



'''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 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()
a     :    Dict      						=					logging.get_logger(__name__)



def    __lowerCamelCase						(      _lowercase	)   ->    List[str]:
							UpperCAmelCase  :	Union[str, Any]							  =	"""huggingface/label-files"""
							UpperCAmelCase  :	Tuple							  =	"""imagenet-1k-id2label.json"""
							UpperCAmelCase  :	Tuple							  =	json.load(open(hf_hub_download(__lowerCAmelCase			,       __lowerCAmelCase			,       repo_type="""dataset"""	)			,       """r"""	)	)
							UpperCAmelCase  :	str							  =	{int(__lowerCAmelCase	): v for k, v in idalabel.items()}
							UpperCAmelCase  :	str							  =	{v: k for k, v in idalabel.items()}

							UpperCAmelCase  :	Optional[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  :	Dict							  =	BitConfig(
							    conv_layer=__lowerCAmelCase			,       num_labels=1_0_0_0			,       idalabel=__lowerCAmelCase			,       labelaid=__lowerCAmelCase			,       )

							return config



def    __lowerCamelCase						(      _lowercase	)   ->    Any:
							if "stem.conv" in name:
														UpperCAmelCase  :	str							  =	name.replace("""stem.conv"""			,       """bit.embedder.convolution"""	)
							if "blocks" in name:
														UpperCAmelCase  :	str							  =	name.replace("""blocks"""			,       """layers"""	)
							if "head.fc" in name:
														UpperCAmelCase  :	int							  =	name.replace("""head.fc"""			,       """classifier.1"""	)
							if name.startswith("""norm"""	):
														UpperCAmelCase  :	str							  =	"""bit.""" + name
							if "bit" not in name and "classifier" not in name:
														UpperCAmelCase  :	Optional[int]							  =	"""bit.encoder.""" + name

							return name



def    __lowerCamelCase						(      )   ->    List[str]:
							UpperCAmelCase  :	Union[str, Any]							  =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase  :	Tuple							  =	Image.open(requests.get(__lowerCAmelCase			,       stream=__lowerCAmelCase	).raw	)
							return im



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase=False	)   ->    Optional[int]:
							UpperCAmelCase  :	List[Any]							  =	get_config(__lowerCAmelCase	)

							# load original model from timm
							UpperCAmelCase  :	str							  =	create_model(__lowerCAmelCase			,       pretrained=__lowerCAmelCase	)
							timm_model.eval()

							# load state_dict of original model
							UpperCAmelCase  :	int							  =	timm_model.state_dict()
							for key in state_dict.copy().keys():
														UpperCAmelCase  :	int							  =	state_dict.pop(__lowerCAmelCase	)
														UpperCAmelCase  :	Union[str, Any]							  =	val.squeeze() if """head""" in key else val

							# load HuggingFace model
							UpperCAmelCase  :	Any							  =	BitForImageClassification(__lowerCAmelCase	)
							model.eval()
							model.load_state_dict(__lowerCAmelCase	)

							# create image processor
							UpperCAmelCase  :	Dict							  =	create_transform(**resolve_data_config({}			,       model=__lowerCAmelCase	)	)
							UpperCAmelCase  :	Union[str, Any]							  =	transform.transforms

							UpperCAmelCase  :	Union[str, Any]							  =	{
							    """bilinear""": PILImageResampling.BILINEAR,
							    """bicubic""": PILImageResampling.BICUBIC,
							    """nearest""": PILImageResampling.NEAREST,
							}

							UpperCAmelCase  :	Tuple							  =	BitImageProcessor(
							    do_resize=__lowerCAmelCase			,       size={"""shortest_edge""": timm_transforms[0].size}			,       resample=pillow_resamplings[timm_transforms[0].interpolation.value]			,       do_center_crop=__lowerCAmelCase			,       crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}			,       do_normalize=__lowerCAmelCase			,       image_mean=timm_transforms[-1].mean.tolist()			,       image_std=timm_transforms[-1].std.tolist()			,       )

							UpperCAmelCase  :	Union[str, Any]							  =	prepare_img()
							UpperCAmelCase  :	Optional[int]							  =	transform(__lowerCAmelCase	).unsqueeze(0	)
							UpperCAmelCase  :	Any							  =	processor(__lowerCAmelCase			,       return_tensors="""pt"""	).pixel_values

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

							# verify logits
							with torch.no_grad():
														UpperCAmelCase  :	Optional[int]							  =	model(__lowerCAmelCase	)
														UpperCAmelCase  :	List[Any]							  =	outputs.logits

							print("""Logits:"""			,       logits[0, :3]	)
							print("""Predicted class:"""			,       model.config.idalabel[logits.argmax(-1	).item()]	)
							UpperCAmelCase  :	Optional[int]							  =	timm_model(__lowerCAmelCase	)
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(__lowerCAmelCase			,       outputs.logits			,       atol=1e-3	)
							print("""Looks ok!"""	)

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

							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__":
							a     :    str      						=					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.""",
							)

							a     :    List[Any]      						=					parser.parse_args()
							convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''




from scipy.stats import pearsonr

import datasets


a     :    str      						=					"""
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.
"""


a     :    Dict      						=					"""
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
"""


a     :    Optional[int]      						=					"""
@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     UpperCamelCase_				(							datasets.Metric    ):





	def 				_lowercase(						self					)    ->    Optional[int]:
								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 				_lowercase(						self	,	A	,	A	,	A=False					)    ->    int:
								if return_pvalue:
															UpperCAmelCase  :	int							  =	pearsonr(A	,	A					)
															return {"pearsonr": results[0], "p-value": results[1]}
								else:
															return {"pearsonr": float(pearsonr(A	,	A					)[0]					)}

