






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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase : int						   =       logging.get_logger(__name__)


if is_vision_available():
	import PIL

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			List[str]									=      ['''pixel_values''']



					def __init__(  self				:	str		,     __a				:	bool = True		,     __a				:	Dict[str, int] = None		,     __a				:	PILImageResampling = PILImageResampling.BICUBIC		,     __a				:	bool = True		,     __a				:	Dict[str, int] = None		,     __a				:	bool = True		,     __a				:	Union[int, float] = 1 / 255		,     __a				:	bool = True		,     __a				:	Optional[Union[float, List[float]]] = None		,     __a				:	Optional[Union[float, List[float]]] = None		,     __a				:	bool = True		,     **__a				:	Optional[Any]		,     )			->  None:


												"""simple docstring"""

												super().__init__(**__a							)
												__lowercase      :							Tuple				=     size if size is not None else {"""shortest_edge""": 224}
												__lowercase      :							Optional[Any]				=     get_size_dict(__a		,     default_to_square=__a							)
												__lowercase      :							Tuple				=     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
												__lowercase      :							Union[str, Any]				=     get_size_dict(__a		,     default_to_square=__a		,     param_name="""crop_size"""							)

												__lowercase      :							int				=     do_resize
												__lowercase      :							Optional[Any]				=     size
												__lowercase      :							Tuple				=     resample
												__lowercase      :							Optional[Any]				=     do_center_crop
												__lowercase      :							Dict				=     crop_size
												__lowercase      :							Any				=     do_rescale
												__lowercase      :							Optional[int]				=     rescale_factor
												__lowercase      :							Any				=     do_normalize
												__lowercase      :							Optional[int]				=     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
												__lowercase      :							Tuple				=     image_std if image_std is not None else OPENAI_CLIP_STD
												__lowercase      :							Optional[int]				=     do_convert_rgb



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


												"""simple docstring"""

												__lowercase      :							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()}"							)
												__lowercase      :							Union[str, 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       lowerCAmelCase				(  self				:	Any		,     __a				:	np.ndarray		,     __a				:	Dict[str, int]		,     __a				:	Optional[Union[str, ChannelDimension]] = None		,     **__a				:	Any		,     )			->  np.ndarray:


												"""simple docstring"""

												__lowercase      :							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, width). Got {size.keys()}"							)
												return center_crop(__a		,     size=(size["""height"""], size["""width"""])		,     data_format=__a		,     **__a							)



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


												"""simple docstring"""

												return rescale(__a		,     scale=__a		,     data_format=__a		,     **__a							)



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


												"""simple docstring"""

												return normalize(__a		,     mean=__a		,     std=__a		,     data_format=__a		,     **__a							)



					def       lowerCAmelCase				(  self				:	List[str]		,     __a				:	ImageInput		,     __a				:	bool = None		,     __a				:	Dict[str, int] = None		,     __a				:	PILImageResampling = None		,     __a				:	bool = None		,     __a				:	int = None		,     __a				:	bool = None		,     __a				:	float = None		,     __a				:	bool = None		,     __a				:	Optional[Union[float, List[float]]] = None		,     __a				:	Optional[Union[float, List[float]]] = None		,     __a				:	bool = None		,     __a				:	Optional[Union[str, TensorType]] = None		,     __a				:	Optional[ChannelDimension] = ChannelDimension.FIRST		,     **__a				:	Union[str, Any]		,     )			->  PIL.Image.Image:


												"""simple docstring"""

												__lowercase      :							Tuple				=     do_resize if do_resize is not None else self.do_resize
												__lowercase      :							List[str]				=     size if size is not None else self.size
												__lowercase      :							Optional[int]				=     get_size_dict(__a		,     param_name="""size"""		,     default_to_square=__a							)
												__lowercase      :							str				=     resample if resample is not None else self.resample
												__lowercase      :							Optional[int]				=     do_center_crop if do_center_crop is not None else self.do_center_crop
												__lowercase      :							Optional[int]				=     crop_size if crop_size is not None else self.crop_size
												__lowercase      :							Tuple				=     get_size_dict(__a		,     param_name="""crop_size"""		,     default_to_square=__a							)
												__lowercase      :							Optional[int]				=     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      :							Optional[int]				=     do_normalize if do_normalize is not None else self.do_normalize
												__lowercase      :							Tuple				=     image_mean if image_mean is not None else self.image_mean
												__lowercase      :							Tuple				=     image_std if image_std is not None else self.image_std
												__lowercase      :							str				=     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

												__lowercase      :							Tuple				=     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."""							)

												# PIL RGBA images are converted to RGB
												if do_convert_rgb:
																			__lowercase      :							Optional[int]				=     [convert_to_rgb(__a							) for image in images]

												# All transformations expect numpy arrays.
												__lowercase      :							Any				=     [to_numpy_array(__a							) for image in images]

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

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

												if do_rescale:
																			__lowercase      :							Tuple				=     [self.rescale(image=__a		,     scale=__a							) for image in images]

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

												__lowercase      :							str				=     [to_channel_dimension_format(__a		,     __a							) for image in images]

												__lowercase      :							Any				=     {"""pixel_values""": images}
												return BatchFeature(data=__a		,     tensor_type=__a							)







# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowerCamelCase : int						   =       '''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''
def       snake_case_		(     ):
							__lowercase      :							List[Any]				=     _ask_options(
							    """In which compute environment are you running?"""       ,       ["""This machine""", """AWS (Amazon SageMaker)"""]       ,       _convert_compute_environment       ,       )
							if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
														__lowercase      :							Union[str, Any]				=     get_sagemaker_input()
							else:
														__lowercase      :							str				=     get_cluster_input()
							return config
def       snake_case_		(     lowerCAmelCase_       :						List[str]=None      ):
							if subparsers is not None:
														__lowercase      :							Optional[int]				=     subparsers.add_parser("""config"""       ,       description=lowerCAmelCase_      )
							else:
														__lowercase      :							List[str]				=     argparse.ArgumentParser("""Accelerate config command"""       ,       description=lowerCAmelCase_      )

							parser.add_argument(
							    """--config_file"""       ,       default=lowerCAmelCase_       ,       help=(
							        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
							        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
							        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
							        """with 'huggingface'."""
							    )       ,       )

							if subparsers is not None:
														parser.set_defaults(func=lowerCAmelCase_      )
							return parser
def       snake_case_		(     lowerCAmelCase_       :						Tuple      ):
							__lowercase      :							Union[str, Any]				=     get_user_input()
							if args.config_file is not None:
														__lowercase      :							List[Any]				=     args.config_file
							else:
														if not os.path.isdir(lowerCAmelCase_      ):
																					os.makedirs(lowerCAmelCase_      )
														__lowercase      :							Any				=     default_yaml_config_file

							if config_file.endswith(""".json"""      ):
														config.to_json_file(lowerCAmelCase_      )
							else:
														config.to_yaml_file(lowerCAmelCase_      )
							print(F"accelerate configuration saved at {config_file}"      )
def       snake_case_		(     ):
							__lowercase      :							str				=     config_command_parser()
							__lowercase      :							str				=     parser.parse_args()
							config_command(lowerCAmelCase_      )


if __name__ == "__main__":
	main()