




"""simple docstring"""






import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


lowercase__  		=		logging.get_logger(__name__)







def   _snake_case    (       ):
				# Get the sagemaker specific mp parameters from smp_options variable.
				_lowerCamelCase  :    Tuple					=					os.getenv('SM_HP_MP_PARAMETERS'							,					'{}'	)
				try:
								# Parse it and check the field "partitions" is included, it is required for model parallel.
								_lowerCamelCase  :    Union[str, Any]					=					json.loads(lowercase__	)
								if "partitions" not in smp_options:
												return False
				except json.JSONDecodeError:
								return False

				# Get the sagemaker specific framework parameters from mpi_options variable.
				_lowerCamelCase  :    int					=					os.getenv('SM_FRAMEWORK_PARAMS'							,					'{}'	)
				try:
								# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
								_lowerCamelCase  :    List[Any]					=					json.loads(lowercase__	)
								if not mpi_options.get('sagemaker_mpi_enabled'							,					lowercase__	):
												return False
				except json.JSONDecodeError:
								return False
				# Lastly, check if the `smdistributed` module is present.
				return importlib.util.find_spec('smdistributed'	) is not None


if is_sagemaker_model_parallel_available():
			import smdistributed.modelparallel.torch as smp

			smp.init()






@dataclass
class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    field(
			    default="""""",			metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""},			)
			def       A_					(						self						):
							super().__post_init__()
							warnings.warn(
							    '`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
							    '`TrainingArguments` instead.'						,   lowercase						,   )
			@cached_property
			def       A_					(						self						):
							logger.info('PyTorch: setting up devices'						)
							if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
											logger.warning(
											    'torch.distributed process group is initialized, but local_rank == -1. '
											    'In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'						)
							if self.no_cuda:
											_lowerCamelCase  :    List[Any]					=					torch.device('cpu'						)
											_lowerCamelCase  :    List[str]					=					0
							elif is_sagemaker_model_parallel_available():
											_lowerCamelCase  :    Union[str, Any]					=					smp.local_rank()
											_lowerCamelCase  :    List[str]					=					torch.device('cuda'						,   lowercase						)
											_lowerCamelCase  :    Optional[int]					=					1
							elif is_sagemaker_dp_enabled():
											import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

											torch.distributed.init_process_group(backend='smddp'						,   timeout=self.ddp_timeout_delta						)
											_lowerCamelCase  :    Tuple					=					int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'						)						)
											_lowerCamelCase  :    Union[str, Any]					=					torch.device('cuda'						,   self.local_rank						)
											_lowerCamelCase  :    Union[str, Any]					=					1
							elif self.local_rank == -1:
											# if n_gpu is > 1 we'll use nn.DataParallel.
											# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
											# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
											# trigger an error that a device index is missing. Index 0 takes into account the
											# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
											# will use the first GPU in that env, i.e. GPU#1
											_lowerCamelCase  :    str					=					torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'						)
											# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
											# the default value.
											_lowerCamelCase  :    List[str]					=					torch.cuda.device_count()
							else:
											# Here, we'll use torch.distributed.
											# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
											if not torch.distributed.is_initialized():
															torch.distributed.init_process_group(backend='nccl'						,   timeout=self.ddp_timeout_delta						)
											_lowerCamelCase  :    int					=					torch.device('cuda'						,   self.local_rank						)
											_lowerCamelCase  :    Tuple					=					1

							if device.type == "cuda":
											torch.cuda.set_device(lowercase						)

							return device
			@property
			def       A_					(						self						):
							if is_sagemaker_model_parallel_available():
											return smp.dp_size()

							return super().world_size
			@property
			def       A_					(						self						):
							return not is_sagemaker_model_parallel_available()






			@property
			def       A_					(						self						):
							return False









"""simple docstring"""






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt






class lowerCAmelCase__			:






			'''simple docstring'''



			def __init__(						self						):
							_lowerCamelCase  :    Tuple					=					''
							_lowerCamelCase  :    Any					=					''
							_lowerCamelCase  :    List[Any]					=					[]
							_lowerCamelCase  :    Tuple					=					0
							_lowerCamelCase  :    Dict					=					256
							_lowerCamelCase  :    Optional[Any]					=					0
							_lowerCamelCase  :    List[Any]					=					0
							_lowerCamelCase  :    int					=					0
							_lowerCamelCase  :    Dict					=					0
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Any					=					cva.imread(lowercase						,   0						)
							_lowerCamelCase  :    Optional[int]					=					copy.deepcopy(self.img						)
							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					plt.hist(self.img.ravel()						,   256						,   [0, 256]						,   label='x'						)
							_lowerCamelCase  :    Optional[int]					=					np.sum(lowercase						)
							for i in range(len(lowercase						)						):
											_lowerCamelCase  :    List[Any]					=					x[i] / self.k
											self.sk += prk
											_lowerCamelCase  :    int					=					(self.L - 1) * self.sk
											if self.rem != 0:
															_lowerCamelCase  :    Union[str, Any]					=					int(last % last						)
											_lowerCamelCase  :    int					=					int(last + 1 if self.rem >= 0.5 else last						)
											self.last_list.append(lowercase						)
											_lowerCamelCase  :    List[str]					=					int(np.ma.count(self.img						) / self.img[1].size						)
											_lowerCamelCase  :    int					=					self.img[1].size
							for i in range(self.number_of_cols						):
											for j in range(self.number_of_rows						):
															_lowerCamelCase  :    Optional[Any]					=					self.img[j][i]
															if num != self.last_list[num]:
																			_lowerCamelCase  :    Any					=					self.last_list[num]
							cva.imwrite('output_data/output.jpg'						,   self.img						)
			def       A_					(						self						):
							plt.hist(self.img.ravel()						,   256						,   [0, 256]						)






			def       A_					(						self						):
							cva.imshow('Output-Image'						,   self.img						)
							cva.imshow('Input-Image'						,   self.original_image						)
							cva.waitKey(5000						)
							cva.destroyAllWindows()


if __name__ == "__main__":
			lowercase__  		=		os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
			lowercase__  		=		ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()