



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()
snake_case__					:     List[Any]    		=   logging.get_logger(__name__)



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			"huggingface/label-files"
	__lowercase		       =			"imagenet-1k-id2label.json"
	__lowercase		       =			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
	__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
	__lowercase		       =			{v: k for k, v in idalabel.items()}

	__lowercase		       =			"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		       =			BitConfig(
	    conv_layer=_SCREAMING_SNAKE_CASE  ,	num_labels=1_0_0_0  ,	idalabel=_SCREAMING_SNAKE_CASE  ,	labelaid=_SCREAMING_SNAKE_CASE  ,	)

	return config



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	if "stem.conv" in name:
		__lowercase		       =			name.replace("stem.conv"  ,	"bit.embedder.convolution"   )
	if "blocks" in name:
		__lowercase		       =			name.replace("blocks"  ,	"layers"   )
	if "head.fc" in name:
		__lowercase		       =			name.replace("head.fc"  ,	"classifier.1"   )
	if name.startswith("norm"   ):
		__lowercase		       =			"bit." + name
	if "bit" not in name and "classifier" not in name:
		__lowercase		       =			"bit.encoder." + name

	return name



def    snake_case_				(    ):
	__lowercase		       =			"http://images.cocodataset.org/val2017/000000039769.jpg"
	__lowercase		       =			Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,	stream=_SCREAMING_SNAKE_CASE   ).raw   )
	return im



@torch.no_grad()
def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False   ):
	__lowercase		       =			get_config(_SCREAMING_SNAKE_CASE   )

	# load original model from timm
	__lowercase		       =			create_model(_SCREAMING_SNAKE_CASE  ,	pretrained=_SCREAMING_SNAKE_CASE   )
	timm_model.eval()

	# load state_dict of original model
	__lowercase		       =			timm_model.state_dict()
	for key in state_dict.copy().keys():
		__lowercase		       =			state_dict.pop(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			val.squeeze() if "head" in key else val

	# load HuggingFace model
	__lowercase		       =			BitForImageClassification(_SCREAMING_SNAKE_CASE   )
	model.eval()
	model.load_state_dict(_SCREAMING_SNAKE_CASE   )

	# create image processor
	__lowercase		       =			create_transform(**resolve_data_config({}  ,	model=_SCREAMING_SNAKE_CASE   )   )
	__lowercase		       =			transform.transforms

	__lowercase		       =			{
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

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

	__lowercase		       =			prepare_img()
	__lowercase		       =			transform(_SCREAMING_SNAKE_CASE   ).unsqueeze(0   )
	__lowercase		       =			processor(_SCREAMING_SNAKE_CASE  ,	return_tensors="pt"   ).pixel_values

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

	# verify logits
	with torch.no_grad():
		__lowercase		       =			model(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			outputs.logits

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

	if pytorch_dump_folder_path is not None:
		Path(_SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE   )
		print(F"""Saving model {model_name} and processor to {pytorch_dump_folder_path}"""   )
		model.save_pretrained(_SCREAMING_SNAKE_CASE   )
		processor.save_pretrained(_SCREAMING_SNAKE_CASE   )

	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__":
	snake_case__					:     str    		=   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.""",
	)

	snake_case__					:     Tuple    		=   parser.parse_args()
	convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








from __future__ import annotations

from typing import Any

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							Union[str, Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							__lowercase		       =			num_of_nodes
							__lowercase		       =			[]
							__lowercase		       =			{}



						def     _snake_case	(	self	:							Dict							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							self.m_edges.append([u_node, v_node, weight]	)



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							if self.m_component[u_node] == u_node:
								return u_node
							return self.find_component(self.m_component[u_node]	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							if self.m_component[u_node] != u_node:
								for k in self.m_component:
									__lowercase		       =			self.find_component(lowerCamelCase	)



						def     _snake_case	(	self	:							Union[str, Any]							,      lowerCamelCase	:							list[int]							,      lowerCamelCase	:							int							,      lowerCamelCase	:							int	):






							'''simple docstring'''



							if component_size[u_node] <= component_size[v_node]:
								__lowercase		       =			v_node
								component_size[v_node] += component_size[u_node]
								self.set_component(lowerCamelCase	)

							elif component_size[u_node] >= component_size[v_node]:
								__lowercase		       =			self.find_component(lowerCamelCase	)
								component_size[u_node] += component_size[v_node]
								self.set_component(lowerCamelCase	)



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							__lowercase		       =			[]
							__lowercase		       =			0

							__lowercase		       =			[-1] * self.m_num_of_nodes

							# A list of components (initialized to all of the nodes)
							for node in range(self.m_num_of_nodes	):
								self.m_component.update({node: node}	)
								component_size.append(1	)

							__lowercase		       =			self.m_num_of_nodes

							while num_of_components > 1:
								for edge in self.m_edges:
									__lowercase       ,    __lowercase       ,    __lowercase		       =			edge

									__lowercase		       =			self.m_component[u]
									__lowercase		       =			self.m_component[v]

									if u_component != v_component:

										for component in (u_component, v_component):
											if (
											    minimum_weight_edge[component] == -1
											    or minimum_weight_edge[component][2] > w
											):
												__lowercase		       =			[u, v, w]

								for edge in minimum_weight_edge:
									if isinstance(lowerCamelCase							,      lowerCamelCase	):
										__lowercase       ,    __lowercase       ,    __lowercase		       =			edge

										__lowercase		       =			self.m_component[u]
										__lowercase		       =			self.m_component[v]

										if u_component != v_component:
											mst_weight += w
											self.union(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)
											print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""	)
											num_of_components -= 1

								__lowercase		       =			[-1] * self.m_num_of_nodes
							print(f"""The total weight of the minimal spanning tree is: {mst_weight}"""	)



def    snake_case_				(    ):
	pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()



