
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


lowercase_											=	logging.get_logger(__name__)





class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	['''pixel_values''']



     def __init__(							self    :    List[Any]      ,		_A    :    bool = True      ,		_A    :    Dict[str, int] = None      ,		_A    :    PILImageResampling = PIL.Image.NEAREST      ,		_A    :    bool = False      ,		_A    :    Dict[str, int] = None      ,		_A    :    Union[int, float] = 1 / 255      ,		_A    :    bool = False      ,		_A    :    bool = True      ,		_A    :    bool = True      ,		_A    :    Optional[Union[float, List[float]]] = None      ,		_A    :    Optional[Union[float, List[float]]] = None      ,		_A    :    bool = True      ,		**_A    :    Tuple      ,		):







       """simple docstring"""




       super().__init__(**_A				)
       __SCREAMING_SNAKE_CASE     :					Any			=						size if size is not None else {'''height''': 346, '''width''': 346}
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						get_size_dict(_A				)
       __SCREAMING_SNAKE_CASE     :					str			=						crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
       __SCREAMING_SNAKE_CASE     :					Any			=						get_size_dict(_A      ,		param_name='''crop_size'''				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						do_resize
       __SCREAMING_SNAKE_CASE     :					Tuple			=						size
       __SCREAMING_SNAKE_CASE     :					Any			=						resample
       __SCREAMING_SNAKE_CASE     :					Tuple			=						do_center_crop
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						crop_size
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						do_rescale
       __SCREAMING_SNAKE_CASE     :					List[str]			=						rescale_factor
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						rescale_offset
       __SCREAMING_SNAKE_CASE     :					Tuple			=						do_normalize
       __SCREAMING_SNAKE_CASE     :					Dict			=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						image_std if image_std is not None else IMAGENET_STANDARD_STD
       __SCREAMING_SNAKE_CASE     :					Any			=						include_top



     def 				UpperCAmelCase__    (							self    :    Optional[Any]      ,		_A    :    np.ndarray      ,		_A    :    Dict[str, int]      ,		_A    :    PILImageResampling = PIL.Image.NEAREST      ,		_A    :    Optional[Union[str, ChannelDimension]] = None      ,		**_A    :    List[Any]      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						get_size_dict(_A				)
       if "height" not in size or "width" not in size:
         raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''				)
       return resize(
           _A      ,		size=(size['''height'''], size['''width'''])      ,		resample=_A      ,		data_format=_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    np.ndarray      ,		_A    :    Dict[str, int]      ,		_A    :    Optional[Union[str, ChannelDimension]] = None      ,		**_A    :    str      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						get_size_dict(_A				)
       if "height" not in size or "width" not in size:
         raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''				)
       return center_crop(_A      ,		size=(size['''height'''], size['''width'''])      ,		data_format=_A      ,		**_A				)



     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		_A    :    np.ndarray      ,		_A    :    Union[int, float]      ,		_A    :    bool = True      ,		_A    :    Optional[Union[str, ChannelDimension]] = None      ,		**_A    :    Tuple      ,		):







       """simple docstring"""




       if offset:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						(image - 1_27.5) * scale
         if data_format is not None:
           __SCREAMING_SNAKE_CASE     :					int			=						to_channel_dimension_format(_A      ,		_A				)
         __SCREAMING_SNAKE_CASE     :					int			=						rescaled_image.astype(np.floataa				)
       else:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						rescale(_A      ,		scale=_A      ,		data_format=_A      ,		**_A				)
       return rescaled_image



     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    np.ndarray      ,		_A    :    Union[float, List[float]]      ,		_A    :    Union[float, List[float]]      ,		_A    :    Optional[Union[str, ChannelDimension]] = None      ,		**_A    :    int      ,		):







       """simple docstring"""




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




     def 				UpperCAmelCase__    (							self    :    str      ,		_A    :    ImageInput      ,		_A    :    bool = None      ,		_A    :    Dict[str, int] = None      ,		_A    :    Union[str, Any]=None      ,		_A    :    bool = None      ,		_A    :    Dict[str, int] = None      ,		_A    :    bool = None      ,		_A    :    float = None      ,		_A    :    bool = None      ,		_A    :    bool = None      ,		_A    :    Optional[Union[float, List[float]]] = None      ,		_A    :    Optional[Union[float, List[float]]] = None      ,		_A    :    bool = None      ,		_A    :    Optional[Union[str, TensorType]] = None      ,		_A    :    ChannelDimension = ChannelDimension.FIRST      ,		**_A    :    Any      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						do_resize if do_resize is not None else self.do_resize
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						resample if resample is not None else self.resample
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						do_center_crop if do_center_crop is not None else self.do_center_crop
       __SCREAMING_SNAKE_CASE     :					Tuple			=						do_rescale if do_rescale is not None else self.do_rescale
       __SCREAMING_SNAKE_CASE     :					List[str]			=						rescale_factor if rescale_factor is not None else self.rescale_factor
       __SCREAMING_SNAKE_CASE     :					int			=						rescale_offset if rescale_offset is not None else self.rescale_offset
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						do_normalize if do_normalize is not None else self.do_normalize
       __SCREAMING_SNAKE_CASE     :					Any			=						image_mean if image_mean is not None else self.image_mean
       __SCREAMING_SNAKE_CASE     :					int			=						image_std if image_std is not None else self.image_std
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						include_top if include_top is not None else self.include_top

       __SCREAMING_SNAKE_CASE     :					Any			=						size if size is not None else self.size
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						get_size_dict(_A				)
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						crop_size if crop_size is not None else self.crop_size
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						get_size_dict(_A      ,		param_name='''crop_size'''				)

       __SCREAMING_SNAKE_CASE     :					Any			=						make_list_of_images(_A				)

       if not valid_images(_A				):
         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 or resample is None:
         raise ValueError('''Size and resample 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     :					Any			=						[to_numpy_array(_A				) for image in images]

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

       if do_center_crop:
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						[self.center_crop(image=_A      ,		size=_A				) for image in images]

       if do_rescale:
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						[self.rescale(image=_A      ,		scale=_A      ,		offset=_A				) for image in images]

       if do_normalize:
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						[self.normalize(image=_A      ,		mean=_A      ,		std=_A				) for image in images]

       if include_top:
         __SCREAMING_SNAKE_CASE     :					int			=						[self.normalize(image=_A      ,		mean=[0, 0, 0]      ,		std=_A				) for image in images]

       __SCREAMING_SNAKE_CASE     :					List[str]			=						[to_channel_dimension_format(_A      ,		_A				) for image in images]

       __SCREAMING_SNAKE_CASE     :					str			=						{'''pixel_values''': images}
       return BatchFeature(data=_A      ,		tensor_type=_A				)


from typing import TYPE_CHECKING

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


lowercase_											=	{
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	[
       """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LEDForConditionalGeneration""",
       """LEDForQuestionAnswering""",
       """LEDForSequenceClassification""",
       """LEDModel""",
       """LEDPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
   from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
   from .tokenization_led import LEDTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_led_fast import LEDTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_led import (
          LED_PRETRAINED_MODEL_ARCHIVE_LIST,
          LEDForConditionalGeneration,
          LEDForQuestionAnswering,
          LEDForSequenceClassification,
          LEDModel,
          LEDPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
   import sys

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