



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



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



def    __lowerCamelCase						(      _lowercase	)   ->    Optional[Any]:
							UpperCAmelCase  :	Optional[int]							  =	["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(_lowercase			,       _lowercase	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Optional[int]:
							UpperCAmelCase  :	str							  =	dct.pop(_lowercase	)
							UpperCAmelCase  :	Union[str, Any]							  =	val



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



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase=True	)   ->    Dict:
							UpperCAmelCase  :	int							  =	ViTConfig()
							# patch_size
							if model_name[-1] == "8":
														UpperCAmelCase  :	List[str]							  =	8
							# set labels if required
							if not base_model:
														UpperCAmelCase  :	Optional[int]							  =	1_0_0_0
														UpperCAmelCase  :	List[Any]							  =	"""huggingface/label-files"""
														UpperCAmelCase  :	int							  =	"""imagenet-1k-id2label.json"""
														UpperCAmelCase  :	Union[str, Any]							  =	json.load(open(hf_hub_download(_lowercase			,       _lowercase			,       repo_type="""dataset"""	)			,       """r"""	)	)
														UpperCAmelCase  :	str							  =	{int(_lowercase	): v for k, v in idalabel.items()}
														UpperCAmelCase  :	str							  =	idalabel
														UpperCAmelCase  :	List[str]							  =	{v: k for k, v in idalabel.items()}
							# size of the architecture
							if model_name in ["dino_vits8", "dino_vits16"]:
														UpperCAmelCase  :	Optional[int]							  =	3_8_4
														UpperCAmelCase  :	List[str]							  =	1_5_3_6
														UpperCAmelCase  :	int							  =	1_2
														UpperCAmelCase  :	Union[str, Any]							  =	6

							# load original model from torch hub
							UpperCAmelCase  :	Union[str, Any]							  =	torch.hub.load("""facebookresearch/dino:main"""			,       _lowercase	)
							original_model.eval()

							# load state_dict of original model, remove and rename some keys
							UpperCAmelCase  :	Dict							  =	original_model.state_dict()
							if base_model:
														remove_classification_head_(_lowercase	)
							UpperCAmelCase  :	Optional[int]							  =	create_rename_keys(_lowercase			,       base_model=_lowercase	)
							for src, dest in rename_keys:
														rename_key(_lowercase			,       _lowercase			,       _lowercase	)
							read_in_q_k_v(_lowercase			,       _lowercase			,       _lowercase	)

							# load HuggingFace model
							if base_model:
														UpperCAmelCase  :	Optional[int]							  =	ViTModel(_lowercase			,       add_pooling_layer=_lowercase	).eval()
							else:
														UpperCAmelCase  :	Tuple							  =	ViTForImageClassification(_lowercase	).eval()
							model.load_state_dict(_lowercase	)

							# Check outputs on an image, prepared by ViTImageProcessor
							UpperCAmelCase  :	Tuple							  =	ViTImageProcessor()
							UpperCAmelCase  :	Union[str, Any]							  =	image_processor(images=prepare_img()			,       return_tensors="""pt"""	)
							UpperCAmelCase  :	Optional[int]							  =	encoding["""pixel_values"""]
							UpperCAmelCase  :	Dict							  =	model(_lowercase	)

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

							Path(_lowercase	).mkdir(exist_ok=_lowercase	)
							print(F'''Saving model {model_name} 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__":
							a     :    List[Any]      						=					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)
							a     :    Optional[Any]      						=					parser.parse_args()
							convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)






'''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]					)}

