


from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__a	 =      logging.get_logger(__name__)






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							['''pixel_values''']





      def __init__(		self    :       Tuple       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Union[int, float] = 1 / 2_5_5       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       int = 8       ,							**__lowerCamelCase    :       Optional[Any]       ,							)     -> None:






         """simple docstring"""







         super().__init__(**__lowerCamelCase							)

         UpperCAmelCase      					=  do_rescale
         UpperCAmelCase      					=  rescale_factor
         UpperCAmelCase      					=  do_pad
         UpperCAmelCase      					=  pad_size





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






         """simple docstring"""







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





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       np.ndarray       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       Optional[Union[str, ChannelDimension]] = None							)     -> int:






         """simple docstring"""







         UpperCAmelCase					,	UpperCAmelCase      					=  get_image_size(__lowerCamelCase							)
         UpperCAmelCase      					=  (old_height // size + 1) * size - old_height
         UpperCAmelCase      					=  (old_width // size + 1) * size - old_width

         return pad(__lowerCamelCase       ,							((0, pad_height), (0, pad_width))       ,							mode="""symmetric"""       ,							data_format=__lowerCamelCase							)





      def 							_lowercase					(		self    :       int       ,							__lowerCamelCase    :       ImageInput       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       Optional[float] = None       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       Optional[int] = None       ,							__lowerCamelCase    :       Optional[Union[str, TensorType]] = None       ,							__lowerCamelCase    :       Union[str, ChannelDimension] = ChannelDimension.FIRST       ,							**__lowerCamelCase    :       Optional[Any]       ,							)     -> int:






         """simple docstring"""







         UpperCAmelCase      					=  do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase      					=  rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase      					=  do_pad if do_pad is not None else self.do_pad
         UpperCAmelCase      					=  pad_size if pad_size is not None else self.pad_size

         UpperCAmelCase      					=  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_rescale and rescale_factor is None:
            raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

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

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

         if do_pad:
            UpperCAmelCase      					=  [self.pad(__lowerCamelCase       ,							size=__lowerCamelCase							) for image in images]

         UpperCAmelCase      					=  [to_channel_dimension_format(__lowerCamelCase       ,							__lowerCamelCase							) for image in images]

         UpperCAmelCase      					=  {"""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,
)


__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__)



