






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()
a_		:						str             =    logging.get_logger(__name__)

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






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






def 				lowerCamelCase__				(_UpperCAmelCase):
			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 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= dct.pop(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= val






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






@torch.no_grad()
def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= SwiftFormerConfig()

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

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

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

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

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

			SCREAMING_SNAKE_CASE						= create_rename_keys(_UpperCAmelCase)
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase)

			# load HuggingFace model
			SCREAMING_SNAKE_CASE						= SwiftFormerForImageClassification(_UpperCAmelCase).eval()
			hf_model.load_state_dict(_UpperCAmelCase)

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

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

			assert hf_logits.shape == torch.Size([1, 1000])
			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__":
				a_		:						List[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.')

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_		:						List[Any]             =    {
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Dict             =    [
				    'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BigBirdPegasusForCausalLM',
				    'BigBirdPegasusForConditionalGeneration',
				    'BigBirdPegasusForQuestionAnswering',
				    'BigBirdPegasusForSequenceClassification',
				    'BigBirdPegasusModel',
				    'BigBirdPegasusPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_bigbird_pegasus import (
				    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BigBirdPegasusConfig,
				    BigBirdPegasusOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bigbird_pegasus import (
								    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BigBirdPegasusForCausalLM,
								    BigBirdPegasusForConditionalGeneration,
								    BigBirdPegasusForQuestionAnswering,
								    BigBirdPegasusForSequenceClassification,
								    BigBirdPegasusModel,
								    BigBirdPegasusPreTrainedModel,
								)


else:
				import sys

				a_		:						Tuple             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




