



'''simple docstring'''




import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment



def      a      (			lowerCamelCase__						,				lowerCamelCase__=()						,				lowerCamelCase__=None						,				lowerCamelCase__="no"						,				lowerCamelCase__="29500"   ):
			'''simple docstring'''





			A_     :						List[Any]     =					False
			A_     :						Tuple     =					False
			if any(key.startswith("""KAGGLE"""   ) for key in os.environ.keys()   ):
						A_     :						List[str]     =					True
			elif "IPython" in sys.modules:
						A_     :						str     =					"""google.colab""" in str(sys.modules["""IPython"""].get_ipython()   )

			try:
						A_     :						Optional[int]     =					PrecisionType(mixed_precision.lower()   )
			except ValueError:
						raise ValueError(
						    f'Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'   )

			if (in_colab or in_kaggle) and (os.environ.get("""TPU_NAME"""						,				lowerCamelCase__   ) is not None):
						# TPU launch
						import torch_xla.distributed.xla_multiprocessing as xmp

						if len(AcceleratorState._shared_state   ) > 0:
									raise ValueError(
									    """To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside """
									    """your training function. Restart your notebook and make sure no cells initializes an """
									    """`Accelerator`."""   )
						if num_processes is None:
									A_     :						str     =					8

						A_     :						List[str]     =					PrepareForLaunch(lowerCamelCase__						,				distributed_type="""TPU"""   )
						print(f'Launching a training on {num_processes} TPU cores.'   )
						xmp.spawn(lowerCamelCase__						,				args=lowerCamelCase__						,				nprocs=lowerCamelCase__						,				start_method="""fork"""   )
			elif in_colab:
						# No need for a distributed launch otherwise as it's either CPU or one GPU.
						if torch.cuda.is_available():
									print("""Launching training on one GPU."""   )
						else:
									print("""Launching training on one CPU."""   )
						function(*lowerCamelCase__   )
			else:
						if num_processes is None:
									raise ValueError(
									    """You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."""   )

						if num_processes > 1:
									# Multi-GPU launch
									from torch.multiprocessing import start_processes
									from torch.multiprocessing.spawn import ProcessRaisedException

									if len(AcceleratorState._shared_state   ) > 0:
												raise ValueError(
												    """To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized """
												    """inside your training function. Restart your notebook and make sure no cells initializes an """
												    """`Accelerator`."""   )

									if torch.cuda.is_initialized():
												raise ValueError(
												    """To launch a multi-GPU training from your notebook, you need to avoid running any instruction """
												    """using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA """
												    """function."""   )

									# torch.distributed will expect a few environment variable to be here. We set the ones common to each
									# process here (the other ones will be set be the launcher).
									with patch_environment(
									    world_size=lowerCamelCase__						,				master_addr="""127.0.01"""						,				master_port=lowerCamelCase__						,				mixed_precision=lowerCamelCase__   ):
												A_     :						Union[str, Any]     =					PrepareForLaunch(lowerCamelCase__						,				distributed_type="""MULTI_GPU"""   )
												print(f'Launching training on {num_processes} GPUs.'   )
												try:
															start_processes(lowerCamelCase__						,				args=lowerCamelCase__						,				nprocs=lowerCamelCase__						,				start_method="""fork"""   )
												except ProcessRaisedException as e:
															if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																		raise RuntimeError(
																		    """CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. """
																		    """This likely stems from an outside import causing issues once the `notebook_launcher()` is called. """
																		    """Please review your imports and test them when running the `notebook_launcher()` to identify """
																		    """which one is problematic."""   ) from e

						else:
									# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
									if is_mps_available():
												A_     :						List[Any]     =					"""1"""
												print("""Launching training on MPS."""   )
									elif torch.cuda.is_available():
												print("""Launching training on one GPU."""   )
									else:
												print("""Launching training on CPU."""   )
									function(*lowerCamelCase__   )



def      a      (			lowerCamelCase__						,				lowerCamelCase__=()						,				lowerCamelCase__=2   ):
			'''simple docstring'''





			from torch.multiprocessing import start_processes

			with tempfile.NamedTemporaryFile() as tmp_file:
						# torch.distributed will expect a few environment variable to be here. We set the ones common to each
						# process here (the other ones will be set be the launcher).
						with patch_environment(
						    world_size=lowerCamelCase__						,				master_addr="""127.0.01"""						,				master_port="""29500"""						,				accelerate_mixed_precision="""no"""						,				accelerate_debug_rdv_file=tmp_file.name						,				accelerate_use_cpu="""yes"""						,				):
									A_     :						int     =					PrepareForLaunch(lowerCamelCase__						,				debug=lowerCamelCase__   )
									start_processes(lowerCamelCase__						,				args=lowerCamelCase__						,				nprocs=lowerCamelCase__						,				start_method="""fork"""   )




