
'''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()
lowercase					:							Any   =      logging.get_logger(__name__)

lowercase					:							int   =      torch.device("cpu")







def        SCREAMING_SNAKE_CASE__			(     ) ->			Dict:
	_snake_case     		=	'http://images.cocodataset.org/val2017/000000039769.jpg'
	_snake_case     		=	Image.open(requests.get(__A    ,       stream=__A			).raw			)
	return im







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			Union[str, 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        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			str:
	_snake_case     		=	dct.pop(__A			)
	_snake_case     		=	val







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			Tuple:
	_snake_case     		=	[]
	for k in state_dict.keys():
		_snake_case     		=	k
		if ".pwconv" in k:
			_snake_case     		=	k_new.replace('.pwconv'    ,       '.point_wise_conv'			)
		if ".dwconv" in k:
			_snake_case     		=	k_new.replace('.dwconv'    ,       '.depth_wise_conv'			)
		if ".Proj." in k:
			_snake_case     		=	k_new.replace('.Proj.'    ,       '.proj.'			)
		if "patch_embed" in k_new:
			_snake_case     		=	k_new.replace('patch_embed'    ,       'swiftformer.patch_embed.patch_embedding'			)
		if "network" in k_new:
			_snake_case     		=	k_new.split('.'			)
			if ls[2].isdigit():
				_snake_case     		=	'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]			)
			else:
				_snake_case     		=	k_new.replace('network'    ,       'swiftformer.encoder.network'			)
		rename_keys.append((k, k_new)			)
	return rename_keys







@torch.no_grad()
def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			Dict:
	_snake_case     		=	SwiftFormerConfig()

	# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
	_snake_case     		=	1_000
	_snake_case     		=	'huggingface/label-files'
	_snake_case     		=	'imagenet-1k-id2label.json'
	_snake_case     		=	json.load(open(hf_hub_download(__A    ,       __A    ,       repo_type='dataset'			)    ,       'r'			)			)
	_snake_case     		=	{int(__A			): v for k, v in idalabel.items()}
	_snake_case     		=	idalabel
	_snake_case     		=	{v: k for k, v in idalabel.items()}

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

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

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

	elif swiftformer_name == "swiftformer_l3":
		_snake_case     		=	[4, 4, 12, 6]
		_snake_case     		=	[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     		=	torch.hub.load_state_dict_from_url(__A    ,       map_location='cpu'    ,       check_hash=__A			)
		else:
			_snake_case     		=	torch.load(__A    ,       map_location='cpu'			)
	_snake_case     		=	checkpoint

	_snake_case     		=	create_rename_keys(__A			)
	for rename_key_src, rename_key_dest in rename_keys:
		rename_key(__A    ,       __A    ,       __A			)

	# load HuggingFace model
	_snake_case     		=	SwiftFormerForImageClassification(__A			).eval()
	hf_model.load_state_dict(__A			)

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

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

	assert hf_logits.shape == torch.Size([1, 1_000]			)
	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__":
					lowercase					:							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.")

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



'''simple docstring'''







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A=5			) ->			Dict:
	# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
	assert masked_input.count('<mask>'			) == 1
	_snake_case     		=	torch.tensor(tokenizer.encode(__A    ,       add_special_tokens=__A			)			).unsqueeze(0			)  # Batch size 1
	_snake_case     		=	model(__A			)[0]  # The last hidden-state is the first element of the output tuple
	_snake_case     		=	(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
	_snake_case     		=	logits[0, masked_index, :]
	_snake_case     		=	logits.softmax(dim=0			)
	_snake_case		,				_snake_case     		=	prob.topk(k=__A    ,       dim=0			)
	_snake_case     		=	' '.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()			) for i in range(len(__A			)			)]			)
	_snake_case     		=	tokenizer.mask_token
	_snake_case     		=	[]
	for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' '			)			):
		_snake_case     		=	predicted_token_bpe.replace('\u2581'    ,       ' '			)
		if " {0}".format(__A			) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(' {0}'.format(__A			)    ,       __A			),
			        values[index].item(),
			        predicted_token,
			    )			)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(__A    ,       __A			),
			        values[index].item(),
			        predicted_token,
			    )			)
	return topk_filled_outputs


lowercase					:							str   =      CamembertTokenizer.from_pretrained("camembert-base")
lowercase					:							str   =      CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowercase					:							Tuple   =      "Le camembert est <mask> :)"





print(fill_mask(masked_input, model, tokenizer, topk=3))

