




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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


__A						:							Tuple							=  logging.get_logger(__name__)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					b.T
			SCREAMING_SNAKE_CASE           =					np.sum(np.square(SCREAMING_SNAKE_CASE   )					,	axis=1   )
			SCREAMING_SNAKE_CASE           =					np.sum(np.square(SCREAMING_SNAKE_CASE   )					,	axis=0   )
			SCREAMING_SNAKE_CASE           =					np.matmul(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					aa[:, None] - 2 * ab + ba[None, :]
			return d
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					x.reshape(-1					,	3   )
			SCREAMING_SNAKE_CASE           =					squared_euclidean_distance(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			return np.argmin(SCREAMING_SNAKE_CASE					,	axis=1   )




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     ['''pixel_values''']





							def __init__(					self       :	str       ,     a       :	Optional[Union[List[List[int]], np.ndarray]] = None       ,     a       :	bool = True       ,     a       :	Dict[str, int] = None       ,     a       :	PILImageResampling = PILImageResampling.BILINEAR       ,     a       :	bool = True       ,     a       :	bool = True       ,     **a       :	List[str]       ,     )			->					None:
										super().__init__(**a			)
										SCREAMING_SNAKE_CASE           =					size if size is not None else {"""height""": 256, """width""": 256}
										SCREAMING_SNAKE_CASE           =					get_size_dict(a			)
										SCREAMING_SNAKE_CASE           =					np.array(a			) if clusters is not None else None
										SCREAMING_SNAKE_CASE           =					do_resize
										SCREAMING_SNAKE_CASE           =					size
										SCREAMING_SNAKE_CASE           =					resample
										SCREAMING_SNAKE_CASE           =					do_normalize
										SCREAMING_SNAKE_CASE           =					do_color_quantize





							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	np.ndarray       ,     a       :	Dict[str, int]       ,     a       :	PILImageResampling = PILImageResampling.BILINEAR       ,     a       :	Optional[Union[str, ChannelDimension]] = None       ,     **a       :	List[Any]       ,     )			->					np.ndarray:
										SCREAMING_SNAKE_CASE           =					get_size_dict(a			)
										if "height" not in size or "width" not in size:
													raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""			)
										return resize(
										    a       ,     size=(size["""height"""], size["""width"""])       ,     resample=a       ,     data_format=a       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	np.ndarray       ,     a       :	Optional[Union[str, ChannelDimension]] = None       ,     )			->					np.ndarray:
										SCREAMING_SNAKE_CASE           =					rescale(image=a       ,     scale=1 / 127.5       ,     data_format=a			)
										SCREAMING_SNAKE_CASE           =					image - 1
										return image







							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	ImageInput       ,     a       :	bool = None       ,     a       :	Dict[str, int] = None       ,     a       :	PILImageResampling = None       ,     a       :	bool = None       ,     a       :	Optional[bool] = None       ,     a       :	Optional[Union[List[List[int]], np.ndarray]] = None       ,     a       :	Optional[Union[str, TensorType]] = None       ,     a       :	Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST       ,     **a       :	List[Any]       ,     )			->					PIL.Image.Image:
										SCREAMING_SNAKE_CASE           =					do_resize if do_resize is not None else self.do_resize
										SCREAMING_SNAKE_CASE           =					size if size is not None else self.size
										SCREAMING_SNAKE_CASE           =					get_size_dict(a			)
										SCREAMING_SNAKE_CASE           =					resample if resample is not None else self.resample
										SCREAMING_SNAKE_CASE           =					do_normalize if do_normalize is not None else self.do_normalize
										SCREAMING_SNAKE_CASE           =					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
										SCREAMING_SNAKE_CASE           =					clusters if clusters is not None else self.clusters
										SCREAMING_SNAKE_CASE           =					np.array(a			)

										SCREAMING_SNAKE_CASE           =					make_list_of_images(a			)

										if not valid_images(a			):
													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_color_quantize and clusters is None:
													raise ValueError("""Clusters must be specified if do_color_quantize is True."""			)

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

										if do_resize:
													SCREAMING_SNAKE_CASE           =					[self.resize(image=a       ,     size=a       ,     resample=a			) for image in images]

										if do_normalize:
													SCREAMING_SNAKE_CASE           =					[self.normalize(image=a			) for image in images]

										if do_color_quantize:
													SCREAMING_SNAKE_CASE           =					[to_channel_dimension_format(a       ,     ChannelDimension.LAST			) for image in images]
													# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
													SCREAMING_SNAKE_CASE           =					np.array(a			)
													SCREAMING_SNAKE_CASE           =					color_quantize(a       ,     a			).reshape(images.shape[:-1]			)

													# flatten to (batch_size, height*width)
													SCREAMING_SNAKE_CASE           =					images.shape[0]
													SCREAMING_SNAKE_CASE           =					images.reshape(a       ,     -1			)

													# We need to convert back to a list of images to keep consistent behaviour across processors.
													SCREAMING_SNAKE_CASE           =					list(a			)
										else:
													SCREAMING_SNAKE_CASE           =					[to_channel_dimension_format(a       ,     a			) for image in images]

										SCREAMING_SNAKE_CASE           =					{"""input_ids""": images}
										return BatchFeature(data=a       ,     tensor_type=a			)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A						:							Dict							=  {
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A						:							List[Any]							=  [
		    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """NllbMoeForConditionalGeneration""",
		    """NllbMoeModel""",
		    """NllbMoePreTrainedModel""",
		    """NllbMoeTop2Router""",
		    """NllbMoeSparseMLP""",
		]


if TYPE_CHECKING:
		from .configuration_nllb_moe import (
		    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    NllbMoeConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_nllb_moe import (
				    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    NllbMoeForConditionalGeneration,
				    NllbMoeModel,
				    NllbMoePreTrainedModel,
				    NllbMoeSparseMLP,
				    NllbMoeTopaRouter,
				)


else:
		import sys

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






