
'''simple docstring'''







from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__lowerCAmelCase   =       logging.get_logger(__name__)







class UpperCAmelCase__      (					lowercase__      ):





  """simple docstring"""

  __UpperCAmelCase							:							List[str]    					=   ['''pixel_values''']


  def __init__(						self :	List[Any]				,_a :	bool = True				,_a :	Union[int, float] = 1 / 255				,_a :	bool = True				,_a :	int = 8				,**_a :	Dict				,):


     '''simple docstring'''






     super().__init__(**__a			)

     _a							:      List[Any]     =  do_rescale
     _a							:      Any     =  rescale_factor
     _a							:      Tuple     =  do_pad
     _a							:      Any     =  pad_size


  def     __lowercase						(						self :	List[str]				,_a :	np.ndarray				,_a :	float				,_a :	Optional[Union[str, ChannelDimension]] = None				,**_a :	int			):


     '''simple docstring'''






     return rescale(__a				,scale=__a				,data_format=__a				,**__a			)


  def     __lowercase						(						self :	List[str]				,_a :	np.ndarray				,_a :	int				,_a :	Optional[Union[str, ChannelDimension]] = None			):


     '''simple docstring'''






     _a,    _a							:      str     =  get_image_size(__a			)
     _a							:      str     =  (old_height // size + 1) * size - old_height
     _a							:      List[str]     =  (old_width // size + 1) * size - old_width

     return pad(__a				,((0, pad_height), (0, pad_width))				,mode='symmetric'				,data_format=__a			)


  def     __lowercase						(						self :	Dict				,_a :	ImageInput				,_a :	Optional[bool] = None				,_a :	Optional[float] = None				,_a :	Optional[bool] = None				,_a :	Optional[int] = None				,_a :	Optional[Union[str, TensorType]] = None				,_a :	Union[str, ChannelDimension] = ChannelDimension.FIRST				,**_a :	Optional[Any]				,):


     '''simple docstring'''






     _a							:      Tuple     =  do_rescale if do_rescale is not None else self.do_rescale
     _a							:      Tuple     =  rescale_factor if rescale_factor is not None else self.rescale_factor
     _a							:      int     =  do_pad if do_pad is not None else self.do_pad
     _a							:      Optional[Any]     =  pad_size if pad_size is not None else self.pad_size

     _a							:      Optional[Any]     =  make_list_of_images(__a			)

     if not valid_images(__a			):
        raise ValueError(
            'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
            'torch.Tensor, tf.Tensor or jax.ndarray.'			)

     if do_rescale and rescale_factor is None:
        raise ValueError('Rescale factor must be specified if do_rescale is True.'			)

     # All transformations expect numpy arrays.
     _a							:      str     =  [to_numpy_array(__a			) for image in images]

     if do_rescale:
        _a							:      Union[str, Any]     =  [self.rescale(image=__a				,scale=__a			) for image in images]

     if do_pad:
        _a							:      str     =  [self.pad(__a				,size=__a			) for image in images]

     _a							:      Optional[int]     =  [to_channel_dimension_format(__a				,__a			) for image in images]

     _a							:      Tuple     =  {'pixel_values': images}
     return BatchFeature(data=__a				,tensor_type=__a			)




'''simple docstring'''


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    lowerCAmelCase       (		UpperCamelCase__					: Dict							):


	"""simple docstring"""
	__UpperCAmelCase      					=      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."""							)
	__UpperCAmelCase      					=      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."""							)

	__UpperCAmelCase      					=      components[:-1] + [test_fn.replace('''.py'''     ,       ''''''							)]
	__UpperCAmelCase      					=      '''.'''.join(UpperCamelCase__							)

	return test_module_path





def    lowerCAmelCase       (		UpperCamelCase__					: int							):


	"""simple docstring"""
	__UpperCAmelCase      					=      get_module_path(UpperCamelCase__							)
	__UpperCAmelCase      					=      importlib.import_module(UpperCamelCase__							)

	return test_module





def    lowerCAmelCase       (		UpperCamelCase__					: Tuple							):


