





'''simple docstring'''


from ...processing_utils import ProcessorMixin



class 							__UpperCAmelCase     (				__lowerCAmelCase  ):







		'''simple docstring'''







		__lowerCAmelCase        =			'''SpeechT5FeatureExtractor'''
		__lowerCAmelCase        =			'''SpeechT5Tokenizer'''







		def __init__(self  :					Union[str, Any]					,					_lowerCAmelCase  :					str					,					_lowerCAmelCase  :					int					):
			super().__init__(_lowerCAmelCase					,					_lowerCAmelCase					)







		def __call__(self  :					List[str]					,					*_lowerCAmelCase  :					Any					,					**_lowerCAmelCase  :					Optional[Any]					):
			A							     =					kwargs.pop("""audio"""					,					_lowerCAmelCase					)
			A							     =					kwargs.pop("""text"""					,					_lowerCAmelCase					)
			A							     =					kwargs.pop("""text_target"""					,					_lowerCAmelCase					)
			A							     =					kwargs.pop("""audio_target"""					,					_lowerCAmelCase					)
			A							     =					kwargs.pop("""sampling_rate"""					,					_lowerCAmelCase					)

			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(_lowerCAmelCase					,					*_lowerCAmelCase					,					sampling_rate=_lowerCAmelCase					,					**_lowerCAmelCase					)
			elif text is not None:
				A							     =					self.tokenizer(_lowerCAmelCase					,					**_lowerCAmelCase					)
			else:
				A							     =					None

			if audio_target is not None:
				A							     =					self.feature_extractor(audio_target=_lowerCAmelCase					,					*_lowerCAmelCase					,					sampling_rate=_lowerCAmelCase					,					**_lowerCAmelCase					)
				A							     =					targets['input_values']
			elif text_target is not None:
				A							     =					self.tokenizer(_lowerCAmelCase					,					**_lowerCAmelCase					)
				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 				A   (self  :					Any					,					*_lowerCAmelCase  :					Union[str, Any]					,					**_lowerCAmelCase  :					Optional[Any]					):
			A							     =					kwargs.pop("""input_values"""					,					_lowerCAmelCase					)
			A							     =					kwargs.pop("""input_ids"""					,					_lowerCAmelCase					)
			A							     =					kwargs.pop("""labels"""					,					_lowerCAmelCase					)

			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(_lowerCAmelCase					,					*_lowerCAmelCase					,					**_lowerCAmelCase					)
			elif input_ids is not None:
				A							     =					self.tokenizer.pad(_lowerCAmelCase					,					**_lowerCAmelCase					)
			else:
				A							     =					None

			if labels is not None:
				if "input_ids" in labels or (isinstance(_lowerCAmelCase					,					_lowerCAmelCase					) and "input_ids" in labels[0]):
					A							     =					self.tokenizer.pad(_lowerCAmelCase					,					**_lowerCAmelCase					)
					A							     =					targets['input_ids']
				else:
					A							     =					self.feature_extractor.feature_size
					A							     =					self.feature_extractor.num_mel_bins
					A							     =					self.feature_extractor.pad(_lowerCAmelCase					,					*_lowerCAmelCase					,					**_lowerCAmelCase					)
					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 				A   (self  :					Union[str, Any]					,					*_lowerCAmelCase  :					str					,					**_lowerCAmelCase  :					Any					):
			return self.tokenizer.batch_decode(*_lowerCAmelCase					,					**_lowerCAmelCase					)







		def 				A   (self  :					int					,					*_lowerCAmelCase  :					Dict					,					**_lowerCAmelCase  :					Optional[Any]					):
			return self.tokenizer.decode(*_lowerCAmelCase					,					**_lowerCAmelCase					)


"""simple docstring"""
import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
 import torch

if is_tf_available():
 import tensorflow as tf




@require_vision
@require_torchvision
class    __snake_case       (     unittest.TestCase				):



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Optional[Any]										=	tempfile.mkdtemp()
        a__:     Optional[int]										=	SamImageProcessor()
        a__:     Tuple										=	SamProcessor(lowercase)
        processor.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self    ,			**lowercase)	->			List[Any]:





