


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
							import torch

if is_vision_available():
							import PIL


a					  =     logging.get_logger(__name__)


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					def constraint_to_multiple_of(UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__=0						,							UpperCAmelCase__=None  ):
										lowercase_  							=			round(val / multiple  ) * multiple

										if max_val is not None and x > max_val:
															lowercase_  							=			math.floor(val / multiple  ) * multiple

										if x < min_val:
															lowercase_  							=			math.ceil(val / multiple  ) * multiple

										return x

					lowercase_  							=			(output_size, output_size) if isinstance(UpperCAmelCase__						,							UpperCAmelCase__  ) else output_size

					lowercase_  							=			get_image_size(UpperCAmelCase__  )
					lowercase_  							=			output_size

					# determine new height and width
					lowercase_  							=			output_height / input_height
					lowercase_  							=			output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width  ) < abs(1 - scale_height  ):
															# fit width
															lowercase_  							=			scale_width
										else:
															# fit height
															lowercase_  							=			scale_height

					lowercase_  							=			constraint_to_multiple_of(scale_height * input_height						,							multiple=UpperCAmelCase__  )
					lowercase_  							=			constraint_to_multiple_of(scale_width * input_width						,							multiple=UpperCAmelCase__  )

					return (new_height, new_width)







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

					def __init__(     self       :   Optional[int]							,					UpperCamelCase__       :   Any = True							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   Optional[int] = PILImageResampling.BILINEAR							,					UpperCamelCase__       :   Union[str, Any] = False							,					UpperCamelCase__       :   str = 1							,					UpperCamelCase__       :   str = True							,					UpperCamelCase__       :   Tuple = 1 / 255							,					UpperCamelCase__       :   str = True							,					UpperCamelCase__       :   Optional[int] = None							,					UpperCamelCase__       :   str = None							,					**UpperCamelCase__       :   str							,					):






										'''simple docstring'''






										super().__init__(**__a       )
										lowercase_  							=			size if size is not None else {'height': 384, 'width': 384}
										lowercase_  							=			get_size_dict(__a       )
										lowercase_  							=			do_resize
										lowercase_  							=			size
										lowercase_  							=			keep_aspect_ratio
										lowercase_  							=			ensure_multiple_of
										lowercase_  							=			resample
										lowercase_  							=			do_rescale
										lowercase_  							=			rescale_factor
										lowercase_  							=			do_normalize
										lowercase_  							=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowercase_  							=			image_std if image_std is not None else IMAGENET_STANDARD_STD

					def   UpperCAmelCase__       (     self       :   Union[str, Any]							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   Dict							,					UpperCamelCase__       :   Dict = False							,					UpperCamelCase__       :   List[str] = 1							,					UpperCamelCase__       :   Optional[Any] = PILImageResampling.BICUBIC							,					UpperCamelCase__       :   List[str] = None							,					**UpperCamelCase__       :   Any							,					):






										'''simple docstring'''






										lowercase_  							=			get_size_dict(__a       )
										if "height" not in size or "width" not in size:
															raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''       )
										lowercase_  							=			get_resize_output_image_size(
										    __a							,					output_size=(size["""height"""], size["""width"""])							,					keep_aspect_ratio=__a							,					multiple=__a							,					)
										return resize(__a							,					size=__a							,					resample=__a							,					data_format=__a							,					**__a       )

					def   UpperCAmelCase__       (     self       :   List[str]							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   Any							,					UpperCamelCase__       :   Optional[Any] = None							,					**UpperCamelCase__       :   Any							,					):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   List[str]							,					UpperCamelCase__       :   List[Any]							,					UpperCamelCase__       :   Dict = None							,					**UpperCamelCase__       :   str							,					):






										'''simple docstring'''






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

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






										'''simple docstring'''






										lowercase_  							=			do_resize if do_resize is not None else self.do_resize
										lowercase_  							=			size if size is not None else self.size
										lowercase_  							=			get_size_dict(__a       )
										lowercase_  							=			keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
										lowercase_  							=			ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
										lowercase_  							=			resample if resample is not None else self.resample
										lowercase_  							=			do_rescale if do_rescale is not None else self.do_rescale
										lowercase_  							=			rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase_  							=			do_normalize if do_normalize is not None else self.do_normalize
										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_  							=			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_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.
										lowercase_  							=			[to_numpy_array(__a       ) for image in images]

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

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

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

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

										lowercase_  							=			{'pixel_values': images}
										return BatchFeature(data=__a							,					tensor_type=__a       )







					def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   Tuple							,					UpperCamelCase__       :   Any = None       ):






										'''simple docstring'''






										lowercase_  							=			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       ):
																				lowercase_  							=			target_sizes.numpy()

															lowercase_  							=			[]

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

										return semantic_segmentation






# 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())


