

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


_lowerCamelCase								=		logging.get_logger(__name__)



class 		a_	(							lowerCamelCase__					):



      """simple docstring"""







      __UpperCAmelCase      		=				['pixel_values']
      def __init__(				self					:							Optional[Any]      ,snake_case					:							List[Any] = True      ,snake_case					:							Tuple = None      ,snake_case					:							int = PIL.Image.NEAREST      ,snake_case					:							Union[str, Any] = False      ,snake_case					:							Dict = None      ,snake_case					:							str = 1 / 255      ,snake_case					:							Optional[Any] = False      ,snake_case					:							Tuple = True      ,snake_case					:							Union[str, Any] = True      ,snake_case					:							Any = None      ,snake_case					:							Optional[Any] = None      ,snake_case					:							Dict = True      ,**snake_case					:							Any      ,):
           super().__init__(**lowercase_    )
           SCREAMING_SNAKE_CASE										=size if size is not None else {'height': 346, 'width': 346}
           SCREAMING_SNAKE_CASE										=get_size_dict(lowercase_    )
           SCREAMING_SNAKE_CASE										=crop_size if crop_size is not None else {'height': 289, 'width': 289}
           SCREAMING_SNAKE_CASE										=get_size_dict(lowercase_      ,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										=rescale_offset
           SCREAMING_SNAKE_CASE										=do_normalize
           SCREAMING_SNAKE_CASE										=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           SCREAMING_SNAKE_CASE										=image_std if image_std is not None else IMAGENET_STANDARD_STD
           SCREAMING_SNAKE_CASE										=include_top
      def 	_lowerCAmelCase					(				self					:							str      ,snake_case					:							Tuple      ,snake_case					:							List[str]      ,snake_case					:							int = PIL.Image.NEAREST      ,snake_case					:							Any = None      ,**snake_case					:							Tuple      ,):
           SCREAMING_SNAKE_CASE										=get_size_dict(lowercase_    )
           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(
               lowercase_      ,size=(size['height'], size['width'])      ,resample=lowercase_      ,data_format=lowercase_      ,**lowercase_    )
      def 	_lowerCAmelCase					(				self					:							Optional[int]      ,snake_case					:							List[Any]      ,snake_case					:							Optional[Any]      ,snake_case					:							List[Any] = None      ,**snake_case					:							int      ,):
           SCREAMING_SNAKE_CASE										=get_size_dict(lowercase_    )
           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(lowercase_      ,size=(size['height'], size['width'])      ,data_format=lowercase_      ,**lowercase_    )
      def 	_lowerCAmelCase					(				self					:							Any      ,snake_case					:							Union[str, Any]      ,snake_case					:							int      ,snake_case					:							int = True      ,snake_case					:							List[str] = None      ,**snake_case					:							Dict      ,):






           if offset:
                SCREAMING_SNAKE_CASE										=(image - 127.5) * scale
                if data_format is not None:
                     SCREAMING_SNAKE_CASE										=to_channel_dimension_format(lowercase_      ,lowercase_    )
                SCREAMING_SNAKE_CASE										=rescaled_image.astype(np.floataa    )
           else:
                SCREAMING_SNAKE_CASE										=rescale(lowercase_      ,scale=lowercase_      ,data_format=lowercase_      ,**lowercase_    )
           return rescaled_image
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]      ,snake_case					:							Dict      ,snake_case					:							Dict      ,snake_case					:							List[str]      ,snake_case					:							List[Any] = None      ,**snake_case					:							Dict      ,):
           return normalize(lowercase_      ,mean=lowercase_      ,std=lowercase_      ,data_format=lowercase_      ,**lowercase_    )


      def 	_lowerCAmelCase					(				self					:							Optional[int]      ,snake_case					:							str      ,snake_case					:							str = None      ,snake_case					:							Any = None      ,snake_case					:							str=None      ,snake_case					:							Tuple = None      ,snake_case					:							Dict = None      ,snake_case					:							Tuple = None      ,snake_case					:							List[str] = None      ,snake_case					:							Any = None      ,snake_case					:							str = None      ,snake_case					:							Any = None      ,snake_case					:							int = None      ,snake_case					:							Optional[int] = None      ,snake_case					:							Optional[int] = None      ,snake_case					:							List[Any] = ChannelDimension.FIRST      ,**snake_case					:							Union[str, Any]      ,):
           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										=rescale_offset if rescale_offset is not None else self.rescale_offset
           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										=include_top if include_top is not None else self.include_top

           SCREAMING_SNAKE_CASE										=size if size is not None else self.size
           SCREAMING_SNAKE_CASE										=get_size_dict(lowercase_    )
           SCREAMING_SNAKE_CASE										=crop_size if crop_size is not None else self.crop_size
           SCREAMING_SNAKE_CASE										=get_size_dict(lowercase_      ,param_name='crop_size'    )

           SCREAMING_SNAKE_CASE										=make_list_of_images(lowercase_    )

           if not valid_images(lowercase_    ):
                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										=[to_numpy_array(lowercase_    ) for image in images]

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

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

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

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

           if include_top:
                SCREAMING_SNAKE_CASE										=[self.normalize(image=lowercase_      ,mean=[0, 0, 0]      ,std=lowercase_    ) for image in images]

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

           SCREAMING_SNAKE_CASE										={'pixel_values': images}
           return BatchFeature(data=lowercase_      ,tensor_type=lowercase_    )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowerCamelCase								={
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase								=[
     "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
     "XLMProphetNetDecoder",
     "XLMProphetNetEncoder",
     "XLMProphetNetForCausalLM",
     "XLMProphetNetForConditionalGeneration",
     "XLMProphetNetModel",
     "XLMProphetNetPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xlm_prophetnet import (
      XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      XLMProphetNetDecoder,
      XLMProphetNetEncoder,
      XLMProphetNetForCausalLM,
      XLMProphetNetForConditionalGeneration,
      XLMProphetNetModel,
      XLMProphetNetPreTrainedModel,
  )

else:
 import sys

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

