"""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    :							Dict													=						logging.get_logger("transformers.models.speecht5")

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







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		for attribute in key.split("""."""     ):
				lowerCAmelCase__        =   getattr(lowerCamelCase__			,  lowerCamelCase__     )

		if weight_type is not None:
				lowerCAmelCase__        =   getattr(lowerCamelCase__			,  lowerCamelCase__     ).shape
		else:
				lowerCAmelCase__        =   hf_pointer.shape

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

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

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







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




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







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   []

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

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

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

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

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

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







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   full_name.split("""conv_layers."""     )[-1]
		lowerCAmelCase__        =   name.split("""."""     )
		lowerCAmelCase__        =   int(items[0]     )
		lowerCAmelCase__        =   int(items[1]     )

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







@torch.no_grad()
def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  ):





		"""simple docstring"""




		if config_path is not None:
				lowerCAmelCase__        =   SpeechTaConfig.from_pretrained(lowerCamelCase__     )
		else:
				lowerCAmelCase__        =   SpeechTaConfig()

		if task == "s2t":
				lowerCAmelCase__        =   config.max_text_positions
				lowerCAmelCase__        =   SpeechTaForSpeechToText(lowerCamelCase__     )
		elif task == "t2s":
				lowerCAmelCase__        =   1876
				lowerCAmelCase__        =   600
				lowerCAmelCase__        =   config.max_speech_positions
				lowerCAmelCase__        =   SpeechTaForTextToSpeech(lowerCamelCase__     )
		elif task == "s2s":
				lowerCAmelCase__        =   1876
				lowerCAmelCase__        =   config.max_speech_positions
				lowerCAmelCase__        =   SpeechTaForSpeechToSpeech(lowerCamelCase__     )
		else:
				raise ValueError(f"""Unknown task name: {task}"""     )

		if vocab_path:
				lowerCAmelCase__        =   SpeechTaTokenizer(lowerCamelCase__			,  model_max_length=config.max_text_positions     )

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

		lowerCAmelCase__        =   SpeechTaFeatureExtractor()
		lowerCAmelCase__        =   SpeechTaProcessor(tokenizer=lowerCamelCase__			,  feature_extractor=lowerCamelCase__     )
		processor.save_pretrained(lowerCamelCase__     )

		lowerCAmelCase__        =   torch.load(lowerCamelCase__     )
		recursively_load_weights(fairseq_checkpoint["""model"""]			,  lowerCamelCase__			,  lowerCamelCase__     )

		model.save_pretrained(lowerCamelCase__     )

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


if __name__ == "__main__":
			__lowerCAmelCase    :							Union[str, 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."
			)

			__lowerCAmelCase    :							Any													=						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


__lowerCAmelCase    :							Optional[Any]													=						get_tests_dir("fixtures/test_sentencepiece_no_bos.model")

@require_sentencepiece
@require_tokenizers
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	Tuple			=    PegasusTokenizer
					UpperCamelCase_ :	Any			=    PegasusTokenizerFast
					UpperCamelCase_ :	int			=    True
					UpperCamelCase_ :	Any			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowerCAmelCase__        =   PegasusTokenizer(snake_case__     )
							tokenizer.save_pretrained(self.tmpdirname     )




					@cached_property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							return PegasusTokenizer.from_pretrained("""google/pegasus-large"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						**snake_case__						:     Optional[Any]     ):
							return PegasusTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     Optional[Any]     ):
							return ("This is a test", "This is a test")




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   """</s>"""
							lowerCAmelCase__        =   1

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   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(snake_case__     )  ,						1103     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							self.assertEqual(self.get_tokenizer().vocab_size  ,						1103     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self.rust_tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase__        =   self.tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase__        =   (
							    """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>"""
							)
							lowerCAmelCase__        =   rust_tokenizer([raw_input_str]  ,						return_tensors=snake_case__  ,						add_special_tokens=snake_case__     ).input_ids[0]
							lowerCAmelCase__        =   py_tokenizer([raw_input_str]  ,						return_tensors=snake_case__  ,						add_special_tokens=snake_case__     ).input_ids[0]
							self.assertListEqual(snake_case__  ,						snake_case__     )




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   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
							lowerCAmelCase__        =   """To ensure a smooth flow of bank resolutions."""
							lowerCAmelCase__        =   [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
							lowerCAmelCase__        =   tokenizer([raw_input_str]  ,						return_tensors=snake_case__     ).input_ids[0]
							self.assertListEqual(snake_case__  ,						snake_case__     )
							assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]     ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




					@require_torch
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   ["""This is going to be way too long.""" * 150, """short example"""]
							lowerCAmelCase__        =   ["""not super long but more than 5 tokens""", """tiny"""]
							lowerCAmelCase__        =   self._large_tokenizer(snake_case__  ,						padding=snake_case__  ,						truncation=snake_case__  ,						return_tensors="""pt"""     )
							lowerCAmelCase__        =   self._large_tokenizer(
							    text_target=snake_case__  ,						max_length=5  ,						padding=snake_case__  ,						truncation=snake_case__  ,						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(snake_case__     ) == 2  # input_ids, attention_mask.




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							# fmt: off
							lowerCAmelCase__        =   {"""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=snake_case__  ,						model_name="""google/bigbird-pegasus-large-arxiv"""  ,						revision="""ba85d0851d708441f91440d509690f1ab6353415"""  ,						)

@require_sentencepiece
@require_tokenizers
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	str			=    PegasusTokenizer
					UpperCamelCase_ :	Optional[int]			=    PegasusTokenizerFast
					UpperCamelCase_ :	Union[str, Any]			=    True
					UpperCamelCase_ :	Optional[int]			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							super().setUp()

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




					@cached_property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						**snake_case__						:     List[Any]     ):
							return PegasusTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Dict     ):
							return ("This is a test", "This is a test")




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




					@require_torch
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   ["""This is going to be way too long.""" * 1000, """short example"""]
							lowerCAmelCase__        =   ["""not super long but more than 5 tokens""", """tiny"""]
							lowerCAmelCase__        =   self._large_tokenizer(snake_case__  ,						padding=snake_case__  ,						truncation=snake_case__  ,						return_tensors="""pt"""     )
							lowerCAmelCase__        =   self._large_tokenizer(
							    text_target=snake_case__  ,						max_length=5  ,						padding=snake_case__  ,						truncation=snake_case__  ,						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(snake_case__     ) == 2  # input_ids, attention_mask.




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   (
							    """This is an example string that is used to test the original TF implementation against the HF"""
							    """ implementation"""
							)

							lowerCAmelCase__        =   self._large_tokenizer(snake_case__     ).input_ids

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




