


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:						List[str]   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Tuple   				=       torch.device('cpu')






def   _lowercase(					):
							a__      ='http://images.cocodataset.org/val2017/000000039769.jpg'
							a__      =Image.open(requests.get(__a    ,     stream=__a     ).raw     )
							return im






def   _lowercase(					__a				:						Optional[Any]     ):
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]     )

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]     )

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]     )

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]     )






def   _lowercase(					__a				:						int    ,     __a				:						int    ,     __a				:						Optional[Any]     ):
							a__      =dct.pop(__a     )
							a__      =val






def   _lowercase(					__a				:						Optional[Any]     ):
							a__      =[]
							for k in state_dict.keys():
														a__      =k
														if ".pwconv" in k:
																					a__      =k_new.replace('.pwconv'    ,     '.point_wise_conv'     )
														if ".dwconv" in k:
																					a__      =k_new.replace('.dwconv'    ,     '.depth_wise_conv'     )
														if ".Proj." in k:
																					a__      =k_new.replace('.Proj.'    ,     '.proj.'     )
														if "patch_embed" in k_new:
																					a__      =k_new.replace('patch_embed'    ,     'swiftformer.patch_embed.patch_embedding'     )
														if "network" in k_new:
																					a__      =k_new.split('.'     )
																					if ls[2].isdigit():
																												a__      ='swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]     )
																					else:
																												a__      =k_new.replace('network'    ,     'swiftformer.encoder.network'     )
														rename_keys.append((k, k_new)     )
							return rename_keys






@torch.no_grad()
def   _lowercase(					__a				:						Union[str, Any]    ,     __a				:						int    ,     __a				:						str     ):
							a__      =SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							a__      =1000
							a__      ='huggingface/label-files'
							a__      ='imagenet-1k-id2label.json'
							a__      =json.load(open(hf_hub_download(__a    ,     __a    ,     repo_type='dataset'     )    ,     'r'     )     )
							a__      ={int(__a     ): v for k, v in idalabel.items()}
							a__      =idalabel
							a__      ={v: k for k, v in idalabel.items()}

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

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

							elif swiftformer_name == "swiftformer_l1":
														a__      =[4, 3, 10, 5]
														a__      =[48, 96, 192, 384]

							elif swiftformer_name == "swiftformer_l3":
														a__      =[4, 4, 12, 6]
														a__      =[64, 128, 320, 512]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith('https'     ):
																					a__      =torch.hub.load_state_dict_from_url(__a    ,     map_location='cpu'    ,     check_hash=__a     )
														else:
																					a__      =torch.load(__a    ,     map_location='cpu'     )
							a__      =checkpoint

							a__      =create_rename_keys(__a     )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(__a    ,     __a    ,     __a     )

							# load HuggingFace model
							a__      =SwiftFormerForImageClassification(__a     ).eval()
							hf_model.load_state_dict(__a     )

							# prepare test inputs
							a__      =prepare_img()
							a__      =ViTImageProcessor.from_pretrained('preprocessor_config'     )
							a__      =processor(images=__a    ,     return_tensors='pt'     )

							# compare outputs from both models
							a__      =get_expected_output(__a     )
							a__      =hf_model(inputs['pixel_values']     ).logits

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

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


if __name__ == "__main__":
	_lowerCAmelCase:						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.')

	_lowerCAmelCase:						Optional[int]   				=       parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)





import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_lowerCAmelCase:						Union[str, Any]   				=       '\\n@inproceedings{lin-2004-rouge,\n    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",\n    author = "Lin, Chin-Yew",\n    booktitle = "Text Summarization Branches Out",\n    month = jul,\n    year = "2004",\n    address = "Barcelona, Spain",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W04-1013",\n    pages = "74--81",\n}\n'

_lowerCAmelCase:						Dict   				=       '\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'

_lowerCAmelCase:						List[Any]   				=       '\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,\n        `"rougeL"`: Longest common subsequence based scoring.\n        `"rougeLSum"`: rougeLsum splits text using `"\n"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric(\'rouge\')\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n    >>> print(results["rouge1"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results["rouge1"].mid.fmeasure)\n    1.0\n'




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





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											return datasets.MetricInfo(
											    description=_DESCRIPTION       ,  citation=_CITATION       ,  inputs_description=_KWARGS_DESCRIPTION       ,  features=datasets.Features(
											        {
											            'predictions': datasets.Value('string'       ,  id='sequence'),
											            'references': datasets.Value('string'       ,  id='sequence'),
											        })       ,  codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge']       ,  reference_urls=[
											        'https://en.wikipedia.org/wiki/ROUGE_(metric)',
											        'https://github.com/google-research/google-research/tree/master/rouge',
											    ]       ,  )





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=True       ,  lowercase_=False)  -> Any:
											if rouge_types is None:
																		a__      =['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

											a__      =rouge_scorer.RougeScorer(rouge_types=lowercase_       ,  use_stemmer=lowercase_)
											if use_aggregator:
																		a__      =scoring.BootstrapAggregator()
											else:
																		a__      =[]

											for ref, pred in zip(lowercase_       ,  lowercase_):
																		a__      =scorer.score(lowercase_       ,  lowercase_)
																		if use_aggregator:
																									aggregator.add_scores(lowercase_)
																		else:
																									scores.append(lowercase_)

											if use_aggregator:
																		a__      =aggregator.aggregate()
											else:
																		a__      ={}
																		for key in scores[0]:
																									a__      =[score[key] for score in scores]

											return result

