






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification



def 		A_    (					_lowerCAmelCase					)						->			Tuple:
 UpperCamelCase     : Any	     =       SwinvaConfig()
 UpperCamelCase     : List[Any]	     =       swinva_name.split("_"					)

 UpperCamelCase     : Tuple	     =       name_split[1]
 if "to" in name_split[3]:
  UpperCamelCase     : Tuple	     =       int(name_split[3][-3:]					)
 else:
  UpperCamelCase     : Union[str, Any]	     =       int(name_split[3]					)
 if "to" in name_split[2]:
  UpperCamelCase     : List[Any]	     =       int(name_split[2][-2:]					)
 else:
  UpperCamelCase     : Optional[Any]	     =       int(name_split[2][6:]					)

 if model_size == "tiny":
  UpperCamelCase     : Tuple	     =       96
  UpperCamelCase     : List[Any]	     =       (2, 2, 6, 2)
  UpperCamelCase     : str	     =       (3, 6, 12, 24)
 elif model_size == "small":
  UpperCamelCase     : List[str]	     =       96
  UpperCamelCase     : List[Any]	     =       (2, 2, 18, 2)
  UpperCamelCase     : Any	     =       (3, 6, 12, 24)
 elif model_size == "base":
  UpperCamelCase     : Optional[Any]	     =       128
  UpperCamelCase     : str	     =       (2, 2, 18, 2)
  UpperCamelCase     : str	     =       (4, 8, 16, 32)
 else:
  UpperCamelCase     : Any	     =       192
  UpperCamelCase     : Union[str, Any]	     =       (2, 2, 18, 2)
  UpperCamelCase     : int	     =       (6, 12, 24, 48)

 if "to" in swinva_name:
  UpperCamelCase     : List[str]	     =       (12, 12, 12, 6)

 if ("22k" in swinva_name) and ("to" not in swinva_name):
  UpperCamelCase     : Optional[Any]	     =       2_1841
  UpperCamelCase     : List[str]	     =       "huggingface/label-files"
  UpperCamelCase     : List[Any]	     =       "imagenet-22k-id2label.json"
  UpperCamelCase     : List[str]	     =       json.load(open(hf_hub_download(_lowerCAmelCase							,       _lowerCAmelCase							,       repo_type="dataset"					)							,       "r"					)					)
  UpperCamelCase     : Optional[int]	     =       {int(_lowerCAmelCase					): v for k, v in idalabel.items()}
  UpperCamelCase     : Union[str, Any]	     =       idalabel
  UpperCamelCase     : Optional[Any]	     =       {v: k for k, v in idalabel.items()}

 else:
  UpperCamelCase     : List[Any]	     =       1000
  UpperCamelCase     : Optional[int]	     =       "huggingface/label-files"
  UpperCamelCase     : Dict	     =       "imagenet-1k-id2label.json"
  UpperCamelCase     : Any	     =       json.load(open(hf_hub_download(_lowerCAmelCase							,       _lowerCAmelCase							,       repo_type="dataset"					)							,       "r"					)					)
  UpperCamelCase     : Optional[Any]	     =       {int(_lowerCAmelCase					): v for k, v in idalabel.items()}
  UpperCamelCase     : Dict	     =       idalabel
  UpperCamelCase     : Optional[Any]	     =       {v: k for k, v in idalabel.items()}

 UpperCamelCase     : Tuple	     =       img_size
 UpperCamelCase     : str	     =       num_classes
 UpperCamelCase     : int	     =       embed_dim
 UpperCamelCase     : Any	     =       depths
 UpperCamelCase     : Union[str, Any]	     =       num_heads
 UpperCamelCase     : Dict	     =       window_size

 return config



def 		A_    (					_lowerCAmelCase					)						->			Optional[int]:
 if "patch_embed.proj" in name:
  UpperCamelCase     : Optional[Any]	     =       name.replace("patch_embed.proj"							,       "embeddings.patch_embeddings.projection"					)
 if "patch_embed.norm" in name:
  UpperCamelCase     : Optional[int]	     =       name.replace("patch_embed.norm"							,       "embeddings.norm"					)
 if "layers" in name:
  UpperCamelCase     : Union[str, Any]	     =       "encoder." + name
 if "attn.proj" in name:
  UpperCamelCase     : Dict	     =       name.replace("attn.proj"							,       "attention.output.dense"					)
 if "attn" in name:
  UpperCamelCase     : int	     =       name.replace("attn"							,       "attention.self"					)
 if "norm1" in name:
  UpperCamelCase     : List[Any]	     =       name.replace("norm1"							,       "layernorm_before"					)
 if "norm2" in name:
  UpperCamelCase     : Tuple	     =       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     : Optional[int]	     =       name.replace("mlp.fc2"							,       "output.dense"					)
 if "q_bias" in name:
  UpperCamelCase     : Union[str, Any]	     =       name.replace("q_bias"							,       "query.bias"					)
 if "k_bias" in name:
  UpperCamelCase     : List[str]	     =       name.replace("k_bias"							,       "key.bias"					)
 if "v_bias" in name:
  UpperCamelCase     : Tuple	     =       name.replace("v_bias"							,       "value.bias"					)
 if "cpb_mlp" in name:
  UpperCamelCase     : Union[str, Any]	     =       name.replace("cpb_mlp"							,       "continuous_position_bias_mlp"					)
 if name == "norm.weight":
  UpperCamelCase     : str	     =       "layernorm.weight"
 if name == "norm.bias":
  UpperCamelCase     : Any	     =       "layernorm.bias"