        '''simple docstring'''





        return AutoProcessor.from_pretrained(self.tmpdirname    ,			**lowercase).image_processor



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        shutil.rmtree(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Any										=	[np.random.randint(2_55    ,			size=(3, 30, 4_00)    ,			dtype=np.uinta)]

        a__:     Optional[Any]										=	[Image.fromarray(np.moveaxis(lowercase    ,			0    ,			-1)) for x in image_inputs]

        return image_inputs



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     List[str]										=	SamProcessor(image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        a__:     Optional[int]										=	self.get_image_processor(do_normalize=lowercase    ,			padding_value=1.0)

        a__:     List[Any]										=	SamProcessor.from_pretrained(self.tmpdirname    ,			do_normalize=lowercase    ,			padding_value=1.0)

        self.assertEqual(processor.image_processor.to_json_string()    ,			image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Union[str, Any]										=	self.get_image_processor()

        a__:     List[Any]										=	SamProcessor(image_processor=lowercase)

        a__:     Optional[int]										=	self.prepare_image_inputs()

        a__:     Optional[Any]										=	image_processor(lowercase    ,			return_tensors='np')
        a__:     Tuple										=	processor(images=lowercase    ,			return_tensors='np')

        input_feat_extract.pop('original_sizes')  # pop original_sizes as it is popped in the processor
        input_feat_extract.pop('reshaped_input_sizes')  # pop original_sizes as it is popped in the processor

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



      @require_torch
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     int										=	self.get_image_processor()

        a__:     List[str]										=	SamProcessor(image_processor=lowercase)
        a__:     Optional[Any]										=	[torch.ones((1, 3, 5, 5))]

        a__:     Union[str, Any]										=	[[17_64, 26_46]]

        a__:     Optional[Any]										=	[[6_83, 10_24]]
        a__:     int										=	processor.post_process_masks(lowercase    ,			lowercase    ,			lowercase)
        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        a__:     Optional[int]										=	processor.post_process_masks(
            lowercase    ,			torch.tensor(lowercase)    ,			torch.tensor(lowercase))
        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        # should also work with np
        a__:     Dict										=	[np.ones((1, 3, 5, 5))]
        a__:     Tuple										=	processor.post_process_masks(lowercase    ,			np.array(lowercase)    ,			np.array(lowercase))

        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        a__:     Tuple										=	[[1, 0], [0, 1]]
        with self.assertRaises(lowercase):
          a__:     List[Any]										=	processor.post_process_masks(lowercase    ,			np.array(lowercase)    ,			np.array(lowercase))







@require_vision
@require_tf
class    __snake_case       (     unittest.TestCase				):



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[Any]										=	tempfile.mkdtemp()
        a__:     List[Any]										=	SamImageProcessor()
        a__:     Optional[int]										=	SamProcessor(lowercase)
        processor.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self    ,			**lowercase)	->			int:





        '''simple docstring'''





        return AutoProcessor.from_pretrained(self.tmpdirname    ,			**lowercase).image_processor



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        shutil.rmtree(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Optional[Any]										=	[np.random.randint(2_55    ,			size=(3, 30, 4_00)    ,			dtype=np.uinta)]

        a__:     Optional[int]										=	[Image.fromarray(np.moveaxis(lowercase    ,			0    ,			-1)) for x in image_inputs]

        return image_inputs



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     List[str]										=	SamProcessor(image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        a__:     Dict										=	self.get_image_processor(do_normalize=lowercase    ,			padding_value=1.0)

        a__:     Union[str, Any]										=	SamProcessor.from_pretrained(self.tmpdirname    ,			do_normalize=lowercase    ,			padding_value=1.0)

        self.assertEqual(processor.image_processor.to_json_string()    ,			image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Optional[Any]										=	self.get_image_processor()

        a__:     str										=	SamProcessor(image_processor=lowercase)

        a__:     int										=	self.prepare_image_inputs()

        a__:     int										=	image_processor(lowercase    ,			return_tensors='np')
        a__:     Dict										=	processor(images=lowercase    ,			return_tensors='np')

        input_feat_extract.pop('original_sizes')  # pop original_sizes as it is popped in the processor
        input_feat_extract.pop('reshaped_input_sizes')  # pop reshaped_input_sizes as it is popped in the processor

