






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()
UpperCamelCase 				=	logging.get_logger('''transformers.models.speecht5''')

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

def        __lowerCamelCase			(		snake_case__       ,snake_case__       ,snake_case__       ,snake_case__       ,snake_case__		)							->  List[str]:






						"""simple docstring"""





						for attribute in key.split("""."""		):
												_SCREAMING_SNAKE_CASE	      =  getattr(__UpperCamelCase       ,__UpperCamelCase		)

						if weight_type is not None:
												_SCREAMING_SNAKE_CASE	      =  getattr(__UpperCamelCase       ,__UpperCamelCase		).shape
						else:
												_SCREAMING_SNAKE_CASE	      =  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":
												_SCREAMING_SNAKE_CASE	      =  value
						elif weight_type == "weight_g":
												_SCREAMING_SNAKE_CASE	      =  value
						elif weight_type == "weight_v":
												_SCREAMING_SNAKE_CASE	      =  value
						elif weight_type == "bias":
												_SCREAMING_SNAKE_CASE	      =  value
						elif weight_type == "running_mean":
												_SCREAMING_SNAKE_CASE	      =  value
						elif weight_type == "running_var":
												_SCREAMING_SNAKE_CASE	      =  value
						elif weight_type == "num_batches_tracked":
												_SCREAMING_SNAKE_CASE	      =  value
						else:
												_SCREAMING_SNAKE_CASE	      =  value

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

def        __lowerCamelCase			(		snake_case__       ,snake_case__		)							->  Optional[Any]:






						"""simple docstring"""





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

def        __lowerCamelCase			(		snake_case__       ,snake_case__       ,snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  []

						if task == "s2t":
												_SCREAMING_SNAKE_CASE	      =  hf_model.speechta.encoder.prenet.feature_encoder
												_SCREAMING_SNAKE_CASE	      =  MAPPING_S2T
												_SCREAMING_SNAKE_CASE	      =  IGNORE_KEYS_S2T
						elif task == "t2s":
												_SCREAMING_SNAKE_CASE	      =  None
												_SCREAMING_SNAKE_CASE	      =  MAPPING_T2S
												_SCREAMING_SNAKE_CASE	      =  IGNORE_KEYS_T2S
						elif task == "s2s":
												_SCREAMING_SNAKE_CASE	      =  hf_model.speechta.encoder.prenet.feature_encoder
												_SCREAMING_SNAKE_CASE	      =  MAPPING_S2S
												_SCREAMING_SNAKE_CASE	      =  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

												_SCREAMING_SNAKE_CASE	      =  False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,__UpperCamelCase       ,hf_model.config.feat_extract_norm == """group"""       ,)
																		_SCREAMING_SNAKE_CASE	      =  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:
																														_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE	      =  key.split(""".*."""		)
																														if prefix in name and suffix in name:
																																				_SCREAMING_SNAKE_CASE	      =  suffix

																																				    # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																								if key in name:
																														_SCREAMING_SNAKE_CASE	      =  True
																														if "*" in mapped_key:
																																				_SCREAMING_SNAKE_CASE	      =  name.split(__UpperCamelCase		)[0].split("""."""		)[-2]
																																				_SCREAMING_SNAKE_CASE	      =  mapped_key.replace("""*"""       ,__UpperCamelCase		)
																														if "weight_g" in name:
																																				_SCREAMING_SNAKE_CASE	      =  """weight_g"""
																														elif "weight_v" in name:
																																				_SCREAMING_SNAKE_CASE	      =  """weight_v"""
																														elif "bias" in name:
																																				_SCREAMING_SNAKE_CASE	      =  """bias"""
																														elif "weight" in name:
																																				_SCREAMING_SNAKE_CASE	      =  """weight"""
																														elif "running_mean" in name:
																																				_SCREAMING_SNAKE_CASE	      =  """running_mean"""
																														elif "running_var" in name:
																																				_SCREAMING_SNAKE_CASE	      =  """running_var"""
																														elif "num_batches_tracked" in name:
																																				_SCREAMING_SNAKE_CASE	      =  """num_batches_tracked"""
																														else:
																																				_SCREAMING_SNAKE_CASE	      =  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			(		snake_case__       ,snake_case__       ,snake_case__       ,snake_case__       ,snake_case__		)							->  Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  full_name.split("""conv_layers."""		)[-1]
						_SCREAMING_SNAKE_CASE	      =  name.split("""."""		)
						_SCREAMING_SNAKE_CASE	      =  int(items[0]		)
						_SCREAMING_SNAKE_CASE	      =  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.'		)
																		_SCREAMING_SNAKE_CASE	      =  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.'		)
																		_SCREAMING_SNAKE_CASE	      =  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.'		)
																		_SCREAMING_SNAKE_CASE	      =  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.'		)
																		_SCREAMING_SNAKE_CASE	      =  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			(		snake_case__       ,snake_case__       ,snake_case__       ,snake_case__=None       ,snake_case__=None       ,snake_case__=None       ,)							->  int:






