





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


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)


if is_vision_available():
						import PIL

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



					'''simple docstring'''

					__snake_case   :     Any		     =      ["pixel_values"]






					def __init__(							self						:   List[str]						,lowerCamelCase__						:   bool = True						,lowerCamelCase__						:   Dict[str, int] = None						,lowerCamelCase__						:   PILImageResampling = PILImageResampling.BICUBIC						,lowerCamelCase__						:   bool = True						,lowerCamelCase__						:   Dict[str, int] = None						,lowerCamelCase__						:   bool = True						,lowerCamelCase__						:   Union[int, float] = 1 / 255						,lowerCamelCase__						:   bool = True						,lowerCamelCase__						:   Optional[Union[float, List[float]]] = None						,lowerCamelCase__						:   Optional[Union[float, List[float]]] = None						,lowerCamelCase__						:   bool = True						,**lowerCamelCase__						:   List[Any]						,)					->		None:




											'''simple docstring'''


											super().__init__(**lowerCamelCase__	)
											SCREAMING_SNAKE_CASE		=						size if size is not None else {"""shortest_edge""": 224}
											SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,default_to_square=lowerCamelCase__	)
											SCREAMING_SNAKE_CASE		=						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
											SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,default_to_square=lowerCamelCase__						,param_name="""crop_size"""	)

											SCREAMING_SNAKE_CASE		=						do_resize
											SCREAMING_SNAKE_CASE		=						size
											SCREAMING_SNAKE_CASE		=						resample
											SCREAMING_SNAKE_CASE		=						do_center_crop
											SCREAMING_SNAKE_CASE		=						crop_size
											SCREAMING_SNAKE_CASE		=						do_rescale
											SCREAMING_SNAKE_CASE		=						rescale_factor
											SCREAMING_SNAKE_CASE		=						do_normalize
											SCREAMING_SNAKE_CASE		=						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
											SCREAMING_SNAKE_CASE		=						image_std if image_std is not None else OPENAI_CLIP_STD
											SCREAMING_SNAKE_CASE		=						do_convert_rgb






					def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   np.ndarray						,lowerCamelCase__						:   Dict[str, int]						,lowerCamelCase__						:   PILImageResampling = PILImageResampling.BICUBIC						,lowerCamelCase__						:   Optional[Union[str, ChannelDimension]] = None						,**lowerCamelCase__						:   Optional[int]						,)					->		np.ndarray:




											'''simple docstring'''


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






					def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   np.ndarray						,lowerCamelCase__						:   Dict[str, int]						,lowerCamelCase__						:   Optional[Union[str, ChannelDimension]] = None						,**lowerCamelCase__						:   Union[str, Any]						,)					->		np.ndarray:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__	)
											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(lowerCamelCase__						,size=(size["""height"""], size["""width"""])						,data_format=lowerCamelCase__						,**lowerCamelCase__	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   np.ndarray						,lowerCamelCase__						:   Union[int, float]						,lowerCamelCase__						:   Optional[Union[str, ChannelDimension]] = None						,**lowerCamelCase__						:   Union[str, Any]						,)					->		Tuple:




											'''simple docstring'''


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






					def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]						,lowerCamelCase__						:   np.ndarray						,lowerCamelCase__						:   Union[float, List[float]]						,lowerCamelCase__						:   Union[float, List[float]]						,lowerCamelCase__						:   Optional[Union[str, ChannelDimension]] = None						,**lowerCamelCase__						:   Optional[Any]						,)					->		np.ndarray:




											'''simple docstring'''


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






					def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   ImageInput						,lowerCamelCase__						:   bool = None						,lowerCamelCase__						:   Dict[str, int] = None						,lowerCamelCase__						:   PILImageResampling = None						,lowerCamelCase__						:   bool = None						,lowerCamelCase__						:   int = None						,lowerCamelCase__						:   bool = None						,lowerCamelCase__						:   float = None						,lowerCamelCase__						:   bool = None						,lowerCamelCase__						:   Optional[Union[float, List[float]]] = None						,lowerCamelCase__						:   Optional[Union[float, List[float]]] = None						,lowerCamelCase__						:   bool = None						,lowerCamelCase__						:   Optional[Union[str, TensorType]] = None						,lowerCamelCase__						:   Optional[ChannelDimension] = ChannelDimension.FIRST						,**lowerCamelCase__						:   int						,)					->		PIL.Image.Image:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						do_resize if do_resize is not None else self.do_resize
											SCREAMING_SNAKE_CASE		=						size if size is not None else self.size
											SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,param_name="""size"""						,default_to_square=lowerCamelCase__	)
											SCREAMING_SNAKE_CASE		=						resample if resample is not None else self.resample
											SCREAMING_SNAKE_CASE		=						do_center_crop if do_center_crop is not None else self.do_center_crop
											SCREAMING_SNAKE_CASE		=						crop_size if crop_size is not None else self.crop_size
											SCREAMING_SNAKE_CASE		=						get_size_dict(lowerCamelCase__						,param_name="""crop_size"""						,default_to_square=lowerCamelCase__	)
											SCREAMING_SNAKE_CASE		=						do_rescale if do_rescale is not None else self.do_rescale
											SCREAMING_SNAKE_CASE		=						rescale_factor if rescale_factor is not None else self.rescale_factor
											SCREAMING_SNAKE_CASE		=						do_normalize if do_normalize is not None else self.do_normalize
											SCREAMING_SNAKE_CASE		=						image_mean if image_mean is not None else self.image_mean
											SCREAMING_SNAKE_CASE		=						image_std if image_std is not None else self.image_std
											SCREAMING_SNAKE_CASE		=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											SCREAMING_SNAKE_CASE		=						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_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:
																	SCREAMING_SNAKE_CASE		=						[convert_to_rgb(lowerCamelCase__	) for image in images]

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

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

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

											if do_rescale:
																	SCREAMING_SNAKE_CASE		=						[self.rescale(image=lowerCamelCase__						,scale=lowerCamelCase__	) for image in images]

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

											SCREAMING_SNAKE_CASE		=						[to_channel_dimension_format(lowerCamelCase__						,lowerCamelCase__	) for image in images]

											SCREAMING_SNAKE_CASE		=						{"""pixel_values""": images}
											return BatchFeature(data=lowerCamelCase__						,tensor_type=lowerCamelCase__	)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_						       =	{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ReformerAttention""",
          """ReformerForMaskedLM""",
          """ReformerForQuestionAnswering""",
          """ReformerForSequenceClassification""",
          """ReformerLayer""",
          """ReformerModel""",
          """ReformerModelWithLMHead""",
          """ReformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer import ReformerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer_fast import ReformerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_reformer import (
                REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                ReformerAttention,
                ReformerForMaskedLM,
                ReformerForQuestionAnswering,
                ReformerForSequenceClassification,
                ReformerLayer,
                ReformerModel,
                ReformerModelWithLMHead,
                ReformerPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE_						       =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


