






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()
SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)

def      _lowerCamelCase	(						__A    :							Any )      ->					Any:
    _UpperCAmelCase				:      Any			  =				'''huggingface/label-files'''
    _UpperCAmelCase				:      str			  =				'''imagenet-1k-id2label.json'''
    _UpperCAmelCase				:      int			  =				json.load(open(hf_hub_download(__A		,		__A		,		repo_type='''dataset''' )		,		'''r''' ) )
    _UpperCAmelCase				:      List[Any]			  =				{int(__A ): v for k, v in idalabel.items()}
    _UpperCAmelCase				:      str			  =				{v: k for k, v in idalabel.items()}

    _UpperCAmelCase				:      List[Any]			  =				'''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"
    _UpperCAmelCase				:      List[Any]			  =				BitConfig(
        conv_layer=__A		,		num_labels=1_000		,		idalabel=__A		,		labelaid=__A		,		)

    return config

def      _lowerCamelCase	(						__A    :							int )      ->					Optional[int]:







    if "stem.conv" in name:
        _UpperCAmelCase				:      List[str]			  =				name.replace('''stem.conv'''		,		'''bit.embedder.convolution''' )
    if "blocks" in name:
        _UpperCAmelCase				:      Union[str, Any]			  =				name.replace('''blocks'''		,		'''layers''' )
    if "head.fc" in name:
        _UpperCAmelCase				:      Optional[int]			  =				name.replace('''head.fc'''		,		'''classifier.1''' )
    if name.startswith('''norm''' ):
        _UpperCAmelCase				:      Any			  =				'''bit.''' + name
    if "bit" not in name and "classifier" not in name:
        _UpperCAmelCase				:      Optional[Any]			  =				'''bit.encoder.''' + name

    return name

def      _lowerCamelCase	(						)      ->					Tuple:
    _UpperCAmelCase				:      Dict			  =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    _UpperCAmelCase				:      Tuple			  =				Image.open(requests.get(__A		,		stream=__A ).raw )
    return im

@torch.no_grad()
def      _lowerCamelCase	(						__A    :							List[Any]		,		__A    :							List[str]		,		__A    :							List[str]=False )      ->					Tuple:
    _UpperCAmelCase				:      Dict			  =				get_config(__A )

    # load original model from timm
    _UpperCAmelCase				:      int			  =				create_model(__A		,		pretrained=__A )
    timm_model.eval()

    # load state_dict of original model
    _UpperCAmelCase				:      List[str]			  =				timm_model.state_dict()
    for key in state_dict.copy().keys():
        _UpperCAmelCase				:      List[Any]			  =				state_dict.pop(__A )
        _UpperCAmelCase				:      List[Any]			  =				val.squeeze() if '''head''' in key else val

    # load HuggingFace model
    _UpperCAmelCase				:      Dict			  =				BitForImageClassification(__A )
    model.eval()
    model.load_state_dict(__A )

    # create image processor
    _UpperCAmelCase				:      Tuple			  =				create_transform(**resolve_data_config({}		,		model=__A ) )
    _UpperCAmelCase				:      List[str]			  =				transform.transforms

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

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

    _UpperCAmelCase				:      int			  =				prepare_img()
    _UpperCAmelCase				:      List[str]			  =				transform(__A ).unsqueeze(0 )
    _UpperCAmelCase				:      Dict			  =				processor(__A		,		return_tensors='''pt''' ).pixel_values

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

    # verify logits
    with torch.no_grad():
        _UpperCAmelCase				:      Any			  =				model(__A )
        _UpperCAmelCase				:      int			  =				outputs.logits

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

    if pytorch_dump_folder_path is not None:
        Path(__A ).mkdir(exist_ok=__A )
        print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}''' )
        model.save_pretrained(__A )
        processor.save_pretrained(__A )

    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__":
  SCREAMING_SNAKE_CASE 							=						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.',
  )

  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












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

import datasets


SCREAMING_SNAKE_CASE 							=						'\\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'


SCREAMING_SNAKE_CASE 							=						'\\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'


SCREAMING_SNAKE_CASE 							=						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 							A_    (						datasets.Metric			):






 '''simple docstring'''




 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     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  snake_case__				(	self							,  _A							,  _A) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				0.0
     for i, j in zip(_A							,  _A):
         n_correct += 1.0 if math_equivalence.is_equiv(_A							,  _A) else 0.0
     _UpperCAmelCase				:      Tuple			  =				n_correct / len(_A)
     return {
         "accuracy": accuracy,
     }




