
"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


A_						       =				logging.get_logger(__name__)




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			str									=						['pixel_values']




				def __init__(							self       ,	UpperCAmelCase = True       ,	UpperCAmelCase = None       ,	UpperCAmelCase = PIL.Image.NEAREST       ,	UpperCAmelCase = False       ,	UpperCAmelCase = None       ,	UpperCAmelCase = 1 / 255       ,	UpperCAmelCase = False       ,	UpperCAmelCase = True       ,	UpperCAmelCase = True       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = True       ,	**UpperCAmelCase       ,	):
							super().__init__(**UpperCAmelCase      )
							lowerCamelCase_ 		=     size if size is not None else {'''height''': 346, '''width''': 346}
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase      )
							lowerCamelCase_ 		=     crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase       ,	param_name='''crop_size'''      )

							lowerCamelCase_ 		=     do_resize
							lowerCamelCase_ 		=     size
							lowerCamelCase_ 		=     resample
							lowerCamelCase_ 		=     do_center_crop
							lowerCamelCase_ 		=     crop_size
							lowerCamelCase_ 		=     do_rescale
							lowerCamelCase_ 		=     rescale_factor
							lowerCamelCase_ 		=     rescale_offset
							lowerCamelCase_ 		=     do_normalize
							lowerCamelCase_ 		=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowerCamelCase_ 		=     image_std if image_std is not None else IMAGENET_STANDARD_STD
							lowerCamelCase_ 		=     include_top




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = PIL.Image.NEAREST       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase      )
							if "height" not in size or "width" not in size:
										raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"      )
							return resize(
							    UpperCAmelCase       ,	size=(size['''height'''], size['''width'''])       ,	resample=UpperCAmelCase       ,	data_format=UpperCAmelCase       ,	**UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase      )
							if "height" not in size or "width" not in size:
										raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"      )
							return center_crop(UpperCAmelCase       ,	size=(size['''height'''], size['''width'''])       ,	data_format=UpperCAmelCase       ,	**UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = True       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							if offset:
										lowerCamelCase_ 		=     (image - 1_2_7.5) * scale
										if data_format is not None:
													lowerCamelCase_ 		=     to_channel_dimension_format(UpperCAmelCase       ,	UpperCAmelCase      )
										lowerCamelCase_ 		=     rescaled_image.astype(np.floataa      )
							else:
										lowerCamelCase_ 		=     rescale(UpperCAmelCase       ,	scale=UpperCAmelCase       ,	data_format=UpperCAmelCase       ,	**UpperCAmelCase      )
							return rescaled_image




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							return normalize(UpperCAmelCase       ,	mean=UpperCAmelCase       ,	std=UpperCAmelCase       ,	data_format=UpperCAmelCase       ,	**UpperCAmelCase      )





				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase=None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = ChannelDimension.FIRST       ,	**UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     do_resize if do_resize is not None else self.do_resize
							lowerCamelCase_ 		=     resample if resample is not None else self.resample
							lowerCamelCase_ 		=     do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCamelCase_ 		=     do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase_ 		=     rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase_ 		=     rescale_offset if rescale_offset is not None else self.rescale_offset
							lowerCamelCase_ 		=     do_normalize if do_normalize is not None else self.do_normalize
							lowerCamelCase_ 		=     image_mean if image_mean is not None else self.image_mean
							lowerCamelCase_ 		=     image_std if image_std is not None else self.image_std
							lowerCamelCase_ 		=     include_top if include_top is not None else self.include_top

							lowerCamelCase_ 		=     size if size is not None else self.size
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase      )
							lowerCamelCase_ 		=     crop_size if crop_size is not None else self.crop_size
							lowerCamelCase_ 		=     get_size_dict(UpperCAmelCase       ,	param_name='''crop_size'''      )

							lowerCamelCase_ 		=     make_list_of_images(UpperCAmelCase      )

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

							if do_resize and size is None or resample is None:
										raise ValueError('''Size and resample must be specified if do_resize is True.'''      )

							if do_center_crop and crop_size is None:
										raise ValueError('''Crop size must be specified if do_center_crop is True.'''      )

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

							if do_normalize and (image_mean is None or image_std is None):
										raise ValueError('''Image mean and std must be specified if do_normalize is True.'''      )

							# All transformations expect numpy arrays.
							lowerCamelCase_ 		=     [to_numpy_array(UpperCAmelCase      ) for image in images]

							if do_resize:
										lowerCamelCase_ 		=     [self.resize(image=UpperCAmelCase       ,	size=UpperCAmelCase       ,	resample=UpperCAmelCase      ) for image in images]

							if do_center_crop:
										lowerCamelCase_ 		=     [self.center_crop(image=UpperCAmelCase       ,	size=UpperCAmelCase      ) for image in images]

							if do_rescale:
										lowerCamelCase_ 		=     [self.rescale(image=UpperCAmelCase       ,	scale=UpperCAmelCase       ,	offset=UpperCAmelCase      ) for image in images]

							if do_normalize:
										lowerCamelCase_ 		=     [self.normalize(image=UpperCAmelCase       ,	mean=UpperCAmelCase       ,	std=UpperCAmelCase      ) for image in images]

							if include_top:
										lowerCamelCase_ 		=     [self.normalize(image=UpperCAmelCase       ,	mean=[0, 0, 0]       ,	std=UpperCAmelCase      ) for image in images]

							lowerCamelCase_ 		=     [to_channel_dimension_format(UpperCAmelCase       ,	UpperCAmelCase      ) for image in images]

							lowerCamelCase_ 		=     {'''pixel_values''': images}
							return BatchFeature(data=UpperCAmelCase       ,	tensor_type=UpperCAmelCase      )





"""simple docstring"""


import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
						A_						       =				[]

						A_						       =				os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
						A_						       =				glob.glob(pattern)
						# TODO: deal with TF/Flax too
						A_						       =				[
						    x for x in test_files if not (x.startswith("""test_modeling_tf_""") or x.startswith("""test_modeling_flax_"""))
						]

						for test_file in test_files:
												A_						       =				get_tester_classes(test_file)
												for tester_class in tester_classes:
																		# A few tester classes don't have `parent` parameter in `__init__`.
																		# TODO: deal this better
																		try:
																								A_						       =				tester_class(parent=None)
																		except Exception:
																								continue
																		if hasattr(tester, """get_config"""):
																								A_						       =				tester.get_config()
																								for k, v in config.to_dict().items():
																														if isinstance(v, int):
																																				A_						       =				None
																																				if k in ["vocab_size"]:
																																										A_						       =				100
																																				elif k in ["max_position_embeddings"]:
																																										A_						       =				128
																																				elif k in ["hidden_size", "d_model"]:
																																										A_						       =				40
																																				elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																										A_						       =				5
																																				if target is not None and v > target:
																																										failures.append(
																																										    f"{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"
																																										    f" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"
																																										    f" than {target}."
																																										)

						if len(failures) > 0:
												raise Exception(f"There were {len(failures)} failures:\n" + """\n""".join(failures))



