



from ...processing_utils import ProcessorMixin
class 					__A       (			__lowercase   ):
			UpperCamelCase       :str	 =      "SpeechT5FeatureExtractor"
			UpperCamelCase       :Tuple	 =      "SpeechT5Tokenizer"





			def __init__(self				,     __magic_name__				,     __magic_name__					):
						super().__init__(_a				,     _a					)





			def __call__(self				,     *__magic_name__				,     **__magic_name__					):
						lowerCamelCase__			:   Union[str, Any]										=		kwargs.pop("""audio"""				,     _a					)
						lowerCamelCase__			:   Tuple										=		kwargs.pop("""text"""				,     _a					)
						lowerCamelCase__			:   Union[str, Any]										=		kwargs.pop("""text_target"""				,     _a					)
						lowerCamelCase__			:   Dict										=		kwargs.pop("""audio_target"""				,     _a					)
						lowerCamelCase__			:   Union[str, Any]										=		kwargs.pop("""sampling_rate"""				,     _a					)

						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:
									lowerCamelCase__			:   int										=		self.feature_extractor(_a				,     *_a				,     sampling_rate=_a				,     **_a					)
						elif text is not None:
									lowerCamelCase__			:   Dict										=		self.tokenizer(_a				,     **_a					)
						else:
									lowerCamelCase__			:   Tuple										=		None

						if audio_target is not None:
									lowerCamelCase__			:   Optional[int]										=		self.feature_extractor(audio_target=_a				,     *_a				,     sampling_rate=_a				,     **_a					)
									lowerCamelCase__			:   str										=		targets['''input_values''']
						elif text_target is not None:
									lowerCamelCase__			:   int										=		self.tokenizer(_a				,     **_a					)
									lowerCamelCase__			:   Optional[Any]										=		targets['''input_ids''']
						else:
									lowerCamelCase__			:   Any										=		None

						if inputs is None:
									return targets

						if targets is not None:
									lowerCamelCase__			:   int										=		labels

									lowerCamelCase__			:   List[str]										=		targets.get("""attention_mask"""					)
									if decoder_attention_mask is not None:
												lowerCamelCase__			:   Optional[Any]										=		decoder_attention_mask

						return inputs





			def 							_snake_case     (self				,     *__magic_name__				,     **__magic_name__					):
						lowerCamelCase__			:   str										=		kwargs.pop("""input_values"""				,     _a					)
						lowerCamelCase__			:   Optional[Any]										=		kwargs.pop("""input_ids"""				,     _a					)
						lowerCamelCase__			:   List[str]										=		kwargs.pop("""labels"""				,     _a					)

						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:
									lowerCamelCase__			:   int										=		self.feature_extractor.pad(_a				,     *_a				,     **_a					)
						elif input_ids is not None:
									lowerCamelCase__			:   List[Any]										=		self.tokenizer.pad(_a				,     **_a					)
						else:
									lowerCamelCase__			:   Tuple										=		None

						if labels is not None:
									if "input_ids" in labels or (isinstance(_a				,     _a					) and "input_ids" in labels[0]):
												lowerCamelCase__			:   List[Any]										=		self.tokenizer.pad(_a				,     **_a					)
												lowerCamelCase__			:   Any										=		targets['''input_ids''']
									else:
												lowerCamelCase__			:   Union[str, Any]										=		self.feature_extractor.feature_size
												lowerCamelCase__			:   Any										=		self.feature_extractor.num_mel_bins
												lowerCamelCase__			:   Optional[int]										=		self.feature_extractor.pad(_a				,     *_a				,     **_a					)
												lowerCamelCase__			:   Optional[int]										=		feature_size_hack
												lowerCamelCase__			:   Dict										=		targets['''input_values''']
						else:
									lowerCamelCase__			:   Dict										=		None

						if inputs is None:
									return targets

						if targets is not None:
									lowerCamelCase__			:   Dict										=		labels

									lowerCamelCase__			:   Union[str, Any]										=		targets.get("""attention_mask"""					)
									if decoder_attention_mask is not None:
												lowerCamelCase__			:   Any										=		decoder_attention_mask

						return inputs





			def 							_snake_case     (self				,     *__magic_name__				,     **__magic_name__					):
						return self.tokenizer.batch_decode(*_a				,     **_a					)





			def 							_snake_case     (self				,     *__magic_name__				,     **__magic_name__					):
						return self.tokenizer.decode(*_a				,     **_a					)






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
			from transformers import TvltImageProcessor

if is_speech_available():
			from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       int:
												_a      :       Dict   =						'''ZinengTang/tvlt-base'''
												_a      :       List[str]   =						tempfile.mkdtemp()


							def 							__lowercase							(     self			,				**_a			)       ->       int:
												return TvltImageProcessor.from_pretrained(self.checkpoint			,				**_a			)


							def 							__lowercase							(     self			,				**_a			)       ->       List[Any]:
												return TvltFeatureExtractor.from_pretrained(self.checkpoint			,				**_a			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												shutil.rmtree(self.tmpdirname			)


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Union[str, Any]   =						self.get_image_processor()
												_a      :       Dict   =						self.get_feature_extractor()

												_a      :       Optional[int]   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)
												processor.save_pretrained(self.tmpdirname			)
												_a      :       Any   =						TvltProcessor.from_pretrained(self.tmpdirname			)

												self.assertIsInstance(processor.feature_extractor			,				_a			)
												self.assertIsInstance(processor.image_processor			,				_a			)


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Optional[Any]   =						self.get_image_processor()
												_a      :       Dict   =						self.get_feature_extractor()

												_a      :       Dict   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       Union[str, Any]   =						np.ones([1_2_0_0_0]			)

												_a      :       Dict   =						feature_extractor(_a			,				return_tensors='''np'''			)
												_a      :       Tuple   =						processor(audio=_a			,				return_tensors='''np'''			)

												for key in audio_dict.keys():
																	self.assertAlmostEqual(audio_dict[key].sum()			,				input_processor[key].sum()			,				delta=1e-2			)


							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[Any]   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Optional[Any]   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       List[Any]   =						np.ones([3, 2_2_4, 2_2_4]			)

												_a      :       int   =						image_processor(_a			,				return_tensors='''np'''			)
												_a      :       Optional[int]   =						processor(images=_a			,				return_tensors='''np'''			)

												for key in image_dict.keys():
																	self.assertAlmostEqual(image_dict[key].sum()			,				input_processor[key].sum()			,				delta=1e-2			)


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       int   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Any   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												_a      :       List[str]   =						np.ones([1_2_0_0_0]			)
												_a      :       Optional[int]   =						np.ones([3, 2_2_4, 2_2_4]			)

												_a      :       int   =						processor(audio=_a			,				images=_a			)

												self.assertListEqual(list(inputs.keys()			)			,				['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']			)

												# test if it raises when no input is passed
												with pytest.raises(_a			):
																	processor()




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       str   =						self.get_image_processor()
												_a      :       Union[str, Any]   =						self.get_feature_extractor()

												_a      :       Dict   =						TvltProcessor(image_processor=_a			,				feature_extractor=_a			)

												self.assertListEqual(
												    processor.model_input_names			,				image_processor.model_input_names + feature_extractor.model_input_names			,				msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''			,				)



