






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 YolosImageProcessor

class 		_snake_case  (  unittest.TestCase					):






   def __init__(       self		,					a		,					a=7		,					a=3		,					a=30		,					a=400		,					a=True		,					a=None		,					a=True		,					a=[0.5, 0.5, 0.5]		,					a=[0.5, 0.5, 0.5]		,					a=True		,					a=1 / 255		,					a=True		,					)      ->       Any:
      # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
      SCREAMING_SNAKE_CASE						= size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
      SCREAMING_SNAKE_CASE						= parent
      SCREAMING_SNAKE_CASE						= batch_size
      SCREAMING_SNAKE_CASE						= num_channels
      SCREAMING_SNAKE_CASE						= min_resolution
      SCREAMING_SNAKE_CASE						= max_resolution
      SCREAMING_SNAKE_CASE						= do_resize
      SCREAMING_SNAKE_CASE						= size
      SCREAMING_SNAKE_CASE						= do_normalize
      SCREAMING_SNAKE_CASE						= image_mean
      SCREAMING_SNAKE_CASE						= image_std
      SCREAMING_SNAKE_CASE						= do_rescale
      SCREAMING_SNAKE_CASE						= rescale_factor
      SCREAMING_SNAKE_CASE						= do_pad






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

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

      return expected_height, expected_width



@require_torch
@require_vision
class 		_snake_case  (  A__   ,    unittest.TestCase					):
   _lowercase		:    int			     =							YolosImageProcessor if is_vision_available() else None






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
      SCREAMING_SNAKE_CASE						= YolosImageProcessingTester(self)






   @property
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
      return self.image_processor_tester.prepare_image_processor_dict()






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
      SCREAMING_SNAKE_CASE						= 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		,					'size'))






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
      SCREAMING_SNAKE_CASE						= self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size		,					{'shortest_edge': 18, 'longest_edge': 1333})
      self.assertEqual(image_processor.do_pad		,					a)

      SCREAMING_SNAKE_CASE						= self.image_processing_class.from_dict(
          self.image_processor_dict		,					size=42		,					max_size=84		,					pad_and_return_pixel_mask=a)
      self.assertEqual(image_processor.size		,					{'shortest_edge': 42, 'longest_edge': 84})
      self.assertEqual(image_processor.do_pad		,					a)






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
      pass






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

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

      SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= 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
      SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.image_processor_tester.get_expected_values(a		,					batched=a)

      SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
      # Initialize image_processing
      SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE						= 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
      SCREAMING_SNAKE_CASE						= image_processing(image_inputs[0]		,					return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= 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
      SCREAMING_SNAKE_CASE						= image_processing(a		,					return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
      # Initialize image_processing
      SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE						= 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
      SCREAMING_SNAKE_CASE						= image_processing(image_inputs[0]		,					return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= 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
      SCREAMING_SNAKE_CASE						= image_processing(a		,					return_tensors='pt').pixel_values

      SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
      # Initialize image_processings
      SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
      SCREAMING_SNAKE_CASE						= self.image_processing_class(do_resize=a		,					do_normalize=a		,					do_rescale=a)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					torchify=a)
      for image in image_inputs:
         self.assertIsInstance(a		,					torch.Tensor)

      # Test whether the method "pad" and calling the image processor return the same tensors
      SCREAMING_SNAKE_CASE						= image_processing_a.pad(a		,					return_tensors='pt')
      SCREAMING_SNAKE_CASE						= image_processing_a(a		,					return_tensors='pt')

      self.assertTrue(
          torch.allclose(encoded_images_with_method['pixel_values']		,					encoded_images['pixel_values']		,					atol=1E-4))






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

      SCREAMING_SNAKE_CASE						= {'image_id': 3_9769, 'annotations': target}

      # encode them
      SCREAMING_SNAKE_CASE						= YolosImageProcessor.from_pretrained('hustvl/yolos-small')
      SCREAMING_SNAKE_CASE						= image_processing(images=a		,					annotations=a		,					return_tensors='pt')

      # verify pixel values
      SCREAMING_SNAKE_CASE						= torch.Size([1, 3, 800, 1066])
      self.assertEqual(encoding['pixel_values'].shape		,					a)

      SCREAMING_SNAKE_CASE						= torch.tensor([0.27_96, 0.31_38, 0.34_81])
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,					a		,					atol=1E-4))

      # verify area
      SCREAMING_SNAKE_CASE						= torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438])
      self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,					a))
      # verify boxes
      SCREAMING_SNAKE_CASE						= torch.Size([6, 4])
      self.assertEqual(encoding['labels'][0]['boxes'].shape		,					a)
      SCREAMING_SNAKE_CASE						= torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,					a		,					atol=1E-3))
      # verify image_id
      SCREAMING_SNAKE_CASE						= torch.tensor([3_9769])
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,					a))
      # verify is_crowd
      SCREAMING_SNAKE_CASE						= torch.tensor([0, 0, 0, 0, 0, 0])
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,					a))
      # verify class_labels
      SCREAMING_SNAKE_CASE						= torch.tensor([75, 75, 63, 65, 17, 17])
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,					a))
      # verify orig_size
      SCREAMING_SNAKE_CASE						= torch.tensor([480, 640])
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,					a))
      # verify size
      SCREAMING_SNAKE_CASE						= torch.tensor([800, 1066])
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,					a))







