



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__			= logging.get_logger(__name__)

lowerCamelCase__			= torch.device("""cpu""")

def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		"http://images.cocodataset.org/val2017/000000039769.jpg"
				__a       =		Image.open(requests.get(__lowerCAmelCase				,     stream=__lowerCAmelCase    ).raw    )
				return im

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			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__						(							_SCREAMING_SNAKE_CASE       :			Tuple				,     _SCREAMING_SNAKE_CASE       :			Tuple				,     _SCREAMING_SNAKE_CASE       :			Dict    ):
				"""simple docstring"""







				__a       =		dct.pop(__lowerCAmelCase    )
				__a       =		val

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Any    ):
				"""simple docstring"""







				__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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[Any]				,     _SCREAMING_SNAKE_CASE       :			Any				,     _SCREAMING_SNAKE_CASE       :			Optional[int]    ):
				"""simple docstring"""







				__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(__lowerCAmelCase				,     __lowerCAmelCase				,     repo_type="""dataset"""    )				,     """r"""    )    )
				__a       =		{int(__lowerCAmelCase    ): 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(__lowerCAmelCase				,     map_location="""cpu"""				,     check_hash=__lowerCAmelCase    )
								else:
												__a       =		torch.load(__lowerCAmelCase				,     map_location="""cpu"""    )
				__a       =		checkpoint

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

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

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

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

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

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


if __name__ == "__main__":
						lowerCamelCase__			= 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__			= 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_tokenizers_available, is_torch_available


lowerCamelCase__			= {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NezhaForNextSentencePrediction""",
						    """NezhaForMaskedLM""",
						    """NezhaForPreTraining""",
						    """NezhaForMultipleChoice""",
						    """NezhaForQuestionAnswering""",
						    """NezhaForSequenceClassification""",
						    """NezhaForTokenClassification""",
						    """NezhaModel""",
						    """NezhaPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nezha import (
												    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NezhaForMaskedLM,
												    NezhaForMultipleChoice,
												    NezhaForNextSentencePrediction,
												    NezhaForPreTraining,
												    NezhaForQuestionAnswering,
												    NezhaForSequenceClassification,
												    NezhaForTokenClassification,
												    NezhaModel,
												    NezhaPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
