

"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__UpperCAmelCase			=					logging.get_logger(__name__)

__UpperCAmelCase			=					{
    'microsoft/swin-tiny-patch4-window7-224': (
        'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}







class   __lowercase      (     __lowerCamelCase						,      __lowerCamelCase       ):
  snake_case_												=							"""swin"""

  snake_case_												=							{
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }

  def __init__(	self		:      List[str]       ,A		:      List[str]=224       ,A		:      Tuple=4       ,A		:      str=3       ,A		:      Union[str, Any]=96       ,A		:      List[str]=[2, 2, 6, 2]       ,A		:      List[Any]=[3, 6, 12, 24]       ,A		:      List[Any]=7       ,A		:      List[str]=4.0       ,A		:      Optional[int]=True       ,A		:      Optional[int]=0.0       ,A		:      List[str]=0.0       ,A		:      List[str]=0.1       ,A		:      Dict="gelu"       ,A		:      Any=False       ,A		:      List[str]=0.0_2       ,A		:      str=1e-5       ,A		:      Optional[Any]=32       ,A		:      List[str]=None       ,A		:      Dict=None       ,**A		:      int       ,):






   '''simple docstring'''
   super().__init__(**A     )

   UpperCAmelCase__       :	str        =       image_size
   UpperCAmelCase__       :	str        =       patch_size
   UpperCAmelCase__       :	Union[str, Any]        =       num_channels
   UpperCAmelCase__       :	List[str]        =       embed_dim
   UpperCAmelCase__       :	int        =       depths
   UpperCAmelCase__       :	Dict        =       len(A     )
   UpperCAmelCase__       :	Tuple        =       num_heads
   UpperCAmelCase__       :	Any        =       window_size
   UpperCAmelCase__       :	Any        =       mlp_ratio
   UpperCAmelCase__       :	str        =       qkv_bias
   UpperCAmelCase__       :	List[str]        =       hidden_dropout_prob
   UpperCAmelCase__       :	Union[str, Any]        =       attention_probs_dropout_prob
   UpperCAmelCase__       :	int        =       drop_path_rate
   UpperCAmelCase__       :	Any        =       hidden_act
   UpperCAmelCase__       :	Any        =       use_absolute_embeddings
   UpperCAmelCase__       :	Optional[Any]        =       layer_norm_eps
   UpperCAmelCase__       :	Tuple        =       initializer_range
   UpperCAmelCase__       :	List[str]        =       encoder_stride
   # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   UpperCAmelCase__       :	Optional[Any]        =       int(embed_dim * 2 ** (len(A     ) - 1)     )
   UpperCAmelCase__       :	Optional[int]        =       ["""stem"""] + [f"stage{idx}" for idx in range(1       ,len(A     ) + 1     )]
   UpperCAmelCase__ ,   UpperCAmelCase__       :	List[Any]        =       get_aligned_output_features_output_indices(
       out_features=A       ,out_indices=A       ,stage_names=self.stage_names     )







class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							version.parse("""1.11"""       )

  @property
  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   return OrderedDict(
       [
           ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
       ]     )




  @property
  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   return 1e-4












import gc
import threading
import time

import psutil
import torch

class   lowerCAmelCase				:
 def __init__( self    : str      )     ->      Union[str, Any]:
     lowerCamelCase__       : Optional[Any]   					=						psutil.Process()
     lowerCamelCase__       : Union[str, Any]   					=						False
 def 	A_      ( self    : Optional[int]      )     ->      int:
     lowerCamelCase__       : Optional[Any]   					=						-1

     while True:
         lowerCamelCase__       : Dict   					=						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      )     ->      Dict:
     lowerCamelCase__       : List[Any]   					=						True
     lowerCamelCase__       : List[str]   					=						threading.Thread(target=self.peak_monitor      )
     lowerCamelCase__       : Union[str, Any]   					=						True
     self.thread.start()







 def 	A_      ( self    : str      )     ->      Dict:
     lowerCamelCase__       : int   					=						False
     self.thread.join()
     return self.cpu_memory_peak


_UpperCAmelCase							:			Dict        =							PeakCPUMemory()


def 			SCREAMING_SNAKE_CASE (			)     ->	Union[str, Any]:
    # Time
    lowerCamelCase__       : List[Any]   					=						{'time': time.time()}

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

    # CPU mem
    lowerCamelCase__       : List[str]   					=						psutil.Process().memory_info().rss
    cpu_peak_tracker.start()

    # GPU mem
    for i in range(torch.cuda.device_count()					):
        lowerCamelCase__       : Union[str, Any]   					=						torch.cuda.memory_allocated(_UpperCAmelCase					)
    torch.cuda.reset_peak_memory_stats()

    return measures


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Tuple:
    # Time
    lowerCamelCase__       : Optional[int]   					=						{'time': time.time() - start_measures['time']}

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

    # CPU mem
    lowerCamelCase__       : Dict   					=						(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
    lowerCamelCase__       : int   					=						(cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

    # GPU mem
    for i in range(torch.cuda.device_count()					):
        lowerCamelCase__       : List[str]   					=						(torch.cuda.memory_allocated(_UpperCAmelCase					) - start_measures[str(_UpperCAmelCase					)]) / 2**20
        lowerCamelCase__       : Optional[Any]   					=						(torch.cuda.max_memory_allocated(_UpperCAmelCase					) - start_measures[str(_UpperCAmelCase					)]) / 2**20

    return measures


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Dict:
    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(_UpperCAmelCase					)]:.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"""					)





