


import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')




def  a   (lowerCAmelCase__   ):
   __a					=  test_file.split(os.path.sep   )
   if components[0:2] != ["tests", "models"]:
      raise ValueError(
          """`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got """
          f'''{test_file} instead.'''   )
   __a					=  components[-1]
   if not test_fn.endswith("""py"""   ):
      raise ValueError(f'''`test_file` should be a python file. Got {test_fn} instead.'''   )
   if not test_fn.startswith("""test_modeling_"""   ):
      raise ValueError(
          f'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'''   )

   __a					=  components[:-1] + [test_fn.replace(""".py"""		,				""""""   )]
   __a					=  """.""".join(lowerCAmelCase__   )

   return test_module_path




def  a   (lowerCAmelCase__   ):
   __a					=  get_module_path(lowerCAmelCase__   )
   __a					=  importlib.import_module(lowerCAmelCase__   )

   return test_module




def  a   (lowerCAmelCase__   ):
   __a					=  []
   __a					=  get_test_module(lowerCAmelCase__   )
   for attr in dir(lowerCAmelCase__   ):
      if attr.endswith("""ModelTester"""   ):
         tester_classes.append(getattr(lowerCAmelCase__		,				lowerCAmelCase__   )   )

    # sort with class names
   return sorted(lowerCAmelCase__		,				key=lambda lowerCAmelCase__   : x.__name__   )




def  a   (lowerCAmelCase__   ):
   __a					=  []
   __a					=  get_test_module(lowerCAmelCase__   )
   for attr in dir(lowerCAmelCase__   ):
      __a					=  getattr(lowerCAmelCase__		,				lowerCAmelCase__   )
      # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
      # `all_model_classes` is not empty (which also excludes other special classes).
      __a					=  getattr(lowerCAmelCase__		,				"""all_model_classes"""		,				[]   )
      if len(lowerCAmelCase__   ) > 0:
         test_classes.append(lowerCAmelCase__   )

    # sort with class names
   return sorted(lowerCAmelCase__		,				key=lambda lowerCAmelCase__   : x.__name__   )




def  a   (lowerCAmelCase__   ):
   __a					=  get_test_classes(lowerCAmelCase__   )
   __a					=  set()
   for test_class in test_classes:
      model_classes.update(test_class.all_model_classes   )

   # sort with class names
   return sorted(lowerCAmelCase__		,				key=lambda lowerCAmelCase__   : x.__name__   )




def  a   (lowerCAmelCase__   ):
   __a					=  test_class()
   if hasattr(lowerCAmelCase__		,				"""setUp"""   ):
      test.setUp()

   __a					=  None
   if hasattr(lowerCAmelCase__		,				"""model_tester"""   ):
      # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
      if test.model_tester is not None:
         __a					=  test.model_tester.__class__

   return model_tester




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  get_test_classes(lowerCAmelCase__   )

   __a					=  []
   for test_class in test_classes:
      if model_class in test_class.all_model_classes:
         target_test_classes.append(lowerCAmelCase__   )

    # sort with class names
   return sorted(lowerCAmelCase__		,				key=lambda lowerCAmelCase__   : x.__name__   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  get_test_classes_for_model(lowerCAmelCase__		,				lowerCAmelCase__   )

   __a					=  []
   for test_class in test_classes:
      __a					=  get_model_tester_from_test_class(lowerCAmelCase__   )
      if tester_class is not None:
         tester_classes.append(lowerCAmelCase__   )

    # sort with class names
   return sorted(lowerCAmelCase__		,				key=lambda lowerCAmelCase__   : x.__name__   )




def  a   (lowerCAmelCase__   ):
   __a					=  get_test_classes(lowerCAmelCase__   )
   __a					=  {test_class: get_model_tester_from_test_class(lowerCAmelCase__   ) for test_class in test_classes}
   return test_tester_mapping




def  a   (lowerCAmelCase__   ):
   __a					=  get_model_classes(lowerCAmelCase__   )
   __a					=  {
       model_class: get_test_classes_for_model(lowerCAmelCase__		,				lowerCAmelCase__   ) for model_class in model_classes
   }
   return model_test_mapping




