

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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)


if is_vision_available():
     import PIL

class 				a  (	UpperCAmelCase				):
      _lowercase    						=			["pixel_values"]


      def __init__(       self						,					A_ = True						,					A_ = None						,					A_ = PILImageResampling.BICUBIC						,					A_ = True						,					A_ = None						,					A_ = True						,					A_ = 1 / 255						,					A_ = True						,					A_ = None						,					A_ = None						,					A_ = True						,					**A_						,					):







          '''simple docstring'''


          super().__init__(**A_	)
          _UpperCAmelCase       :		Tuple 		=    size if size is not None else {"shortest_edge": 224}
          _UpperCAmelCase       :		Tuple 		=    get_size_dict(A_						,					default_to_square=A_	)
          _UpperCAmelCase       :		Any 		=    crop_size if crop_size is not None else {"height": 224, "width": 224}
          _UpperCAmelCase       :		Optional[Any] 		=    get_size_dict(A_						,					default_to_square=A_						,					param_name="crop_size"	)

          _UpperCAmelCase       :		Any 		=    do_resize
          _UpperCAmelCase       :		Dict 		=    size
          _UpperCAmelCase       :		int 		=    resample
          _UpperCAmelCase       :		str 		=    do_center_crop
          _UpperCAmelCase       :		Any 		=    crop_size
          _UpperCAmelCase       :		List[Any] 		=    do_rescale
          _UpperCAmelCase       :		List[Any] 		=    rescale_factor
          _UpperCAmelCase       :		List[str] 		=    do_normalize
          _UpperCAmelCase       :		Any 		=    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          _UpperCAmelCase       :		Union[str, Any] 		=    image_std if image_std is not None else OPENAI_CLIP_STD
          _UpperCAmelCase       :		List[str] 		=    do_convert_rgb


      def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = PILImageResampling.BICUBIC						,					A_ = None						,					**A_						,					):







          '''simple docstring'''


          _UpperCAmelCase       :		Optional[int] 		=    get_size_dict(A_						,					default_to_square=A_	)
          if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'	)
          _UpperCAmelCase       :		Optional[int] 		=    get_resize_output_image_size(A_						,					size=size["shortest_edge"]						,					default_to_square=A_	)
          return resize(A_						,					size=A_						,					resample=A_						,					data_format=A_						,					**A_	)


      def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = None						,					**A_						,					):







          '''simple docstring'''


          _UpperCAmelCase       :		List[str] 		=    get_size_dict(A_	)
          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(A_						,					size=(size["height"], size["width"])						,					data_format=A_						,					**A_	)


      def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = None						,					**A_						,					):







          '''simple docstring'''


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


      def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_ = None						,					**A_						,					):







          '''simple docstring'''


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





      def        _UpperCAmelCase				(       self						,					A_						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = ChannelDimension.FIRST						,					**A_						,					):







          '''simple docstring'''


          _UpperCAmelCase       :		List[Any] 		=    do_resize if do_resize is not None else self.do_resize
          _UpperCAmelCase       :		Dict 		=    size if size is not None else self.size
          _UpperCAmelCase       :		str 		=    get_size_dict(A_						,					param_name="size"						,					default_to_square=A_	)
          _UpperCAmelCase       :		Dict 		=    resample if resample is not None else self.resample
          _UpperCAmelCase       :		List[str] 		=    do_center_crop if do_center_crop is not None else self.do_center_crop
          _UpperCAmelCase       :		Optional[Any] 		=    crop_size if crop_size is not None else self.crop_size
          _UpperCAmelCase       :		Union[str, Any] 		=    get_size_dict(A_						,					param_name="crop_size"						,					default_to_square=A_	)
          _UpperCAmelCase       :		Any 		=    do_rescale if do_rescale is not None else self.do_rescale
          _UpperCAmelCase       :		Optional[Any] 		=    rescale_factor if rescale_factor is not None else self.rescale_factor
          _UpperCAmelCase       :		Optional[Any] 		=    do_normalize if do_normalize is not None else self.do_normalize
          _UpperCAmelCase       :		str 		=    image_mean if image_mean is not None else self.image_mean
          _UpperCAmelCase       :		Union[str, Any] 		=    image_std if image_std is not None else self.image_std
          _UpperCAmelCase       :		Dict 		=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          _UpperCAmelCase       :		str 		=    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:
              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:
              _UpperCAmelCase       :		Tuple 		=    [convert_to_rgb(A_	) for image in images]

          # All transformations expect numpy arrays.
          _UpperCAmelCase       :		Union[str, Any] 		=    [to_numpy_array(A_	) for image in images]

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

          if do_center_crop:
              _UpperCAmelCase       :		Any 		=    [self.center_crop(image=A_						,					size=A_	) for image in images]

          if do_rescale:
              _UpperCAmelCase       :		Optional[Any] 		=    [self.rescale(image=A_						,					scale=A_	) for image in images]

          if do_normalize:
              _UpperCAmelCase       :		Optional[Any] 		=    [self.normalize(image=A_						,					mean=A_						,					std=A_	) for image in images]

          _UpperCAmelCase       :		Dict 		=    [to_channel_dimension_format(A_						,					A_	) for image in images]

          _UpperCAmelCase       :		Tuple 		=    {"pixel_values": images}
          return BatchFeature(data=A_						,					tensor_type=A_	)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_        =      {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_        =      [
					    'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'NllbMoeForConditionalGeneration',
					    'NllbMoeModel',
					    'NllbMoePreTrainedModel',
					    'NllbMoeTop2Router',
					    'NllbMoeSparseMLP',
					]


if TYPE_CHECKING:
					from .configuration_nllb_moe import (
					    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    NllbMoeConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nllb_moe import (
										    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NllbMoeForConditionalGeneration,
										    NllbMoeModel,
										    NllbMoePreTrainedModel,
										    NllbMoeSparseMLP,
										    NllbMoeTopaRouter,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_        =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


