

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      snake_case_				(__A					:						int					)      ->     str:
   __lowerCAmelCase  :      Union[str, Any]    =						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.'''					)
   __lowerCAmelCase  :      Optional[Any]    =						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.'''					)

   __lowerCAmelCase  :      Tuple    =						components[:-1] + [test_fn.replace(""".py""" ,       """"""					)]
   __lowerCAmelCase  :      Tuple    =						""".""".join(__A					)

   return test_module_path





def      snake_case_				(__A					:						Union[str, Any]					)      ->     int:
   __lowerCAmelCase  :      Tuple    =						get_module_path(__A					)
   __lowerCAmelCase  :      List[Any]    =						importlib.import_module(__A					)

   return test_module





def      snake_case_				(__A					:						str					)      ->     Optional[Any]:
   __lowerCAmelCase  :      int    =						[]
   __lowerCAmelCase  :      str    =						get_test_module(__A					)
   for attr in dir(__A					):
      if attr.endswith("""ModelTester"""					):
         tester_classes.append(getattr(__A ,       __A					)					)

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





def      snake_case_				(__A					:						List[Any]					)      ->     Any:
   __lowerCAmelCase  :      Any    =						[]
   __lowerCAmelCase  :      Any    =						get_test_module(__A					)
   for attr in dir(__A					):
      __lowerCAmelCase  :      Optional[Any]    =						getattr(__A ,       __A					)
      # (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).
      __lowerCAmelCase  :      List[str]    =						getattr(__A ,       """all_model_classes""" ,       []					)
      if len(__A					) > 0:
         test_classes.append(__A					)

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





def      snake_case_				(__A					:						List[Any]					)      ->     Tuple:
   __lowerCAmelCase  :      List[str]    =						get_test_classes(__A					)
   __lowerCAmelCase  :      Optional[int]    =						set()
   for test_class in test_classes:
      model_classes.update(test_class.all_model_classes					)

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





def      snake_case_				(__A					:						Union[str, Any]					)      ->     Optional[Any]:
   __lowerCAmelCase  :      List[Any]    =						test_class()
   if hasattr(__A ,       """setUp"""					):
      test.setUp()

   __lowerCAmelCase  :      Any    =						None
   if hasattr(__A ,       """model_tester"""					):
      # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
      if test.model_tester is not None:
         __lowerCAmelCase  :      Optional[Any]    =						test.model_tester.__class__

   return model_tester





def      snake_case_				(__A					:						Optional[Any] ,       __A					:						str					)      ->     List[Any]:
   __lowerCAmelCase  :      Any    =						get_test_classes(__A					)

   __lowerCAmelCase  :      Tuple    =						[]
   for test_class in test_classes:
      if model_class in test_class.all_model_classes:
         target_test_classes.append(__A					)

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





def      snake_case_				(__A					:						List[Any] ,       __A					:						Optional[Any]					)      ->     Any:
   __lowerCAmelCase  :      List[Any]    =						get_test_classes_for_model(__A ,       __A					)

   __lowerCAmelCase  :      str    =						[]
   for test_class in test_classes:
      __lowerCAmelCase  :      List[str]    =						get_model_tester_from_test_class(__A					)
      if tester_class is not None:
         tester_classes.append(__A					)

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





def      snake_case_				(__A					:						Union[str, Any]					)      ->     Tuple:
   __lowerCAmelCase  :      Any    =						get_test_classes(__A					)
   __lowerCAmelCase  :      int    =						{test_class: get_model_tester_from_test_class(__A					) for test_class in test_classes}
   return test_tester_mapping





def      snake_case_				(__A					:						Union[str, Any]					)      ->     Union[str, Any]:
   __lowerCAmelCase  :      List[str]    =						get_model_classes(__A					)
   __lowerCAmelCase  :      str    =						{
       model_class: get_test_classes_for_model(__A ,       __A					) for model_class in model_classes
   }
   return model_test_mapping





def      snake_case_				(__A					:						List[str]					)      ->     List[str]:
   __lowerCAmelCase  :      Tuple    =						get_model_classes(__A					)
   __lowerCAmelCase  :      Union[str, Any]    =						{
       model_class: get_tester_classes_for_model(__A ,       __A					) for model_class in model_classes
   }
   return model_to_tester_mapping





def      snake_case_				(__A					:						List[str]					)      ->     str:
   if isinstance(__A ,       __A					):
      return o
   elif isinstance(__A ,       __A					):
      return o.__name__
   elif isinstance(__A ,       (list, tuple)					):
      return [to_json(__A					) for x in o]
   elif isinstance(__A ,       __A					):
      return {to_json(__A					): to_json(__A					) for k, v in o.items()}
   else:
      return o





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 MobileViTImageProcessor



class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Optional[Any]=7	,							lowerCAmelCase			:   List[str]=3	,							lowerCAmelCase			:   int=18	,							lowerCAmelCase			:   int=30	,							lowerCAmelCase			:   Optional[int]=4_00	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   Tuple=None	,							lowerCAmelCase			:   Any=True	,							)							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						size if size is not None else {"""shortest_edge""": 20}
     __lowerCAmelCase  :      Any    =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
     __lowerCAmelCase  :      str    =						parent
     __lowerCAmelCase  :      List[str]    =						batch_size
     __lowerCAmelCase  :      int    =						num_channels
     __lowerCAmelCase  :      List[str]    =						image_size
     __lowerCAmelCase  :      Optional[int]    =						min_resolution
     __lowerCAmelCase  :      List[str]    =						max_resolution
     __lowerCAmelCase  :      List[Any]    =						do_resize
     __lowerCAmelCase  :      Optional[int]    =						size
     __lowerCAmelCase  :      List[Any]    =						do_center_crop
     __lowerCAmelCase  :      Optional[Any]    =						crop_size
     __lowerCAmelCase  :      int    =						do_flip_channel_order







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






     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_flip_channel_order": self.do_flip_channel_order,
     }






@require_torch
@require_vision
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      List[str]										=MobileViTImageProcessor if is_vision_available() else None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						MobileViTImageProcessingTester(self   )







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






     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_resize"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""size"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_center_crop"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""center_crop"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_flip_channel_order"""   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.image_processing_class.from_dict(self.image_processor_dict   )
     self.assertEqual(image_processor.size	,							{"""shortest_edge""": 20}   )
     self.assertEqual(image_processor.crop_size	,							{"""height""": 18, """width""": 18}   )

     __lowerCAmelCase  :      Optional[Any]    =						self.image_processing_class.from_dict(self.image_processor_dict	,							size=42	,							crop_size=84   )
     self.assertEqual(image_processor.size	,							{"""shortest_edge""": 42}   )
     self.assertEqual(image_processor.crop_size	,							{"""height""": 84, """width""": 84}   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     int:






     """simple docstring"""







     pass







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PIL images
     __lowerCAmelCase  :      Union[str, Any]    =						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  :      List[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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)

     # Test batched
     __lowerCAmelCase  :      str    =						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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)







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






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     # create random numpy tensors
     __lowerCAmelCase  :      Any    =						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  :      Optional[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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)

     # Test batched
     __lowerCAmelCase  :      Tuple    =						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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PyTorch tensors
     __lowerCAmelCase  :      str    =						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  :      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  :      Tuple    =						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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)


