



from ...processing_utils import ProcessorMixin






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	Optional[Any]    ='SpeechT5FeatureExtractor'
							__lowerCamelCase	:	Optional[Any]    ='SpeechT5Tokenizer'





							def __init__(   self		:		Tuple      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		Union[str, Any]	):

											'''simple docstring'''
											super().__init__(__lowercase      ,				__lowercase	)





							def __call__(   self		:		Any      ,				*__lowercase		:		List[str]      ,				**__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		kwargs.pop("""audio"""      ,				__lowercase	)
											__a       =		kwargs.pop("""text"""      ,				__lowercase	)
											__a       =		kwargs.pop("""text_target"""      ,				__lowercase	)
											__a       =		kwargs.pop("""audio_target"""      ,				__lowercase	)
											__a       =		kwargs.pop("""sampling_rate"""      ,				__lowercase	)

											if audio is not None and text is not None:
															raise ValueError(
															    """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""	)
											if audio_target is not None and text_target is not None:
															raise ValueError(
															    """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""	)
											if audio is None and audio_target is None and text is None and text_target is None:
															raise ValueError(
															    """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""	)

											if audio is not None:
															__a       =		self.feature_extractor(__lowercase      ,				*__lowercase      ,				sampling_rate=__lowercase      ,				**__lowercase	)
											elif text is not None:
															__a       =		self.tokenizer(__lowercase      ,				**__lowercase	)
											else:
															__a       =		None

											if audio_target is not None:
															__a       =		self.feature_extractor(audio_target=__lowercase      ,				*__lowercase      ,				sampling_rate=__lowercase      ,				**__lowercase	)
															__a       =		targets["""input_values"""]
											elif text_target is not None:
															__a       =		self.tokenizer(__lowercase      ,				**__lowercase	)
															__a       =		targets["""input_ids"""]
											else:
															__a       =		None

											if inputs is None:
															return targets

											if targets is not None:
															__a       =		labels

															__a       =		targets.get("""attention_mask"""	)
															if decoder_attention_mask is not None:
																			__a       =		decoder_attention_mask

											return inputs





							def 						UpperCamelCase_					(   self		:		List[Any]      ,				*__lowercase		:		Optional[int]      ,				**__lowercase		:		List[Any]	):

											'''simple docstring'''
											__a       =		kwargs.pop("""input_values"""      ,				__lowercase	)
											__a       =		kwargs.pop("""input_ids"""      ,				__lowercase	)
											__a       =		kwargs.pop("""labels"""      ,				__lowercase	)

											if input_values is not None and input_ids is not None:
															raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""	)
											if input_values is None and input_ids is None and labels is None:
															raise ValueError(
															    """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""	)

											if input_values is not None:
															__a       =		self.feature_extractor.pad(__lowercase      ,				*__lowercase      ,				**__lowercase	)
											elif input_ids is not None:
															__a       =		self.tokenizer.pad(__lowercase      ,				**__lowercase	)
											else:
															__a       =		None

											if labels is not None:
															if "input_ids" in labels or (isinstance(__lowercase      ,				__lowercase	) and "input_ids" in labels[0]):
																			__a       =		self.tokenizer.pad(__lowercase      ,				**__lowercase	)
																			__a       =		targets["""input_ids"""]
															else:
																			__a       =		self.feature_extractor.feature_size
																			__a       =		self.feature_extractor.num_mel_bins
																			__a       =		self.feature_extractor.pad(__lowercase      ,				*__lowercase      ,				**__lowercase	)
																			__a       =		feature_size_hack
																			__a       =		targets["""input_values"""]
											else:
															__a       =		None

											if inputs is None:
															return targets

											if targets is not None:
															__a       =		labels

															__a       =		targets.get("""attention_mask"""	)
															if decoder_attention_mask is not None:
																			__a       =		decoder_attention_mask

											return inputs





							def 						UpperCamelCase_					(   self		:		List[str]      ,				*__lowercase		:		Optional[Any]      ,				**__lowercase		:		List[Any]	):

											'''simple docstring'''
											return self.tokenizer.batch_decode(*__lowercase      ,				**__lowercase	)







							def 						UpperCamelCase_					(   self		:		str      ,				*__lowercase		:		Any      ,				**__lowercase		:		Union[str, Any]	):

											'''simple docstring'''
											return self.tokenizer.decode(*__lowercase      ,				**__lowercase	)





import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	Optional[int]    =BlenderbotSmallTokenizer
							__lowerCamelCase	:	str    =False





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

											'''simple docstring'''
											super().setUp()

											__a       =		["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
											__a       =		dict(zip(__lowercase      ,				range(len(__lowercase	)	)	)	)

											__a       =		["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
											__a       =		{"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

											__a       =		os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES["""vocab_file"""]	)
											__a       =		os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES["""merges_file"""]	)
											with open(self.vocab_file      ,				"""w"""      ,				encoding="""utf-8"""	) as fp:
															fp.write(json.dumps(__lowercase	) + """\n"""	)
											with open(self.merges_file      ,				"""w"""      ,				encoding="""utf-8"""	) as fp:
															fp.write("""\n""".join(__lowercase	)	)





							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				**__lowercase		:		Dict	):

											'''simple docstring'''
											kwargs.update(self.special_tokens_map	)
											return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		"""adapt act apte"""
											__a       =		"""adapt act apte"""
											return input_text, output_text





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		BlenderbotSmallTokenizer(self.vocab_file      ,				self.merges_file      ,				**self.special_tokens_map	)
											__a       =		"""adapt act apte"""
											__a       =		["""adapt""", """act""", """ap@@""", """te"""]
											__a       =		tokenizer.tokenize(__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)

											__a       =		[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

											__a       =		[0, 1, 2, 3, 4, 5]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase	)      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""	)
											assert tok("""sam"""	).input_ids == [1384]
											__a       =		"""I am a small frog."""
											__a       =		tok([src_text]      ,				padding=__lowercase      ,				truncation=__lowercase	)["""input_ids"""]
											__a       =		tok.batch_decode(__lowercase      ,				skip_special_tokens=__lowercase      ,				clean_up_tokenization_spaces=__lowercase	)[0]
											assert src_text != decoded  # I wish it did!
											assert decoded == "i am a small frog ."







							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""	)
											__a       =		"""I am a small frog ."""
											__a       =		"""."""
											__a       =		tok(__lowercase	)["""input_ids"""]
											__a       =		tok(__lowercase	)["""input_ids"""]

											assert encoded[-1] == encoded_dot[0]
