


'''simple docstring'''



import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




def 			_a			(		_lowercase      :			Dict						):





     '''simple docstring'''






     if "cls_token" in name:
          __UpperCAmelCase			:				Tuple													=    name.replace('''cls_token'''    ,      '''vit.embeddings.cls_token'''						)
     if "mask_token" in name:
          __UpperCAmelCase			:				str													=    name.replace('''mask_token'''    ,      '''decoder.mask_token'''						)
     if "decoder_pos_embed" in name:
          __UpperCAmelCase			:				Optional[Any]													=    name.replace('''decoder_pos_embed'''    ,      '''decoder.decoder_pos_embed'''						)
     if "pos_embed" in name and "decoder" not in name:
          __UpperCAmelCase			:				Tuple													=    name.replace('''pos_embed'''    ,      '''vit.embeddings.position_embeddings'''						)
     if "patch_embed.proj" in name:
          __UpperCAmelCase			:				Dict													=    name.replace('''patch_embed.proj'''    ,      '''vit.embeddings.patch_embeddings.projection'''						)
     if "patch_embed.norm" in name:
          __UpperCAmelCase			:				List[str]													=    name.replace('''patch_embed.norm'''    ,      '''vit.embeddings.norm'''						)
     if "decoder_blocks" in name:
          __UpperCAmelCase			:				List[Any]													=    name.replace('''decoder_blocks'''    ,      '''decoder.decoder_layers'''						)
     if "blocks" in name:
          __UpperCAmelCase			:				Optional[int]													=    name.replace('''blocks'''    ,      '''vit.encoder.layer'''						)
     if "attn.proj" in name:
          __UpperCAmelCase			:				int													=    name.replace('''attn.proj'''    ,      '''attention.output.dense'''						)
     if "attn" in name:
          __UpperCAmelCase			:				List[str]													=    name.replace('''attn'''    ,      '''attention.self'''						)
     if "norm1" in name:
          __UpperCAmelCase			:				Dict													=    name.replace('''norm1'''    ,      '''layernorm_before'''						)
     if "norm2" in name:
          __UpperCAmelCase			:				Any													=    name.replace('''norm2'''    ,      '''layernorm_after'''						)
     if "mlp.fc1" in name:
          __UpperCAmelCase			:				List[Any]													=    name.replace('''mlp.fc1'''    ,      '''intermediate.dense'''						)
     if "mlp.fc2" in name:
          __UpperCAmelCase			:				str													=    name.replace('''mlp.fc2'''    ,      '''output.dense'''						)
     if "decoder_embed" in name:
          __UpperCAmelCase			:				int													=    name.replace('''decoder_embed'''    ,      '''decoder.decoder_embed'''						)
     if "decoder_norm" in name:
          __UpperCAmelCase			:				Union[str, Any]													=    name.replace('''decoder_norm'''    ,      '''decoder.decoder_norm'''						)
     if "decoder_pred" in name:
          __UpperCAmelCase			:				int													=    name.replace('''decoder_pred'''    ,      '''decoder.decoder_pred'''						)
     if "norm.weight" in name and "decoder" not in name:
          __UpperCAmelCase			:				Tuple													=    name.replace('''norm.weight'''    ,      '''vit.layernorm.weight'''						)
     if "norm.bias" in name and "decoder" not in name:
          __UpperCAmelCase			:				Union[str, Any]													=    name.replace('''norm.bias'''    ,      '''vit.layernorm.bias'''						)

     return name




def 			_a			(		_lowercase      :			Union[str, Any]    ,      _lowercase      :			Tuple						):





     '''simple docstring'''






     for key in orig_state_dict.copy().keys():
          __UpperCAmelCase			:				Tuple													=    orig_state_dict.pop(_lowercase						)

          if "qkv" in key:
               __UpperCAmelCase			:				Dict													=    key.split('''.'''						)
               __UpperCAmelCase			:				List[Any]													=    int(key_split[1]						)
               if "decoder_blocks" in key:
                    __UpperCAmelCase			:				Optional[Any]													=    config.decoder_hidden_size
                    __UpperCAmelCase			:				Any													=    '''decoder.decoder_layers.'''
                    if "weight" in key:
                         __UpperCAmelCase			:				int													=    val[:dim, :]
                         __UpperCAmelCase			:				List[Any]													=    val[dim : dim * 2, :]
                         __UpperCAmelCase			:				Tuple													=    val[-dim:, :]
                    elif "bias" in key:
                         __UpperCAmelCase			:				Any													=    val[:dim]
                         __UpperCAmelCase			:				Tuple													=    val[dim : dim * 2]
                         __UpperCAmelCase			:				Union[str, Any]													=    val[-dim:]
               else:
                    __UpperCAmelCase			:				List[Any]													=    config.hidden_size
                    __UpperCAmelCase			:				Tuple													=    '''vit.encoder.layer.'''
                    if "weight" in key:
                         __UpperCAmelCase			:				Optional[int]													=    val[:dim, :]
                         __UpperCAmelCase			:				Dict													=    val[dim : dim * 2, :]
                         __UpperCAmelCase			:				List[str]													=    val[-dim:, :]
                    elif "bias" in key:
                         __UpperCAmelCase			:				List[Any]													=    val[:dim]
                         __UpperCAmelCase			:				Any													=    val[dim : dim * 2]
                         __UpperCAmelCase			:				Tuple													=    val[-dim:]

