

from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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






class  UpperCAmelCase__	( A__ ):






   """simple docstring"""


   a     =      ["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, Iterable[float]]] = IMAGENET_DEFAULT_MEAN			,     __lowerCamelCase		:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD			,     **__lowerCamelCase		:	List[Any]			,     )	->							None:
        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			,     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 IMAGENET_DEFAULT_MEAN
        SCREAMING_SNAKE_CASE__					=					image_std if image_std is not None else IMAGENET_DEFAULT_STD






   def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Dict[str, int]			,     __lowerCamelCase		:	PILImageResampling = PILImageResampling.BICUBIC			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	List[str]			,     )	->							np.ndarray:
        SCREAMING_SNAKE_CASE__					=					get_size_dict(__lowerCamelCase			,     default_to_square=__lowerCamelCase      )
        # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
        if "shortest_edge" in size:
             SCREAMING_SNAKE_CASE__					=					int((256 / 224) * size['''shortest_edge''']      )
             SCREAMING_SNAKE_CASE__					=					get_resize_output_image_size(__lowerCamelCase			,     size=__lowerCamelCase			,     default_to_square=__lowerCamelCase      )
             SCREAMING_SNAKE_CASE__					=					{'''height''': output_size[0], '''width''': output_size[1]}
        if "height" not in size_dict or "width" not in size_dict:
             raise ValueError(
                 f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''      )
        return resize(
            __lowerCamelCase			,     size=(size_dict['''height'''], size_dict['''width'''])			,     resample=__lowerCamelCase			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )






   def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Dict[str, int]			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	Optional[int]			,     )	->							np.ndarray:
        SCREAMING_SNAKE_CASE__					=					get_size_dict(__lowerCamelCase      )
        if "height" not in size or "width" not in size:
             raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''      )
        return center_crop(__lowerCamelCase			,     size=(size['''height'''], size['''width'''])			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )






   def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Union[int, float]			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	str			,     )	->							np.ndarray:
        return rescale(__lowerCamelCase			,     scale=__lowerCamelCase			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )






   def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Union[float, List[float]]			,     __lowerCamelCase		:	Union[float, List[float]]			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	int			,     )	->							np.ndarray:
        return normalize(__lowerCamelCase			,     mean=__lowerCamelCase			,     std=__lowerCamelCase			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )







   def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	ImageInput			,     __lowerCamelCase		:	Optional[bool] = None			,     __lowerCamelCase		:	Optional[Dict[str, int]] = None			,     __lowerCamelCase		:	PILImageResampling = None			,     __lowerCamelCase		:	Optional[bool] = None			,     __lowerCamelCase		:	Optional[Dict[str, int]] = None			,     __lowerCamelCase		:	Optional[bool] = None			,     __lowerCamelCase		:	Optional[float] = None			,     __lowerCamelCase		:	Optional[bool] = None			,     __lowerCamelCase		:	Optional[Union[float, Iterable[float]]] = None			,     __lowerCamelCase		:	Optional[Union[float, Iterable[float]]] = None			,     __lowerCamelCase		:	Optional[TensorType] = None			,     __lowerCamelCase		:	ChannelDimension = ChannelDimension.FIRST			,     **__lowerCamelCase		:	Any			,     )	->							BatchFeature:
        SCREAMING_SNAKE_CASE__					=					do_resize if do_resize is not None else self.do_resize
        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__					=					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__					=					size if size is not None else self.size
        SCREAMING_SNAKE_CASE__					=					get_size_dict(__lowerCamelCase			,     default_to_square=__lowerCamelCase      )
        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'''      )

        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.'''      )

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

        if do_resize:
             SCREAMING_SNAKE_CASE__					=					[self.resize(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      ) for image in images]

        if do_center_crop:
             SCREAMING_SNAKE_CASE__					=					[self.center_crop(__lowerCamelCase			,     __lowerCamelCase      ) for image in images]

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

        if do_normalize:
             SCREAMING_SNAKE_CASE__					=					[self.normalize(__lowerCamelCase			,     __lowerCamelCase			,     __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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE  :						Dict         =    {
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Tuple         =    ['''LongformerTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''LongformerForMaskedLM''',
           '''LongformerForMultipleChoice''',
           '''LongformerForQuestionAnswering''',
           '''LongformerForSequenceClassification''',
           '''LongformerForTokenClassification''',
           '''LongformerModel''',
           '''LongformerPreTrainedModel''',
           '''LongformerSelfAttention''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFLongformerForMaskedLM''',
           '''TFLongformerForMultipleChoice''',
           '''TFLongformerForQuestionAnswering''',
           '''TFLongformerForSequenceClassification''',
           '''TFLongformerForTokenClassification''',
           '''TFLongformerModel''',
           '''TFLongformerPreTrainedModel''',
           '''TFLongformerSelfAttention''',
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

       _SCREAMING_SNAKE_CASE  :						int         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


