
"""simple docstring"""




import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor







class     lowercase(unittest.TestCase       ):



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=7     ,				__SCREAMING_SNAKE_CASE=3     ,				__SCREAMING_SNAKE_CASE=1_8     ,				__SCREAMING_SNAKE_CASE=3_0     ,				__SCREAMING_SNAKE_CASE=4_0_0     ,				__SCREAMING_SNAKE_CASE=True     ,				__SCREAMING_SNAKE_CASE=None     ,				__SCREAMING_SNAKE_CASE=True     ,				__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]     ,				__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]     ,				)		->     Dict:







         """simple docstring"""



         a__		=			size if size is not None else {'height': 1_8, 'width': 1_8}
         a__		=			parent
         a__		=			batch_size
         a__		=			num_channels
         a__		=			image_size
         a__		=			min_resolution
         a__		=			max_resolution
         a__		=			do_resize
         a__		=			size
         a__		=			do_normalize
         a__		=			image_mean
         a__		=			image_std




     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
         }







@require_torch
@require_vision
class     lowercase(a_    ,						unittest.TestCase       ):
     __snake_case:	Dict						=     DPTImageProcessor if is_vision_available() else None



     def 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         a__		=			DPTImageProcessingTester(self   )



     @property
     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()



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







         """simple docstring"""



         a__		=			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_     ,				'size'   )   )



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         a__		=			self.image_processing_class.from_dict(self.image_processor_dict   )
         self.assertEqual(image_processor.size     ,				{'height': 1_8, 'width': 1_8}   )

         a__		=			self.image_processing_class.from_dict(self.image_processor_dict     ,				size=4_2   )
         self.assertEqual(image_processor.size     ,				{'height': 4_2, 'width': 4_2}   )



     def 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         a__		=			self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         a__		=			prepare_image_inputs(self.image_processor_tester     ,				equal_resolution=lowerCAmelCase_   )
         for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase_     ,				Image.Image   )

         # Test not batched input
         a__		=			image_processing(image_inputs[0]     ,				return_tensors='pt'   ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )     ,				)

         # Test batched
         a__		=			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,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )     ,				)



     def 			lowercase__  (							self   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			self.image_processing_class(**self.image_processor_dict   )
         # create random numpy tensors
         a__		=			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
         a__		=			image_processing(image_inputs[0]     ,				return_tensors='pt'   ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )     ,				)

         # Test batched
         a__		=			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,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )     ,				)




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







         """simple docstring"""



         a__		=			self.image_processing_class(**self.image_processor_dict   )
         # create random PyTorch tensors
         a__		=			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
         a__		=			image_processing(image_inputs[0]     ,				return_tensors='pt'   ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )     ,				)

         # Test batched
         a__		=			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,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )     ,				)






'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__  			=				{
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				[
					    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TrOCRForCausalLM''',
					    '''TrOCRPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
					from .processing_trocr import TrOCRProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
					import sys

					UpperCAmelCase__  			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



