


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()
snake_case__			     =						logging.get_logger(__name__)

snake_case__			     =						torch.device('''cpu''')


def 				lowerCamelCase__	(      )    ->	List[str]:





			"""simple docstring"""
			a__ :Union[str, Any]						  =    "http://images.cocodataset.org/val2017/000000039769.jpg"
			a__ :Dict						  =    Image.open(requests.get(a	,  stream=a				).raw				)
			return im


def 				lowerCamelCase__	(      a	:   Tuple				)    ->	Dict:





			"""simple docstring"""

			if swiftformer_name == "swiftformer_xs":
						return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01]				)

			elif swiftformer_name == "swiftformer_s":
						return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01]				)

			elif swiftformer_name == "swiftformer_l1":
						return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02]				)

			elif swiftformer_name == "swiftformer_l3":
						return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02]				)


def 				lowerCamelCase__	(      a	:   Optional[int]	,  a	:   int	,  a	:   Dict				)    ->	List[str]:





			"""simple docstring"""
			a__ :str						  =    dct.pop(a				)
			a__ :Optional[Any]						  =    val


def 				lowerCamelCase__	(      a	:   Any				)    ->	Tuple:





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


@torch.no_grad()
def 				lowerCamelCase__	(      a	:   Any	,  a	:   List[Any]	,  a	:   List[Any]				)    ->	Optional[int]:





			"""simple docstring"""
			a__ :Dict						  =    SwiftFormerConfig()

			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			a__ :List[Any]						  =    1_000
			a__ :Any						  =    "huggingface/label-files"
			a__ :List[str]						  =    "imagenet-1k-id2label.json"
			a__ :int						  =    json.load(open(hf_hub_download(a	,  a	,  repo_type="dataset"				)	,  "r"				)				)
			a__ :Tuple						  =    {int(a				): v for k, v in idalabel.items()}
			a__ :Optional[Any]						  =    idalabel
			a__ :List[str]						  =    {v: k for k, v in idalabel.items()}

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

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

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

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

			# load state_dict of original model, remove and rename some keys
			if original_ckpt:
						if original_ckpt.startswith("https"				):
									a__ :List[Any]						  =    torch.hub.load_state_dict_from_url(a	,  map_location="cpu"	,  check_hash=a				)
						else:
									a__ :int						  =    torch.load(a	,  map_location="cpu"				)
			a__ :str						  =    checkpoint

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

			# load HuggingFace model
			a__ :List[str]						  =    SwiftFormerForImageClassification(a				).eval()
			hf_model.load_state_dict(a				)

			# prepare test inputs
			a__ :Union[str, Any]						  =    prepare_img()
			a__ :Any						  =    ViTImageProcessor.from_pretrained("preprocessor_config"				)
			a__ :Dict						  =    processor(images=a	,  return_tensors="pt"				)

			# compare outputs from both models
			a__ :Tuple						  =    get_expected_output(a				)
			a__ :Tuple						  =    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__":
		snake_case__			     =						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.''')

		snake_case__			     =						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


snake_case__			 =						{
    '''configuration_mgp_str''': ['''MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MgpstrConfig'''],
    '''processing_mgp_str''': ['''MgpstrProcessor'''],
    '''tokenization_mgp_str''': ['''MgpstrTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		snake_case__			 =						[
		    '''MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MgpstrModel''',
		    '''MgpstrPreTrainedModel''',
		    '''MgpstrForSceneTextRecognition''',
		]

if TYPE_CHECKING:
		from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
		from .processing_mgp_str import MgpstrProcessor
		from .tokenization_mgp_str import MgpstrTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mgp_str import (
				    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MgpstrForSceneTextRecognition,
				    MgpstrModel,
				    MgpstrPreTrainedModel,
				)
else:
		import sys

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


