

'''simple docstring'''





import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A_	= logging.get_logger()







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase = True	)       ->			int:
					print(f'''Converting {name}...'''	)

					with torch.no_grad():
										if hidden_sizes == 1_28:
															if name[-1] == "S":
																				lowerCamelCase_  				=      timm.create_model('levit_128s'    ,pretrained=A__	)
															else:
																				lowerCamelCase_  				=      timm.create_model('levit_128'    ,pretrained=A__	)
										if hidden_sizes == 1_92:
															lowerCamelCase_  				=      timm.create_model('levit_192'    ,pretrained=A__	)
										if hidden_sizes == 2_56:
															lowerCamelCase_  				=      timm.create_model('levit_256'    ,pretrained=A__	)
										if hidden_sizes == 3_84:
															lowerCamelCase_  				=      timm.create_model('levit_384'    ,pretrained=A__	)

										from_model.eval()
										lowerCamelCase_  				=      LevitForImageClassificationWithTeacher(A__	).eval()
										lowerCamelCase_  				=      OrderedDict()

										lowerCamelCase_  				=      from_model.state_dict()
										lowerCamelCase_  				=      list(from_model.state_dict().keys()	)
										lowerCamelCase_  				=      list(our_model.state_dict().keys()	)
										print(len(A__	)    ,len(A__	)	)
										for i in range(len(A__	)	):
															lowerCamelCase_  				=      weights[og_keys[i]]
										our_model.load_state_dict(A__	)

										lowerCamelCase_  				=      torch.randn((2, 3, 2_24, 2_24)	)
										lowerCamelCase_  				=      from_model(A__	)
										lowerCamelCase_  				=      our_model(A__	).logits

					assert torch.allclose(A__    ,A__	), "The model logits don't match the original one."

					lowerCamelCase_  				=      name
					print(A__	)

					if push_to_hub:
										our_model.save_pretrained(save_directory / checkpoint_name	)
										lowerCamelCase_  				=      LevitImageProcessor()
										image_processor.save_pretrained(save_directory / checkpoint_name	)

										print(f'''Pushed {checkpoint_name}'''	)







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase = None    ,__UpperCamelCase = True	)       ->			Any:
					lowerCamelCase_  				=      """imagenet-1k-id2label.json"""
					lowerCamelCase_  				=      10_00
					lowerCamelCase_  				=      (1, num_labels)

					lowerCamelCase_  				=      """huggingface/label-files"""
					lowerCamelCase_  				=      num_labels
					lowerCamelCase_  				=      json.load(open(hf_hub_download(A__    ,A__    ,repo_type='dataset'	)    ,'r'	)	)
					lowerCamelCase_  				=      {int(A__	): v for k, v in idalabel.items()}

					lowerCamelCase_  				=      idalabel
					lowerCamelCase_  				=      {v: k for k, v in idalabel.items()}

					lowerCamelCase_  				=      partial(A__    ,num_labels=A__    ,idalabel=A__    ,labelaid=A__	)

					lowerCamelCase_  				=      {
					    """levit-128S""": 1_28,
					    """levit-128""": 1_28,
					    """levit-192""": 1_92,
					    """levit-256""": 2_56,
					    """levit-384""": 3_84,
					}

					lowerCamelCase_  				=      {
					    """levit-128S""": ImageNetPreTrainedConfig(
					        hidden_sizes=[1_28, 2_56, 3_84]    ,num_attention_heads=[4, 6, 8]    ,depths=[2, 3, 4]    ,key_dim=[16, 16, 16]    ,drop_path_rate=0    ,),
					    """levit-128""": ImageNetPreTrainedConfig(
					        hidden_sizes=[1_28, 2_56, 3_84]    ,num_attention_heads=[4, 8, 12]    ,depths=[4, 4, 4]    ,key_dim=[16, 16, 16]    ,drop_path_rate=0    ,),
					    """levit-192""": ImageNetPreTrainedConfig(
					        hidden_sizes=[1_92, 2_88, 3_84]    ,num_attention_heads=[3, 5, 6]    ,depths=[4, 4, 4]    ,key_dim=[32, 32, 32]    ,drop_path_rate=0    ,),
					    """levit-256""": ImageNetPreTrainedConfig(
					        hidden_sizes=[2_56, 3_84, 5_12]    ,num_attention_heads=[4, 6, 8]    ,depths=[4, 4, 4]    ,key_dim=[32, 32, 32]    ,drop_path_rate=0    ,),
					    """levit-384""": ImageNetPreTrainedConfig(
					        hidden_sizes=[3_84, 5_12, 7_68]    ,num_attention_heads=[6, 9, 12]    ,depths=[4, 4, 4]    ,key_dim=[32, 32, 32]    ,drop_path_rate=0.1    ,),
					}

					if model_name:
										convert_weight_and_push(
										    names_to_hidden_sizes[model_name]    ,A__    ,names_to_config[model_name]    ,A__    ,A__	)
					else:
										for model_name, config in names_to_config.items():
															convert_weight_and_push(names_to_hidden_sizes[model_name]    ,A__    ,A__    ,A__    ,A__	)
					return config, expected_shape


if __name__ == "__main__":
				A_	= argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--model_name",
				    default=None,
				    type=str,
				    help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default="levit-dump-folder/",
				    type=Path,
				    required=False,
				    help="Path to the output PyTorch model directory.",
				)
				parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
				parser.add_argument(
				    "--no-push_to_hub",
				    dest="push_to_hub",
				    action="store_false",
				    help="Do not push model and image processor to the hub",
				)

				A_	= parser.parse_args()
				A_	= args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







'''simple docstring'''






import math







def 							UpperCamelCase_     (       A__   :  float				,       A__   :  float   ):
  '''simple docstring'''





  if initial_intensity < 0:
    raise ValueError("""The value of intensity cannot be negative"""   )
    # handling of negative values of initial intensity
  if angle < 0 or angle > 3_60:
    raise ValueError("""In Malus Law, the angle is in the range 0-360 degrees"""   )
    # handling of values out of allowed range
  return initial_intensity * (math.cos(math.radians(A__   )   ) ** 2)


if __name__ == "__main__":
  import doctest

  doctest.testmod(name="malus_law")



