






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()
__UpperCAmelCase								=						logging.get_logger()




def      lowercase__      (     __snake_case    :							int     ,       __snake_case    :							str     ,       __snake_case    :							LevitConfig     ,       __snake_case    :							Path     ,       __snake_case    :							bool = True  ):


					'''simple docstring'''


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

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

										from_model.eval()
										UpperCAmelCase_    :      Dict            =						LevitForImageClassificationWithTeacher(__snake_case  ).eval()
										UpperCAmelCase_    :      Tuple            =						OrderedDict()

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

										UpperCAmelCase_    :      str            =						torch.randn((2, 3, 224, 224)  )
										UpperCAmelCase_    :      List[Any]            =						from_model(__snake_case  )
										UpperCAmelCase_    :      Any            =						our_model(__snake_case  ).logits

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

					UpperCAmelCase_    :      str            =						name
					print(__snake_case  )

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

										print(F"Pushed {checkpoint_name}"  )




def      lowercase__      (     __snake_case    :							Path     ,       __snake_case    :							str = None     ,       __snake_case    :							bool = True  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						'imagenet-1k-id2label.json'
					UpperCAmelCase_    :      Union[str, Any]            =						1_000
					UpperCAmelCase_    :      Tuple            =						(1, num_labels)

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

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

					UpperCAmelCase_    :      Optional[int]            =						partial(__snake_case     ,       num_labels=__snake_case     ,       idalabel=__snake_case     ,       labelaid=__snake_case  )

					UpperCAmelCase_    :      Tuple            =						{
					    'levit-128S': 128,
					    'levit-128': 128,
					    'levit-192': 192,
					    'levit-256': 256,
					    'levit-384': 384,
					}

					UpperCAmelCase_    :      int            =						{
					    '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]     ,       __snake_case     ,       names_to_config[model_name]     ,       __snake_case     ,       __snake_case  )
					else:
										for model_name, config in names_to_config.items():
															convert_weight_and_push(names_to_hidden_sizes[model_name]     ,       __snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case  )
					return config, expected_shape


if __name__ == "__main__":
		__UpperCAmelCase								=						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',
		)

		__UpperCAmelCase								=						parser.parse_args()
		__UpperCAmelCase								=						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)













import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


_snake_case  :    Tuple						    =  '\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n'


_snake_case  :    str						    =  '\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'


_snake_case  :    List[str]						    =  R'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n    >>> metric = datasets.load_metric("competition_math")\n    >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'



@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION		,    _KWARGS_DESCRIPTION    )
class 				_UpperCAmelCase    (    datasets.Metric    ):

  """simple docstring"""


  def 				lowercase		(				self :		str     )							->						int:
     return datasets.MetricInfo(
         description=_DESCRIPTION					,  citation=_CITATION					,  inputs_description=_KWARGS_DESCRIPTION					,  features=datasets.Features(
             {
                 'predictions': datasets.Value('string'     ),
                 'references': datasets.Value('string'     ),
             }     )					,  homepage='https://github.com/hendrycks/math'					,  codebase_urls=['https://github.com/hendrycks/math']					,  )






  def 				lowercase		(				self :		str					,  lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		Optional[int]     )							->						List[Any]:
     __lowerCAmelCase								=	0.0
     for i, j in zip(lowerCAmelCase_					,  lowerCAmelCase_     ):
        n_correct += 1.0 if math_equivalence.is_equiv(lowerCAmelCase_					,  lowerCAmelCase_     ) else 0.0
     __lowerCAmelCase								=	n_correct / len(lowerCAmelCase_     )
     return {
         "accuracy": accuracy,
     }

