

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 DeiTImageProcessor






class 							A_			(		unittest.TestCase ):


     def __init__(							self      :		Union[str, Any]      ,  UpperCAmelCase      :		int      ,  UpperCAmelCase      :		Union[str, Any]=7      ,  UpperCAmelCase      :		List[str]=3      ,  UpperCAmelCase      :		int=1_8      ,  UpperCAmelCase      :		Tuple=3_0      ,  UpperCAmelCase      :		Union[str, Any]=4_0_0      ,  UpperCAmelCase      :		Any=True      ,  UpperCAmelCase      :		Union[str, Any]=None      ,  UpperCAmelCase      :		Optional[Any]=True      ,  UpperCAmelCase      :		List[str]=None      ,  UpperCAmelCase      :		Dict=True      ,  UpperCAmelCase      :		int=[0.5, 0.5, 0.5]      ,  UpperCAmelCase      :		int=[0.5, 0.5, 0.5]      ,  )							->     str:
      __lowerCAmelCase:       Any            =				size if size is not None else {'height': 2_0, 'width': 2_0}
      __lowerCAmelCase:       Optional[Any]            =				crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

      __lowerCAmelCase:       Tuple            =				parent
      __lowerCAmelCase:       List[Any]            =				batch_size
      __lowerCAmelCase:       Tuple            =				num_channels
      __lowerCAmelCase:       Optional[int]            =				image_size
      __lowerCAmelCase:       List[Any]            =				min_resolution
      __lowerCAmelCase:       str            =				max_resolution
      __lowerCAmelCase:       List[str]            =				do_resize
      __lowerCAmelCase:       Dict            =				size
      __lowerCAmelCase:       List[Any]            =				do_center_crop
      __lowerCAmelCase:       Tuple            =				crop_size
      __lowerCAmelCase:       Dict            =				do_normalize
      __lowerCAmelCase:       Union[str, Any]            =				image_mean
      __lowerCAmelCase:       Optional[int]            =				image_std


     def   UpperCAmelCase  (							self      :		List[Any] )							->     Optional[Any]:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
      }







@require_torch
@require_vision
class 							A_			(		SCREAMING_SNAKE_CASE__							,     unittest.TestCase ):
     _lowercase      :					Tuple       =     DeiTImageProcessor if is_vision_available() else None
     _lowercase      :					int       =     True


     def   UpperCAmelCase  (							self      :		str )							->     int:
      __lowerCAmelCase:       Optional[Any]            =				DeiTImageProcessingTester(self )


     @property
     def   UpperCAmelCase  (							self      :		Tuple )							->     int:
      return self.image_processor_tester.prepare_image_processor_dict()


     def   UpperCAmelCase  (							self      :		str )							->     str:
      __lowerCAmelCase:       List[str]            =				self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(UpperCAmelCase      ,  'do_resize' ) )
      self.assertTrue(hasattr(UpperCAmelCase      ,  'size' ) )
      self.assertTrue(hasattr(UpperCAmelCase      ,  'do_center_crop' ) )
      self.assertTrue(hasattr(UpperCAmelCase      ,  'center_crop' ) )
      self.assertTrue(hasattr(UpperCAmelCase      ,  'do_normalize' ) )
      self.assertTrue(hasattr(UpperCAmelCase      ,  'image_mean' ) )
      self.assertTrue(hasattr(UpperCAmelCase      ,  'image_std' ) )


     def   UpperCAmelCase  (							self      :		List[str] )							->     Dict:
      __lowerCAmelCase:       int            =				self.image_processing_class.from_dict(self.image_processor_dict )
      self.assertEqual(image_processor.size      ,  {'height': 2_0, 'width': 2_0} )
      self.assertEqual(image_processor.crop_size      ,  {'height': 1_8, 'width': 1_8} )

      __lowerCAmelCase:       List[str]            =				self.image_processing_class.from_dict(self.image_processor_dict      ,  size=4_2      ,  crop_size=8_4 )
      self.assertEqual(image_processor.size      ,  {'height': 4_2, 'width': 4_2} )
      self.assertEqual(image_processor.crop_size      ,  {'height': 8_4, 'width': 8_4} )


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


     def   UpperCAmelCase  (							self      :		str )							->     int:
      __lowerCAmelCase:       Optional[int]            =				self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      __lowerCAmelCase:       Dict            =				prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=UpperCAmelCase )
      for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase      ,  Image.Image )

      # Test not batched input
      __lowerCAmelCase:       Optional[int]            =				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.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,  )

      # Test batched
      __lowerCAmelCase:       Optional[int]            =				image_processing(UpperCAmelCase      ,  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.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,  )


     def   UpperCAmelCase  (							self      :		List[str] )							->     List[str]:
      __lowerCAmelCase:       List[Any]            =				self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      __lowerCAmelCase:       Dict            =				prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=UpperCAmelCase      ,  numpify=UpperCAmelCase )
      for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase      ,  np.ndarray )

      # Test not batched input
      __lowerCAmelCase:       List[str]            =				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.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,  )

      # Test batched
      __lowerCAmelCase:       Any            =				image_processing(UpperCAmelCase      ,  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.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,  )


     def   UpperCAmelCase  (							self      :		Dict )							->     List[str]:
      __lowerCAmelCase:       Optional[int]            =				self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      __lowerCAmelCase:       int            =				prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=UpperCAmelCase      ,  torchify=UpperCAmelCase )
      for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase      ,  torch.Tensor )

      # Test not batched input
      __lowerCAmelCase:       Dict            =				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.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,  )

      # Test batched
      __lowerCAmelCase:       Tuple            =				image_processing(UpperCAmelCase      ,  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.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,  )








import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig






class 							A_			(		snake_case__ ):
     _lowercase      :					Any       =     CustomConfig


     def __init__(							self      :		List[Any]      ,  UpperCAmelCase      :		Optional[Any] )							->     int:
      super().__init__(UpperCAmelCase )
      __lowerCAmelCase:       Tuple            =				torch.nn.Linear(config.hidden_size      ,  config.hidden_size )


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Tuple )							->     Union[str, Any]:
      return self.linear(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		str      ,  UpperCAmelCase      :		str )							->     Optional[int]:
      pass







class 							A_			(		snake_case__ ):
     _lowercase      :					str       =     NoSuperInitConfig


     def __init__(							self      :		List[Any]      ,  UpperCAmelCase      :		Union[str, Any] )							->     int:
      super().__init__(UpperCAmelCase )
      __lowerCAmelCase:       int            =				torch.nn.Linear(config.attribute      ,  config.attribute )


     def   UpperCAmelCase  (							self      :		Tuple      ,  UpperCAmelCase      :		Tuple )							->     Tuple:
      return self.linear(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Optional[Any]      ,  UpperCAmelCase      :		Any )							->     str:
      pass





