

'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_    						=       logging.get_logger(__name__)






def 				_lowerCAmelCase			(   __magic_name__						:     Dict						)				->  List[Any]:
						lowercase :							Union[str, Any]    		='''huggingface/label-files'''
						lowercase :							Optional[int]    		='''imagenet-1k-id2label.json'''
						lowercase :							Optional[Any]    		=json.load(open(hf_hub_download(__magic_name__   , __magic_name__   , repo_type='''dataset'''						)   , '''r'''						)						)
						lowercase :							List[Any]    		={int(__magic_name__						): v for k, v in idalabel.items()}
						lowercase :							Any    		={v: k for k, v in idalabel.items()}

						lowercase :							Union[str, Any]    		='''std_conv''' if '''bit''' in model_name else False

						# note that when using BiT as backbone for ViT-hybrid checkpoints,
						# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
						# config.conv_layer = "std_conv_same"
						lowercase :							Any    		=BitConfig(
						    conv_layer=__magic_name__   , num_labels=1000   , idalabel=__magic_name__   , labelaid=__magic_name__   , )

						return config






def 				_lowerCAmelCase			(   __magic_name__						:     Optional[Any]						)				->  List[str]:
						if "stem.conv" in name:
												lowercase :							Tuple    		=name.replace('''stem.conv'''   , '''bit.embedder.convolution'''						)
						if "blocks" in name:
												lowercase :							Union[str, Any]    		=name.replace('''blocks'''   , '''layers'''						)
						if "head.fc" in name:
												lowercase :							Tuple    		=name.replace('''head.fc'''   , '''classifier.1'''						)
						if name.startswith('''norm'''						):
												lowercase :							Tuple    		='''bit.''' + name
						if "bit" not in name and "classifier" not in name:
												lowercase :							Optional[int]    		='''bit.encoder.''' + name

						return name






def 				_lowerCAmelCase			(   )				->  Tuple:
						lowercase :							int    		='''http://images.cocodataset.org/val2017/000000039769.jpg'''
						lowercase :							Any    		=Image.open(requests.get(__magic_name__   , stream=__magic_name__						).raw						)
						return im






@torch.no_grad()
def 				_lowerCAmelCase			(   __magic_name__						:     Any   , __magic_name__						:     List[str]   , __magic_name__						:     List[Any]=False						)				->  Tuple:
						lowercase :							Optional[Any]    		=get_config(__magic_name__						)

						# load original model from timm
						lowercase :							Dict    		=create_model(__magic_name__   , pretrained=__magic_name__						)
						timm_model.eval()

						# load state_dict of original model
						lowercase :							List[str]    		=timm_model.state_dict()
						for key in state_dict.copy().keys():
												lowercase :							Union[str, Any]    		=state_dict.pop(__magic_name__						)
												lowercase :							List[Any]    		=val.squeeze() if '''head''' in key else val

						# load HuggingFace model
						lowercase :							str    		=BitForImageClassification(__magic_name__						)
						model.eval()
						model.load_state_dict(__magic_name__						)

						# create image processor
						lowercase :							str    		=create_transform(**resolve_data_config({}   , model=__magic_name__						)						)
						lowercase :							int    		=transform.transforms

						lowercase :							Union[str, Any]    		={
						    '''bilinear''': PILImageResampling.BILINEAR,
						    '''bicubic''': PILImageResampling.BICUBIC,
						    '''nearest''': PILImageResampling.NEAREST,
						}

						lowercase :							Optional[Any]    		=BitImageProcessor(
						    do_resize=__magic_name__   , size={'''shortest_edge''': timm_transforms[0].size}   , resample=pillow_resamplings[timm_transforms[0].interpolation.value]   , do_center_crop=__magic_name__   , crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}   , do_normalize=__magic_name__   , image_mean=timm_transforms[-1].mean.tolist()   , image_std=timm_transforms[-1].std.tolist()   , )

						lowercase :							Optional[Any]    		=prepare_img()
						lowercase :							Optional[Any]    		=transform(__magic_name__						).unsqueeze(0						)
						lowercase :							Optional[Any]    		=processor(__magic_name__   , return_tensors='''pt'''						).pixel_values

						# verify pixel values
						assert torch.allclose(__magic_name__   , __magic_name__						)

						# verify logits
						with torch.no_grad():
												lowercase :							List[str]    		=model(__magic_name__						)
												lowercase :							Any    		=outputs.logits

						print('''Logits:'''   , logits[0, :3]						)
						print('''Predicted class:'''   , model.config.idalabel[logits.argmax(-1						).item()]						)
						lowercase :							List[str]    		=timm_model(__magic_name__						)
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(__magic_name__   , outputs.logits   , atol=1E-3						)
						print('''Looks ok!'''						)

						if pytorch_dump_folder_path is not None:
												Path(__magic_name__						).mkdir(exist_ok=__magic_name__						)
												print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''						)
												model.save_pretrained(__magic_name__						)
												processor.save_pretrained(__magic_name__						)

						if push_to_hub:
												print(f'''Pushing model {model_name} and processor to the hub'''						)
												model.push_to_hub(f'''ybelkada/{model_name}'''						)
												processor.push_to_hub(f'''ybelkada/{model_name}'''						)


if __name__ == "__main__":
							UpperCamelCase_    						=       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--model_name""",
							    default="""resnetv2_50x1_bitm""",
							    type=str,
							    help="""Name of the BiT timm model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
							)
							parser.add_argument(
							    """--push_to_hub""",
							    action="""store_true""",
							    help="""Whether to push the model to the hub.""",
							)

							UpperCamelCase_    						=       parser.parse_args()
							convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








from random import randint
from tempfile import TemporaryFile

import numpy as np



def   __UpperCAmelCase					(      __a :     Optional[Any]						,__a :     int						,__a :     Any      )     ->	int:

					"""simple docstring"""

					_a      :       int   =						0
					if start < end:
										_a      :       Tuple   =						randint(__a						,__a      )
										_a      :       Tuple   =						a[end]
										_a      :       List[str]   =						a[pivot]
										_a      :       Any   =						temp

										_a ,   _a      :       Optional[int]   =						_in_place_partition(__a						,__a						,__a      )
										count += _in_place_quick_sort(__a						,__a						,p - 1      )
										count += _in_place_quick_sort(__a						,p + 1						,__a      )
					return count



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Tuple						,__a :     Dict      )     ->	Dict:

					"""simple docstring"""

					_a      :       Dict   =						0
					_a      :       Tuple   =						randint(__a						,__a      )
					_a      :       List[Any]   =						a[end]
					_a      :       str   =						a[pivot]
					_a      :       str   =						temp
					_a      :       Dict   =						start - 1
					for index in range(__a						,__a      ):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															_a      :       int   =						new_pivot_index + 1
															_a      :       Any   =						a[new_pivot_index]
															_a      :       Optional[int]   =						a[index]
															_a      :       str   =						temp

					_a      :       Union[str, Any]   =						a[new_pivot_index + 1]
					_a      :       Tuple   =						a[end]
					_a      :       Any   =						temp
					return new_pivot_index + 1, count


a__												=						TemporaryFile()
a__												=						100  # 1000 elements are to be sorted


a__						,     a__												=						0, 1  # mean and standard deviation
a__												=						np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a__												=						np.load(outfile)
a__												=						len(M) - 1
a__												=						_in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)







print(z)