          else:
               __UpperCAmelCase			:				Dict													=    val

     return orig_state_dict




def 			_a			(		_lowercase      :			List[Any]    ,      _lowercase      :			str						):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[int]													=    ViTMAEConfig()
     if "large" in checkpoint_url:
          __UpperCAmelCase			:				List[str]													=    1024
          __UpperCAmelCase			:				int													=    4096
          __UpperCAmelCase			:				int													=    24
          __UpperCAmelCase			:				Union[str, Any]													=    16
     elif "huge" in checkpoint_url:
          __UpperCAmelCase			:				List[str]													=    14
          __UpperCAmelCase			:				str													=    1280
          __UpperCAmelCase			:				List[Any]													=    5120
          __UpperCAmelCase			:				Union[str, Any]													=    32
          __UpperCAmelCase			:				List[str]													=    16

     __UpperCAmelCase			:				Any													=    ViTMAEForPreTraining(_lowercase						)

     __UpperCAmelCase			:				Optional[int]													=    torch.hub.load_state_dict_from_url(_lowercase    ,      map_location='''cpu'''						)['''model''']

     __UpperCAmelCase			:				Union[str, Any]													=    ViTMAEImageProcessor(size=config.image_size						)

     __UpperCAmelCase			:				int													=    convert_state_dict(_lowercase    ,      _lowercase						)

     model.load_state_dict(_lowercase						)
     model.eval()

     __UpperCAmelCase			:				List[str]													=    '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

     __UpperCAmelCase			:				Any													=    Image.open(requests.get(_lowercase    ,      stream=_lowercase						).raw						)
     __UpperCAmelCase			:				Optional[Any]													=    ViTMAEImageProcessor(size=config.image_size						)
     __UpperCAmelCase			:				int													=    image_processor(images=_lowercase    ,      return_tensors='''pt'''						)

     # forward pass
     torch.manual_seed(2						)
     __UpperCAmelCase			:				Dict													=    model(**_lowercase						)
     __UpperCAmelCase			:				Dict													=    outputs.logits

     if "large" in checkpoint_url:
          __UpperCAmelCase			:				Dict													=    torch.tensor(
              [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]						)
     elif "huge" in checkpoint_url:
          __UpperCAmelCase			:				Union[str, Any]													=    torch.tensor(
              [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]						)
     else:
          __UpperCAmelCase			:				str													=    torch.tensor(
              [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]						)

     # verify logits
     assert torch.allclose(logits[0, :3, :3]    ,      _lowercase    ,      atol=1E-4						)

     print(F'Saving model to {pytorch_dump_folder_path}'						)
     model.save_pretrained(_lowercase						)

     print(F'Saving image processor to {pytorch_dump_folder_path}'						)
     image_processor.save_pretrained(_lowercase						)


if __name__ == "__main__":
      __UpperCAmelCase    :int				       =     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--checkpoint_url",
          default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
          type=str,
          help="URL of the checkpoint you'd like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
      )

      __UpperCAmelCase    :Optional[Any]				       =     parser.parse_args()
      convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



'''simple docstring'''



from scipy.stats import pearsonr

import datasets


__UpperCAmelCase    :Tuple				       =     "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n"


__UpperCAmelCase    :List[str]				       =     "\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        ['p-value', 'pearsonr']\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n        >>> print(round(results['p-value'], 2))\n        0.15\n"


__UpperCAmelCase    :List[Any]				       =     "\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,						_KWARGS_DESCRIPTION )
class 			a       (	datasets.Metric ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Union[str, Any]:
          return datasets.MetricInfo(
              description=_DESCRIPTION							,				citation=_CITATION							,				inputs_description=_KWARGS_DESCRIPTION							,				features=datasets.Features(
                  {
                      '''predictions''': datasets.Value('''float'''    ),
                      '''references''': datasets.Value('''float'''    ),
                  }    )							,				reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html''']							,				)






     def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	Union[str, Any]							,				snake_case  :	int							,				snake_case  :	Dict=False    )		->							List[Any]:
          if return_pvalue:
               __UpperCAmelCase			:				Tuple													=    pearsonr(snake_case							,				snake_case    )
               return {"pearsonr": results[0], "p-value": results[1]}
          else:
               return {"pearsonr": float(pearsonr(snake_case							,				snake_case    )[0]    )}