



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       ,     _lowerCAmelCase       ,     _lowerCAmelCase=7       ,     _lowerCAmelCase=3       ,     _lowerCAmelCase=30       ,     _lowerCAmelCase=400       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=[0.5, 0.5, 0.5]       ,     _lowerCAmelCase=[0.5, 0.5, 0.5]       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=1 / 255       ,     _lowerCAmelCase=True       ,     ):
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         _lowerCAmelCase 			=  size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
         _lowerCAmelCase 			=  parent
         _lowerCAmelCase 			=  batch_size
         _lowerCAmelCase 			=  num_channels
         _lowerCAmelCase 			=  min_resolution
         _lowerCAmelCase 			=  max_resolution
         _lowerCAmelCase 			=  do_resize
         _lowerCAmelCase 			=  size
         _lowerCAmelCase 			=  do_normalize
         _lowerCAmelCase 			=  image_mean
         _lowerCAmelCase 			=  image_std
         _lowerCAmelCase 			=  do_rescale
         _lowerCAmelCase 			=  rescale_factor
         _lowerCAmelCase 			=  do_pad





   def 	__lowerCAmelCase							(	self			):
         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 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=False			):
         if not batched:
               _lowerCAmelCase 			=  image_inputs[0]
               if isinstance(_lowerCAmelCase       ,     Image.Image			):
                     _lowerCAmelCase			,   _lowerCAmelCase 			=  image.size
               else:
                     _lowerCAmelCase			,   _lowerCAmelCase 			=  image.shape[1], image.shape[2]
               if w < h:
                     _lowerCAmelCase 			=  int(self.size['''shortest_edge'''] * h / w			)
                     _lowerCAmelCase 			=  self.size['''shortest_edge''']
               elif w > h:
                     _lowerCAmelCase 			=  self.size['''shortest_edge''']
                     _lowerCAmelCase 			=  int(self.size['''shortest_edge'''] * w / h			)
               else:
                     _lowerCAmelCase 			=  self.size['''shortest_edge''']
                     _lowerCAmelCase 			=  self.size['''shortest_edge''']

         else:
               _lowerCAmelCase 			=  []
               for image in image_inputs:
                     _lowerCAmelCase			,   _lowerCAmelCase 			=  self.get_expected_values([image]			)
                     expected_values.append((expected_height, expected_width)			)
               _lowerCAmelCase 			=  max(_lowerCAmelCase       ,     key=lambda _lowerCAmelCase			: item[0]			)[0]
               _lowerCAmelCase 			=  max(_lowerCAmelCase       ,     key=lambda _lowerCAmelCase			: item[1]			)[1]

         return expected_height, expected_width






@require_torch
@require_vision
class 		UpperCAmelCase						(						snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     DetaImageProcessor if is_vision_available() else None





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  DetaImageProcessingTester(self			)





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.image_processor_tester.prepare_image_processor_dict()





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.image_processing_class(**self.image_processor_dict			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''image_mean'''			)			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''image_std'''			)			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''do_normalize'''			)			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''do_resize'''			)			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''do_rescale'''			)			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''do_pad'''			)			)
         self.assertTrue(hasattr(_lowerCAmelCase       ,     '''size'''			)			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.image_processing_class.from_dict(self.image_processor_dict			)
         self.assertEqual(image_processor.size       ,     {'''shortest_edge''': 18, '''longest_edge''': 1_333}			)
         self.assertEqual(image_processor.do_pad       ,     _lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         pass





   def 	__lowerCAmelCase							(	self			):
         # Initialize image_processing
         _lowerCAmelCase 			=  self.image_processing_class(**self.image_processor_dict			)
         # create random PIL images
         _lowerCAmelCase 			=  prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=_lowerCAmelCase			)
         for image in image_inputs:
               self.assertIsInstance(_lowerCAmelCase       ,     Image.Image			)

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

         _lowerCAmelCase			,   _lowerCAmelCase 			=  self.image_processor_tester.get_expected_values(_lowerCAmelCase			)

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

         # Test batched
         _lowerCAmelCase			,   _lowerCAmelCase 			=  self.image_processor_tester.get_expected_values(_lowerCAmelCase       ,     batched=_lowerCAmelCase			)

         _lowerCAmelCase 			=  image_processing(_lowerCAmelCase       ,     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 	__lowerCAmelCase							(	self			):
         # Initialize image_processing
         _lowerCAmelCase 			=  self.image_processing_class(**self.image_processor_dict			)
         # create random numpy tensors
         _lowerCAmelCase 			=  prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=_lowerCAmelCase       ,     numpify=_lowerCAmelCase			)
         for image in image_inputs:
               self.assertIsInstance(_lowerCAmelCase       ,     np.ndarray			)

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

         _lowerCAmelCase			,   _lowerCAmelCase 			=  self.image_processor_tester.get_expected_values(_lowerCAmelCase			)

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

         # Test batched
         _lowerCAmelCase 			=  image_processing(_lowerCAmelCase       ,     return_tensors='''pt'''			).pixel_values

         _lowerCAmelCase			,   _lowerCAmelCase 			=  self.image_processor_tester.get_expected_values(_lowerCAmelCase       ,     batched=_lowerCAmelCase			)

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





   def 	__lowerCAmelCase							(	self			):
         # Initialize image_processing
         _lowerCAmelCase 			=  self.image_processing_class(**self.image_processor_dict			)
         # create random PyTorch tensors
         _lowerCAmelCase 			=  prepare_image_inputs(self.image_processor_tester       ,     equal_resolution=_lowerCAmelCase       ,     torchify=_lowerCAmelCase			)
         for image in image_inputs:
               self.assertIsInstance(_lowerCAmelCase       ,     torch.Tensor			)