						"""simple docstring"""





						if config_path is not None:
												_SCREAMING_SNAKE_CASE	      =  SpeechTaConfig.from_pretrained(__UpperCamelCase		)
						else:
												_SCREAMING_SNAKE_CASE	      =  SpeechTaConfig()

						if task == "s2t":
												_SCREAMING_SNAKE_CASE	      =  config.max_text_positions
												_SCREAMING_SNAKE_CASE	      =  SpeechTaForSpeechToText(__UpperCamelCase		)
						elif task == "t2s":
												_SCREAMING_SNAKE_CASE	      =  18_76
												_SCREAMING_SNAKE_CASE	      =  6_00
												_SCREAMING_SNAKE_CASE	      =  config.max_speech_positions
												_SCREAMING_SNAKE_CASE	      =  SpeechTaForTextToSpeech(__UpperCamelCase		)
						elif task == "s2s":
												_SCREAMING_SNAKE_CASE	      =  18_76
												_SCREAMING_SNAKE_CASE	      =  config.max_speech_positions
												_SCREAMING_SNAKE_CASE	      =  SpeechTaForSpeechToSpeech(__UpperCamelCase		)
						else:
												raise ValueError(F'Unknown task name: {task}'		)

						if vocab_path:
												_SCREAMING_SNAKE_CASE	      =  SpeechTaTokenizer(__UpperCamelCase       ,model_max_length=config.max_text_positions		)

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

						_SCREAMING_SNAKE_CASE	      =  SpeechTaFeatureExtractor()
						_SCREAMING_SNAKE_CASE	      =  SpeechTaProcessor(tokenizer=__UpperCamelCase       ,feature_extractor=__UpperCamelCase		)
						processor.save_pretrained(__UpperCamelCase		)

						_SCREAMING_SNAKE_CASE	      =  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__":
		UpperCamelCase 				=	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.'''
		)

		UpperCamelCase 				=	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 unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase			: str							   =		get_tests_dir('''fixtures/test_sentencepiece.model''')

if is_sentencepiece_available():
   import sentencepiece as sp


__lowerCamelCase			: Any							   =		5
__lowerCamelCase			: Dict							   =		10







@require_sentencepiece
@require_tokenizers
class __snake_case				(						lowerCamelCase_   ,		unittest.TestCase		):
   lowerCAmelCase_														=							SpeechaTextTokenizer
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							True




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     super().setUp()

     SCREAMING_SNAKE_CASE__										=      sp.SentencePieceProcessor()
     spm_model.Load(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      ["""<s>""", """<pad>""", """</s>""", """<unk>"""]

     vocab += [spm_model.IdToPiece(id_      ) for id_ in range(len(_lowercase      )      )]
     SCREAMING_SNAKE_CASE__										=      dict(zip(_lowercase       ,	range(len(_lowercase      )      )      )      )

     SCREAMING_SNAKE_CASE__										=      Path(self.tmpdirname      )
     save_json(_lowercase       ,	save_dir / VOCAB_FILES_NAMES["""vocab_file"""]      )
     if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
       copyfile(_lowercase       ,	save_dir / VOCAB_FILES_NAMES["""spm_file"""]      )

     SCREAMING_SNAKE_CASE__										=      SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )
     tokenizer.save_pretrained(self.tmpdirname      )




   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """<pad>"""
     SCREAMING_SNAKE_CASE__										=      1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase      )       ,	_lowercase      )
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase      )       ,	_lowercase      )




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      list(self.get_tokenizer().get_vocab().keys()      )

     self.assertEqual(vocab_keys[0]       ,	"""<s>"""      )
     self.assertEqual(vocab_keys[1]       ,	"""<pad>"""      )
     self.assertEqual(vocab_keys[-1]       ,	"""j"""      )
     self.assertEqual(len(_lowercase      )       ,	10_01      )




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     self.assertEqual(self.get_tokenizer().vocab_size       ,	10_01      )




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )

     SCREAMING_SNAKE_CASE__										=      tokenizer.tokenize("""This is a test"""      )
     self.assertListEqual(_lowercase       ,	["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]      )

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(_lowercase      )       ,	[2_89, 50, 14, 1_74, 3_86]       ,	)

     SCREAMING_SNAKE_CASE__										=      tokenizer.tokenize("""I was born in 92000, and this is falsé."""      )
     self.assertListEqual(
         _lowercase       ,	[SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """9""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """é""", """."""]       ,	)
     SCREAMING_SNAKE_CASE__										=      tokenizer.convert_tokens_to_ids(_lowercase      )
     self.assertListEqual(_lowercase       ,	[12, 25, 88, 59, 28, 23, 11, 4, 6_06, 3_51, 3_51, 3_51, 7, 16, 70, 50, 76, 84, 10, 4, 8]      )

     SCREAMING_SNAKE_CASE__										=      tokenizer.convert_ids_to_tokens(_lowercase      )
     self.assertListEqual(
         _lowercase       ,	[SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """<unk>""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """<unk>""", """."""]       ,	)




