"""simple docstring"""





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import DetaImageProcessor


class      UpperCAmelCase_	(    unittest.TestCase):


      def __init__(   self		,			a		,			a=7		,			a=3		,			a=3_0		,			a=4_0_0		,			a=True		,			a=None		,			a=True		,			a=[0.5, 0.5, 0.5]		,			a=[0.5, 0.5, 0.5]		,			a=True		,			a=1 / 2_5_5		,			a=True		,			)       ->					Any:
             # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
             lowercase__			:		Dict       =	size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
             lowercase__			:		Optional[int]       =	parent
             lowercase__			:		Tuple       =	batch_size
             lowercase__			:		List[str]       =	num_channels
             lowercase__			:		List[str]       =	min_resolution
             lowercase__			:		Tuple       =	max_resolution
             lowercase__			:		Union[str, Any]       =	do_resize
             lowercase__			:		Dict       =	size
             lowercase__			:		str       =	do_normalize
             lowercase__			:		List[Any]       =	image_mean
             lowercase__			:		int       =	image_std
             lowercase__			:		List[Any]       =	do_rescale
             lowercase__			:		int       =	rescale_factor
             lowercase__			:		int       =	do_pad


      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_rescale": self.do_rescale,
                 "rescale_factor": self.rescale_factor,
                 "do_pad": self.do_pad,
             }


      def 		_UpperCAmelCase       (   self		,			a		,			a=False							)       ->					Dict:
             if not batched:
                    lowercase__			:		str       =	image_inputs[0]
                    if isinstance(a		,			Image.Image							):
                           lowercase__    ,      lowercase__			:		List[str]       =	image.size
                    else:
                           lowercase__    ,      lowercase__			:		int       =	image.shape[1], image.shape[2]
                    if w < h:
                           lowercase__			:		Any       =	int(self.size['shortest_edge'] * h / w							)
                           lowercase__			:		Dict       =	self.size['shortest_edge']
                    elif w > h:
                           lowercase__			:		int       =	self.size['shortest_edge']
                           lowercase__			:		Tuple       =	int(self.size['shortest_edge'] * w / h							)
                    else:
                           lowercase__			:		Optional[Any]       =	self.size['shortest_edge']
                           lowercase__			:		List[Any]       =	self.size['shortest_edge']

             else:
                    lowercase__			:		Union[str, Any]       =	[]
                    for image in image_inputs:
                           lowercase__    ,      lowercase__			:		int       =	self.get_expected_values([image]							)
                           expected_values.append((expected_height, expected_width)							)
                    lowercase__			:		Optional[Any]       =	max(a		,			key=lambda a							: item[0]							)[0]
                    lowercase__			:		Optional[Any]       =	max(a		,			key=lambda a							: item[1]							)[1]

             return expected_height, expected_width





@require_torch
@require_vision
class      UpperCAmelCase_	(    _a ,							unittest.TestCase):
      lowerCamelCase__		:    List[str]   =  DetaImageProcessor if is_vision_available() else None


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		Optional[Any]       =	DetaImageProcessingTester(self							)


      @property
      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             return self.image_processor_tester.prepare_image_processor_dict()


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		Dict       =	self.image_processing_class(**self.image_processor_dict							)
             self.assertTrue(hasattr(a		,			'image_mean'							)							)
             self.assertTrue(hasattr(a		,			'image_std'							)							)
             self.assertTrue(hasattr(a		,			'do_normalize'							)							)
             self.assertTrue(hasattr(a		,			'do_resize'							)							)
             self.assertTrue(hasattr(a		,			'do_rescale'							)							)
             self.assertTrue(hasattr(a		,			'do_pad'							)							)
             self.assertTrue(hasattr(a		,			'size'							)							)


      def 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		Tuple       =	self.image_processing_class.from_dict(self.image_processor_dict							)
             self.assertEqual(image_processor.size		,			{'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}							)
             self.assertEqual(image_processor.do_pad		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             pass


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             # Initialize image_processing
             lowercase__			:		Tuple       =	self.image_processing_class(**self.image_processor_dict							)
             # create random PIL images
             lowercase__			:		Any       =	prepare_image_inputs(self.image_processor_tester		,			equal_resolution=a							)
             for image in image_inputs:
                    self.assertIsInstance(a		,			Image.Image							)