'''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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
						import PIL

if is_torch_available():
						import torch


lowerCamelCase							:str       = logging.get_logger(__name__)







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Tuple		      = ['pixel_values']


	def __init__(self	,				lowercase = True	,				lowercase = None	,				lowercase = PILImageResampling.BILINEAR	,				lowercase = True	,				lowercase = 1 / 255	,				lowercase = True	,				lowercase = None	,				lowercase = True	,				**lowercase	,				):
				super().__init__(**lowercase							)
				A_     :						Union[str, Any]     =					size if size is not None else {"""shortest_edge""": 224}
				A_     :						Union[str, Any]     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				A_     :						Optional[Any]     =					crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
				A_     :						Tuple     =					get_size_dict(lowercase	,				param_name="""crop_size"""							)

				A_     :						List[Any]     =					do_resize
				A_     :						List[str]     =					size
				A_     :						Dict     =					resample
				A_     :						int     =					do_rescale
				A_     :						str     =					rescale_factor
				A_     :						Tuple     =					do_center_crop
				A_     :						Tuple     =					crop_size
				A_     :						List[str]     =					do_flip_channel_order


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = PIL.Image.BILINEAR	,				lowercase = None	,				**lowercase	,				):
				A_     :						Any     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				if "shortest_edge" not in size:
							raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'							)
				A_     :						Any     =					get_resize_output_image_size(lowercase	,				size=size["""shortest_edge"""]	,				default_to_square=lowercase							)
				return resize(lowercase	,				size=lowercase	,				resample=lowercase	,				data_format=lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				A_     :						Tuple     =					get_size_dict(lowercase							)
				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()}'							)
				return center_crop(lowercase	,				size=(size["""height"""], size["""width"""])	,				data_format=lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase = None	,				**lowercase	,				):
				return rescale(lowercase	,				scale=lowercase	,				data_format=lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None							):
				return flip_channel_order(lowercase	,				data_format=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = None	,				lowercase = ChannelDimension.FIRST	,				**lowercase	,				):
				A_     :						str     =					do_resize if do_resize is not None else self.do_resize
				A_     :						Optional[int]     =					resample if resample is not None else self.resample
				A_     :						str     =					do_rescale if do_rescale is not None else self.do_rescale
				A_     :						str     =					rescale_factor if rescale_factor is not None else self.rescale_factor
				A_     :						str     =					do_center_crop if do_center_crop is not None else self.do_center_crop
				A_     :						Dict     =					(
				    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
				)

				A_     :						Union[str, Any]     =					size if size is not None else self.size
				A_     :						Dict     =					get_size_dict(lowercase	,				default_to_square=lowercase							)
				A_     :						Any     =					crop_size if crop_size is not None else self.crop_size
				A_     :						Union[str, Any]     =					get_size_dict(lowercase	,				param_name="""crop_size"""							)

				A_     :						Union[str, Any]     =					make_list_of_images(lowercase							)

				if not valid_images(lowercase							):
							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_rescale and rescale_factor is None:
							raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

				if do_center_crop and crop_size is None:
							raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

				# All transformations expect numpy arrays.
				A_     :						Optional[Any]     =					[to_numpy_array(lowercase							) for image in images]

				if do_resize:
							A_     :						List[Any]     =					[self.resize(image=lowercase	,				size=lowercase	,				resample=lowercase							) for image in images]

				if do_center_crop:
							A_     :						str     =					[self.center_crop(image=lowercase	,				size=lowercase							) for image in images]

				if do_rescale:
							A_     :						Dict     =					[self.rescale(image=lowercase	,				scale=lowercase							) for image in images]

				# the pretrained checkpoints assume images are BGR, not RGB
				if do_flip_channel_order:
							A_     :						List[str]     =					[self.flip_channel_order(image=lowercase							) for image in images]

				A_     :						List[str]     =					[to_channel_dimension_format(lowercase	,				lowercase							) for image in images]

				A_     :						str     =					{"""pixel_values""": images}
				return BatchFeature(data=lowercase	,				tensor_type=lowercase							)


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Optional[Any]     =					outputs.logits

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

							if is_torch_tensor(lowercase							):
										A_     :						Dict     =					target_sizes.numpy()

							A_     :						Union[str, Any]     =					[]

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

				return semantic_segmentation