   @slow
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      {"""input_ids""": [[37_91, 7_97, 31, 11, 64, 7_97, 31, 24_29, 4_33, 12, 11_76, 12, 20, 7_86, 9_15, 1_42, 24_13, 2_40, 37, 32_38, 7_97, 31, 11, 35, 93, 9_15, 1_42, 24_13, 2_40, 37, 55_40, 5_67, 12_76, 93, 37, 6_10, 40, 62, 4_55, 6_57, 10_42, 1_23, 7_80, 1_77, 37, 3_09, 2_41, 12_98, 5_14, 20, 2_92, 27_37, 1_14, 24_69, 2_41, 85, 64, 3_02, 5_48, 5_28, 4_23, 4, 5_09, 4_06, 4_23, 37, 6_01, 4, 7_77, 3_02, 5_48, 5_28, 4_23, 2_84, 4, 33_88, 5_11, 4_59, 4, 35_55, 40, 3_21, 3_02, 7_05, 4, 33_88, 5_11, 5_83, 3_26, 5, 5, 5, 62, 33_10, 5_60, 1_77, 26_80, 2_17, 15_08, 32, 31, 8_53, 4_18, 64, 5_83, 5_11, 16_05, 62, 35, 93, 5_60, 1_77, 26_80, 2_17, 15_08, 15_21, 64, 5_83, 5_11, 5_19, 62, 20, 15_15, 7_64, 20, 1_49, 2_61, 56_25, 79_72, 20, 55_40, 5_67, 12_76, 93, 39_25, 16_75, 11, 15, 8_02, 79_72, 5_76, 2_17, 15_08, 11, 35, 93, 12_53, 24_41, 15, 2_89, 6_52, 31, 4_16, 3_21, 38_42, 1_15, 40, 9_11, 8, 4_76, 6_19, 4, 3_80, 1_42, 4_23, 3_35, 2_40, 35, 93, 2_64, 8, 11, 3_35, 5_69, 4_20, 1_63, 5, 2], [2_60, 5_48, 5_28, 4_23, 20, 4_51, 20, 26_81, 11_53, 34_34, 20, 55_40, 37, 5_67, 1_26, 12_53, 24_41, 33_76, 4_49, 2_10, 4_31, 15_63, 1_77, 7_67, 55_40, 11, 12_03, 4_72, 11, 29_53, 6_85, 2_85, 3_64, 7_06, 11_53, 20, 67_99, 20, 28_69, 20, 44_64, 1_26, 40, 24_29, 20, 10_40, 8_66, 26_64, 4_18, 20, 3_18, 20, 17_26, 1_86, 20, 2_65, 5_22, 35, 93, 21_91, 46_34, 20, 10_40, 12, 67_99, 15, 2_28, 23_56, 1_42, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_75, 26_66, 6_84, 15_82, 11_76, 12, 6_27, 1_49, 6_19, 20, 49_02, 5_63, 11, 20, 1_49, 2_61, 34_20, 23_56, 1_74, 1_42, 47_14, 1_31, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=_lowercase       ,	model_name="""facebook/s2t-small-mustc-en-de-st"""       ,	revision="""a14f04cf0776c02f62a8cb800cf7909e15ea23ad"""       ,	)







@require_sentencepiece
class __snake_case				(						unittest.TestCase		):
   lowerCAmelCase_														=							"valhalla/s2t_mustc_multilinguial_medium"

   lowerCAmelCase_														=							"C'est trop cool"
   lowerCAmelCase_														=							"Esto es genial"




   @classmethod
   def 	__a						(    cls			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name      )
     return cls




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     self.assertEqual(self.tokenizer.lang_code_to_id["""pt"""]       ,	4      )
     self.assertEqual(self.tokenizer.lang_code_to_id["""ru"""]       ,	6      )
     self.assertEqual(self.tokenizer.lang_code_to_id["""it"""]       ,	9      )
     self.assertEqual(self.tokenizer.lang_code_to_id["""de"""]       ,	11      )




   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     self.assertEqual(self.tokenizer.vocab_size       ,	1_00_00      )




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     self.assertIn(_lowercase       ,	self.tokenizer.all_special_ids      )
     SCREAMING_SNAKE_CASE__										=      [ES_CODE, 4, 16_01, 47, 76_47, 2]
     SCREAMING_SNAKE_CASE__										=      self.tokenizer.decode(_lowercase       ,	skip_special_tokens=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      self.tokenizer.decode(generated_ids[1:]       ,	skip_special_tokens=_lowercase      )
     self.assertEqual(_lowercase       ,	_lowercase      )
     self.assertNotIn(self.tokenizer.eos_token       ,	_lowercase      )




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """fr"""
     SCREAMING_SNAKE_CASE__										=      self.tokenizer(self.french_text      ).input_ids
     self.assertEqual(encoded[0]       ,	_lowercase      )
     self.assertEqual(encoded[-1]       ,	self.tokenizer.eos_token_id      )




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """fr"""
     self.assertListEqual(self.tokenizer.prefix_tokens       ,	[FR_CODE]      )

     SCREAMING_SNAKE_CASE__										=      """es"""
     self.assertListEqual(self.tokenizer.prefix_tokens       ,	[ES_CODE]      )