             # Test not batched input
             lowercase__			:		List[str]       =	image_processing(image_inputs[0]		,			return_tensors='pt'							).pixel_values

             lowercase__    ,      lowercase__			:		Optional[int]       =	self.image_processor_tester.get_expected_values(a							)

             self.assertEqual(
                 encoded_images.shape		,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,			)

             # Test batched
             lowercase__    ,      lowercase__			:		Tuple       =	self.image_processor_tester.get_expected_values(a		,			batched=a							)

             lowercase__			:		List[str]       =	image_processing(a		,			return_tensors='pt'							).pixel_values
             self.assertEqual(
                 encoded_images.shape		,			(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )		,			)


      def 		_UpperCAmelCase       (   self							)       ->					str:
             # Initialize image_processing
             lowercase__			:		List[str]       =	self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             lowercase__			:		int       =	prepare_image_inputs(self.image_processor_tester		,			equal_resolution=a		,			numpify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a		,			np.ndarray							)

             # Test not batched input
             lowercase__			:		int       =	image_processing(image_inputs[0]		,			return_tensors='pt'							).pixel_values

             lowercase__    ,      lowercase__			:		List[Any]       =	self.image_processor_tester.get_expected_values(a							)

             self.assertEqual(
                 encoded_images.shape		,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,			)

             # Test batched
             lowercase__			:		Union[str, Any]       =	image_processing(a		,			return_tensors='pt'							).pixel_values

             lowercase__    ,      lowercase__			:		int       =	self.image_processor_tester.get_expected_values(a		,			batched=a							)

             self.assertEqual(
                 encoded_images.shape		,			(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )		,			)


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             # Initialize image_processing
             lowercase__			:		Optional[Any]       =	self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             lowercase__			:		str       =	prepare_image_inputs(self.image_processor_tester		,			equal_resolution=a		,			torchify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a		,			torch.Tensor							)

             # Test not batched input
             lowercase__			:		Dict       =	image_processing(image_inputs[0]		,			return_tensors='pt'							).pixel_values

             lowercase__    ,      lowercase__			:		Optional[Any]       =	self.image_processor_tester.get_expected_values(a							)

             self.assertEqual(
                 encoded_images.shape		,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,			)

             # Test batched
             lowercase__			:		Optional[int]       =	image_processing(a		,			return_tensors='pt'							).pixel_values

             lowercase__    ,      lowercase__			:		Optional[Any]       =	self.image_processor_tester.get_expected_values(a		,			batched=a							)

             self.assertEqual(
                 encoded_images.shape		,			(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )		,			)


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             # prepare image and target
             lowercase__			:		Optional[Any]       =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
             with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'		,			'r'							) as f:
                    lowercase__			:		Tuple       =	json.loads(f.read()							)

             lowercase__			:		Optional[int]       =	{'image_id': 3_9_7_6_9, 'annotations': target}

             # encode them
             lowercase__			:		Union[str, Any]       =	DetaImageProcessor()
             lowercase__			:		List[str]       =	image_processing(images=a		,			annotations=a		,			return_tensors='pt'							)

             # verify pixel values
             lowercase__			:		int       =	torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
             self.assertEqual(encoding['pixel_values'].shape		,			a							)

             lowercase__			:		List[str]       =	torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,			a		,			atol=1e-4							)							)

             # verify area
             lowercase__			:		List[str]       =	torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,			a							)							)
             # verify boxes
             lowercase__			:		Dict       =	torch.Size([6, 4]							)
             self.assertEqual(encoding['labels'][0]['boxes'].shape		,			a							)
             lowercase__			:		str       =	torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,			a		,			atol=1e-3							)							)
             # verify image_id
             lowercase__			:		Tuple       =	torch.tensor([3_9_7_6_9]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,			a							)							)
             # verify is_crowd
             lowercase__			:		Tuple       =	torch.tensor([0, 0, 0, 0, 0, 0]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,			a							)							)
             # verify class_labels
             lowercase__			:		List[Any]       =	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,			a							)							)
             # verify orig_size
             lowercase__			:		Tuple       =	torch.tensor([4_8_0, 6_4_0]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,			a							)							)
             # verify size
             lowercase__			:		Optional[int]       =	torch.tensor([8_0_0, 1_0_6_6]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,			a							)							)


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             # prepare image, target and masks_path
             lowercase__			:		str       =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
             with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'		,			'r'							) as f:
                    lowercase__			:		int       =	json.loads(f.read()							)

             lowercase__			:		Optional[Any]       =	{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

             lowercase__			:		Any       =	pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'							)

