
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	:      List[Any]  					=					logging.get_logger(__name__)

A	:      int  					=					torch.device('cpu')


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


def        __lowerCAmelCase     (     a__    )			->    Tuple:
				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__		,       a__		,       a__    )			->    Optional[Any]:
				__a     				=       dct.pop(__lowerCAmelCase    )
				__a     				=       val


def        __lowerCAmelCase     (     a__    )			->    Any:
				__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     (     a__		,       a__		,       a__    )			->    Any:
				__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__":
					A	:      Optional[int]  					=					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	:      List[str]  					=					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,
    is_vision_available,
)


__magic_name__  :  Any					=       {
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  List[str]					=       ["""LayoutLMv2TokenizerFast"""]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  Any					=       ["""LayoutLMv2FeatureExtractor"""]
      __magic_name__  :  Any					=       ["""LayoutLMv2ImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  Any					=       [
          """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """LayoutLMv2ForQuestionAnswering""",
          """LayoutLMv2ForSequenceClassification""",
          """LayoutLMv2ForTokenClassification""",
          """LayoutLMv2Layer""",
          """LayoutLMv2Model""",
          """LayoutLMv2PreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

      __magic_name__  :  Union[str, Any]					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

