

import gc
import threading
import time

import psutil
import torch






class 	A_		:
							def __init__(		self		:      Dict):
								__lowerCamelCase    :    Any 	=			psutil.Process()
								__lowerCamelCase    :    List[str] 	=			False
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    Optional[Any] 	=			-1

								while True:
									__lowerCamelCase    :    int 	=			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    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    Dict 	=			True
								__lowerCamelCase    :    List[Any] 	=			threading.Thread(target=self.peak_monitor)
								__lowerCamelCase    :    int 	=			True
								self.thread.start()


							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    List[str] 	=			False
								self.thread.join()
								return self.cpu_memory_peak
a		=PeakCPUMemory()






def        SCREAMING_SNAKE_CASE__	(  )  ->					List[str]:
	# Time
	__lowerCamelCase    :    List[Any] 	=			{'time': time.time()}

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

	# CPU mem
	__lowerCamelCase    :    Union[str, Any] 	=			psutil.Process().memory_info().rss
	cpu_peak_tracker.start()

	# GPU mem
	for i in range(torch.cuda.device_count()      ):
		__lowerCamelCase    :    Dict 	=			torch.cuda.memory_allocated(lowerCamelCase__      )
	torch.cuda.reset_peak_memory_stats()

	return measures






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Dict:
	# Time
	__lowerCamelCase    :    Any 	=			{'time': time.time() - start_measures['time']}

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

	# CPU mem
	__lowerCamelCase    :    List[str] 	=			(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**2_0
	__lowerCamelCase    :    Tuple 	=			(cpu_peak_tracker.stop() - start_measures['cpu']) / 2**2_0

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

	return measures






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[Any]:
	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(lowerCamelCase__      )]:.2f}MiB"      )
		__lowerCamelCase    :    List[str] 	=			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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a		={
    """configuration_pix2struct""": [
        """PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Pix2StructConfig""",
        """Pix2StructTextConfig""",
        """Pix2StructVisionConfig""",
    ],
    """processing_pix2struct""": ["""Pix2StructProcessor"""],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""Pix2StructImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Pix2StructPreTrainedModel""",
					    """Pix2StructForConditionalGeneration""",
					    """Pix2StructVisionModel""",
					    """Pix2StructTextModel""",
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




