




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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


UpperCAmelCase__    :							str       		=     logging.get_logger(__name__)

class   UpperCAmelCase    (      a_ ):
   __SCREAMING_SNAKE_CASE		:				List[str]      					=       ["pixel_values"]






   def __init__(						self :							Dict    ,       UpperCamelCase_ :							bool = True    ,       UpperCamelCase_ :							Dict[str, int] = None    ,       UpperCamelCase_ :							bool = True    ,       UpperCamelCase_ :							Dict[str, int] = None    ,       UpperCamelCase_ :							PILImageResampling = PILImageResampling.BICUBIC    ,       UpperCamelCase_ :							bool = True    ,       UpperCamelCase_ :							Union[int, float] = 1 / 2_55    ,       UpperCamelCase_ :							bool = True    ,       UpperCamelCase_ :							Optional[Union[float, List[float]]] = None    ,       UpperCamelCase_ :							Optional[Union[float, List[float]]] = None    ,       **UpperCamelCase_ :							Union[str, Any]    ,       )->      Optional[Any]:
     super().__init__(**UpperCAmelCase_  )
     _lowercase:			str      =     crop_size if crop_size is not None else {'height': 2_56, 'width': 2_56}
     _lowercase:			Dict      =     get_size_dict(UpperCAmelCase_    ,       param_name='crop_size'  )
     _lowercase:			Dict      =     size if size is not None else {'height': 2_24, 'width': 2_24}
     _lowercase:			Tuple      =     get_size_dict(UpperCAmelCase_  )

     _lowercase:			List[str]      =     do_center_crop
     _lowercase:			List[Any]      =     crop_size
     _lowercase:			List[str]      =     do_resize
     _lowercase:			Dict      =     size
     _lowercase:			Any      =     resample
     _lowercase:			Tuple      =     do_rescale
     _lowercase:			List[str]      =     rescale_factor
     _lowercase:			int      =     do_normalize
     _lowercase:			Optional[Any]      =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     _lowercase:			Union[str, Any]      =     image_std if image_std is not None else IMAGENET_DEFAULT_STD






   def 			_a    (						self :							List[Any]    ,       UpperCamelCase_ :							np.ndarray    ,       UpperCamelCase_ :							Dict[str, int]    ,       UpperCamelCase_ :							Optional[int] = None    ,       UpperCamelCase_ :							Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_ :							str    ,       )->      Union[str, Any]:
     _lowercase:			Any      =     self.size if size is None else size
     _lowercase:			Union[str, Any]      =     get_size_dict(UpperCAmelCase_  )
     _lowercase:			List[Any]      =     get_size_dict(UpperCAmelCase_    ,       param_name='crop_size'  )

     _lowercase:			int      =     get_image_size(UpperCAmelCase_  )
     _lowercase:			Dict      =     min(UpperCAmelCase_    ,       UpperCAmelCase_  )
     _lowercase:			Dict      =     (size['height'] / crop_size['height']) * min_dim
     _lowercase:			Tuple      =     (size['width'] / crop_size['width']) * min_dim
     return center_crop(UpperCAmelCase_    ,       size=(cropped_height, cropped_width)    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_  )






   def 			_a    (						self :							int    ,       UpperCamelCase_ :							np.ndarray    ,       UpperCamelCase_ :							Dict[str, int]    ,       UpperCamelCase_ :							PILImageResampling = PIL.Image.BICUBIC    ,       UpperCamelCase_ :							Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_ :							List[str]    ,       )->      List[Any]:
     _lowercase:			int      =     get_size_dict(UpperCAmelCase_  )
     if "height" not in size or "width" not in size:
       raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'  )
     return resize(
         UpperCAmelCase_    ,       size=(size['height'], size['width'])    ,       resample=UpperCAmelCase_    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_  )






   def 			_a    (						self :							int    ,       UpperCamelCase_ :							np.ndarray    ,       UpperCamelCase_ :							Union[int, float]    ,       UpperCamelCase_ :							Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_ :							Optional[Any]    ,       )->      Tuple:
     return rescale(UpperCAmelCase_    ,       scale=UpperCAmelCase_    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_  )






   def 			_a    (						self :							int    ,       UpperCamelCase_ :							np.ndarray    ,       UpperCamelCase_ :							Union[float, List[float]]    ,       UpperCamelCase_ :							Union[float, List[float]]    ,       UpperCamelCase_ :							Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_ :							Tuple    ,       )->      Union[str, Any]:
     return normalize(UpperCAmelCase_    ,       mean=UpperCAmelCase_    ,       std=UpperCAmelCase_    ,       data_format=UpperCAmelCase_    ,       **UpperCAmelCase_  )






