



from typing import Dict, Iterable, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a__      :				List[Any]   	=			logging.get_logger(__name__)



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Union[str, Any]       				=    ["pixel_values"]


   def __init__(						self  :			str			,				lowerCAmelCase  :			bool = True			,				lowerCAmelCase  :			Dict[str, int] = None			,				lowerCAmelCase  :			PILImageResampling = PILImageResampling.BICUBIC			,				lowerCAmelCase  :			bool = True			,				lowerCAmelCase  :			Dict[str, int] = None			,				lowerCAmelCase  :			bool = True			,				lowerCAmelCase  :			Union[int, float] = 1 / 2_55			,				lowerCAmelCase  :			bool = True			,				lowerCAmelCase  :			Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN			,				lowerCAmelCase  :			Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD			,				**lowerCAmelCase  :			Optional[Any]			,				)	->				None:




         """simple docstring"""

         super().__init__(**__UpperCamelCase)
         lowercase__     =  size if size is not None else {'shortest_edge': 2_24}
         lowercase__     =  get_size_dict(__UpperCamelCase			,				default_to_square=__UpperCamelCase)
         lowercase__     =  crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
         lowercase__     =  get_size_dict(__UpperCamelCase			,				param_name='crop_size')

         lowercase__     =  do_resize
         lowercase__     =  size
         lowercase__     =  resample
         lowercase__     =  do_center_crop
         lowercase__     =  crop_size
         lowercase__     =  do_rescale
         lowercase__     =  rescale_factor
         lowercase__     =  do_normalize
         lowercase__     =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         lowercase__     =  image_std if image_std is not None else IMAGENET_DEFAULT_STD


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			np.ndarray			,				lowerCAmelCase  :			Dict[str, int]			,				lowerCAmelCase  :			PILImageResampling = PILImageResampling.BICUBIC			,				lowerCAmelCase  :			Optional[Union[str, ChannelDimension]] = None			,				**lowerCAmelCase  :			str			,				)	->				np.ndarray:




         """simple docstring"""

         lowercase__     =  get_size_dict(__UpperCamelCase			,				default_to_square=__UpperCamelCase)
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
               lowercase__     =  int((2_56 / 2_24) * size['shortest_edge'])
               lowercase__     =  get_resize_output_image_size(__UpperCamelCase			,				size=__UpperCamelCase			,				default_to_square=__UpperCamelCase)
               lowercase__     =  {'height': output_size[0], 'width': output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
               raise ValueError(
                   f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}''')
         return resize(
             __UpperCamelCase			,				size=(size_dict['height'], size_dict['width'])			,				resample=__UpperCamelCase			,				data_format=__UpperCamelCase			,				**__UpperCamelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			np.ndarray			,				lowerCAmelCase  :			Dict[str, int]			,				lowerCAmelCase  :			Optional[Union[str, ChannelDimension]] = None			,				**lowerCAmelCase  :			List[Any]			,				)	->				np.ndarray:




         """simple docstring"""

         lowercase__     =  get_size_dict(__UpperCamelCase)
         if "height" not in size or "width" not in size:
               raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}''')
         return center_crop(__UpperCamelCase			,				size=(size['height'], size['width'])			,				data_format=__UpperCamelCase			,				**__UpperCamelCase)


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			np.ndarray			,				lowerCAmelCase  :			Union[int, float]			,				lowerCAmelCase  :			Optional[Union[str, ChannelDimension]] = None			,				**lowerCAmelCase  :			int			,				)	->				np.ndarray:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			np.ndarray			,				lowerCAmelCase  :			Union[float, List[float]]			,				lowerCAmelCase  :			Union[float, List[float]]			,				lowerCAmelCase  :			Optional[Union[str, ChannelDimension]] = None			,				**lowerCAmelCase  :			Optional[Any]			,				)	->				np.ndarray:




         """simple docstring"""

