





'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowercase__       : List[str]         =       logging.get_logger(__name__)


if is_vision_available():
  import PIL


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			['''pixel_values''']


     def __init__(		self		,      _UpperCAmelCase = True		,      _UpperCAmelCase = None		,      _UpperCAmelCase = PILImageResampling.BICUBIC		,      _UpperCAmelCase = True		,      _UpperCAmelCase = None		,      _UpperCAmelCase = True		,      _UpperCAmelCase = 1 / 255		,      _UpperCAmelCase = True		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = True		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__(**_UpperCAmelCase)
          __A		:					int						=			size if size is not None else {'shortest_edge': 224}
          __A		:					Any						=			get_size_dict(_UpperCAmelCase		,      default_to_square=_UpperCAmelCase)
          __A		:					Union[str, Any]						=			crop_size if crop_size is not None else {'height': 224, 'width': 224}
          __A		:					List[str]						=			get_size_dict(_UpperCAmelCase		,      default_to_square=_UpperCAmelCase		,      param_name='crop_size')

          __A		:					Optional[int]						=			do_resize
          __A		:					str						=			size
          __A		:					List[Any]						=			resample
          __A		:					Dict						=			do_center_crop
          __A		:					Any						=			crop_size
          __A		:					Tuple						=			do_rescale
          __A		:					List[str]						=			rescale_factor
          __A		:					List[Any]						=			do_normalize
          __A		:					Optional[Any]						=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          __A		:					Optional[int]						=			image_std if image_std is not None else OPENAI_CLIP_STD
          __A		:					Optional[Any]						=			do_convert_rgb


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = PILImageResampling.BICUBIC		,      _UpperCAmelCase = None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					List[str]						=			get_size_dict(_UpperCAmelCase		,      default_to_square=_UpperCAmelCase)
          if "shortest_edge" not in size:
               raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
          __A		:					List[str]						=			get_resize_output_image_size(_UpperCAmelCase		,      size=size['shortest_edge']		,      default_to_square=_UpperCAmelCase)
          return resize(_UpperCAmelCase		,      size=_UpperCAmelCase		,      resample=_UpperCAmelCase		,      data_format=_UpperCAmelCase		,      **_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					List[str]						=			get_size_dict(_UpperCAmelCase)
          if "height" not in size or "width" not in size:
               raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}')
          return center_crop(_UpperCAmelCase		,      size=(size['height'], size['width'])		,      data_format=_UpperCAmelCase		,      **_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		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 = ChannelDimension.FIRST		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					Tuple						=			do_resize if do_resize is not None else self.do_resize
          __A		:					int						=			size if size is not None else self.size
          __A		:					Any						=			get_size_dict(_UpperCAmelCase		,      param_name='size'		,      default_to_square=_UpperCAmelCase)
          __A		:					Dict						=			resample if resample is not None else self.resample
          __A		:					str						=			do_center_crop if do_center_crop is not None else self.do_center_crop
          __A		:					str						=			crop_size if crop_size is not None else self.crop_size
          __A		:					str						=			get_size_dict(_UpperCAmelCase		,      param_name='crop_size'		,      default_to_square=_UpperCAmelCase)
          __A		:					Optional[Any]						=			do_rescale if do_rescale is not None else self.do_rescale
          __A		:					int						=			rescale_factor if rescale_factor is not None else self.rescale_factor
          __A		:					Union[str, Any]						=			do_normalize if do_normalize is not None else self.do_normalize
          __A		:					Union[str, Any]						=			image_mean if image_mean is not None else self.image_mean
          __A		:					Any						=			image_std if image_std is not None else self.image_std
          __A		:					Any						=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          __A		:					int						=			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:
               raise ValueError('Size 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.')

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
               __A		:					Any						=			[convert_to_rgb(_UpperCAmelCase) for image in images]

          # All transformations expect numpy arrays.
          __A		:					Union[str, Any]						=			[to_numpy_array(_UpperCAmelCase) for image in images]

          if do_resize:
               __A		:					Any						=			[self.resize(image=_UpperCAmelCase		,      size=_UpperCAmelCase		,      resample=_UpperCAmelCase) for image in images]

          if do_center_crop:
               __A		:					Tuple						=			[self.center_crop(image=_UpperCAmelCase		,      size=_UpperCAmelCase) for image in images]

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

          if do_normalize:
               __A		:					Any						=			[self.normalize(image=_UpperCAmelCase		,      mean=_UpperCAmelCase		,      std=_UpperCAmelCase) for image in images]

          __A		:					Optional[Any]						=			[to_channel_dimension_format(_UpperCAmelCase		,      _UpperCAmelCase) for image in images]

          __A		:					int						=			{'pixel_values': images}
          return BatchFeature(data=_UpperCAmelCase		,      tensor_type=_UpperCAmelCase)

'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__						       =      {
    "configuration_poolformer": [
        "POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "PoolFormerConfig",
        "PoolFormerOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["PoolFormerFeatureExtractor"]
						lowercase__						       =      ["PoolFormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      [
						    "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PoolFormerForImageClassification",
						    "PoolFormerModel",
						    "PoolFormerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_poolformer import (
						    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    PoolFormerConfig,
						    PoolFormerOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_poolformer import PoolFormerFeatureExtractor
												from .image_processing_poolformer import PoolFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_poolformer import (
												    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PoolFormerForImageClassification,
												    PoolFormerModel,
												    PoolFormerPreTrainedModel,
												)


else:
						import sys

						lowercase__						       =      _LazyModule(__name__, globals()["__file__"], _import_structure)


