
'''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 (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase   :					Optional[int]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					Optional[int]   							=					torch.device('''cpu''')

def 	__UpperCAmelCase       ( )->    Optional[int]:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       "http://images.cocodataset.org/val2017/000000039769.jpg"
			snake_case_				:					Any   =       Image.open(requests.get(__magic_name__     ,stream=__magic_name__						).raw						)
			return im

def 	__UpperCAmelCase       ( __magic_name__						)->    int:



			"""simple docstring"""



			if swiftformer_name == "swiftformer_xs":
						return torch.tensor([-2.17_03E00, 2.11_07E00, -2.08_11E00, 8.86_85E-01, 2.43_60E-01]						)

			elif swiftformer_name == "swiftformer_s":
						return torch.tensor([3.96_36E-01, 2.34_78E-01, -1.69_63E00, -1.73_81E00, -8.63_37E-01]						)

			elif swiftformer_name == "swiftformer_l1":
						return torch.tensor([-4.27_68E-01, -4.74_29E-01, -1.08_97E00, -1.02_48E00, 3.55_23E-02]						)

			elif swiftformer_name == "swiftformer_l3":
						return torch.tensor([-2.53_30E-01, 2.42_11E-01, -6.01_85E-01, -8.27_89E-01, -6.04_46E-02]						)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    List[str]:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       dct.pop(__magic_name__						)
			snake_case_				:					Union[str, Any]   =       val

def 	__UpperCAmelCase       ( __magic_name__						)->    List[Any]:



			"""simple docstring"""



			snake_case_				:					Dict   =       []
			for k in state_dict.keys():
						snake_case_				:					str   =       k
						if ".pwconv" in k:
									snake_case_				:					List[str]   =       k_new.replace(".pwconv"     ,".point_wise_conv"						)
						if ".dwconv" in k:
									snake_case_				:					Dict   =       k_new.replace(".dwconv"     ,".depth_wise_conv"						)
						if ".Proj." in k:
									snake_case_				:					Tuple   =       k_new.replace(".Proj."     ,".proj."						)
						if "patch_embed" in k_new:
									snake_case_				:					List[Any]   =       k_new.replace("patch_embed"     ,"swiftformer.patch_embed.patch_embedding"						)
						if "network" in k_new:
									snake_case_				:					int   =       k_new.split("."						)
									if ls[2].isdigit():
												snake_case_				:					Dict   =       "swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:]						)
									else:
												snake_case_				:					Dict   =       k_new.replace("network"     ,"swiftformer.encoder.network"						)
						rename_keys.append((k, k_new)						)
			return rename_keys

@torch.no_grad()
def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    int:



			"""simple docstring"""



			snake_case_				:					Any   =       SwiftFormerConfig()

			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			snake_case_				:					Optional[Any]   =       1000
			snake_case_				:					Tuple   =       "huggingface/label-files"
			snake_case_				:					Union[str, Any]   =       "imagenet-1k-id2label.json"
			snake_case_				:					List[str]   =       json.load(open(hf_hub_download(__magic_name__     ,__magic_name__     ,repo_type="dataset"						)     ,"r"						)						)
			snake_case_				:					List[Any]   =       {int(__magic_name__						): v for k, v in idalabel.items()}
			snake_case_				:					Any   =       idalabel
			snake_case_				:					Tuple   =       {v: k for k, v in idalabel.items()}

			# size of the architecture
			if swiftformer_name == "swiftformer_xs":
						snake_case_				:					Optional[Any]   =       [3, 3, 6, 4]
						snake_case_				:					Any   =       [48, 56, 112, 220]

			elif swiftformer_name == "swiftformer_s":
						snake_case_				:					int   =       [3, 3, 9, 6]
						snake_case_				:					int   =       [48, 64, 168, 224]

			elif swiftformer_name == "swiftformer_l1":
						snake_case_				:					Optional[int]   =       [4, 3, 10, 5]
						snake_case_				:					Dict   =       [48, 96, 192, 384]

			elif swiftformer_name == "swiftformer_l3":
						snake_case_				:					Union[str, Any]   =       [4, 4, 12, 6]
						snake_case_				:					List[str]   =       [64, 128, 320, 512]

			# load state_dict of original model, remove and rename some keys
			if original_ckpt:
						if original_ckpt.startswith("https"						):
									snake_case_				:					Optional[int]   =       torch.hub.load_state_dict_from_url(__magic_name__     ,map_location="cpu"     ,check_hash=__magic_name__						)
						else:
									snake_case_				:					Optional[int]   =       torch.load(__magic_name__     ,map_location="cpu"						)
			snake_case_				:					List[str]   =       checkpoint

			snake_case_				:					int   =       create_rename_keys(__magic_name__						)
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(__magic_name__     ,__magic_name__     ,__magic_name__						)

			# load HuggingFace model
			snake_case_				:					Tuple   =       SwiftFormerForImageClassification(__magic_name__						).eval()
			hf_model.load_state_dict(__magic_name__						)

			# prepare test inputs
			snake_case_				:					Tuple   =       prepare_img()
			snake_case_				:					int   =       ViTImageProcessor.from_pretrained("preprocessor_config"						)
			snake_case_				:					List[Any]   =       processor(images=__magic_name__     ,return_tensors="pt"						)

			# compare outputs from both models
			snake_case_				:					Optional[Any]   =       get_expected_output(__magic_name__						)
			snake_case_				:					Any   =       hf_model(inputs["pixel_values"]						).logits

			assert hf_logits.shape == torch.Size([1, 1000]						)
			assert torch.allclose(hf_logits[0, 0:5]     ,__magic_name__     ,atol=1E-3						)

			Path(__magic_name__						).mkdir(exist_ok=__magic_name__						)
			print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''						)
			hf_model.save_pretrained(__magic_name__						)


if __name__ == "__main__":
		__lowerCamelCase   :					List[str]   							=					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--swiftformer_name''',
		    default='''swiftformer_xs''',
		    choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
		    type=str,
		    help='''Name of the SwiftFormer model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default='''./converted_outputs/''',
		    type=str,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

		__lowerCamelCase   :					Dict   							=					parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)












'''simple docstring'''




from scipy.stats import spearmanr

import datasets


__lowerCamelCase   :					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   :					int   							=					'''
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   :					List[str]   							=					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							):
							"""simple docstring"""




							def 							_A	(		self			:str				)				->  Union[str, Any]:


										'''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 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Optional[Any]=False				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       spearmanr(lowerCAmelCase__       ,							lowerCAmelCase__				)
										if return_pvalue:
													return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
										else:
													return {"spearmanr": results[0]}




