





from typing import TYPE_CHECKING

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


UpperCAmelCase_							=				{
    """configuration_blenderbot_small""": [
        """BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlenderbotSmallConfig""",
        """BlenderbotSmallOnnxConfig""",
    ],
    """tokenization_blenderbot_small""": ["""BlenderbotSmallTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				["""BlenderbotSmallTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BlenderbotSmallForCausalLM""",
							    """BlenderbotSmallForConditionalGeneration""",
							    """BlenderbotSmallModel""",
							    """BlenderbotSmallPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """TFBlenderbotSmallForConditionalGeneration""",
							    """TFBlenderbotSmallModel""",
							    """TFBlenderbotSmallPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """FlaxBlenderbotSmallForConditionalGeneration""",
							    """FlaxBlenderbotSmallModel""",
							    """FlaxBlenderbotSmallPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_blenderbot_small import (
							    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotSmallConfig,
							    BlenderbotSmallOnnxConfig,
							)
							from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot_small import (
														    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotSmallForCausalLM,
														    BlenderbotSmallForConditionalGeneration,
														    BlenderbotSmallModel,
														    BlenderbotSmallPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot_small import (
														    TFBlenderbotSmallForConditionalGeneration,
														    TFBlenderbotSmallModel,
														    TFBlenderbotSmallPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot_small import (
														    FlaxBlenderbotSmallForConditionalGeneration,
														    FlaxBlenderbotSmallModel,
														    FlaxBlenderbotSmallPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase_							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
							import PIL

UpperCAmelCase_							=				logging.get_logger(__name__)





def  __magic_name__  (				lowercase				)				-> List[List[ImageInput]]:

							"""simple docstring"""



							if isinstance(lowercase				,       (list, tuple)				) and isinstance(videos[0]				,       (list, tuple)				) and is_valid_image(videos[0][0]				):
														return videos

							elif isinstance(lowercase				,       (list, tuple)				) and is_valid_image(videos[0]				):
														return [videos]

							elif is_valid_image(lowercase				):
														return [[videos]]

							raise ValueError(f"""Could not make batched video from {videos}"""				)




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       List[Any]       =	["""pixel_values"""]






		def __init__(							self,   snake_case__ = True,   snake_case__ = None,   snake_case__ = PILImageResampling.BILINEAR,   snake_case__ = True,   snake_case__ = None,   snake_case__ = True,   snake_case__ = 1 / 2_55,   snake_case__ = True,   snake_case__ = True,   snake_case__ = None,   snake_case__ = None,   **snake_case__,   )       -> None:






									"""simple docstring"""
									super().__init__(**snake_case__  )
									lowercase_	:   str  				=     size if size is not None else {"""shortest_edge""": 2_56}
									lowercase_	:   int  				=     get_size_dict(snake_case__,   default_to_square=snake_case__  )
									lowercase_	:   int  				=     crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
									lowercase_	:   Optional[int]  				=     get_size_dict(snake_case__,   param_name="""crop_size"""  )

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






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = PILImageResampling.BILINEAR,   snake_case__ = None,   **snake_case__,   )       -> np.ndarray:






									"""simple docstring"""
									lowercase_	:   int  				=     get_size_dict(snake_case__,   default_to_square=snake_case__  )
									if "shortest_edge" in size:
																lowercase_	:   Union[str, Any]  				=     get_resize_output_image_size(snake_case__,   size["""shortest_edge"""],   default_to_square=snake_case__  )
									elif "height" in size and "width" in size:
																lowercase_	:   Dict  				=     (size["""height"""], size["""width"""])
									else:
																raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""  )
									return resize(snake_case__,   size=snake_case__,   resample=snake_case__,   data_format=snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = None,   **snake_case__,   )       -> np.ndarray:






									"""simple docstring"""
									lowercase_	:   List[Any]  				=     get_size_dict(snake_case__  )
									if "height" not in size or "width" not in size:
																raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""  )
									return center_crop(snake_case__,   size=(size["""height"""], size["""width"""]),   data_format=snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = True,   snake_case__ = None,   **snake_case__,   )       -> str:






									"""simple docstring"""
									lowercase_	:   Dict  				=     image.astype(np.floataa  )
									if offset:
																lowercase_	:   Optional[Any]  				=     image - (scale / 2)
									return rescale(snake_case__,   scale=snake_case__,   data_format=snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = None,   **snake_case__,   )       -> np.ndarray:






									"""simple docstring"""
									return normalize(snake_case__,   mean=snake_case__,   std=snake_case__,   data_format=snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = ChannelDimension.FIRST,   )       -> np.ndarray:






									"""simple docstring"""
									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."""  )

									if offset and not do_rescale:
																raise ValueError("""For offset, do_rescale must also be set to True."""  )

									# All transformations expect numpy arrays.
									lowercase_	:   Optional[int]  				=     to_numpy_array(snake_case__  )

									if do_resize:
																lowercase_	:   Dict  				=     self.resize(image=snake_case__,   size=snake_case__,   resample=snake_case__  )

									if do_center_crop:
																lowercase_	:   Optional[int]  				=     self.center_crop(snake_case__,   size=snake_case__  )

									if do_rescale:
																lowercase_	:   Dict  				=     self.rescale(image=snake_case__,   scale=snake_case__,   offset=snake_case__  )

									if do_normalize:
																lowercase_	:   Optional[Any]  				=     self.normalize(image=snake_case__,   mean=snake_case__,   std=snake_case__  )

									lowercase_	:   Tuple  				=     to_channel_dimension_format(snake_case__,   snake_case__  )
									return image






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = ChannelDimension.FIRST,   **snake_case__,   )       -> PIL.Image.Image:






									"""simple docstring"""
									lowercase_	:   Any  				=     do_resize if do_resize is not None else self.do_resize
									lowercase_	:   str  				=     resample if resample is not None else self.resample
									lowercase_	:   List[str]  				=     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_	:   str  				=     rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase_	:   List[Any]  				=     offset if offset is not None else self.offset
									lowercase_	:   Optional[Any]  				=     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_	:   Optional[Any]  				=     image_std if image_std is not None else self.image_std

									lowercase_	:   Optional[Any]  				=     size if size is not None else self.size
									lowercase_	:   Dict  				=     get_size_dict(snake_case__,   default_to_square=snake_case__  )
									lowercase_	:   Union[str, Any]  				=     crop_size if crop_size is not None else self.crop_size
									lowercase_	:   Tuple  				=     get_size_dict(snake_case__,   param_name="""crop_size"""  )

									if not valid_images(snake_case__  ):
																raise ValueError(
																    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																    """torch.Tensor, tf.Tensor or jax.ndarray."""  )

									lowercase_	:   Optional[Any]  				=     make_batched(snake_case__  )

									lowercase_	:   Optional[Any]  				=     [
									    [
									        self._preprocess_image(
									            image=snake_case__,   do_resize=snake_case__,   size=snake_case__,   resample=snake_case__,   do_center_crop=snake_case__,   crop_size=snake_case__,   do_rescale=snake_case__,   rescale_factor=snake_case__,   offset=snake_case__,   do_normalize=snake_case__,   image_mean=snake_case__,   image_std=snake_case__,   data_format=snake_case__,   )
									        for img in video
									    ]
									    for video in videos
									]

									lowercase_	:   List[str]  				=     {"""pixel_values""": videos}
									return BatchFeature(data=snake_case__,   tensor_type=snake_case__  )