

'''simple docstring'''



from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
		import torch


__snake_case			:     Optional[int]										=      logging.get_logger(__name__)



class lowerCamelCase			(  UpperCamelCase__						):



			'''simple docstring'''






			__snake_case        =	["""pixel_values"""]


			def __init__(				self	:						int		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Optional[Dict[str, int]] = None		,      lowerCAmelCase_	:						PILImageResampling = PILImageResampling.BILINEAR		,      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, List[float]]] = None		,      lowerCAmelCase_	:						Optional[Union[float, List[float]]] = None		,      **lowerCAmelCase_	:						Optional[Any]		,      )      ->		Tuple:





									'''simple docstring'''
									super().__init__(**__a			)
									A__					:       Union[str, Any]     =size if size is not None else {"""shortest_edge""": 2_56}
									A__					:       int     =get_size_dict(__a		,      default_to_square=__a			)
									A__					:       Any     =crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
									A__					:       str     =get_size_dict(__a		,      param_name="""crop_size"""			)
									A__					:       Union[str, Any]     =do_resize
									A__					:       Union[str, Any]     =size
									A__					:       Any     =resample
									A__					:       Tuple     =do_center_crop
									A__					:       List[Any]     =crop_size
									A__					:       int     =do_rescale
									A__					:       Optional[int]     =rescale_factor
									A__					:       str     =do_normalize
									A__					:       int     =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									A__					:       int     =image_std if image_std is not None else IMAGENET_STANDARD_STD


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





									'''simple docstring'''
									A__					:       int     =get_size_dict(__a		,      default_to_square=__a			)
									if "shortest_edge" not in size:
															raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"			)
									A__					:       Optional[Any]     =get_resize_output_image_size(__a		,      size=size["""shortest_edge"""]		,      default_to_square=__a			)
									return resize(__a		,      size=__a		,      resample=__a		,      data_format=__a		,      **__a			)


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





									'''simple docstring'''
									A__					:       List[Any]     =get_size_dict(__a			)
									if "height" not in size or "width" not in size:
															raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"			)
									return center_crop(__a		,      size=(size["""height"""], size["""width"""])		,      data_format=__a		,      **__a			)


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





									'''simple docstring'''
									return rescale(__a		,      scale=__a		,      data_format=__a		,      **__a			)


			def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						np.ndarray		,      lowerCAmelCase_	:						Union[float, List[float]]		,      lowerCAmelCase_	:						Union[float, List[float]]		,      lowerCAmelCase_	:						Optional[Union[str, ChannelDimension]] = None		,      **lowerCAmelCase_	:						List[Any]		,      )      ->		int:





									'''simple docstring'''
									return normalize(__a		,      mean=__a		,      std=__a		,      data_format=__a		,      **__a			)


			def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						ImageInput		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Dict[str, int] = None		,      lowerCAmelCase_	:						PILImageResampling = None		,      lowerCAmelCase_	:						bool = None		,      lowerCAmelCase_	:						Dict[str, int] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[float] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[Union[float, List[float]]] = None		,      lowerCAmelCase_	:						Optional[Union[float, List[float]]] = None		,      lowerCAmelCase_	:						Optional[Union[str, TensorType]] = None		,      lowerCAmelCase_	:						Union[str, ChannelDimension] = ChannelDimension.FIRST		,      **lowerCAmelCase_	:						int		,      )      ->		Tuple:





									'''simple docstring'''
									A__					:       Optional[int]     =do_resize if do_resize is not None else self.do_resize
									A__					:       Any     =size if size is not None else self.size
									A__					:       int     =get_size_dict(__a		,      default_to_square=__a			)
									A__					:       List[str]     =resample if resample is not None else self.resample
									A__					:       Optional[Any]     =do_center_crop if do_center_crop is not None else self.do_center_crop
									A__					:       Any     =crop_size if crop_size is not None else self.crop_size
									A__					:       Tuple     =get_size_dict(__a		,      param_name="""crop_size"""			)
									A__					:       Tuple     =do_rescale if do_rescale is not None else self.do_rescale
									A__					:       Union[str, Any]     =rescale_factor if rescale_factor is not None else self.rescale_factor
									A__					:       int     =do_normalize if do_normalize is not None else self.do_normalize
									A__					:       Tuple     =image_mean if image_mean is not None else self.image_mean
									A__					:       List[str]     =image_std if image_std is not None else self.image_std

									A__					:       Optional[int]     =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:
															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.
									A__					:       Dict     =[to_numpy_array(__a			) for image in images]

									if do_resize:
															A__					:       int     =[self.resize(image=__a		,      size=__a		,      resample=__a			) for image in images]

									if do_center_crop:
															A__					:       Any     =[self.center_crop(image=__a		,      size=__a			) for image in images]

									if do_rescale:
															A__					:       Union[str, Any]     =[self.rescale(image=__a		,      scale=__a			) for image in images]

									if do_normalize:
															A__					:       Optional[int]     =[self.normalize(image=__a		,      mean=__a		,      std=__a			) for image in images]

									A__					:       Optional[Any]     =[to_channel_dimension_format(__a		,      __a			) for image in images]

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



			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						Optional[int]		,      lowerCAmelCase_	:						List[Tuple] = None			)      ->		Any:





									'''simple docstring'''
									A__					:       int     =outputs.logits

									# Resize logits and compute semantic segmentation maps
									if target_sizes is not None:
															if len(__a			) != len(__a			):
																					raise ValueError(
																					    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""			)

															if is_torch_tensor(__a			):
																					A__					:       Dict     =target_sizes.numpy()

															A__					:       int     =[]

															for idx in range(len(__a			)			):
																					A__					:       Dict     =torch.nn.functional.interpolate(
																					    logits[idx].unsqueeze(dim=0			)		,      size=target_sizes[idx]		,      mode="""bilinear"""		,      align_corners=__a			)
																					A__					:       Any     =resized_logits[0].argmax(dim=0			)
																					semantic_segmentation.append(__a			)
									else:
															A__					:       Dict     =logits.argmax(dim=1			)
															A__					:       List[str]     =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]			)]

									return semantic_segmentation




'''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	(       )	->							List[Any]:






						"""simple docstring"""





						A__					:       List[Any]     =ArgumentParser("""Transformers CLI tool""",		usage="""transformers-cli <command> [<args>]"""	)
						A__					:       List[Any]     =parser.add_subparsers(help="""transformers-cli command helpers"""	)

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

						# Let's go
						A__					:       List[str]     =parser.parse_args()

						if not hasattr(__snake_case,		"""func"""	):
												parser.print_help()
												exit(1	)

						# Run
						A__					:       Optional[Any]     =args.func(__snake_case	)
						service.run()


if __name__ == "__main__":
		main()

