





'''simple docstring'''


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,
    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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase :					List[str] 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      ["pixel_values"]





     def __init__(			self		: List[str]     ,				lowercase		: bool = True     ,				lowercase		: Optional[Dict[str, int]] = None     ,				lowercase		: PILImageResampling = PILImageResampling.BICUBIC     ,				lowercase		: bool = True     ,				lowercase		: bool = True     ,				lowercase		: Union[int, float] = 1 / 255     ,				lowercase		: Dict[str, int] = None     ,				lowercase		: bool = True     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				lowercase		: Optional[Union[float, List[float]]] = None     ,				**lowercase		: int     ,				):





        """simple docstring"""




        super().__init__(**lowercase						)
        lowercase_      :int         =			size if size is not None else {"height": 224, "width": 224}
        lowercase_      :Union[str, Any]         =			get_size_dict(lowercase						)
        lowercase_      :Optional[int]         =			crop_size if crop_size is not None else {"height": 224, "width": 224}
        lowercase_      :Any         =			get_size_dict(lowercase     ,				default_to_square=lowercase     ,				param_name="crop_size"						)

        lowercase_      :Optional[int]         =			do_resize
        lowercase_      :Tuple         =			do_rescale
        lowercase_      :Optional[Any]         =			do_normalize
        lowercase_      :List[Any]         =			do_center_crop
        lowercase_      :Optional[Any]         =			crop_size
        lowercase_      :int         =			size
        lowercase_      :Union[str, Any]         =			resample
        lowercase_      :Any         =			rescale_factor
        lowercase_      :Any         =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowercase_      :List[Any]         =			image_std if image_std is not None else IMAGENET_DEFAULT_STD





     def 							lowercase__				(			self		: int     ,				lowercase		: np.ndarray     ,				lowercase		: Dict[str, int]     ,				lowercase		: PILImageResampling = PILImageResampling.BILINEAR     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: Tuple     ,				):





        """simple docstring"""




        lowercase_      :Tuple         =			get_size_dict(lowercase						)

        if "shortest_edge" in size:
           lowercase_      :List[str]         =			get_resize_output_image_size(lowercase     ,				size=size["shortest_edge"]     ,				default_to_square=lowercase						)
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
           lowercase_      :Tuple         =			(size["height"], size["width"])
        else:
           raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'						)
        return resize(lowercase     ,				size=lowercase     ,				resample=lowercase     ,				data_format=lowercase     ,				**lowercase						)





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





        """simple docstring"""




        lowercase_      :Any         =			get_size_dict(lowercase						)
        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(lowercase     ,				size=(size["height"], size["width"])     ,				data_format=lowercase     ,				**lowercase						)





     def 							lowercase__				(			self		: Union[str, Any]     ,				lowercase		: np.ndarray     ,				lowercase		: float     ,				lowercase		: Optional[Union[str, ChannelDimension]] = None     ,				**lowercase		: Tuple						):





        """simple docstring"""




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





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





        """simple docstring"""




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







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





        """simple docstring"""




        lowercase_      :Any         =			do_resize if do_resize is not None else self.do_resize
        lowercase_      :Any         =			do_rescale if do_rescale is not None else self.do_rescale
        lowercase_      :Tuple         =			do_normalize if do_normalize is not None else self.do_normalize
        lowercase_      :str         =			do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_      :str         =			crop_size if crop_size is not None else self.crop_size
        lowercase_      :str         =			get_size_dict(lowercase     ,				param_name="crop_size"     ,				default_to_square=lowercase						)
        lowercase_      :Tuple         =			resample if resample is not None else self.resample
        lowercase_      :Dict         =			rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_      :Union[str, Any]         =			image_mean if image_mean is not None else self.image_mean
        lowercase_      :Union[str, Any]         =			image_std if image_std is not None else self.image_std

        lowercase_      :Union[str, Any]         =			size if size is not None else self.size
        lowercase_      :str         =			get_size_dict(lowercase						)

        if not is_batched(lowercase						):
           lowercase_      :str         =			[images]

        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:
           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."						)

        # All transformations expect numpy arrays.
        lowercase_      :List[Any]         =			[to_numpy_array(lowercase						) for image in images]

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

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

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

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

        lowercase_      :List[Any]         =			[to_channel_dimension_format(lowercase     ,				lowercase						) for image in images]

        lowercase_      :Any         =			{"pixel_values": images}
        return BatchFeature(data=lowercase     ,				tensor_type=lowercase						)









'''simple docstring'''


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase :					Dict 							={
    '''configuration_xmod''': [
        '''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XmodConfig''',
        '''XmodOnnxConfig''',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					Union[str, Any] 							=[
     '''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''XmodForCausalLM''',
     '''XmodForMaskedLM''',
     '''XmodForMultipleChoice''',
     '''XmodForQuestionAnswering''',
     '''XmodForSequenceClassification''',
     '''XmodForTokenClassification''',
     '''XmodModel''',
     '''XmodPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_xmod import (
      XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
      XmodForCausalLM,
      XmodForMaskedLM,
      XmodForMultipleChoice,
      XmodForQuestionAnswering,
      XmodForSequenceClassification,
      XmodForTokenClassification,
      XmodModel,
      XmodPreTrainedModel,
  )

else:
 import sys

 lowerCAmelCase :					Optional[Any] 							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


