


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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a					  =     logging.get_logger(__name__)







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  Any						=   ['pixel_values']

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






										'''simple docstring'''






										super().__init__(**UpperCamelCase__       )
										lowercase_  							=			size if size is not None else {"""height""": 224, """width""": 224}
										lowercase_  							=			get_size_dict(UpperCamelCase__       )
										lowercase_  							=			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										lowercase_  							=			get_size_dict(UpperCamelCase__							,					default_to_square=UpperCamelCase__							,					param_name="""crop_size"""       )

										lowercase_  							=			do_resize
										lowercase_  							=			do_rescale
										lowercase_  							=			do_normalize
										lowercase_  							=			do_center_crop
										lowercase_  							=			crop_size
										lowercase_  							=			size
										lowercase_  							=			resample
										lowercase_  							=			rescale_factor
										lowercase_  							=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										lowercase_  							=			image_std if image_std is not None else IMAGENET_DEFAULT_STD

					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Dict[str, int]							,					UpperCamelCase__       :   PILImageResampling = PILImageResampling.BILINEAR							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Tuple							,					):






										'''simple docstring'''






										lowercase_  							=			get_size_dict(UpperCamelCase__       )

										if "shortest_edge" in size:
															lowercase_  							=			get_resize_output_image_size(UpperCamelCase__							,					size=size["""shortest_edge"""]							,					default_to_square=UpperCamelCase__       )
															# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
										elif "height" in size and "width" in size:
															lowercase_  							=			(size["""height"""], size["""width"""])
										else:
															raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''       )
										return resize(UpperCamelCase__							,					size=UpperCamelCase__							,					resample=UpperCamelCase__							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Dict[str, int]							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   str							,					):






										'''simple docstring'''






										lowercase_  							=			get_size_dict(UpperCamelCase__       )
										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(UpperCamelCase__							,					size=(size["""height"""], size["""width"""])							,					data_format=UpperCamelCase__							,					**UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[str]							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   str       ):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   Tuple							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   Union[float, List[float]]							,					UpperCamelCase__       :   Union[float, List[float]]							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Optional[int]							,					):






										'''simple docstring'''






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







					def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   ImageInput							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Dict[str, int] = None							,					UpperCamelCase__       :   PILImageResampling = None							,					UpperCamelCase__       :   bool = None							,					UpperCamelCase__       :   int = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[float] = None							,					UpperCamelCase__       :   Optional[bool] = None							,					UpperCamelCase__       :   Optional[Union[float, List[float]]] = None							,					UpperCamelCase__       :   Optional[Union[float, List[float]]] = None							,					UpperCamelCase__       :   Optional[Union[str, TensorType]] = None							,					UpperCamelCase__       :   Union[str, ChannelDimension] = ChannelDimension.FIRST							,					**UpperCamelCase__       :   Dict							,					):






										'''simple docstring'''






										lowercase_  							=			do_resize if do_resize is not None else self.do_resize
										lowercase_  							=			do_rescale if do_rescale is not None else self.do_rescale
										lowercase_  							=			do_normalize if do_normalize is not None else self.do_normalize
										lowercase_  							=			do_center_crop if do_center_crop is not None else self.do_center_crop
										lowercase_  							=			crop_size if crop_size is not None else self.crop_size
										lowercase_  							=			get_size_dict(UpperCamelCase__							,					param_name="""crop_size"""							,					default_to_square=UpperCamelCase__       )
										lowercase_  							=			resample if resample is not None else self.resample
										lowercase_  							=			rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase_  							=			image_mean if image_mean is not None else self.image_mean
										lowercase_  							=			image_std if image_std is not None else self.image_std

										lowercase_  							=			size if size is not None else self.size
										lowercase_  							=			get_size_dict(UpperCamelCase__       )

										if not is_batched(UpperCamelCase__       ):
															lowercase_  							=			[images]

										if not valid_images(UpperCamelCase__       ):
															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."""       )

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

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

										if do_center_crop:
															lowercase_  							=			[self.center_crop(image=UpperCamelCase__							,					size=UpperCamelCase__       ) for image in images]

										if do_rescale:
															lowercase_  							=			[self.rescale(image=UpperCamelCase__							,					scale=UpperCamelCase__       ) for image in images]

										if do_normalize:
															lowercase_  							=			[self.normalize(image=UpperCamelCase__							,					mean=UpperCamelCase__							,					std=UpperCamelCase__       ) for image in images]

										lowercase_  							=			[to_channel_dimension_format(UpperCamelCase__							,					UpperCamelCase__       ) for image in images]

										lowercase_  							=			{"""pixel_values""": images}
										return BatchFeature(data=UpperCamelCase__							,					tensor_type=UpperCamelCase__       )






# Copyright 2022 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
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available


def 			UpperCAmelCase_				(      UpperCAmelCase__=None  ):
					if subparsers is not None:
										lowercase_  							=			subparsers.add_parser("""env"""  )
					else:
										lowercase_  							=			argparse.ArgumentParser("""Accelerate env command"""  )

					parser.add_argument(
					    """--config_file"""						,							default=UpperCAmelCase__						,							help="""The config file to use for the default values in the launching script."""  )

					if subparsers is not None:
										parser.set_defaults(func=UpperCAmelCase__  )
					return parser


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			torch.__version__
					lowercase_  							=			torch.cuda.is_available()
					lowercase_  							=			is_xpu_available()
					lowercase_  							=			is_npu_available()

					lowercase_  							=			"""Not found"""
					# Get the default from the config file.
					if args.config_file is not None or os.path.isfile(UpperCAmelCase__  ):
										lowercase_  							=			load_config_from_file(args.config_file  ).to_dict()

					lowercase_  							=			{
					    """`Accelerate` version""": version,
					    """Platform""": platform.platform(),
					    """Python version""": platform.python_version(),
					    """Numpy version""": np.__version__,
					    """PyTorch version (GPU?)""": F'''{pt_version} ({pt_cuda_available})''',
					    """PyTorch XPU available""": str(UpperCAmelCase__  ),
					    """PyTorch NPU available""": str(UpperCAmelCase__  ),
					    """System RAM""": F'''{psutil.virtual_memory().total / 1_0_2_4 ** 3:.2f} GB''',
					}
					if pt_cuda_available:
										lowercase_  							=			torch.cuda.get_device_name()

					print("""\nCopy-and-paste the text below in your GitHub issue\n"""  )
					print("""\n""".join([F'''- {prop}: {val}''' for prop, val in info.items()]  )  )

					print("""- `Accelerate` default config:""" if args.config_file is None else """- `Accelerate` config passed:"""  )
					lowercase_  							=			(
					    """\n""".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]  )
					    if isinstance(UpperCAmelCase__						,							UpperCAmelCase__  )
					    else F'''\t{accelerate_config}'''
					)
					print(UpperCAmelCase__  )

					lowercase_  							=			accelerate_config

					return info


def 			UpperCAmelCase_				(      ):
					lowercase_  							=			env_command_parser()
					lowercase_  							=			parser.parse_args()
					env_command(UpperCAmelCase__  )
					return 0


if __name__ == "__main__":
							raise SystemExit(main())


