




"""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()
lowercase__     						=						logging.get_logger()







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = True		):
				print(f'''Converting {name}...'''		)

				with torch.no_grad():
								if hidden_sizes == 128:
												if name[-1] == "S":
																_lowerCamelCase     :			Dict    =    timm.create_model('levit_128s'							,    pretrained=lowercase__		)
												else:
																_lowerCamelCase     :			int    =    timm.create_model('levit_128'							,    pretrained=lowercase__		)
								if hidden_sizes == 192:
												_lowerCamelCase     :			Optional[int]    =    timm.create_model('levit_192'							,    pretrained=lowercase__		)
								if hidden_sizes == 256:
												_lowerCamelCase     :			Optional[Any]    =    timm.create_model('levit_256'							,    pretrained=lowercase__		)
								if hidden_sizes == 384:
												_lowerCamelCase     :			str    =    timm.create_model('levit_384'							,    pretrained=lowercase__		)

								from_model.eval()
								_lowerCamelCase     :			List[Any]    =    LevitForImageClassificationWithTeacher(lowercase__		).eval()
								_lowerCamelCase     :			Optional[int]    =    OrderedDict()

								_lowerCamelCase     :			List[Any]    =    from_model.state_dict()
								_lowerCamelCase     :			List[Any]    =    list(from_model.state_dict().keys()		)
								_lowerCamelCase     :			Union[str, Any]    =    list(our_model.state_dict().keys()		)
								print(len(lowercase__		)							,    len(lowercase__		)		)
								for i in range(len(lowercase__		)		):
												_lowerCamelCase     :			Union[str, Any]    =    weights[og_keys[i]]
								our_model.load_state_dict(lowercase__		)

								_lowerCamelCase     :			Optional[Any]    =    torch.randn((2, 3, 224, 224)		)
								_lowerCamelCase     :			Union[str, Any]    =    from_model(lowercase__		)
								_lowerCamelCase     :			Tuple    =    our_model(lowercase__		).logits

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

				_lowerCamelCase     :			Any    =    name
				print(lowercase__		)

				if push_to_hub:
								our_model.save_pretrained(save_directory / checkpoint_name		)
								_lowerCamelCase     :			List[Any]    =    LevitImageProcessor()
								image_processor.save_pretrained(save_directory / checkpoint_name		)

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







def      _snake_case			(				lowercase__							,    lowercase__ = None							,    lowercase__ = True		):
				_lowerCamelCase     :			str    =    'imagenet-1k-id2label.json'
				_lowerCamelCase     :			Dict    =    1000
				_lowerCamelCase     :			Union[str, Any]    =    (1, num_labels)

				_lowerCamelCase     :			Optional[Any]    =    'huggingface/label-files'
				_lowerCamelCase     :			Tuple    =    num_labels
				_lowerCamelCase     :			str    =    json.load(open(hf_hub_download(lowercase__							,    lowercase__							,    repo_type='dataset'		)							,    'r'		)		)
				_lowerCamelCase     :			List[Any]    =    {int(lowercase__		): v for k, v in idalabel.items()}

				_lowerCamelCase     :			int    =    idalabel
				_lowerCamelCase     :			Dict    =    {v: k for k, v in idalabel.items()}

				_lowerCamelCase     :			int    =    partial(lowercase__							,    num_labels=lowercase__							,    idalabel=lowercase__							,    labelaid=lowercase__		)

				_lowerCamelCase     :			Optional[Any]    =    {
				    'levit-128S': 128,
				    'levit-128': 128,
				    'levit-192': 192,
				    'levit-256': 256,
				    'levit-384': 384,
				}

				_lowerCamelCase     :			Optional[Any]    =    {
				    'levit-128S': ImageNetPreTrainedConfig(
				        hidden_sizes=[128, 256, 384]							,    num_attention_heads=[4, 6, 8]							,    depths=[2, 3, 4]							,    key_dim=[16, 16, 16]							,    drop_path_rate=0							,    ),
				    'levit-128': ImageNetPreTrainedConfig(
				        hidden_sizes=[128, 256, 384]							,    num_attention_heads=[4, 8, 12]							,    depths=[4, 4, 4]							,    key_dim=[16, 16, 16]							,    drop_path_rate=0							,    ),
				    'levit-192': ImageNetPreTrainedConfig(
				        hidden_sizes=[192, 288, 384]							,    num_attention_heads=[3, 5, 6]							,    depths=[4, 4, 4]							,    key_dim=[32, 32, 32]							,    drop_path_rate=0							,    ),
				    'levit-256': ImageNetPreTrainedConfig(
				        hidden_sizes=[256, 384, 512]							,    num_attention_heads=[4, 6, 8]							,    depths=[4, 4, 4]							,    key_dim=[32, 32, 32]							,    drop_path_rate=0							,    ),
				    'levit-384': ImageNetPreTrainedConfig(
				        hidden_sizes=[384, 512, 768]							,    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]							,    lowercase__							,    names_to_config[model_name]							,    lowercase__							,    lowercase__		)
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(names_to_hidden_sizes[model_name]							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		)
				return config, expected_shape


if __name__ == "__main__":
			lowercase__     						=						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""",
			)

			lowercase__     						=						parser.parse_args()
			lowercase__     						=						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"""






def      _snake_case			(				lowercase__							,    lowercase__		):
				# "extended trapezoidal rule"
				# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
				_lowerCamelCase     :			List[Any]    =    (boundary[1] - boundary[0]) / steps
				_lowerCamelCase     :			Tuple    =    boundary[0]
				_lowerCamelCase     :			Dict    =    boundary[1]
				_lowerCamelCase     :			List[Any]    =    make_points(lowercase__							,    lowercase__							,    lowercase__		)
				_lowerCamelCase     :			List[Any]    =    0.0
				y += (h / 2.0) * f(lowercase__		)
				for i in x_i:
								# print(i)
								y += h * f(lowercase__		)
				y += (h / 2.0) * f(lowercase__		)
				return y







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			str    =    a + h
				while x < (b - h):
								yield x
								_lowerCamelCase     :			int    =    x + h







def      _snake_case			(				lowercase__		):  # enter your function here
				_lowerCamelCase     :			Optional[Any]    =    (x - 0) * (x - 0)
				return y







def      _snake_case			(				):
				_lowerCamelCase     :			int    =    0.0  # Lower bound of integration
				_lowerCamelCase     :			Optional[int]    =    1.0  # Upper bound of integration
				_lowerCamelCase     :			List[str]    =    1_0.0  # define number of steps or resolution
				_lowerCamelCase     :			List[Any]    =    [a, b]  # define boundary of integration
				_lowerCamelCase     :			Optional[Any]    =    method_a(lowercase__							,    lowercase__		)
				print(f'''y = {y}'''		)


if __name__ == "__main__":
			main()