]											=      dict(zip(__SCREAMING_SNAKE_CASE  ,						range(len(__SCREAMING_SNAKE_CASE					)					)					)					)
							a_		:  Union[str, Any]											=      ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
							a_		:  Any											=      {'''unk_token''': '''<unk>'''}

							a_		:  Union[str, Any]											=      os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES['''vocab_file''']					)
							a_		:  Optional[int]											=      os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES['''merges_file''']					)
							with open(self.vocab_file  ,						'''w'''  ,						encoding='''utf-8'''					) as fp:
											fp.write(json.dumps(__SCREAMING_SNAKE_CASE					) + '''\n'''					)
							with open(self.merges_file  ,						'''w'''  ,						encoding='''utf-8'''					) as fp:
											fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						**__SCREAMING_SNAKE_CASE      :			Any					)			->			int:
							kwargs.update(self.special_tokens_map					)
							return self.tokenizer_class.from_pretrained(self.tmpdirname  ,						**__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						**__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			List[str]:
							kwargs.update(self.special_tokens_map					)
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,						**__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Any:
							a_		:  Union[str, Any]											=      '''lower newer'''
							a_		:  List[Any]											=      '''lower newer'''
							return input_text, output_text






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							a_		:  Optional[Any]											=      self.tokenizer_class(self.vocab_file  ,						self.merges_file  ,						**self.special_tokens_map					)
							a_		:  List[str]											=      '''lower newer'''
							a_		:  str											=      ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
							a_		:  Optional[int]											=      tokenizer.tokenize(__SCREAMING_SNAKE_CASE					)  # , add_prefix_space=True)
							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							a_		:  Dict											=      tokens + [tokenizer.unk_token]
							a_		:  Any											=      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE					)  ,						__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Optional[Any]:
							a_		:  Union[str, Any]											=      self.get_tokenizer()

							self.assertListEqual(tokenizer.encode('''Hello world!'''  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)  ,						[0, 3_1414, 232, 328, 2]					)
							self.assertListEqual(
							    tokenizer.encode('''Hello world! cécé herlolip 418'''  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)  ,						[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]  ,						)






			@slow
			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Optional[Any]:
							a_		:  Dict											=      self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''					)

							a_		:  Tuple											=      tokenizer.encode('''sequence builders'''  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      tokenizer.encode('''multi-sequence build'''  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)

							a_		:  Any											=      tokenizer.encode(
							    '''sequence builders'''  ,						add_special_tokens=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      tokenizer.encode(
							    '''sequence builders'''  ,						'''multi-sequence build'''  ,						add_special_tokens=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE					)

							a_		:  List[str]											=      tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			int:
							a_		:  str											=      self.get_tokenizer()

							a_		:  int											=      '''Encode this sequence.'''
							a_		:  List[str]											=      tokenizer.byte_encoder[''' '''.encode('''utf-8'''					)[0]]

							# Testing encoder arguments
							a_		:  Dict											=      tokenizer.encode(__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[Any]											=      tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
							self.assertNotEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							a_		:  Dict											=      tokenizer.encode(__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
							self.assertEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}					)
							a_		:  Dict											=      tokenizer.encode(__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
							a_		:  Dict											=      tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
							self.assertNotEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							# Testing spaces after special tokens
							a_		:  Optional[Any]											=      '''<mask>'''
							tokenizer.add_special_tokens(
							    {'''mask_token''': AddedToken(__SCREAMING_SNAKE_CASE  ,						lstrip=__SCREAMING_SNAKE_CASE  ,						rstrip=__SCREAMING_SNAKE_CASE					)}					)  # mask token has a left space
							a_		:  Optional[int]											=      tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE					)

							a_		:  List[Any]											=      '''Encode <mask> sequence'''
							a_		:  List[str]											=      '''Encode <mask>sequence'''

							a_		:  int											=      tokenizer.encode(__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      encoded.index(__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
							self.assertEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)

							a_		:  Union[str, Any]											=      tokenizer.encode(__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      encoded.index(__SCREAMING_SNAKE_CASE					)
							a_		:  str											=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
							self.assertNotEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			Union[str, Any]:
							pass






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Any:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'					):
															a_		:  Any											=      self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)
															a_		:  Any											=      self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)
															a_		:  str											=      '''A, <mask> AllenNLP sentence.'''
															a_		:  List[Any]											=      tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE  ,						return_token_type_ids=__SCREAMING_SNAKE_CASE					)
															a_		:  Dict											=      tokenizer_p.encode_plus(__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE  ,						return_token_type_ids=__SCREAMING_SNAKE_CASE					)

															# token_type_ids should put 0 everywhere
															self.assertEqual(sum(tokens_r['''token_type_ids''']					)  ,						sum(tokens_p['''token_type_ids''']					)					)

															# attention_mask should put 1 everywhere, so sum over length should be 1
															self.assertEqual(
															    sum(tokens_r['''attention_mask''']					) / len(tokens_r['''attention_mask''']					)  ,						sum(tokens_p['''attention_mask''']					) / len(tokens_p['''attention_mask''']					)  ,						)

															a_		:  str											=      tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']					)
															a_		:  Tuple											=      tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']					)

