
from sklearn.metrics import recall_score

import datasets


A     :	Optional[Any]    = '\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n'


A     :	Optional[Any]    = '\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the \'positive class\' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n    - `\'binary\'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n    - `\'micro\'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n    - `\'weighted\'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n    - `\'samples\'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n    - `\'warn\'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n    - `0`: If there is a zero division, the return value is `0`.\n    - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n    Example 1-A simple example with some errors\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n        >>> print(results)\n        {\'recall\': 0.6666666666666666}\n\n    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n        >>> print(results)\n        {\'recall\': 0.5}\n\n    Example 3-The same example as Example 1, but with `sample_weight` included.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n        >>> print(results)\n        {\'recall\': 0.55}\n\n    Example 4-A multiclass example, using different averages.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'macro\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'micro\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'weighted\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {\'recall\': array([1., 0., 0.])}\n'


A     :	Tuple    = '\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n'







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




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


								'''simple docstring'''




								return datasets.MetricInfo(
								    description=_DESCRIPTION		,     citation=_CITATION		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
								        {
								            '''predictions''': datasets.Sequence(datasets.Value('''int32'''				)				),
								            '''references''': datasets.Sequence(datasets.Value('''int32'''				)				),
								        }
								        if self.config_name == '''multilabel'''
								        else {
								            '''predictions''': datasets.Value('''int32'''				),
								            '''references''': datasets.Value('''int32'''				),
								        }				)		,     reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html''']		,     )




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case=None		,     _snake_case=1		,     _snake_case="binary"		,     _snake_case=None		,     _snake_case="warn"		,     )    ->      Any:


								'''simple docstring'''




								__a										=    recall_score(
								    _snake_case		,     _snake_case		,     labels=_snake_case		,     pos_label=_snake_case		,     average=_snake_case		,     sample_weight=_snake_case		,     zero_division=_snake_case		,     )
								return {"recall": float(_snake_case				) if score.size == 1 else score}



"""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()
UpperCAmelCase    :     Dict   						=		logging.get_logger(__name__)

UpperCAmelCase    :     Union[str, Any]   						=		torch.device('cpu')
def      lowerCamelCase       (     )		->       str:

							'''simple docstring'''



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

							'''simple docstring'''



							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]							)

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]							)

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]							)

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]							)
def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]       ,			_UpperCamelCase		:      Optional[int]       ,			_UpperCamelCase		:      List[Any]							)		->       Union[str, Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Any      							=					dct.pop(_UpperCamelCase							)
							__UpperCAmelCase   :						List[str]      							=					val
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]							)		->       List[Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Tuple      							=					[]
							for k in state_dict.keys():
														__UpperCAmelCase   :						int      							=					k
														if ".pwconv" in k:
																					__UpperCAmelCase   :						Optional[int]      							=					k_new.replace(""".pwconv"""       ,			""".point_wise_conv"""							)
														if ".dwconv" in k:
																					__UpperCAmelCase   :						List[Any]      							=					k_new.replace(""".dwconv"""       ,			""".depth_wise_conv"""							)
														if ".Proj." in k:
																					__UpperCAmelCase   :						str      							=					k_new.replace(""".Proj."""       ,			""".proj."""							)
														if "patch_embed" in k_new:
																					__UpperCAmelCase   :						int      							=					k_new.replace("""patch_embed"""       ,			"""swiftformer.patch_embed.patch_embedding"""							)
														if "network" in k_new:
																					__UpperCAmelCase   :						str      							=					k_new.split("""."""							)
																					if ls[2].isdigit():
																												__UpperCAmelCase   :						Dict      							=					"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]							)
																					else:
																												__UpperCAmelCase   :						List[str]      							=					k_new.replace("""network"""       ,			"""swiftformer.encoder.network"""							)
														rename_keys.append((k, k_new)							)
							return rename_keys
@torch.no_grad()
def      lowerCamelCase       (     _UpperCamelCase		:      int       ,			_UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      int							)		->       Union[str, Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Optional[int]      							=					SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							__UpperCAmelCase   :						Optional[int]      							=					1_0_0_0
							__UpperCAmelCase   :						Optional[int]      							=					"""huggingface/label-files"""
							__UpperCAmelCase   :						str      							=					"""imagenet-1k-id2label.json"""
							__UpperCAmelCase   :						List[Any]      							=					json.load(open(hf_hub_download(_UpperCamelCase       ,			_UpperCamelCase       ,			repo_type="""dataset"""							)       ,			"""r"""							)							)
							__UpperCAmelCase   :						Any      							=					{int(_UpperCamelCase							): v for k, v in idalabel.items()}
							__UpperCAmelCase   :						Optional[Any]      							=					idalabel
							__UpperCAmelCase   :						Optional[int]      							=					{v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														__UpperCAmelCase   :						Tuple      							=					[3, 3, 6, 4]
														__UpperCAmelCase   :						List[Any]      							=					[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														__UpperCAmelCase   :						Optional[int]      							=					[3, 3, 9, 6]
														__UpperCAmelCase   :						Tuple      							=					[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														__UpperCAmelCase   :						Dict      							=					[4, 3, 1_0, 5]
														__UpperCAmelCase   :						Optional[Any]      							=					[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														__UpperCAmelCase   :						str      							=					[4, 4, 1_2, 6]
														__UpperCAmelCase   :						str      							=					[6_4, 1_2_8, 3_2_0, 5_1_2]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith("""https"""							):
																					__UpperCAmelCase   :						str      							=					torch.hub.load_state_dict_from_url(_UpperCamelCase       ,			map_location="""cpu"""       ,			check_hash=_UpperCamelCase							)
														else:
																					__UpperCAmelCase   :						Dict      							=					torch.load(_UpperCamelCase       ,			map_location="""cpu"""							)
							__UpperCAmelCase   :						str      							=					checkpoint

							__UpperCAmelCase   :						Union[str, Any]      							=					create_rename_keys(_UpperCamelCase							)
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase							)

							# load HuggingFace model
							__UpperCAmelCase   :						int      							=					SwiftFormerForImageClassification(_UpperCamelCase							).eval()
							hf_model.load_state_dict(_UpperCamelCase							)

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

							# compare outputs from both models
							__UpperCAmelCase   :						Tuple      							=					get_expected_output(_UpperCamelCase							)
							__UpperCAmelCase   :						Dict      							=					hf_model(inputs["""pixel_values"""]							).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]							)
							assert torch.allclose(hf_logits[0, 0:5]       ,			_UpperCamelCase       ,			atol=1E-3							)

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


if __name__ == "__main__":
							UpperCAmelCase    :     Optional[Any]   						=		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.')

							UpperCAmelCase    :     Tuple   						=		parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
