





'''simple docstring'''


import gc
import threading
import time

import psutil
import torch

class    lowerCAmelCase_		:




 def __init__(	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      psutil.Process()
     _lowerCAmelCase				=      False




 def 							_snake_case   (	self		)    ->							int:
     _lowerCAmelCase				=      -1

     while True:
         _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      True
     _lowerCAmelCase				=      threading.Thread(target=self.peak_monitor		)
     _lowerCAmelCase				=      True
     self.thread.start()





 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      False
     self.thread.join()
     return self.cpu_memory_peak







_SCREAMING_SNAKE_CASE	 =							PeakCPUMemory()


def 	__a():



    '''simple docstring'''





    _lowerCAmelCase				=      {"time": time.time()}

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

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

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

    return measures


def 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      {"time": time.time() - start_measures["time"]}

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

    # CPU mem
    _lowerCAmelCase				=      (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
    _lowerCAmelCase				=      (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

    # GPU mem
    for i in range(torch.cuda.device_count()    ):
        _lowerCAmelCase				=      (torch.cuda.memory_allocated(SCREAMING_SNAKE_CASE_    ) - start_measures[str(SCREAMING_SNAKE_CASE_    )]) / 2**20
        _lowerCAmelCase				=      (torch.cuda.max_memory_allocated(SCREAMING_SNAKE_CASE_    ) - start_measures[str(SCREAMING_SNAKE_CASE_    )]) / 2**20

    return measures





def 	__a(SCREAMING_SNAKE_CASE_  :					Tuple		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    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(SCREAMING_SNAKE_CASE_    )]:.2f}MiB'''    )
        _lowerCAmelCase				=      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'''    )











'''simple docstring'''


from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       import torch

       from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

@add_end_docstrings(__magic_name__		)
class    lowerCAmelCase_		(		__magic_name__		):




 def __init__(	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							List[Any]:
     super().__init__(*_lowerCAmelCase			,     **_lowerCAmelCase		)
     requires_backends(self			,     "vision"		)
     self.check_model_type(_lowerCAmelCase		)




 def __call__(	self			,     _lowerCAmelCase			,     **_lowerCAmelCase		)    ->							str:
     return super().__call__(_lowerCAmelCase			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Tuple:
     return {}, {}, {}




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      load_image(_lowerCAmelCase		)
     _lowerCAmelCase				=      image.size
     _lowerCAmelCase				=      self.image_processor(images=_lowerCAmelCase			,     return_tensors=self.framework		)
     return model_inputs




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase				=      self.model(**_lowerCAmelCase		)
     return model_outputs





 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Optional[Any]:
     _lowerCAmelCase				=      model_outputs.predicted_depth
     _lowerCAmelCase				=      torch.nn.functional.interpolate(
         predicted_depth.unsqueeze(1		)			,     size=self.image_size[::-1]			,     mode="bicubic"			,     align_corners=_lowerCAmelCase		)
     _lowerCAmelCase				=      prediction.squeeze().cpu().numpy()
     _lowerCAmelCase				=      (output * 255 / np.max(_lowerCAmelCase		)).astype("uint8"		)
     _lowerCAmelCase				=      Image.fromarray(_lowerCAmelCase		)
     _lowerCAmelCase				=      {}
     _lowerCAmelCase				=      predicted_depth
     _lowerCAmelCase				=      depth
     return output_dict