															# Rust correctly handles the space before the mask while python doesnt
															self.assertSequenceEqual(tokens_p['''input_ids''']  ,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)
															self.assertSequenceEqual(tokens_r['''input_ids''']  ,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)

															self.assertSequenceEqual(
															    __SCREAMING_SNAKE_CASE  ,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']					)
															self.assertSequenceEqual(
															    __SCREAMING_SNAKE_CASE  ,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[Any]:
							for trim_offsets, add_prefix_space in itertools.product([True, False]  ,						repeat=2					):
											a_		:  Any											=      self.rust_tokenizer_class.from_pretrained(
											    self.tmpdirname  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)

											a_		:  Union[str, Any]											=      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
											a_		:  str											=      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

											self.assertEqual(pre_tokenizer_state['''add_prefix_space''']  ,						__SCREAMING_SNAKE_CASE					)

											self.assertEqual(post_processor_state['''add_prefix_space''']  ,						__SCREAMING_SNAKE_CASE					)
											self.assertEqual(post_processor_state['''trim_offsets''']  ,						__SCREAMING_SNAKE_CASE					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Optional[int]:
							# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
							# `trim_offsets`
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'					):
															a_		:  Dict											=      '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
															a_		:  Union[str, Any]											=      f'{text_of_1_token} {text_of_1_token}'

															a_		:  Any											=      self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)
															a_		:  Optional[int]											=      tokenizer_r(__SCREAMING_SNAKE_CASE  ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(encoding.offset_mapping[0]  ,						(0, len(__SCREAMING_SNAKE_CASE					))					)
															self.assertEqual(
															    encoding.offset_mapping[1]  ,						(len(__SCREAMING_SNAKE_CASE					) + 1, len(__SCREAMING_SNAKE_CASE					) + 1 + len(__SCREAMING_SNAKE_CASE					))  ,						)

															a_		:  Any											=      self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)
															a_		:  str											=      tokenizer_r(__SCREAMING_SNAKE_CASE  ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(encoding.offset_mapping[0]  ,						(0, len(__SCREAMING_SNAKE_CASE					))					)
															self.assertEqual(
															    encoding.offset_mapping[1]  ,						(len(__SCREAMING_SNAKE_CASE					) + 1, len(__SCREAMING_SNAKE_CASE					) + 1 + len(__SCREAMING_SNAKE_CASE					))  ,						)

															a_		:  int											=      self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)
															a_		:  Union[str, Any]											=      tokenizer_r(__SCREAMING_SNAKE_CASE  ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(encoding.offset_mapping[0]  ,						(0, len(__SCREAMING_SNAKE_CASE					))					)
															self.assertEqual(
															    encoding.offset_mapping[1]  ,						(len(__SCREAMING_SNAKE_CASE					), len(__SCREAMING_SNAKE_CASE					) + 1 + len(__SCREAMING_SNAKE_CASE					))  ,						)

															a_		:  Tuple											=      self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)
															a_		:  Any											=      tokenizer_r(__SCREAMING_SNAKE_CASE  ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(encoding.offset_mapping[0]  ,						(0, len(__SCREAMING_SNAKE_CASE					))					)
															self.assertEqual(
															    encoding.offset_mapping[1]  ,						(len(__SCREAMING_SNAKE_CASE					), len(__SCREAMING_SNAKE_CASE					) + 1 + len(__SCREAMING_SNAKE_CASE					))  ,						)

															a_		:  Union[str, Any]											=      f' {text}'

															# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
															#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
															# )
															# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
															# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
															# self.assertEqual(
															#     encoding.offset_mapping[1],
															#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
															# )

															a_		:  str											=      self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)
															a_		:  int											=      tokenizer_r(__SCREAMING_SNAKE_CASE  ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(encoding.offset_mapping[0]  ,						(1, 1 + len(__SCREAMING_SNAKE_CASE					))					)
															self.assertEqual(
															    encoding.offset_mapping[1]  ,						(1 + len(__SCREAMING_SNAKE_CASE					) + 1, 1 + len(__SCREAMING_SNAKE_CASE					) + 1 + len(__SCREAMING_SNAKE_CASE					))  ,						)

															a_		:  int											=      self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)
															a_		:  str											=      tokenizer_r(__SCREAMING_SNAKE_CASE  ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(encoding.offset_mapping[0]  ,						(0, 1 + len(__SCREAMING_SNAKE_CASE					))					)
															self.assertEqual(
															    encoding.offset_mapping[1]  ,						(1 + len(__SCREAMING_SNAKE_CASE					), 1 + len(__SCREAMING_SNAKE_CASE					) + 1 + len(__SCREAMING_SNAKE_CASE					))  ,						)

															a_		:  Optional[int]											=      self.rust_tokenizer_class.from_pretrained(
															    __SCREAMING_SNAKE_CASE  ,						use_fast=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE					)
															a_		:  int											=      tokenizer_r(__SCREAMING_SNAKE_CASE  ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE  ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
															self.assertEqual(encoding.offset_mapping[0]  ,						(0, 1 + len(__SCREAMING_SNAKE_CASE					))					)
															self.assertEqual(
															    encoding.offset_mapping[1]  ,						(1 + len(__SCREAMING_SNAKE_CASE					), 1 + len(__SCREAMING_SNAKE_CASE					) + 1 + len(__SCREAMING_SNAKE_CASE					))  ,						)




