
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 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__ = True	)							->							Dict:
				"""simple docstring"""

				print(F'Converting {name}...'	)

				with torch.no_grad():
								if hidden_sizes == 128:
												if name[-1] == "S":
																snake_case				  =      timm.create_model("levit_128s"				,	pretrained=A__	)
												else:
																snake_case				  =      timm.create_model("levit_128"				,	pretrained=A__	)
								if hidden_sizes == 192:
												snake_case				  =      timm.create_model("levit_192"				,	pretrained=A__	)
								if hidden_sizes == 256:
												snake_case				  =      timm.create_model("levit_256"				,	pretrained=A__	)
								if hidden_sizes == 384:
												snake_case				  =      timm.create_model("levit_384"				,	pretrained=A__	)

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

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

								snake_case				  =      torch.randn((2, 3, 224, 224)	)
								snake_case				  =      from_model(A__	)
								snake_case				  =      our_model(A__	).logits

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

				snake_case				  =      name
				print(A__	)

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

								print(F'Pushed {checkpoint_name}'	)



def 		lowercase_    (     A__				,	A__ = None				,	A__ = True	)							->							List[Any]:
				"""simple docstring"""

				snake_case				  =      "imagenet-1k-id2label.json"
				snake_case				  =      1000
				snake_case				  =      (1, num_labels)

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

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

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

				snake_case				  =      {
				    "levit-128S": 128,
				    "levit-128": 128,
				    "levit-192": 192,
				    "levit-256": 256,
				    "levit-384": 384,
				}

				snake_case				  =      {
				    "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]				,	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)



def 		lowercase_    (     A__				,	A__	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      [0 for i in range(r + 1	)]
    # nc0 = 1
    snake_case				  =      1
    for i in range(1				,	n + 1	):
        # to compute current row from previous row.
        snake_case				  =      min(A__				,	A__	)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]





print(binomial_coefficient(n=10, r=5))

