




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


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

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

					snake_case_									=				'''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"
					snake_case_									=				BitConfig(
					    conv_layer=__UpperCAmelCase,      num_labels=1000,      idalabel=__UpperCAmelCase,      labelaid=__UpperCAmelCase,      )

					return config


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

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

					return name


def 		__magic_name__    (	)		->					Tuple:

					'''simple docstring'''

					snake_case_									=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					snake_case_									=				Image.open(requests.get(__UpperCAmelCase,      stream=__UpperCAmelCase	).raw	)
					return im


@torch.no_grad()
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase=False	)		->					str:

					'''simple docstring'''

					snake_case_									=				get_config(__UpperCAmelCase	)

					# load original model from timm
					snake_case_									=				create_model(__UpperCAmelCase,      pretrained=__UpperCAmelCase	)
					timm_model.eval()

					# load state_dict of original model
					snake_case_									=				timm_model.state_dict()
					for key in state_dict.copy().keys():
										snake_case_									=				state_dict.pop(__UpperCAmelCase	)
										snake_case_									=				val.squeeze() if '''head''' in key else val

					# load HuggingFace model
					snake_case_									=				BitForImageClassification(__UpperCAmelCase	)
					model.eval()
					model.load_state_dict(__UpperCAmelCase	)

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

					snake_case_									=				{
					    '''bilinear''': PILImageResampling.BILINEAR,
					    '''bicubic''': PILImageResampling.BICUBIC,
					    '''nearest''': PILImageResampling.NEAREST,
					}

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

					snake_case_									=				prepare_img()
					snake_case_									=				transform(__UpperCAmelCase	).unsqueeze(0	)
					snake_case_									=				processor(__UpperCAmelCase,      return_tensors='''pt'''	).pixel_values

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

					# verify logits
					with torch.no_grad():
										snake_case_									=				model(__UpperCAmelCase	)
										snake_case_									=				outputs.logits

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

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

					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__":
							a  :				Optional[Any]									=  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.',
							)

							a  :				Any									=  parser.parse_args()
							convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''






from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
UpperCamelCase__:   Tuple								=			numpy.array([0, 0])
UpperCamelCase__:   Union[str, Any]								=			numpy.array([0.5, 0.8660254])
UpperCamelCase__:   Dict								=			numpy.array([1, 0])
UpperCamelCase__:   int								=			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]


def  snake_case_     (					_lowerCAmelCase  :						list[numpy.ndarray]   ,      _lowerCAmelCase  :						int  )      ->   list[numpy.ndarray]:
							UpperCAmelCase :       Union[str, Any] 							=		initial_vectors
							for _ in range(_lowerCAmelCase  ):
														UpperCAmelCase :       Union[str, Any] 							=		iteration_step(_lowerCAmelCase  )
							return vectors


def  snake_case_     (					_lowerCAmelCase  :						list[numpy.ndarray]  )      ->   list[numpy.ndarray]:
							UpperCAmelCase :       Tuple 							=		[]
							for i, start_vector in enumerate(vectors[:-1]  ):
														UpperCAmelCase :       List[str] 							=		vectors[i + 1]
														new_vectors.append(_lowerCAmelCase  )
														UpperCAmelCase :       Optional[Any] 							=		end_vector - start_vector
														new_vectors.append(start_vector + difference_vector / 3  )
														new_vectors.append(
														    start_vector + difference_vector / 3 + rotate(difference_vector / 3   ,      60  )  )
														new_vectors.append(start_vector + difference_vector * 2 / 3  )
							new_vectors.append(vectors[-1]  )
							return new_vectors


def  snake_case_     (					_lowerCAmelCase  :						numpy.ndarray   ,      _lowerCAmelCase  :						float  )      ->   numpy.ndarray:
							UpperCAmelCase :       List[str] 							=		numpy.radians(_lowerCAmelCase  )
							UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		numpy.cos(_lowerCAmelCase  ), numpy.sin(_lowerCAmelCase  )
							UpperCAmelCase :       Union[str, Any] 							=		numpy.array(((c, -s), (s, c))  )
							return numpy.dot(_lowerCAmelCase   ,      _lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						list[numpy.ndarray]  )      ->   None:
							UpperCAmelCase :       List[Any] 							=		plt.gca()
							axes.set_aspect('''equal'''  )

							# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
							# y-coordinates as inputs, which are constructed from the vector-list using
							# zip()
							UpperCAmelCase							,		UpperCAmelCase :       str 							=		zip(*_lowerCAmelCase  )
							plt.plot(_lowerCAmelCase   ,      _lowerCAmelCase  )
							plt.show()


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						UpperCamelCase__:   List[Any]								=			iterate(INITIAL_VECTORS, 5)
						plot(processed_vectors)





