



'''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()
a     :    Tuple      						=					logging.get_logger("""transformers.models.speecht5""")

a     :    Any      						=					{
    """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""",
}
a     :    Union[str, Any]      						=					{
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
a     :    str      						=					{
    """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""",
}
a     :    Dict      						=					{
    """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""",
}
a     :    Union[str, Any]      						=					{
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
a     :    List[str]      						=					{
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
a     :    List[str]      						=					{
    """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""",
}
a     :    Union[str, Any]      						=					{
    """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""",
}
a     :    Union[str, Any]      						=					{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
a     :    Any      						=					{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a     :    Tuple      						=					{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a     :    Any      						=					[]
a     :    List[str]      						=					[
    """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""",
]
a     :    Optional[Any]      						=					IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
a     :    Optional[int]      						=					IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
a     :    Optional[Any]      						=					IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)   ->    Optional[int]:
							for attribute in key.split("""."""	):
														UpperCAmelCase  :	str							  =	getattr(_lowercase			,       _lowercase	)

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

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



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    List[Any]:
							for key in ignore_keys:
														if key.endswith(""".*"""	):
																					if name.startswith(key[:-1]	):
																												return True
														elif ".*." in key:
																					UpperCAmelCase  ,  UpperCAmelCase  :	Union[str, Any]							  =	key.split(""".*."""	)
																					if prefix in name and suffix in name:
																												return True
														elif key in name:
																					return True
							return False



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    List[Any]:
							UpperCAmelCase  :	Optional[Any]							  =	[]

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

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

														UpperCAmelCase  :	List[Any]							  =	False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       hf_model.config.feat_extract_norm == """group"""			,       )
																					UpperCAmelCase  :	List[str]							  =	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  :	Union[str, Any]							  =	key.split(""".*."""	)
																																			if prefix in name and suffix in name:
																																										UpperCAmelCase  :	List[str]							  =	suffix

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

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



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



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None			,       )   ->    Union[str, Any]:
							if config_path is not None:
														UpperCAmelCase  :	Any							  =	SpeechTaConfig.from_pretrained(_lowercase	)
							else:
														UpperCAmelCase  :	str							  =	SpeechTaConfig()

							if task == "s2t":
														UpperCAmelCase  :	Optional[Any]							  =	config.max_text_positions
														UpperCAmelCase  :	int							  =	SpeechTaForSpeechToText(_lowercase	)
							elif task == "t2s":
														UpperCAmelCase  :	Dict							  =	1_8_7_6
														UpperCAmelCase  :	Any							  =	6_0_0
														UpperCAmelCase  :	Optional[Any]							  =	config.max_speech_positions
														UpperCAmelCase  :	List[str]							  =	SpeechTaForTextToSpeech(_lowercase	)
							elif task == "s2s":
														UpperCAmelCase  :	List[Any]							  =	1_8_7_6
														UpperCAmelCase  :	Optional[Any]							  =	config.max_speech_positions
														UpperCAmelCase  :	List[Any]							  =	SpeechTaForSpeechToSpeech(_lowercase	)
							else:
														raise ValueError(F'''Unknown task name: {task}'''	)

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

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

							UpperCAmelCase  :	List[Any]							  =	SpeechTaFeatureExtractor()
							UpperCAmelCase  :	List[Any]							  =	SpeechTaProcessor(tokenizer=_lowercase			,       feature_extractor=_lowercase	)
							processor.save_pretrained(_lowercase	)

							UpperCAmelCase  :	str							  =	torch.load(_lowercase	)
							recursively_load_weights(fairseq_checkpoint["""model"""]			,       _lowercase			,       _lowercase	)

							model.save_pretrained(_lowercase	)

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


if __name__ == "__main__":
							a     :    List[Any]      						=					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."""
							)

							a     :    Tuple      						=					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 unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a     :    List[Any]      						=					get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")

@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_				(							__magic_name__   ,  unittest.TestCase    ):
	lowercase    =				PegasusTokenizer
	lowercase    =				PegasusTokenizerFast
	lowercase    =				True
	lowercase    =				True





	def 				_lowercase(						self					)    ->    Tuple:
								super().setUp()

								# We have a SentencePiece fixture for testing
								UpperCAmelCase  :	int							  =	PegasusTokenizer(A					)
								tokenizer.save_pretrained(self.tmpdirname					)





	@cached_property
	def 				_lowercase(						self					)    ->    int:
								return PegasusTokenizer.from_pretrained("""google/pegasus-large"""					)





	def 				_lowercase(						self	,	**A					)    ->    PegasusTokenizer:
								return PegasusTokenizer.from_pretrained(self.tmpdirname	,	**A					)





	def 				_lowercase(						self	,	A					)    ->    List[str]:
								return ("This is a test", "This is a test")





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Optional[int]							  =	"""</s>"""
								UpperCAmelCase  :	Optional[int]							  =	1

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





	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  :	str							  =	list(self.get_tokenizer().get_vocab().keys()					)

								self.assertEqual(vocab_keys[0]	,	"""<pad>"""					)
								self.assertEqual(vocab_keys[1]	,	"""</s>"""					)
								self.assertEqual(vocab_keys[-1]	,	"""v"""					)
								self.assertEqual(len(A					)	,	1103					)





	def 				_lowercase(						self					)    ->    Optional[Any]:
								self.assertEqual(self.get_tokenizer().vocab_size	,	1103					)





