

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			:					int						=			logging.get_logger(__name__)





def 	__a     (      A__			:  Tuple			,  A__			:  List[Any]       ):
    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 	__a     (      A__			:  Dict			,  A__			:  Tuple       ):
    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 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      ["pixel_values"]





   def __init__(       self					:			Union[str, Any]     , __lowerCamelCase					:			Optional[Union[List[List[int]], np.ndarray]] = None     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			Dict[str, int] = None     , __lowerCamelCase					:			PILImageResampling = PILImageResampling.BILINEAR     , __lowerCamelCase					:			bool = True     , __lowerCamelCase					:			bool = True     , **__lowerCamelCase					:			Union[str, Any]     , ):
       super().__init__(**_lowercase			)
       SCREAMING_SNAKE_CASE										=   size if size is not None else {"height": 256, "width": 256}
       SCREAMING_SNAKE_CASE										=   get_size_dict(_lowercase			)
       SCREAMING_SNAKE_CASE										=   np.array(_lowercase			) 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      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			np.ndarray     , __lowerCamelCase					:			Dict[str, int]     , __lowerCamelCase					:			PILImageResampling = PILImageResampling.BILINEAR     , __lowerCamelCase					:			Optional[Union[str, ChannelDimension]] = None     , **__lowerCamelCase					:			str     , ):
       SCREAMING_SNAKE_CASE										=   get_size_dict(_lowercase			)
       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(
           _lowercase     , size=(size["height"], size["width"])     , resample=_lowercase     , data_format=_lowercase     , **_lowercase			)





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			np.ndarray     , __lowerCamelCase					:			Optional[Union[str, ChannelDimension]] = None     , ):
       SCREAMING_SNAKE_CASE										=   rescale(image=_lowercase     , scale=1 / 127.5     , data_format=_lowercase			)
       SCREAMING_SNAKE_CASE										=   image - 1
       return image





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			ImageInput     , __lowerCamelCase					:			bool = None     , __lowerCamelCase					:			Dict[str, int] = None     , __lowerCamelCase					:			PILImageResampling = None     , __lowerCamelCase					:			bool = None     , __lowerCamelCase					:			Optional[bool] = None     , __lowerCamelCase					:			Optional[Union[List[List[int]], np.ndarray]] = None     , __lowerCamelCase					:			Optional[Union[str, TensorType]] = None     , __lowerCamelCase					:			Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST     , **__lowerCamelCase					:			Dict     , ):
       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(_lowercase			)
       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(_lowercase			)

       SCREAMING_SNAKE_CASE										=   make_list_of_images(_lowercase			)

       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 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(_lowercase			) for image in images]

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

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

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

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

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

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





'''simple docstring'''
from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands





def 			lowerCamelCase(							)		->		int:
			A_									= ArgumentParser('Transformers CLI tool'       ,usage='transformers-cli <command> [<args>]'     )
			A_									= parser.add_subparsers(help='transformers-cli command helpers'     )

			# Register commands
			ConvertCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			DownloadCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			EnvironmentCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			RunCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			ServeCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			UserCommands.register_subcommand(SCREAMING_SNAKE_CASE_     )
			AddNewModelCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			AddNewModelLikeCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			LfsCommands.register_subcommand(SCREAMING_SNAKE_CASE_     )
			PTtoTFCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )

			# Let's go
			A_									= parser.parse_args()

			if not hasattr(SCREAMING_SNAKE_CASE_       ,'func'     ):
						parser.print_help()
						exit(1     )

			# Run
			A_									= args.func(SCREAMING_SNAKE_CASE_     )
			service.run()


if __name__ == "__main__":
							main()




