

"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_     :			Any	  =			logging.get_logger(__name__)


class 		_A       (			__SCREAMING_SNAKE_CASE					):
      __a								=	['pixel_values']





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = 32    ,						SCREAMING_SNAKE_CASE__=PILImageResampling.BILINEAR    ,						SCREAMING_SNAKE_CASE__ = True    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					Dict:
         lowerCamelCase__						=			do_resize
         lowerCamelCase__						=			do_rescale
         lowerCamelCase__						=			size_divisor
         lowerCamelCase__						=			resample
         super().__init__(**_a		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						**SCREAMING_SNAKE_CASE__		)   ->					List[str]:
         lowerCamelCase__						=			get_image_size(_a		)
         # Rounds the height and width down to the closest multiple of size_divisor
         lowerCamelCase__						=			height // size_divisor * size_divisor
         lowerCamelCase__						=			width // size_divisor * size_divisor
         lowerCamelCase__						=			resize(_a    ,						(new_h, new_w)    ,						resample=_a    ,						data_format=_a    ,						**_a		)
         return image





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						**SCREAMING_SNAKE_CASE__		)   ->					Optional[int]:
         return rescale(image=_a    ,						scale=_a    ,						data_format=_a    ,						**_a		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					Dict:
         lowerCamelCase__						=			do_resize if do_resize is not None else self.do_resize
         lowerCamelCase__						=			do_rescale if do_rescale is not None else self.do_rescale
         lowerCamelCase__						=			size_divisor if size_divisor is not None else self.size_divisor
         lowerCamelCase__						=			resample if resample is not None else self.resample

         if do_resize and size_divisor is None:
            raise ValueError("size_divisor is required for resizing"		)

         lowerCamelCase__						=			make_list_of_images(_a		)

         if not valid_images(_a		):
            raise ValueError("Invalid image(s)"		)

         # All transformations expect numpy arrays.
         lowerCamelCase__						=			[to_numpy_array(_a		) for img in images]

         if do_resize:
            lowerCamelCase__						=			[self.resize(_a    ,						size_divisor=_a    ,						resample=_a		) for image in images]

         if do_rescale:
            lowerCamelCase__						=			[self.rescale(_a    ,						scale=1 / 255		) for image in images]

         lowerCamelCase__						=			[to_channel_dimension_format(_a    ,						_a		) for image in images]

         lowerCamelCase__						=			{"""pixel_values""": images}
         return BatchFeature(data=_a    ,						tensor_type=_a		)







"""simple docstring"""




from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_     :			List[Any]	  =			{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			[
         '''RagModel''',
         '''RagPreTrainedModel''',
         '''RagSequenceForGeneration''',
         '''RagTokenForGeneration''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			str	  =			[
         '''TFRagModel''',
         '''TFRagPreTrainedModel''',
         '''TFRagSequenceForGeneration''',
         '''TFRagTokenForGeneration''',
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

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




