




import gc
import threading
import time

import psutil
import torch






class  __magic_name__						:
				'''simple docstring'''







				def __init__(							self:   str		):
											SCREAMING_SNAKE_CASE_   	=    psutil.Process()
											SCREAMING_SNAKE_CASE_   	=    False


				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    -1

											while True:
																		SCREAMING_SNAKE_CASE_   	=    max(self.process.memory_info().rss		,					self.cpu_memory_peak		)

																		# can't sleep or will not catch the peak right (this comment is here on purpose)
																		if not self.peak_monitoring:
																									break


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    True
											SCREAMING_SNAKE_CASE_   	=    threading.Thread(target=self.peak_monitor		)
											SCREAMING_SNAKE_CASE_   	=    True
											self.thread.start()





				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    False
											self.thread.join()
											return self.cpu_memory_peak






__SCREAMING_SNAKE_CASE 						=PeakCPUMemory()

def 			a   ():
							# Time
							SCREAMING_SNAKE_CASE_   	=    {"time": time.time()}

							gc.collect()
							torch.cuda.empty_cache()

							# CPU mem
							SCREAMING_SNAKE_CASE_   	=    psutil.Process().memory_info().rss
							cpu_peak_tracker.start()

							# GPU mem
							for i in range(torch.cuda.device_count()     ):
														SCREAMING_SNAKE_CASE_   	=    torch.cuda.memory_allocated(snake_case_     )
							torch.cuda.reset_peak_memory_stats()

							return measures

def 			a   (_lowerCAmelCase     ):
							# Time
							SCREAMING_SNAKE_CASE_   	=    {"time": time.time() - start_measures["time"]}

							gc.collect()
							torch.cuda.empty_cache()

							# CPU mem
							SCREAMING_SNAKE_CASE_   	=    (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**2_0
							SCREAMING_SNAKE_CASE_   	=    (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**2_0

							# GPU mem
							for i in range(torch.cuda.device_count()     ):
														SCREAMING_SNAKE_CASE_   	=    (torch.cuda.memory_allocated(snake_case_     ) - start_measures[str(snake_case_     )]) / 2**2_0
														SCREAMING_SNAKE_CASE_   	=    (torch.cuda.max_memory_allocated(snake_case_     ) - start_measures[str(snake_case_     )]) / 2**2_0

							return measures


def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							print(F"{description}:"     )
							print(F"- Time: {measures['time']:.2f}s"     )
							for i in range(torch.cuda.device_count()     ):
														print(F"- GPU {i} allocated: {measures[str(snake_case_     )]:.2f}MiB"     )
														SCREAMING_SNAKE_CASE_   	=    measures[F"{i}-peak"]
														print(F"- GPU {i} peak: {peak:.2f}MiB"     )
							print(F"- CPU RAM allocated: {measures['cpu']:.2f}MiB"     )
							print(F"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"     )








from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE 						={
    """caidas/swin2sr-classicalsr-x2-64""": (
        """https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"""
    ),
}






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					"swin2sr"

				SCREAMING_SNAKE_CASE__					:							str	      =					{
				    "hidden_size": "embed_dim",
				    "num_attention_heads": "num_heads",
				    "num_hidden_layers": "num_layers",
				}


				def __init__(							self:   Optional[int]		,					_lowerCamelCase:   Dict=64		,					_lowerCamelCase:   str=1		,					_lowerCamelCase:   Optional[int]=3		,					_lowerCamelCase:   List[Any]=1_80		,					_lowerCamelCase:   Optional[int]=[6, 6, 6, 6, 6, 6]		,					_lowerCamelCase:   Union[str, Any]=[6, 6, 6, 6, 6, 6]		,					_lowerCamelCase:   Tuple=8		,					_lowerCamelCase:   List[Any]=2.0		,					_lowerCamelCase:   Dict=True		,					_lowerCamelCase:   Dict=0.0		,					_lowerCamelCase:   int=0.0		,					_lowerCamelCase:   Optional[int]=0.1		,					_lowerCamelCase:   Any="gelu"		,					_lowerCamelCase:   int=False		,					_lowerCamelCase:   Tuple=0.02		,					_lowerCamelCase:   List[str]=1E-5		,					_lowerCamelCase:   List[Any]=2		,					_lowerCamelCase:   str=1.0		,					_lowerCamelCase:   List[Any]="1conv"		,					_lowerCamelCase:   str="pixelshuffle"		,					**_lowerCamelCase:   List[str]		,					):
											super().__init__(**_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    image_size
											SCREAMING_SNAKE_CASE_   	=    patch_size
											SCREAMING_SNAKE_CASE_   	=    num_channels
											SCREAMING_SNAKE_CASE_   	=    embed_dim
											SCREAMING_SNAKE_CASE_   	=    depths
											SCREAMING_SNAKE_CASE_   	=    len(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    num_heads
											SCREAMING_SNAKE_CASE_   	=    window_size
											SCREAMING_SNAKE_CASE_   	=    mlp_ratio
											SCREAMING_SNAKE_CASE_   	=    qkv_bias
											SCREAMING_SNAKE_CASE_   	=    hidden_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    drop_path_rate
											SCREAMING_SNAKE_CASE_   	=    hidden_act
											SCREAMING_SNAKE_CASE_   	=    use_absolute_embeddings
											SCREAMING_SNAKE_CASE_   	=    layer_norm_eps
											SCREAMING_SNAKE_CASE_   	=    initializer_range
											SCREAMING_SNAKE_CASE_   	=    upscale
											SCREAMING_SNAKE_CASE_   	=    img_range
											SCREAMING_SNAKE_CASE_   	=    resi_connection
											SCREAMING_SNAKE_CASE_   	=    upsampler


