




"""simple docstring"""






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
			import torch

if is_torch_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm


lowercase__  		=		logging.get_logger(__name__)






@dataclass
class lowerCAmelCase__			(  lowercase_		):






			'''simple docstring'''



			lowerCamelCase__   =    [
			    '''no_inference''',
			    '''no_cuda''',
			    '''no_tpu''',
			    '''no_speed''',
			    '''no_memory''',
			    '''no_env_print''',
			    '''no_multi_process''',
			]
			def __init__(						self						,   **lowercase						):







							for deprecated_arg in self.deprecated_args:
											if deprecated_arg in kwargs:
															_lowerCamelCase  :    Optional[Any]					=					deprecated_arg[3:]
															setattr(self						,   UpperCamelCase__						,   not kwargs.pop(UpperCamelCase__						)						)
															logger.warning(
															    F'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
															    F''' {positive_arg}={kwargs[positive_arg]}'''						)

							_lowerCamelCase  :    Optional[Any]					=					kwargs.pop('torchscript'						,   self.torchscript						)
							_lowerCamelCase  :    Union[str, Any]					=					kwargs.pop('torch_xla_tpu_print_metrics'						,   self.torch_xla_tpu_print_metrics						)
							_lowerCamelCase  :    str					=					kwargs.pop('fp16_opt_level'						,   self.fpaa_opt_level						)
							super().__init__(**UpperCamelCase__						)

			lowerCamelCase__   =    field(default=lowercase_,			metadata={"""help""": """Trace the models using torchscript"""}		)
			lowerCamelCase__   =    field(default=lowercase_,			metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}		)
			lowerCamelCase__   =    field(
			    default="""O1""",			metadata={
			        """help""": (
			            """For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. """
			            """See details at https://nvidia.github.io/apex/amp.html"""
			        )
			    },			)
			@cached_property
			def       A_					(						self						):
							requires_backends(self						,   ['torch']						)
							logger.info('PyTorch: setting up devices'						)
							if not self.cuda:
											_lowerCamelCase  :    Optional[int]					=					torch.device('cpu'						)
											_lowerCamelCase  :    Any					=					0
							elif is_torch_tpu_available():
											_lowerCamelCase  :    List[Any]					=					xm.xla_device()
											_lowerCamelCase  :    Optional[Any]					=					0
							else:
											_lowerCamelCase  :    Tuple					=					torch.device('cuda' if torch.cuda.is_available() else 'cpu'						)
											_lowerCamelCase  :    Any					=					torch.cuda.device_count()
							return device, n_gpu
			@property
			def       A_					(						self						):
							return is_torch_tpu_available() and self.tpu
			@property
			def       A_					(						self						):
							requires_backends(self						,   ['torch']						)
							# TODO(PVP): currently only single GPU is supported
							return torch.cuda.current_device()
			@property
			def       A_					(						self						):
							requires_backends(self						,   ['torch']						)
							return self._setup_devices[0]
			@property
			def       A_					(						self						):
							requires_backends(self						,   ['torch']						)
							return self._setup_devices[1]






			@property
			def       A_					(						self						):
							return self.n_gpu > 0









"""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()