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Any							  =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname					)
								UpperCAmelCase  :	Optional[Any]							  =	self.tokenizer_class.from_pretrained(self.tmpdirname					)
								UpperCAmelCase  :	Union[str, Any]							  =	(
								    """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
								    """ </s> <pad> <pad> <pad>"""
								)
								UpperCAmelCase  :	Optional[Any]							  =	rust_tokenizer([raw_input_str]	,	return_tensors=A	,	add_special_tokens=A					).input_ids[0]
								UpperCAmelCase  :	Optional[Any]							  =	py_tokenizer([raw_input_str]	,	return_tensors=A	,	add_special_tokens=A					).input_ids[0]
								self.assertListEqual(A	,	A					)





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	List[Any]							  =	self._large_tokenizer
								# <mask_1> masks whole sentence while <mask_2> masks single word
								UpperCAmelCase  :	Any							  =	"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
								UpperCAmelCase  :	Optional[Any]							  =	[2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
								UpperCAmelCase  :	Optional[Any]							  =	tokenizer([raw_input_str]	,	return_tensors=A					).input_ids[0]
								self.assertListEqual(A	,	A					)





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	str							  =	self._large_tokenizer
								# The tracebacks for the following asserts are **better** without messages or self.assertEqual
								assert tokenizer.vocab_size == 96103
								assert tokenizer.pad_token_id == 0
								assert tokenizer.eos_token_id == 1
								assert tokenizer.offset == 103
								assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
								assert tokenizer.unk_token == "<unk>"
								assert tokenizer.model_max_length == 1024
								UpperCAmelCase  :	List[Any]							  =	"""To ensure a smooth flow of bank resolutions."""
								UpperCAmelCase  :	Optional[int]							  =	[413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
								UpperCAmelCase  :	Union[str, Any]							  =	tokenizer([raw_input_str]	,	return_tensors=A					).input_ids[0]
								self.assertListEqual(A	,	A					)
								assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]					) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]





	@require_torch
	def 				_lowercase(						self					)    ->    Any:
								UpperCAmelCase  :	int							  =	["""This is going to be way too long.""" * 150, """short example"""]
								UpperCAmelCase  :	Optional[int]							  =	["""not super long but more than 5 tokens""", """tiny"""]
								UpperCAmelCase  :	Tuple							  =	self._large_tokenizer(A	,	padding=A	,	truncation=A	,	return_tensors="""pt"""					)
								UpperCAmelCase  :	List[Any]							  =	self._large_tokenizer(
								    text_target=A	,	max_length=5	,	padding=A	,	truncation=A	,	return_tensors="""pt"""					)

								assert batch.input_ids.shape == (2, 1024)
								assert batch.attention_mask.shape == (2, 1024)
								assert targets["input_ids"].shape == (2, 5)
								assert len(A					) == 2  # input_ids, attention_mask.






	@slow
	def 				_lowercase(						self					)    ->    List[str]:
								# fmt: off
								UpperCAmelCase  :	List[str]							  =	{"""input_ids""": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 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]], """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, 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, 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=A	,	model_name="""google/bigbird-pegasus-large-arxiv"""	,	revision="""ba85d0851d708441f91440d509690f1ab6353415"""	,	)




@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_				(							__magic_name__   ,  unittest.TestCase    ):
	lowercase    =				PegasusTokenizer
	lowercase    =				PegasusTokenizerFast
	lowercase    =				True
	lowercase    =				True





	def 				_lowercase(						self					)    ->    List[Any]:
								super().setUp()

								# We have a SentencePiece fixture for testing
								UpperCAmelCase  :	int							  =	PegasusTokenizer(A	,	offset=0	,	mask_token_sent=A	,	mask_token="""[MASK]"""					)
								tokenizer.save_pretrained(self.tmpdirname					)





	@cached_property
	def 				_lowercase(						self					)    ->    Optional[Any]:
								return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""					)





	def 				_lowercase(						self	,	**A					)    ->    PegasusTokenizer:
								return PegasusTokenizer.from_pretrained(self.tmpdirname	,	**A					)





	def 				_lowercase(						self	,	A					)    ->    str:
								return ("This is a test", "This is a test")





	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Dict							  =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname					)
								UpperCAmelCase  :	Any							  =	self.tokenizer_class.from_pretrained(self.tmpdirname					)
								UpperCAmelCase  :	str							  =	(
								    """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
								    """ <pad> <pad> <pad>"""
								)
								UpperCAmelCase  :	List[str]							  =	rust_tokenizer([raw_input_str]	,	return_tensors=A	,	add_special_tokens=A					).input_ids[0]
								UpperCAmelCase  :	str							  =	py_tokenizer([raw_input_str]	,	return_tensors=A	,	add_special_tokens=A					).input_ids[0]
								self.assertListEqual(A	,	A					)





	@require_torch
	def 				_lowercase(						self					)    ->    Optional[int]:
								UpperCAmelCase  :	Union[str, Any]							  =	["""This is going to be way too long.""" * 1000, """short example"""]
								UpperCAmelCase  :	Any							  =	["""not super long but more than 5 tokens""", """tiny"""]
								UpperCAmelCase  :	int							  =	self._large_tokenizer(A	,	padding=A	,	truncation=A	,	return_tensors="""pt"""					)
								UpperCAmelCase  :	Optional[int]							  =	self._large_tokenizer(
								    text_target=A	,	max_length=5	,	padding=A	,	truncation=A	,	return_tensors="""pt"""					)

								assert batch.input_ids.shape == (2, 4096)
								assert batch.attention_mask.shape == (2, 4096)
								assert targets["input_ids"].shape == (2, 5)
								assert len(A					) == 2  # input_ids, attention_mask.






	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Union[str, Any]							  =	(
								    """This is an example string that is used to test the original TF implementation against the HF"""
								    """ implementation"""
								)

								UpperCAmelCase  :	Optional[Any]							  =	self._large_tokenizer(A					).input_ids

								self.assertListEqual(
								    A	,	[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]	,	)

