





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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Dict    ):

		if swiftformer_name == "swiftformer_xs":
				return torch.tensor([-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0, 8.8_6_8_5e-0_1, 2.4_3_6_0e-0_1]    )

		elif swiftformer_name == "swiftformer_s":
				return torch.tensor([3.9_6_3_6e-0_1, 2.3_4_7_8e-0_1, -1.6_9_6_3e0_0, -1.7_3_8_1e0_0, -8.6_3_3_7e-0_1]    )

		elif swiftformer_name == "swiftformer_l1":
				return torch.tensor([-4.2_7_6_8e-0_1, -4.7_4_2_9e-0_1, -1.0_8_9_7e0_0, -1.0_2_4_8e0_0, 3.5_5_2_3e-0_2]    )

		elif swiftformer_name == "swiftformer_l3":
				return torch.tensor([-2.5_3_3_0e-0_1, 2.4_2_1_1e-0_1, -6.0_1_8_5e-0_1, -8.2_7_8_9e-0_1, -6.0_4_4_6e-0_2]    )






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Any					,   UpperCamelCase__:							Union[str, Any]					,   UpperCamelCase__:							Any    ):
		SCREAMING_SNAKE_CASE__			=      dct.pop(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      val






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]    ):
		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 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Tuple					,   UpperCamelCase__:							Any					,   UpperCamelCase__:							int    ):
		SCREAMING_SNAKE_CASE__			=      SwiftFormerConfig()

		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		SCREAMING_SNAKE_CASE__			=      1_000
		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, 1_000]    )
		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__":
	_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)









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase   				=			{
    'configuration_xmod': [
        'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XmodConfig',
        'XmodOnnxConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			[
	    'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'XmodForCausalLM',
	    'XmodForMaskedLM',
	    'XmodForMultipleChoice',
	    'XmodForQuestionAnswering',
	    'XmodForSequenceClassification',
	    'XmodForTokenClassification',
	    'XmodModel',
	    'XmodPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xmod import (
		    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XmodForCausalLM,
		    XmodForMaskedLM,
		    XmodForMultipleChoice,
		    XmodForQuestionAnswering,
		    XmodForSequenceClassification,
		    XmodForTokenClassification,
		    XmodModel,
		    XmodPreTrainedModel,
		)

else:
	import sys

	_lowerCamelCase   				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)