   def 			_a    (						self :							List[Any]    ,       UpperCamelCase_ :							ImageInput    ,       UpperCamelCase_ :							Optional[bool] = None    ,       UpperCamelCase_ :							Optional[Dict[str, int]] = None    ,       UpperCamelCase_ :							Optional[bool] = None    ,       UpperCamelCase_ :							Optional[Dict[str, int]] = None    ,       UpperCamelCase_ :							PILImageResampling = None    ,       UpperCamelCase_ :							Optional[bool] = None    ,       UpperCamelCase_ :							Optional[float] = None    ,       UpperCamelCase_ :							Optional[bool] = None    ,       UpperCamelCase_ :							Optional[Union[float, List[float]]] = None    ,       UpperCamelCase_ :							Optional[Union[float, List[float]]] = None    ,       UpperCamelCase_ :							Optional[Union[str, TensorType]] = None    ,       UpperCamelCase_ :							ChannelDimension = ChannelDimension.FIRST    ,       **UpperCamelCase_ :							Optional[Any]    ,       )->      Optional[int]:
     _lowercase:			Union[str, Any]      =     do_center_crop if do_center_crop is not None else self.do_center_crop
     _lowercase:			Tuple      =     crop_size if crop_size is not None else self.crop_size
     _lowercase:			Tuple      =     get_size_dict(UpperCAmelCase_    ,       param_name='crop_size'  )
     _lowercase:			str      =     do_resize if do_resize is not None else self.do_resize
     _lowercase:			Union[str, Any]      =     size if size is not None else self.size
     _lowercase:			int      =     get_size_dict(UpperCAmelCase_  )
     _lowercase:			List[Any]      =     resample if resample is not None else self.resample
     _lowercase:			int      =     do_rescale if do_rescale is not None else self.do_rescale
     _lowercase:			Optional[int]      =     rescale_factor if rescale_factor is not None else self.rescale_factor
     _lowercase:			List[Any]      =     do_normalize if do_normalize is not None else self.do_normalize
     _lowercase:			Union[str, Any]      =     image_mean if image_mean is not None else self.image_mean
     _lowercase:			Optional[Any]      =     image_std if image_std is not None else self.image_std

     _lowercase:			List[str]      =     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_center_crop and crop_size is None:
       raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'  )

     if do_resize and size is None:
       raise ValueError('Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'  )

     # All transformations expect numpy arrays.
     _lowercase:			List[Any]      =     [to_numpy_array(UpperCAmelCase_  ) for image in images]

     if do_center_crop:
       _lowercase:			Optional[Any]      =     [self.center_crop(UpperCAmelCase_    ,       UpperCAmelCase_    ,       size=UpperCAmelCase_  ) for image in images]

     if do_resize:
       _lowercase:			str      =     [self.resize(image=UpperCAmelCase_    ,       size=UpperCAmelCase_    ,       resample=UpperCAmelCase_  ) for image in images]

     if do_rescale:
       _lowercase:			str      =     [self.rescale(image=UpperCAmelCase_    ,       scale=UpperCAmelCase_  ) for image in images]

     if do_normalize:
       _lowercase:			List[Any]      =     [self.normalize(image=UpperCAmelCase_    ,       mean=UpperCAmelCase_    ,       std=UpperCAmelCase_  ) for image in images]

     _lowercase:			str      =     [to_channel_dimension_format(UpperCAmelCase_    ,       UpperCAmelCase_  ) for image in images]

     _lowercase:			List[Any]      =     {'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__":
      UpperCamelCase       :     List[Any]			=    []

      UpperCamelCase       :     Tuple			=    os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
      UpperCamelCase       :     List[str]			=    glob.glob(pattern)
      # TODO: deal with TF/Flax too
      UpperCamelCase       :     List[Any]			=    [
          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:
            UpperCamelCase       :     List[str]			=    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:
                        UpperCamelCase       :     int			=    tester_class(parent=None)
                  except Exception:
                        continue
                  if hasattr(tester, """get_config"""):
                        UpperCamelCase       :     Union[str, Any]			=    tester.get_config()
                        for k, v in config.to_dict().items():
                              if isinstance(v, int):
                                    UpperCamelCase       :     List[Any]			=    None
                                    if k in ["vocab_size"]:
                                          UpperCamelCase       :     List[Any]			=    100
                                    elif k in ["max_position_embeddings"]:
                                          UpperCamelCase       :     Tuple			=    128
                                    elif k in ["hidden_size", "d_model"]:
                                          UpperCamelCase       :     Any			=    40
                                    elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
                                          UpperCamelCase       :     Tuple			=    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))

