'''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 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, logging


_lowerCamelCase :				Union[str, Any]														= logging.get_logger(__name__)




class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						["""pixel_values"""]



   def __init__(    self					,      lowerCAmelCase__ = True					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = PILImageResampling.BILINEAR					,      lowerCAmelCase__ = True					,      lowerCAmelCase__ = 1 / 255					,      lowerCAmelCase__ = True					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      **lowerCAmelCase__					,      )				->     None:
        """simple docstring"""





        super().__init__(**lowerCAmelCase__     )
        _UpperCamelCase  :Union[str, Any]       =size if size is not None else {"""height""": 224, """width""": 224}
        _UpperCamelCase  :Union[str, Any]       =get_size_dict(lowerCAmelCase__     )
        _UpperCamelCase  :Optional[int]       =do_resize
        _UpperCamelCase  :Dict       =do_rescale
        _UpperCamelCase  :Tuple       =do_normalize
        _UpperCamelCase  :Any       =size
        _UpperCamelCase  :List[str]       =resample
        _UpperCamelCase  :Optional[Any]       =rescale_factor
        _UpperCamelCase  :List[str]       =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        _UpperCamelCase  :Tuple       =image_std if image_std is not None else IMAGENET_STANDARD_STD



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = PILImageResampling.BILINEAR					,      lowerCAmelCase__ = None					,      **lowerCAmelCase__					,      )				->     np.ndarray:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =get_size_dict(lowerCAmelCase__     )
        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(
            lowerCAmelCase__					,      size=(size["""height"""], size["""width"""])					,      resample=lowerCAmelCase__					,      data_format=lowerCAmelCase__					,      **lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      **lowerCAmelCase__     )				->     np.ndarray:
        """simple docstring"""





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



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      **lowerCAmelCase__					,      )				->     np.ndarray:
        """simple docstring"""





        return normalize(lowerCAmelCase__					,      mean=lowerCAmelCase__					,      std=lowerCAmelCase__					,      data_format=lowerCAmelCase__					,      **lowerCAmelCase__     )




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = ChannelDimension.FIRST					,      **lowerCAmelCase__					,      )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :Dict       =do_resize if do_resize is not None else self.do_resize
        _UpperCamelCase  :Any       =do_rescale if do_rescale is not None else self.do_rescale
        _UpperCamelCase  :int       =do_normalize if do_normalize is not None else self.do_normalize
        _UpperCamelCase  :Optional[Any]       =resample if resample is not None else self.resample
        _UpperCamelCase  :str       =rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCamelCase  :str       =image_mean if image_mean is not None else self.image_mean
        _UpperCamelCase  :Any       =image_std if image_std is not None else self.image_std

        _UpperCamelCase  :Dict       =size if size is not None else self.size
        _UpperCamelCase  :Tuple       =get_size_dict(lowerCAmelCase__     )

        _UpperCamelCase  :int       =make_list_of_images(lowerCAmelCase__     )

        if not valid_images(lowerCAmelCase__     ):
             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:
             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."""     )

        # All transformations expect numpy arrays.
        _UpperCamelCase  :Dict       =[to_numpy_array(lowerCAmelCase__     ) for image in images]

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

        if do_rescale:
             _UpperCamelCase  :List[Any]       =[self.rescale(image=lowerCAmelCase__					,      scale=lowerCAmelCase__     ) for image in images]

        if do_normalize:
             _UpperCamelCase  :Tuple       =[self.normalize(image=lowerCAmelCase__					,      mean=lowerCAmelCase__					,      std=lowerCAmelCase__     ) for image in images]

        _UpperCamelCase  :Tuple       =[to_channel_dimension_format(lowerCAmelCase__					,      lowerCAmelCase__     ) for image in images]

        _UpperCamelCase  :Union[str, Any]       ={"""pixel_values""": images}
        return BatchFeature(data=lowerCAmelCase__					,      tensor_type=lowerCAmelCase__     )
'''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))