 if "head" in name:
  UpperCamelCase     : int	     =       name.replace("head"							,       "classifier"					)
 else:
  UpperCamelCase     : Any	     =       "swinv2." + name

 return name



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Optional[int]:
 for key in orig_state_dict.copy().keys():
  UpperCamelCase     : List[str]	     =       orig_state_dict.pop(_lowerCAmelCase					)

  if "mask" in key:
   continue
  elif "qkv" in key:
   UpperCamelCase     : Tuple	     =       key.split("."					)
   UpperCamelCase     : Union[str, Any]	     =       int(key_split[1]					)
   UpperCamelCase     : Union[str, Any]	     =       int(key_split[3]					)
   UpperCamelCase     : List[Any]	     =       model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

   if "weight" in key:
    UpperCamelCase     : List[str]	     =       val[:dim, :]
    UpperCamelCase     : int	     =       val[dim : dim * 2, :]
    UpperCamelCase     : Union[str, Any]	     =       val[-dim:, :]
   else:
    UpperCamelCase     : List[Any]	     =       val[:dim]
    UpperCamelCase     : List[str]	     =       val[
        dim : dim * 2
    ]
    UpperCamelCase     : Union[str, Any]	     =       val[-dim:]
  else:
   UpperCamelCase     : List[Any]	     =       val

 return orig_state_dict



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			List[Any]:
 UpperCamelCase     : Optional[Any]	     =       timm.create_model(_lowerCAmelCase							,       pretrained=_lowerCAmelCase					)
 timm_model.eval()

 UpperCamelCase     : Optional[int]	     =       get_swinva_config(_lowerCAmelCase					)
 UpperCamelCase     : Union[str, Any]	     =       SwinvaForImageClassification(_lowerCAmelCase					)
 model.eval()

 UpperCamelCase     : Optional[int]	     =       convert_state_dict(timm_model.state_dict()							,       _lowerCAmelCase					)
 model.load_state_dict(_lowerCAmelCase					)

 UpperCamelCase     : Optional[Any]	     =       "http://images.cocodataset.org/val2017/000000039769.jpg"

 UpperCamelCase     : str	     =       AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"							,       "-"					)					)					)
 UpperCamelCase     : Union[str, Any]	     =       Image.open(requests.get(_lowerCAmelCase							,       stream=_lowerCAmelCase					).raw					)
 UpperCamelCase     : str	     =       image_processor(images=_lowerCAmelCase							,       return_tensors="pt"					)

 UpperCamelCase     : str	     =       timm_model(inputs["pixel_values"]					)
 UpperCamelCase     : List[str]	     =       model(**_lowerCAmelCase					).logits

 assert torch.allclose(_lowerCAmelCase							,       _lowerCAmelCase							,       atol=1e-3					)

 print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""					)
 model.save_pretrained(_lowerCAmelCase					)

 print(F"""Saving image processor to {pytorch_dump_folder_path}"""					)
 image_processor.save_pretrained(_lowerCAmelCase					)

 model.push_to_hub(
     repo_path_or_name=Path(_lowerCAmelCase							,       _lowerCAmelCase					)							,       organization="nandwalritik"							,       commit_message="Add model"							,       )


if __name__ == "__main__":
    __lowerCamelCase	:					List[Any]			  =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--swinv2_name""",
        default="""swinv2_tiny_patch4_window8_256""",
        type=str,
        help="""Name of the Swinv2 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."""
    )

    __lowerCamelCase	:					List[Any]			  =					parser.parse_args()
    convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)







from scipy.stats import spearmanr

import datasets


__lowerCamelCase	:					List[str]			  =					"""
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""

__lowerCamelCase	:					List[Any]			  =					"""
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {'spearmanr': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results['spearmanr'])
        -0.7
        >>> print(round(results['spearmanr_pvalue'], 2))
        0.19
"""

__lowerCamelCase	:					Optional[int]			  =					r"""\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
"""



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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       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.spearmanr.html"]	,							)





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_=False       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       spearmanr(A_	,							A_       )
       if return_pvalue:
        return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
       else:
        return {"spearmanr": results[0]}