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

      SCREAMING_SNAKE_CASE						= {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

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

      # encode them
      SCREAMING_SNAKE_CASE						= YolosImageProcessor(format='coco_panoptic')
      SCREAMING_SNAKE_CASE						= image_processing(images=a		,					annotations=a		,					masks_path=a		,					return_tensors='pt')

      # verify pixel values
      SCREAMING_SNAKE_CASE						= torch.Size([1, 3, 800, 1066])
      self.assertEqual(encoding['pixel_values'].shape		,					a)

      SCREAMING_SNAKE_CASE						= torch.tensor([0.27_96, 0.31_38, 0.34_81])
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,					a		,					atol=1E-4))

      # verify area
      SCREAMING_SNAKE_CASE						= torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147])
      self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,					a))
      # verify boxes
      SCREAMING_SNAKE_CASE						= torch.Size([6, 4])
      self.assertEqual(encoding['labels'][0]['boxes'].shape		,					a)
      SCREAMING_SNAKE_CASE						= torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,					a		,					atol=1E-3))
      # verify image_id
      SCREAMING_SNAKE_CASE						= torch.tensor([3_9769])
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,					a))
      # verify is_crowd
      SCREAMING_SNAKE_CASE						= torch.tensor([0, 0, 0, 0, 0, 0])
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,					a))
      # verify class_labels
      SCREAMING_SNAKE_CASE						= torch.tensor([17, 17, 63, 75, 75, 93])
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,					a))
      # verify masks
      SCREAMING_SNAKE_CASE						= 82_2873
      self.assertEqual(encoding['labels'][0]['masks'].sum().item()		,					a)
      # verify orig_size
      SCREAMING_SNAKE_CASE						= torch.tensor([480, 640])
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,					a))
      # verify size
      SCREAMING_SNAKE_CASE						= torch.tensor([800, 1066])
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,					a))












from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 		_snake_case  (  A__					):
			_lowercase		:    Union[str, Any]			     =							['''image_processor''', '''tokenizer''']
			_lowercase		:    Optional[int]			     =							'''Pix2StructImageProcessor'''
			_lowercase		:    str			     =							('''T5Tokenizer''', '''T5TokenizerFast''')






			def __init__(       self		,					a		,					a)      ->       Any:
						SCREAMING_SNAKE_CASE						= False
						super().__init__(a		,					a)






			def __call__(       self		,					a=None		,					a = None		,					a = True		,					a = False		,					a = None		,					a = None		,					a = 2048		,					a = 0		,					a = None		,					a = None		,					a = False		,					a = False		,					a = False		,					a = False		,					a = False		,					a = True		,					a = None		,					**a		,					)      ->       BatchEncoding:
						if images is None and text is None:
									raise ValueError('You have to specify either images or text.')

						# Get only text
						if images is None and not self.image_processor.is_vqa:
									SCREAMING_SNAKE_CASE						= self.tokenizer
									SCREAMING_SNAKE_CASE						= self.tokenizer(
									    text=a		,					add_special_tokens=a		,					padding=a		,					truncation=a		,					max_length=a		,					stride=a		,					pad_to_multiple_of=a		,					return_attention_mask=a		,					return_overflowing_tokens=a		,					return_special_tokens_mask=a		,					return_offsets_mapping=a		,					return_token_type_ids=a		,					return_length=a		,					verbose=a		,					return_tensors=a		,					**a		,					)
									return text_encoding

						if not self.image_processor.is_vqa:
									# add pixel_values
									SCREAMING_SNAKE_CASE						= self.image_processor(
									    a		,					return_tensors=a		,					max_patches=a		,					**a)
						else:
									# add pixel_values and bbox
									SCREAMING_SNAKE_CASE						= self.image_processor(
									    a		,					return_tensors=a		,					max_patches=a		,					header_text=a		,					**a)

						if text is not None and not self.image_processor.is_vqa:
									SCREAMING_SNAKE_CASE						= self.tokenizer(
									    text=a		,					add_special_tokens=a		,					padding=a		,					truncation=a		,					max_length=a		,					stride=a		,					pad_to_multiple_of=a		,					return_attention_mask=a		,					return_overflowing_tokens=a		,					return_special_tokens_mask=a		,					return_offsets_mapping=a		,					return_token_type_ids=a		,					return_length=a		,					verbose=a		,					return_tensors=a		,					**a		,					)

									if "attention_mask" in text_encoding:
												SCREAMING_SNAKE_CASE						= text_encoding.pop('attention_mask')
									if "input_ids" in text_encoding:
												SCREAMING_SNAKE_CASE						= text_encoding.pop('input_ids')
						else:
									SCREAMING_SNAKE_CASE						= None

						if text_encoding is not None:
									encoding_image_processor.update(a)

						return encoding_image_processor






			def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       List[Any]:
						return self.tokenizer.batch_decode(*a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       Optional[int]:
						return self.tokenizer.decode(*a		,					**a)







			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.tokenizer.model_input_names
						SCREAMING_SNAKE_CASE						= self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))