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

         _lowerCAmelCase			,   _lowerCAmelCase 			=  self.image_processor_tester.get_expected_values(_lowerCAmelCase			)

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

         # Test batched
         _lowerCAmelCase 			=  image_processing(_lowerCAmelCase       ,     return_tensors='''pt'''			).pixel_values

         _lowerCAmelCase			,   _lowerCAmelCase 			=  self.image_processor_tester.get_expected_values(_lowerCAmelCase       ,     batched=_lowerCAmelCase			)

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





   @slow
   def 	__lowerCAmelCase							(	self			):
         # prepare image and target
         _lowerCAmelCase 			=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
         with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''       ,     '''r'''			) as f:
               _lowerCAmelCase 			=  json.loads(f.read()			)

         _lowerCAmelCase 			=  {'''image_id''': 39_769, '''annotations''': target}

         # encode them
         _lowerCAmelCase 			=  DetaImageProcessor()
         _lowerCAmelCase 			=  image_processing(images=_lowerCAmelCase       ,     annotations=_lowerCAmelCase       ,     return_tensors='''pt'''			)

         # verify pixel values
         _lowerCAmelCase 			=  torch.Size([1, 3, 800, 1_066]			)
         self.assertEqual(encoding['''pixel_values'''].shape       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  torch.tensor([0.2_796, 0.3_138, 0.3_481]			)
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,     _lowerCAmelCase       ,     atol=1E-4			)			)

         # verify area
         _lowerCAmelCase 			=  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''']       ,     _lowerCAmelCase			)			)
         # verify boxes
         _lowerCAmelCase 			=  torch.Size([6, 4]			)
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,     _lowerCAmelCase			)
         _lowerCAmelCase 			=  torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,     _lowerCAmelCase       ,     atol=1E-3			)			)
         # verify image_id
         _lowerCAmelCase 			=  torch.tensor([39_769]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,     _lowerCAmelCase			)			)
         # verify is_crowd
         _lowerCAmelCase 			=  torch.tensor([0, 0, 0, 0, 0, 0]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,     _lowerCAmelCase			)			)
         # verify class_labels
         _lowerCAmelCase 			=  torch.tensor([75, 75, 63, 65, 17, 17]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,     _lowerCAmelCase			)			)
         # verify orig_size
         _lowerCAmelCase 			=  torch.tensor([480, 640]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,     _lowerCAmelCase			)			)
         # verify size
         _lowerCAmelCase 			=  torch.tensor([800, 1_066]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,     _lowerCAmelCase			)			)





   @slow
   def 	__lowerCAmelCase							(	self			):
         # prepare image, target and masks_path
         _lowerCAmelCase 			=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
         with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''       ,     '''r'''			) as f:
               _lowerCAmelCase 			=  json.loads(f.read()			)

         _lowerCAmelCase 			=  {'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

         _lowerCAmelCase 			=  pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''			)

         # encode them
         _lowerCAmelCase 			=  DetaImageProcessor(format='''coco_panoptic'''			)
         _lowerCAmelCase 			=  image_processing(images=_lowerCAmelCase       ,     annotations=_lowerCAmelCase       ,     masks_path=_lowerCAmelCase       ,     return_tensors='''pt'''			)

         # verify pixel values
         _lowerCAmelCase 			=  torch.Size([1, 3, 800, 1_066]			)
         self.assertEqual(encoding['''pixel_values'''].shape       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  torch.tensor([0.2_796, 0.3_138, 0.3_481]			)
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,     _lowerCAmelCase       ,     atol=1E-4			)			)

         # verify area
         _lowerCAmelCase 			=  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''']       ,     _lowerCAmelCase			)			)
         # verify boxes
         _lowerCAmelCase 			=  torch.Size([6, 4]			)
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,     _lowerCAmelCase			)
         _lowerCAmelCase 			=  torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,     _lowerCAmelCase       ,     atol=1E-3			)			)
         # verify image_id
         _lowerCAmelCase 			=  torch.tensor([39_769]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,     _lowerCAmelCase			)			)
         # verify is_crowd
         _lowerCAmelCase 			=  torch.tensor([0, 0, 0, 0, 0, 0]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,     _lowerCAmelCase			)			)
         # verify class_labels
         _lowerCAmelCase 			=  torch.tensor([17, 17, 63, 75, 75, 93]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,     _lowerCAmelCase			)			)
         # verify masks
         _lowerCAmelCase 			=  822_873
         self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()       ,     _lowerCAmelCase			)
         # verify orig_size
         _lowerCAmelCase 			=  torch.tensor([480, 640]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,     _lowerCAmelCase			)			)
         # verify size
         _lowerCAmelCase 			=  torch.tensor([800, 1_066]			)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,     _lowerCAmelCase			)			)




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     '''ClapFeatureExtractor'''
   SCREAMING_SNAKE_CASE__							=     ('''RobertaTokenizer''', '''RobertaTokenizerFast''')





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         super().__init__(_lowerCAmelCase       ,     _lowerCAmelCase			)





   def __call__(	self       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     **_lowerCAmelCase			):
         _lowerCAmelCase 			=  kwargs.pop('''sampling_rate'''       ,     _lowerCAmelCase			)

         if text is None and audios is None:
               raise ValueError('''You have to specify either text or audios. Both cannot be none.'''			)

         if text is not None:
               _lowerCAmelCase 			=  self.tokenizer(_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if audios is not None:
               _lowerCAmelCase 			=  self.feature_extractor(
                   _lowerCAmelCase       ,     sampling_rate=_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if text is not None and audios is not None:
               _lowerCAmelCase 			=  audio_features.input_features
               return encoding
         elif text is not None:
               return encoding
         else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)       ,     tensor_type=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.batch_decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   @property
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.tokenizer.model_input_names
         _lowerCAmelCase 			=  self.feature_extractor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names			)			)