




"""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


a_        =       logging.get_logger(__name__)


if is_vision_available():
			import PIL

class   UpperCAmelCase_    (			snake_case		):
		UpperCamelCase							=["pixel_values"]
		def __init__(						self				,						UpperCamelCase_ = True				,						UpperCamelCase_ = None				,						UpperCamelCase_ = PILImageResampling.BICUBIC				,						UpperCamelCase_ = True				,						UpperCamelCase_ = None				,						UpperCamelCase_ = True				,						UpperCamelCase_ = 1 / 2_55				,						UpperCamelCase_ = True				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = True				,						**UpperCamelCase_				,						)						->       None:
					super().__init__(**UpperCamelCase_       )
					__lowercase			:  str								=       size if size is not None else {'''shortest_edge''': 2_24}
					__lowercase			:  Tuple								=       get_size_dict(UpperCamelCase_				,						default_to_square=UpperCamelCase_       )
					__lowercase			:  int								=       crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
					__lowercase			:  Optional[int]								=       get_size_dict(UpperCamelCase_				,						default_to_square=UpperCamelCase_				,						param_name='''crop_size'''       )

					__lowercase			:  int								=       do_resize
					__lowercase			:  Any								=       size
					__lowercase			:  int								=       resample
					__lowercase			:  List[Any]								=       do_center_crop
					__lowercase			:  Dict								=       crop_size
					__lowercase			:  Optional[int]								=       do_rescale
					__lowercase			:  str								=       rescale_factor
					__lowercase			:  int								=       do_normalize
					__lowercase			:  str								=       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					__lowercase			:  List[str]								=       image_std if image_std is not None else OPENAI_CLIP_STD
					__lowercase			:  Optional[int]								=       do_convert_rgb
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_ = PILImageResampling.BICUBIC				,						UpperCamelCase_ = None				,						**UpperCamelCase_				,						)						->       np.ndarray:
					__lowercase			:  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()}"""       )
					__lowercase			:  Union[str, Any]								=       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 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_ = None				,						**UpperCamelCase_				,						)						->       np.ndarray:
					__lowercase			:  Union[str, Any]								=       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 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_ = None				,						**UpperCamelCase_				,						)						->       Union[str, Any]:
					return rescale(UpperCamelCase_				,						scale=UpperCamelCase_				,						data_format=UpperCamelCase_				,						**UpperCamelCase_       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_ = None				,						**UpperCamelCase_				,						)						->       np.ndarray:
					return normalize(UpperCamelCase_				,						mean=UpperCamelCase_				,						std=UpperCamelCase_				,						data_format=UpperCamelCase_				,						**UpperCamelCase_       )
		def 	_lowerCamelCase							(						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_				,						)						->       PIL.Image.Image:
					__lowercase			:  Any								=       do_resize if do_resize is not None else self.do_resize
					__lowercase			:  int								=       size if size is not None else self.size
					__lowercase			:  Dict								=       get_size_dict(UpperCamelCase_				,						param_name='''size'''				,						default_to_square=UpperCamelCase_       )
					__lowercase			:  Dict								=       resample if resample is not None else self.resample
					__lowercase			:  Optional[int]								=       do_center_crop if do_center_crop is not None else self.do_center_crop
					__lowercase			:  str								=       crop_size if crop_size is not None else self.crop_size
					__lowercase			:  List[str]								=       get_size_dict(UpperCamelCase_				,						param_name='''crop_size'''				,						default_to_square=UpperCamelCase_       )
					__lowercase			:  Tuple								=       do_rescale if do_rescale is not None else self.do_rescale
					__lowercase			:  Union[str, Any]								=       rescale_factor if rescale_factor is not None else self.rescale_factor
					__lowercase			:  Optional[Any]								=       do_normalize if do_normalize is not None else self.do_normalize
					__lowercase			:  Any								=       image_mean if image_mean is not None else self.image_mean
					__lowercase			:  Dict								=       image_std if image_std is not None else self.image_std
					__lowercase			:  List[Any]								=       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					__lowercase			:  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:
								__lowercase			:  List[Any]								=       [convert_to_rgb(UpperCamelCase_       ) for image in images]

					# All transformations expect numpy arrays.
					__lowercase			:  str								=       [to_numpy_array(UpperCamelCase_       ) for image in images]

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

					if do_center_crop:
								__lowercase			:  List[str]								=       [self.center_crop(image=UpperCamelCase_				,						size=UpperCamelCase_       ) for image in images]

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

					if do_normalize:
								__lowercase			:  Union[str, Any]								=       [self.normalize(image=UpperCamelCase_				,						mean=UpperCamelCase_				,						std=UpperCamelCase_       ) for image in images]

					__lowercase			:  Optional[Any]								=       [to_channel_dimension_format(UpperCamelCase_				,						UpperCamelCase_       ) for image in images]

					__lowercase			:  List[Any]								=       {'''pixel_values''': images}
					return BatchFeature(data=UpperCamelCase_				,						tensor_type=UpperCamelCase_       )







from typing import TYPE_CHECKING

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


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

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

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a	 =      [
     """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

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



