




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


_UpperCAmelCase		:  Any	=   logging.get_logger(__name__)




class       __lowerCAmelCase				(							lowerCAmelCase):
       _a           =  ['''pixel_values''']






       def __init__(      self:    str				,    _lowerCAmelCase:    bool = True				,    _lowerCAmelCase:    Dict[str, int] = None				,    _lowerCAmelCase:    PILImageResampling = PIL.Image.BICUBIC				,    _lowerCAmelCase:    bool = True				,    _lowerCAmelCase:    Dict[str, int] = None				,    _lowerCAmelCase:    Union[int, float] = 1 / 2_55				,    _lowerCAmelCase:    bool = True				,    _lowerCAmelCase:    bool = True				,    _lowerCAmelCase:    Optional[Union[float, List[float]]] = None				,    _lowerCAmelCase:    Optional[Union[float, List[float]]] = None				,    **_lowerCAmelCase:    Optional[int]				,    ):
         super().__init__(**_lowerCAmelCase )
         lowercase			:int							=  size if size is not None else {"height": 2_56, "width": 2_56}
         lowercase			:Union[str, Any]							=  get_size_dict(_lowerCAmelCase )
         lowercase			:Tuple							=  crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
         lowercase			:List[Any]							=  get_size_dict(_lowerCAmelCase				,    param_name="crop_size" )

         lowercase			:Dict							=  do_resize
         lowercase			:int							=  size
         lowercase			:Any							=  resample
         lowercase			:Optional[Any]							=  do_center_crop
         lowercase			:Any							=  crop_size
         lowercase			:List[Any]							=  do_rescale
         lowercase			:int							=  rescale_factor
         lowercase			:List[Any]							=  do_normalize
         lowercase			:Union[str, Any]							=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowercase			:Optional[Any]							=  image_std if image_std is not None else IMAGENET_STANDARD_STD






       def     SCREAMING_SNAKE_CASE			(      self:    Any				,    _lowerCAmelCase:    np.ndarray				,    _lowerCAmelCase:    Dict[str, int]				,    _lowerCAmelCase:    PILImageResampling = PIL.Image.BICUBIC				,    _lowerCAmelCase:    Optional[Union[str, ChannelDimension]] = None				,    **_lowerCAmelCase:    List[Any]				,    ):
         lowercase			:Any							=  get_size_dict(_lowerCAmelCase )
         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(
             _lowerCAmelCase				,    size=(size["height"], size["width"])				,    resample=_lowerCAmelCase				,    data_format=_lowerCAmelCase				,    **_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    np.ndarray				,    _lowerCAmelCase:    Dict[str, int]				,    _lowerCAmelCase:    Optional[Union[str, ChannelDimension]] = None				,    **_lowerCAmelCase:    Tuple				,    ):
         lowercase			:Dict							=  get_size_dict(_lowerCAmelCase )
         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(_lowerCAmelCase				,    size=(size["height"], size["width"])				,    data_format=_lowerCAmelCase				,    **_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any]				,    _lowerCAmelCase:    np.ndarray				,    _lowerCAmelCase:    Union[int, float]				,    _lowerCAmelCase:    Optional[Union[str, ChannelDimension]] = None				,    **_lowerCAmelCase:    Dict				,    ):
         return rescale(_lowerCAmelCase				,    scale=_lowerCAmelCase				,    data_format=_lowerCAmelCase				,    **_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str]				,    _lowerCAmelCase:    np.ndarray				,    _lowerCAmelCase:    Union[float, List[float]]				,    _lowerCAmelCase:    Union[float, List[float]]				,    _lowerCAmelCase:    Optional[Union[str, ChannelDimension]] = None				,    **_lowerCAmelCase:    List[Any]				,    ):
         return normalize(_lowerCAmelCase				,    mean=_lowerCAmelCase				,    std=_lowerCAmelCase				,    data_format=_lowerCAmelCase				,    **_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    ImageInput				,    _lowerCAmelCase:    bool = None				,    _lowerCAmelCase:    Dict[str, int] = None				,    _lowerCAmelCase:    List[str]=None				,    _lowerCAmelCase:    bool = None				,    _lowerCAmelCase:    Dict[str, int] = None				,    _lowerCAmelCase:    bool = None				,    _lowerCAmelCase:    float = None				,    _lowerCAmelCase:    bool = None				,    _lowerCAmelCase:    Optional[Union[float, List[float]]] = None				,    _lowerCAmelCase:    Optional[Union[float, List[float]]] = None				,    _lowerCAmelCase:    Optional[Union[str, TensorType]] = None				,    _lowerCAmelCase:    ChannelDimension = ChannelDimension.FIRST				,    **_lowerCAmelCase:    Optional[Any]				,    ):
         lowercase			:List[str]							=  do_resize if do_resize is not None else self.do_resize
         lowercase			:int							=  resample if resample is not None else self.resample
         lowercase			:Tuple							=  do_center_crop if do_center_crop is not None else self.do_center_crop
         lowercase			:Optional[Any]							=  do_rescale if do_rescale is not None else self.do_rescale
         lowercase			:Tuple							=  rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase			:List[str]							=  do_normalize if do_normalize is not None else self.do_normalize
         lowercase			:Any							=  image_mean if image_mean is not None else self.image_mean
         lowercase			:Any							=  image_std if image_std is not None else self.image_std

         lowercase			:List[str]							=  size if size is not None else self.size
         lowercase			:str							=  get_size_dict(_lowerCAmelCase )
         lowercase			:int							=  crop_size if crop_size is not None else self.crop_size
         lowercase			:int							=  get_size_dict(_lowerCAmelCase				,    param_name="crop_size" )

         lowercase			:Optional[Any]							=  make_list_of_images(_lowerCAmelCase )

         if not valid_images(_lowerCAmelCase ):
           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.
         lowercase			:Tuple							=  [to_numpy_array(_lowerCAmelCase ) for image in images]

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

         if do_center_crop:
           lowercase			:Optional[int]							=  [self.center_crop(image=_lowerCAmelCase				,    size=_lowerCAmelCase ) for image in images]

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

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

         lowercase			:int							=  [to_channel_dimension_format(_lowerCAmelCase				,    _lowerCAmelCase ) for image in images]

         lowercase			:List[str]							=  {"pixel_values": images}
         return BatchFeature(data=_lowerCAmelCase				,    tensor_type=_lowerCAmelCase )









from typing import TYPE_CHECKING

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


_UpperCAmelCase		:  List[str]	=   {
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Optional[int]	=   ["BlenderbotTokenizerFast"]

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


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


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Optional[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

     _UpperCAmelCase		:  Union[str, Any]	=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