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



      @require_tf
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Tuple										=	self.get_image_processor()

        a__:     Any										=	SamProcessor(image_processor=lowercase)
        a__:     str										=	[tf.ones((1, 3, 5, 5))]

        a__:     List[Any]										=	[[17_64, 26_46]]

        a__:     List[Any]										=	[[6_83, 10_24]]
        a__:     List[Any]										=	processor.post_process_masks(lowercase    ,			lowercase    ,			lowercase    ,			return_tensors='tf')
        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        a__:     Tuple										=	processor.post_process_masks(
            lowercase    ,			tf.convert_to_tensor(lowercase)    ,			tf.convert_to_tensor(lowercase)    ,			return_tensors='tf'    ,			)
        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        # should also work with np
        a__:     Optional[Any]										=	[np.ones((1, 3, 5, 5))]
        a__:     int										=	processor.post_process_masks(
            lowercase    ,			np.array(lowercase)    ,			np.array(lowercase)    ,			return_tensors='tf')

        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        a__:     List[str]										=	[[1, 0], [0, 1]]
        with self.assertRaises(tf.errors.InvalidArgumentError):
          a__:     Any										=	processor.post_process_masks(
              lowercase    ,			np.array(lowercase)    ,			np.array(lowercase)    ,			return_tensors='tf')







@require_vision
@require_torchvision
class    __snake_case       (     unittest.TestCase				):



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     str										=	tempfile.mkdtemp()
        a__:     int										=	SamImageProcessor()
        a__:     Union[str, Any]										=	SamProcessor(lowercase)
        processor.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self    ,			**lowercase)	->			Optional[int]:





        '''simple docstring'''





        return AutoProcessor.from_pretrained(self.tmpdirname    ,			**lowercase).image_processor



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        shutil.rmtree(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Any										=	[np.random.randint(2_55    ,			size=(3, 30, 4_00)    ,			dtype=np.uinta)]

        a__:     Any										=	[Image.fromarray(np.moveaxis(lowercase    ,			0    ,			-1)) for x in image_inputs]

        return image_inputs



      @is_pt_tf_cross_test
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Optional[int]										=	self.get_image_processor()

        a__:     int										=	SamProcessor(image_processor=lowercase)
        a__:     int										=	np.random.randint(0    ,			2    ,			size=(1, 3, 5, 5)).astype(np.floataa)
        a__:     Dict										=	[tf.convert_to_tensor(lowercase)]
        a__:     Union[str, Any]										=	[torch.tensor(lowercase)]

        a__:     List[Any]										=	[[17_64, 26_46]]

        a__:     Optional[Any]										=	[[6_83, 10_24]]
        a__:     Tuple										=	processor.post_process_masks(
            lowercase    ,			lowercase    ,			lowercase    ,			return_tensors='tf')
        a__:     str										=	processor.post_process_masks(
            lowercase    ,			lowercase    ,			lowercase    ,			return_tensors='pt')

        self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()))



      @is_pt_tf_cross_test
      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Tuple										=	self.get_image_processor()

        a__:     Dict										=	SamProcessor(image_processor=lowercase)

        a__:     Any										=	self.prepare_image_inputs()

        a__:     List[Any]										=	image_processor(lowercase    ,			return_tensors='pt')['pixel_values'].numpy()
        a__:     Tuple										=	processor(images=lowercase    ,			return_tensors='pt')['pixel_values'].numpy()

        a__:     Any										=	image_processor(lowercase    ,			return_tensors='tf')['pixel_values'].numpy()
        a__:     Any										=	processor(images=lowercase    ,			return_tensors='tf')['pixel_values'].numpy()

        self.assertTrue(np.allclose(lowercase    ,			lowercase))
        self.assertTrue(np.allclose(lowercase    ,			lowercase))
        self.assertTrue(np.allclose(lowercase    ,			lowercase))