             # encode them
             lowercase__			:		List[Any]       =	DetaImageProcessor(format='coco_panoptic'							)
             lowercase__			:		int       =	image_processing(images=a		,			annotations=a		,			masks_path=a		,			return_tensors='pt'							)

             # verify pixel values
             lowercase__			:		List[Any]       =	torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
             self.assertEqual(encoding['pixel_values'].shape		,			a							)

             lowercase__			:		Optional[int]       =	torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,			a		,			atol=1e-4							)							)

             # verify area
             lowercase__			:		List[str]       =	torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,			a							)							)
             # verify boxes
             lowercase__			:		int       =	torch.Size([6, 4]							)
             self.assertEqual(encoding['labels'][0]['boxes'].shape		,			a							)
             lowercase__			:		List[Any]       =	torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,			a		,			atol=1e-3							)							)
             # verify image_id
             lowercase__			:		Union[str, Any]       =	torch.tensor([3_9_7_6_9]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,			a							)							)
             # verify is_crowd
             lowercase__			:		Union[str, Any]       =	torch.tensor([0, 0, 0, 0, 0, 0]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,			a							)							)
             # verify class_labels
             lowercase__			:		Tuple       =	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,			a							)							)
             # verify masks
             lowercase__			:		Dict       =	8_2_2_8_7_3
             self.assertEqual(encoding['labels'][0]['masks'].sum().item()		,			a							)
             # verify orig_size
             lowercase__			:		Any       =	torch.tensor([4_8_0, 6_4_0]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,			a							)							)
             # verify size
             lowercase__			:		List[str]       =	torch.tensor([8_0_0, 1_0_6_6]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,			a							)							)







from ...processing_utils import ProcessorMixin


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''SpeechT5FeatureExtractor'''
    UpperCAmelCase__							  =      '''SpeechT5Tokenizer'''


    def __init__(			self      :	Any					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Tuple)						->Union[str, Any]:





           '''simple docstring'''

           super().__init__(UpperCAmelCase__					, UpperCAmelCase__)


    def __call__(			self      :	Dict					, *UpperCAmelCase__      :	List[str]					, **UpperCAmelCase__      :	Any)						->Optional[Any]:





           '''simple docstring'''

           A__       =    kwargs.pop('''audio'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''text'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''text_target'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''audio_target'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''sampling_rate'''					, UpperCAmelCase__)

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

           if audio_target is not None:
                  A__       =    self.feature_extractor(audio_target=UpperCAmelCase__					, *UpperCAmelCase__					, sampling_rate=UpperCAmelCase__					, **UpperCAmelCase__)
                  A__       =    targets['''input_values''']
           elif text_target is not None:
                  A__       =    self.tokenizer(UpperCAmelCase__					, **UpperCAmelCase__)
                  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        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, *UpperCAmelCase__      :	Tuple					, **UpperCAmelCase__      :	int)						->Optional[int]:





           '''simple docstring'''

           A__       =    kwargs.pop('''input_values'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''input_ids'''					, UpperCAmelCase__)
           A__       =    kwargs.pop('''labels'''					, UpperCAmelCase__)

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

           if labels is not None:
                  if "input_ids" in labels or (isinstance(UpperCAmelCase__					, UpperCAmelCase__) and "input_ids" in labels[0]):
                         A__       =    self.tokenizer.pad(UpperCAmelCase__					, **UpperCAmelCase__)
                         A__       =    targets['''input_ids''']
                  else:
                         A__       =    self.feature_extractor.feature_size
                         A__       =    self.feature_extractor.num_mel_bins
                         A__       =    self.feature_extractor.pad(UpperCAmelCase__					, *UpperCAmelCase__					, **UpperCAmelCase__)
                         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        SCREAMING_SNAKE_CASE							(			self      :	Any					, *UpperCAmelCase__      :	Dict					, **UpperCAmelCase__      :	Optional[Any])						->Optional[Any]:





           '''simple docstring'''

           return self.tokenizer.batch_decode(*UpperCAmelCase__					, **UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, *UpperCAmelCase__      :	List[Any]					, **UpperCAmelCase__      :	Union[str, Any])						->Dict:





           '''simple docstring'''

           return self.tokenizer.decode(*UpperCAmelCase__					, **UpperCAmelCase__)


