


"""simple docstring"""






import argparse
import json
from pathlib import Path

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

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case				=				logging.get_logger(__name__)
def 					A_      (      _lowerCAmelCase   :    str,   _lowerCAmelCase   :    List[str]=False				):






     """simple docstring"""

     _a					= []
     for i in range(config.num_hidden_layers				):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f'blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight')				)
          rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias')				)
          rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight')				)
          rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias')				)
          rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight')				)
          rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias')				)
          rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight')				)
          rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias')				)
          rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight')				)
          rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias')				)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ('''cls_token''', '''vit.embeddings.cls_token'''),
             ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
             ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
             ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
         ]				)

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ('''norm.weight''', '''layernorm.weight'''),
                  ('''norm.bias''', '''layernorm.bias'''),
              ]				)

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          _a					= [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''				) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('''norm.weight''', '''vit.layernorm.weight'''),
                  ('''norm.bias''', '''vit.layernorm.bias'''),
                  ('''head.weight''', '''classifier.weight'''),
                  ('''head.bias''', '''classifier.bias'''),
              ]				)

     return rename_keys
def 					A_      (      _lowerCAmelCase   :    Dict,   _lowerCAmelCase   :    Dict,   _lowerCAmelCase   :    Tuple=False				):






     """simple docstring"""

     for i in range(config.num_hidden_layers				):
          if base_model:
               _a					= """"""
          else:
               _a					= """vit."""
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          _a					= state_dict.pop(f'blocks.{i}.attn.qkv.weight'				)
          _a					= state_dict.pop(f'blocks.{i}.attn.qkv.bias'				)
          # next, add query, keys and values (in that order) to the state dict
          _a					= in_proj_weight[
              : config.hidden_size, :
          ]
          _a					= in_proj_bias[: config.hidden_size]
          _a					= in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _a					= in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          _a					= in_proj_weight[
              -config.hidden_size :, :
          ]
          _a					= in_proj_bias[-config.hidden_size :]
def 					A_      (      _lowerCAmelCase   :    str				):






     """simple docstring"""

     _a					= ["""head.weight""", """head.bias"""]
     for k in ignore_keys:
          state_dict.pop(_lowerCAmelCase,   _lowerCAmelCase				)
def 					A_      (      _lowerCAmelCase   :    int,   _lowerCAmelCase   :    Optional[Any],   _lowerCAmelCase   :    int				):






     """simple docstring"""

     _a					= dct.pop(_lowerCAmelCase				)
     _a					= val
def 					A_      (      ):






     """simple docstring"""

     _a					= """http://images.cocodataset.org/val2017/000000039769.jpg"""
     _a					= Image.open(requests.get(_lowerCAmelCase,   stream=_lowerCAmelCase				).raw				)
     return im
@torch.no_grad()
def 					A_      (      _lowerCAmelCase   :    List[str],   _lowerCAmelCase   :    Union[str, Any],   _lowerCAmelCase   :    Union[str, Any]=True				):






     """simple docstring"""

     _a					= ViTConfig()
     # patch_size
     if model_name[-1] == "8":
          _a					= 8
     # set labels if required
     if not base_model:
          _a					= 10_00
          _a					= """huggingface/label-files"""
          _a					= """imagenet-1k-id2label.json"""
          _a					= json.load(open(hf_hub_download(_lowerCAmelCase,   _lowerCAmelCase,   repo_type='''dataset'''				),   '''r'''				)				)
          _a					= {int(_lowerCAmelCase				): v for k, v in idalabel.items()}
          _a					= idalabel
          _a					= {v: k for k, v in idalabel.items()}
     # size of the architecture
     if model_name in ["dino_vits8", "dino_vits16"]:
          _a					= 3_84
          _a					= 15_36
          _a					= 12
          _a					= 6

     # load original model from torch hub
     _a					= torch.hub.load('''facebookresearch/dino:main''',   _lowerCAmelCase				)
     original_model.eval()

     # load state_dict of original model, remove and rename some keys
     _a					= original_model.state_dict()
     if base_model:
          remove_classification_head_(_lowerCAmelCase				)
     _a					= create_rename_keys(_lowerCAmelCase,   base_model=_lowerCAmelCase				)
     for src, dest in rename_keys:
          rename_key(_lowerCAmelCase,   _lowerCAmelCase,   _lowerCAmelCase				)
     read_in_q_k_v(_lowerCAmelCase,   _lowerCAmelCase,   _lowerCAmelCase				)

     # load HuggingFace model
     if base_model:
          _a					= ViTModel(_lowerCAmelCase,   add_pooling_layer=_lowerCAmelCase				).eval()
     else:
          _a					= ViTForImageClassification(_lowerCAmelCase				).eval()
     model.load_state_dict(_lowerCAmelCase				)

     # Check outputs on an image, prepared by ViTImageProcessor
     _a					= ViTImageProcessor()
     _a					= image_processor(images=prepare_img(),   return_tensors='''pt'''				)
     _a					= encoding["""pixel_values"""]
     _a					= model(_lowerCAmelCase				)

     if base_model:
          _a					= original_model(_lowerCAmelCase				)
          assert torch.allclose(_lowerCAmelCase,   outputs.last_hidden_state[:, 0, :],   atol=1e-1				)
     else:
          _a					= original_model(_lowerCAmelCase				)
          assert logits.shape == outputs.logits.shape
          assert torch.allclose(_lowerCAmelCase,   outputs.logits,   atol=1e-3				)

     Path(_lowerCAmelCase				).mkdir(exist_ok=_lowerCAmelCase				)
     print(f'Saving model {model_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				)


if __name__ == "__main__":
   __snake_case				=				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--model_name''',
       default='''dino_vitb16''',
       type=str,
       help='''Name of the model trained with DINO 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(
       '''--base_model''',
       action='''store_true''',
       help='''Whether to only convert the base model (no projection head weights).''',
   )

   parser.set_defaults(base_model=True)
   __snake_case				=				parser.parse_args()
   convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)

from scipy.stats import pearsonr

import datasets


_lowerCamelCase					   ="""
Pearson correlation coefficient and p-value for testing non-correlation.
The 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.
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.
"""


_lowerCamelCase					   ="""
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    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.
    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.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results['pearsonr'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        ['p-value', 'pearsonr']
        >>> print(round(results['pearsonr'], 2))
        -0.74
        >>> print(round(results['p-value'], 2))
        0.15
"""


_lowerCamelCase					   ="""
@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, Ilhan 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, Antonio 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      ):
       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 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__=False      ):
       if return_pvalue:
        lowerCamelCase		:	Optional[Any]							=      pearsonr(__magic_name__    ,  __magic_name__      )
        return {"pearsonr": results[0], "p-value": results[1]}
       else:
        return {"pearsonr": float(pearsonr(__magic_name__    ,  __magic_name__      )[0]      )}




