



# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


UpperCAmelCase__     = "Create a default config file for Accelerate with only a few flags set."





def 							A			(  _UpperCAmelCase       :     Optional[int]="no"							,     _UpperCAmelCase       :     str = default_json_config_file							,     _UpperCAmelCase       :     bool = False					)       ->				Tuple:







			'''simple docstring'''
			_UpperCAmelCase	 =     Path(_UpperCAmelCase					)
			path.parent.mkdir(parents=_UpperCAmelCase							,     exist_ok=_UpperCAmelCase					)
			if path.exists():
						print(
						    F"Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."					)
						return False
			_UpperCAmelCase	 =     mixed_precision.lower()
			if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
						raise ValueError(
						    F"`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"					)
			_UpperCAmelCase	 =     {
			    'compute_environment': 'LOCAL_MACHINE',
			    'mixed_precision': mixed_precision,
			}
			if torch.cuda.is_available():
						_UpperCAmelCase	 =     torch.cuda.device_count()
						_UpperCAmelCase	 =     num_gpus
						_UpperCAmelCase	 =     False
						if num_gpus > 1:
									_UpperCAmelCase	 =     'MULTI_GPU'
						else:
									_UpperCAmelCase	 =     'NO'
			elif is_xpu_available() and use_xpu:
						_UpperCAmelCase	 =     torch.xpu.device_count()
						_UpperCAmelCase	 =     num_xpus
						_UpperCAmelCase	 =     False
						if num_xpus > 1:
									_UpperCAmelCase	 =     'MULTI_XPU'
						else:
									_UpperCAmelCase	 =     'NO'
			elif is_npu_available():
						_UpperCAmelCase	 =     torch.npu.device_count()
						_UpperCAmelCase	 =     num_npus
						_UpperCAmelCase	 =     False
						if num_npus > 1:
									_UpperCAmelCase	 =     'MULTI_NPU'
						else:
									_UpperCAmelCase	 =     'NO'
			else:
						_UpperCAmelCase	 =     0
						_UpperCAmelCase	 =     True
						_UpperCAmelCase	 =     1
						_UpperCAmelCase	 =     'NO'
			_UpperCAmelCase	 =     ClusterConfig(**_UpperCAmelCase					)
			config.to_json_file(_UpperCAmelCase					)
			return path





def 							A			(  _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     List[str]					)       ->				Tuple:







			'''simple docstring'''
			_UpperCAmelCase	 =     parser.add_parser('default'							,     parents=_UpperCAmelCase							,     help=_UpperCAmelCase							,     formatter_class=_UpperCAmelCase					)
			parser.add_argument(
			    '--config_file'							,     default=_UpperCAmelCase							,     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\'.'
			    )							,     dest='save_location'							,     )

			parser.add_argument(
			    '--mixed_precision'							,     choices=['no', 'fp16', 'bf16']							,     type=_UpperCAmelCase							,     help='Whether or not to use mixed precision training. '
			    'Choose between FP16 and BF16 (bfloat16) training. '
			    'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'							,     default='no'							,     )
			parser.set_defaults(func=_UpperCAmelCase					)
			return parser





def 							A			(  _UpperCAmelCase       :     Optional[int]					)       ->				Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase	 =     write_basic_config(args.mixed_precision							,     args.save_location					)
			if config_file:
						print(F"accelerate configuration saved at {config_file}"					)





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


UpperCAmelCase__     = logging.get_logger(__name__)







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  ['''pixel_values''']



			def __init__(		self     :						Any				,	A     :						bool = True				,	A     :						Optional[Dict[str, int]] = None				,	A     :						PILImageResampling = PILImageResampling.BILINEAR				,	A     :						bool = True				,	A     :						Dict[str, int] = None				,	A     :						bool = True				,	A     :						Union[int, float] = 1 / 2_55				,	A     :						bool = True				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						Optional[Union[float, List[float]]] = None				,	**A     :						Union[str, Any]				,	)     ->					None:



						"""simple docstring"""






						super().__init__(**A)
						_UpperCAmelCase	 =     size if size is not None else {'shortest_edge': 2_56}
						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						_UpperCAmelCase	 =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
						_UpperCAmelCase	 =     get_size_dict(A				,	param_name='crop_size')
						_UpperCAmelCase	 =     do_resize
						_UpperCAmelCase	 =     size
						_UpperCAmelCase	 =     resample
						_UpperCAmelCase	 =     do_center_crop
						_UpperCAmelCase	 =     crop_size
						_UpperCAmelCase	 =     do_rescale
						_UpperCAmelCase	 =     rescale_factor
						_UpperCAmelCase	 =     do_normalize
						_UpperCAmelCase	 =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_UpperCAmelCase	 =     image_std if image_std is not None else IMAGENET_STANDARD_STD



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



						"""simple docstring"""






						_UpperCAmelCase	 =     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()}")
						_UpperCAmelCase	 =     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     :						Union[str, Any]				,	)     ->					np.ndarray:



						"""simple docstring"""






						_UpperCAmelCase	 =     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 					_lowerCamelCase (		self     :						Any				,	A     :						np.ndarray				,	A     :						float				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Dict)     ->					np.ndarray:



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						int				,	A     :						np.ndarray				,	A     :						Union[float, List[float]]				,	A     :						Union[float, List[float]]				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Dict				,	)     ->					np.ndarray:



						"""simple docstring"""






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



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



						"""simple docstring"""






						_UpperCAmelCase	 =     do_resize if do_resize is not None else self.do_resize
						_UpperCAmelCase	 =     size if size is not None else self.size
						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						_UpperCAmelCase	 =     resample if resample is not None else self.resample
						_UpperCAmelCase	 =     do_center_crop if do_center_crop is not None else self.do_center_crop
						_UpperCAmelCase	 =     crop_size if crop_size is not None else self.crop_size
						_UpperCAmelCase	 =     get_size_dict(A				,	param_name='crop_size')
						_UpperCAmelCase	 =     do_rescale if do_rescale is not None else self.do_rescale
						_UpperCAmelCase	 =     rescale_factor if rescale_factor is not None else self.rescale_factor
						_UpperCAmelCase	 =     do_normalize if do_normalize is not None else self.do_normalize
						_UpperCAmelCase	 =     image_mean if image_mean is not None else self.image_mean
						_UpperCAmelCase	 =     image_std if image_std is not None else self.image_std

						_UpperCAmelCase	 =     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.
						_UpperCAmelCase	 =     [to_numpy_array(A) for image in images]

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

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

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

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

						_UpperCAmelCase	 =     [to_channel_dimension_format(A				,	A) for image in images]

						_UpperCAmelCase	 =     {'pixel_values': images}
						return BatchFeature(data=A				,	tensor_type=A)



			def 					_lowerCamelCase (		self     :						str				,	A     :						Any				,	A     :						List[Tuple] = None)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     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):
												_UpperCAmelCase	 =     target_sizes.numpy()

									_UpperCAmelCase	 =     []

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

						return semantic_segmentation