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



   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			ImageInput			,				lowerCAmelCase  :			Optional[bool] = None			,				lowerCAmelCase  :			Optional[Dict[str, int]] = None			,				lowerCAmelCase  :			PILImageResampling = None			,				lowerCAmelCase  :			Optional[bool] = None			,				lowerCAmelCase  :			Optional[Dict[str, int]] = None			,				lowerCAmelCase  :			Optional[bool] = None			,				lowerCAmelCase  :			Optional[float] = None			,				lowerCAmelCase  :			Optional[bool] = None			,				lowerCAmelCase  :			Optional[Union[float, Iterable[float]]] = None			,				lowerCAmelCase  :			Optional[Union[float, Iterable[float]]] = None			,				lowerCAmelCase  :			Optional[TensorType] = None			,				lowerCAmelCase  :			ChannelDimension = ChannelDimension.FIRST			,				**lowerCAmelCase  :			Dict			,				)	->				BatchFeature:




         """simple docstring"""

         lowercase__     =  do_resize if do_resize is not None else self.do_resize
         lowercase__     =  resample if resample is not None else self.resample
         lowercase__     =  do_center_crop if do_center_crop is not None else self.do_center_crop
         lowercase__     =  do_rescale if do_rescale is not None else self.do_rescale
         lowercase__     =  rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase__     =  do_normalize if do_normalize is not None else self.do_normalize
         lowercase__     =  image_mean if image_mean is not None else self.image_mean
         lowercase__     =  image_std if image_std is not None else self.image_std

         lowercase__     =  size if size is not None else self.size
         lowercase__     =  get_size_dict(__UpperCamelCase			,				default_to_square=__UpperCamelCase)
         lowercase__     =  crop_size if crop_size is not None else self.crop_size
         lowercase__     =  get_size_dict(__UpperCamelCase			,				param_name='crop_size')

         lowercase__     =  make_list_of_images(__UpperCamelCase)

         if not valid_images(__UpperCamelCase):
               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.')

         # All transformations expect numpy arrays.
         lowercase__     =  [to_numpy_array(__UpperCamelCase) for image in images]

         if do_resize:
               lowercase__     =  [self.resize(__UpperCamelCase			,				__UpperCamelCase			,				__UpperCamelCase) for image in images]

         if do_center_crop:
               lowercase__     =  [self.center_crop(__UpperCamelCase			,				__UpperCamelCase) for image in images]

         if do_rescale:
               lowercase__     =  [self.rescale(__UpperCamelCase			,				__UpperCamelCase) for image in images]

         if do_normalize:
               lowercase__     =  [self.normalize(__UpperCamelCase			,				__UpperCamelCase			,				__UpperCamelCase) for image in images]

         lowercase__     =  [to_channel_dimension_format(__UpperCamelCase			,				__UpperCamelCase) for image in images]

         lowercase__     =  {'pixel_values': images}
         return BatchFeature(data=__UpperCamelCase			,				tensor_type=__UpperCamelCase)











from typing import TYPE_CHECKING

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


a__      :				Optional[int]   	=			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Any   	=			["BlenderbotTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Dict   	=			[
           "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BlenderbotForCausalLM",
           "BlenderbotForConditionalGeneration",
           "BlenderbotModel",
           "BlenderbotPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				List[str]   	=			[
           "TFBlenderbotForConditionalGeneration",
           "TFBlenderbotModel",
           "TFBlenderbotPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				int   	=			[
           "FlaxBlenderbotForConditionalGeneration",
           "FlaxBlenderbotModel",
           "FlaxBlenderbotPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_blenderbot import (
           BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BlenderbotConfig,
           BlenderbotOnnxConfig,
       )
       from .tokenization_blenderbot import BlenderbotTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_blenderbot import (
                  BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BlenderbotForCausalLM,
                  BlenderbotForConditionalGeneration,
                  BlenderbotModel,
                  BlenderbotPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_blenderbot import (
                  TFBlenderbotForConditionalGeneration,
                  TFBlenderbotModel,
                  TFBlenderbotPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_blenderbot import (
                  FlaxBlenderbotForConditionalGeneration,
                  FlaxBlenderbotModel,
                  FlaxBlenderbotPreTrainedModel,
              )

else:
       import sys

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