	"""simple docstring"""
	__UpperCAmelCase      					=      []
	__UpperCAmelCase      					=      get_test_module(UpperCamelCase__							)
	for attr in dir(UpperCamelCase__							):
		if attr.endswith('''ModelTester'''							):
			tester_classes.append(getattr(UpperCamelCase__     ,       UpperCamelCase__							)							)

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





def    lowerCAmelCase       (		UpperCamelCase__					: List[Any]							):


	"""simple docstring"""
	__UpperCAmelCase      					=      []
	__UpperCAmelCase      					=      get_test_module(UpperCamelCase__							)
	for attr in dir(UpperCamelCase__							):
		__UpperCAmelCase      					=      getattr(UpperCamelCase__     ,       UpperCamelCase__							)
		# (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).
		__UpperCAmelCase      					=      getattr(UpperCamelCase__     ,       '''all_model_classes'''     ,       []							)
		if len(UpperCamelCase__							) > 0:
			test_classes.append(UpperCamelCase__							)

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





def    lowerCAmelCase       (		UpperCamelCase__					: str							):


	"""simple docstring"""
	__UpperCAmelCase      					=      get_test_classes(UpperCamelCase__							)
	__UpperCAmelCase      					=      set()
	for test_class in test_classes:
		model_classes.update(test_class.all_model_classes							)

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





def    lowerCAmelCase       (		UpperCamelCase__					: Any							):


	"""simple docstring"""
	__UpperCAmelCase      					=      test_class()
	if hasattr(UpperCamelCase__     ,       '''setUp'''							):
		test.setUp()

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

	return model_tester





def    lowerCAmelCase       (		UpperCamelCase__					: Dict     ,       UpperCamelCase__					: int							):


	"""simple docstring"""
	__UpperCAmelCase      					=      get_test_classes(UpperCamelCase__							)

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

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





def    lowerCAmelCase       (		UpperCamelCase__					: Tuple     ,       UpperCamelCase__					: Any							):


	"""simple docstring"""
	__UpperCAmelCase      					=      get_test_classes_for_model(UpperCamelCase__     ,       UpperCamelCase__							)

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

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





def    lowerCAmelCase       (		UpperCamelCase__					: int							):


	"""simple docstring"""
	__UpperCAmelCase      					=      get_test_classes(UpperCamelCase__							)
	__UpperCAmelCase      					=      {test_class: get_model_tester_from_test_class(UpperCamelCase__							) for test_class in test_classes}
	return test_tester_mapping





def    lowerCAmelCase       (		UpperCamelCase__					: str							):


	"""simple docstring"""
	__UpperCAmelCase      					=      get_model_classes(UpperCamelCase__							)
	__UpperCAmelCase      					=      {
	    model_class: get_test_classes_for_model(UpperCamelCase__     ,       UpperCamelCase__							) for model_class in model_classes
	}
	return model_test_mapping





def    lowerCAmelCase       (		UpperCamelCase__					: Optional[int]							):


	"""simple docstring"""
	__UpperCAmelCase      					=      get_model_classes(UpperCamelCase__							)
	__UpperCAmelCase      					=      {
	    model_class: get_tester_classes_for_model(UpperCamelCase__     ,       UpperCamelCase__							) for model_class in model_classes
	}
	return model_to_tester_mapping





def    lowerCAmelCase       (		UpperCamelCase__					: Tuple							):


	"""simple docstring"""
	if isinstance(UpperCamelCase__     ,       UpperCamelCase__							):
		return o
	elif isinstance(UpperCamelCase__     ,       UpperCamelCase__							):
		return o.__name__
	elif isinstance(UpperCamelCase__     ,       (list, tuple)							):
		return [to_json(UpperCamelCase__							) for x in o]
	elif isinstance(UpperCamelCase__     ,       UpperCamelCase__							):
		return {to_json(UpperCamelCase__							): to_json(UpperCamelCase__							) for k, v in o.items()}
	else:
		return o