def  a   (lowerCAmelCase__   ):
   __a					=  get_model_classes(lowerCAmelCase__   )
   __a					=  {
       model_class: get_tester_classes_for_model(lowerCAmelCase__		,				lowerCAmelCase__   ) for model_class in model_classes
   }
   return model_to_tester_mapping




def  a   (lowerCAmelCase__   ):
   if isinstance(lowerCAmelCase__		,				lowerCAmelCase__   ):
      return o
   elif isinstance(lowerCAmelCase__		,				lowerCAmelCase__   ):
      return o.__name__
   elif isinstance(lowerCAmelCase__		,				(list, tuple)   ):
      return [to_json(lowerCAmelCase__   ) for x in o]
   elif isinstance(lowerCAmelCase__		,				lowerCAmelCase__   ):
      return {to_json(lowerCAmelCase__   ): to_json(lowerCAmelCase__   ) for k, v in o.items()}
   else:
      return o





"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class       __magic_name__							(							unittest.TestCase			):
       def __init__(  self			,					A_			,					A_=7			,					A_=3			,					A_=18			,					A_=30			,					A_=400			,					A_=True			,					A_=None			,					A_=True			,					A_=False			,					A_=True			,					A_=True			,					A_=[0.5, 0.5, 0.5]			,					A_=[0.5, 0.5, 0.5]			,					)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  parent
         _lowercase:      str   			=  batch_size
         _lowercase:      Any   			=  num_channels
         _lowercase:      Any   			=  image_size
         _lowercase:      Any   			=  min_resolution
         _lowercase:      int   			=  max_resolution
         _lowercase:      Union[str, Any]   			=  do_resize
         _lowercase:      List[Any]   			=  size if size is not None else {'''height''': 18, '''width''': 20}
         _lowercase:      Optional[int]   			=  do_thumbnail
         _lowercase:      List[str]   			=  do_align_axis
         _lowercase:      int   			=  do_pad
         _lowercase:      Optional[Any]   			=  do_normalize
         _lowercase:      List[Any]   			=  image_mean
         _lowercase:      Union[str, Any]   			=  image_std



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





         """simple docstring"""




         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_thumbnail": self.do_thumbnail,
             "do_align_long_axis": self.do_align_axis,
             "do_pad": self.do_pad,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }


@require_torch
@require_vision
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		DonutImageProcessor if is_vision_available() else None
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      str   			=  DonutImageProcessingTester(self							)
       @property
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         return self.image_processor_tester.prepare_image_processor_dict()
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(A_			,					'''do_resize'''							)							)
         self.assertTrue(hasattr(A_			,					'''size'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_thumbnail'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_align_long_axis'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_pad'''							)							)
         self.assertTrue(hasattr(A_			,					'''do_normalize'''							)							)
         self.assertTrue(hasattr(A_			,					'''image_mean'''							)							)
         self.assertTrue(hasattr(A_			,					'''image_std'''							)							)
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  self.image_processing_class.from_dict(self.image_processor_dict							)
         self.assertEqual(image_processor.size			,					{'''height''': 18, '''width''': 20}							)

         _lowercase:      List[str]   			=  self.image_processing_class.from_dict(self.image_processor_dict			,					size=42							)
         self.assertEqual(image_processor.size			,					{'''height''': 42, '''width''': 42}							)

         # Previous config had dimensions in (width, height) order
         _lowercase:      Any   			=  self.image_processing_class.from_dict(self.image_processor_dict			,					size=(42, 84)							)
         self.assertEqual(image_processor.size			,					{'''height''': 84, '''width''': 42}							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         pass
       @is_flaky()
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      str   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         _lowercase:      Dict   			=  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:      Tuple   			=  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
         _lowercase:      List[Any]   			=  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,
                 self.image_processor_tester.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )			,					)
       @is_flaky()
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         _lowercase:      str   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _lowercase:      str   			=  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:      Union[str, Any]   			=  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
         _lowercase:      Dict   			=  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,
                 self.image_processor_tester.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )			,					)



       @is_flaky()
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[Any]   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         _lowercase:      List[Any]   			=  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:      Union[str, Any]   			=  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
         _lowercase:      Dict   			=  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,
                 self.image_processor_tester.size['''height'''],
                 self.image_processor_tester.size['''width'''],
             )			,					)
