




import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def 							lowerCAmelCase_      (	snake_case_	):
      _A				:	Dict     =		SwinConfig()
      _A				:	Optional[int]     =		swin_name.split("""_"""	)

      _A				:	Union[str, Any]     =		name_split[1]
      _A				:	str     =		int(name_split[4]	)
      _A				:	Optional[int]     =		int(name_split[3][-1]	)

      if model_size == "tiny":
            _A				:	Optional[int]     =		96
            _A				:	Optional[int]     =		(2, 2, 6, 2)
            _A				:	Optional[int]     =		(3, 6, 12, 24)
      elif model_size == "small":
            _A				:	Optional[int]     =		96
            _A				:	Union[str, Any]     =		(2, 2, 18, 2)
            _A				:	Dict     =		(3, 6, 12, 24)
      elif model_size == "base":
            _A				:	int     =		128
            _A				:	Optional[Any]     =		(2, 2, 18, 2)
            _A				:	List[Any]     =		(4, 8, 16, 32)
      else:
            _A				:	Optional[Any]     =		192
            _A				:	List[str]     =		(2, 2, 18, 2)
            _A				:	Dict     =		(6, 12, 24, 48)

      if "in22k" in swin_name:
            _A				:	Union[str, Any]     =		21841
      else:
            _A				:	Optional[Any]     =		1000
            _A				:	int     =		"""huggingface/label-files"""
            _A				:	Tuple     =		"""imagenet-1k-id2label.json"""
            _A				:	Tuple     =		json.load(open(hf_hub_download(snake_case_,snake_case_,repo_type="""dataset"""	),"""r"""	)	)
            _A				:	int     =		{int(snake_case_	): v for k, v in idalabel.items()}
            _A				:	List[Any]     =		idalabel
            _A				:	List[str]     =		{v: k for k, v in idalabel.items()}

      _A				:	List[Any]     =		img_size
      _A				:	Any     =		num_classes
      _A				:	str     =		embed_dim
      _A				:	str     =		depths
      _A				:	int     =		num_heads
      _A				:	str     =		window_size

      return config







def 							lowerCAmelCase_      (	snake_case_	):
      if "patch_embed.proj" in name:
            _A				:	Optional[Any]     =		name.replace("""patch_embed.proj""","""embeddings.patch_embeddings.projection"""	)
      if "patch_embed.norm" in name:
            _A				:	Optional[Any]     =		name.replace("""patch_embed.norm""","""embeddings.norm"""	)
      if "layers" in name:
            _A				:	Any     =		"""encoder.""" + name
      if "attn.proj" in name:
            _A				:	str     =		name.replace("""attn.proj""","""attention.output.dense"""	)
      if "attn" in name:
            _A				:	Union[str, Any]     =		name.replace("""attn""","""attention.self"""	)
      if "norm1" in name:
            _A				:	List[str]     =		name.replace("""norm1""","""layernorm_before"""	)
      if "norm2" in name:
            _A				:	Tuple     =		name.replace("""norm2""","""layernorm_after"""	)
      if "mlp.fc1" in name:
            _A				:	Tuple     =		name.replace("""mlp.fc1""","""intermediate.dense"""	)
      if "mlp.fc2" in name:
            _A				:	Any     =		name.replace("""mlp.fc2""","""output.dense"""	)

      if name == "norm.weight":
            _A				:	int     =		"""layernorm.weight"""
      if name == "norm.bias":
            _A				:	List[Any]     =		"""layernorm.bias"""

      if "head" in name:
            _A				:	str     =		name.replace("""head""","""classifier"""	)
      else:
            _A				:	List[str]     =		"""swin.""" + name

      return name







def 							lowerCAmelCase_      (	snake_case_,snake_case_	):
      for key in orig_state_dict.copy().keys():
            _A				:	List[Any]     =		orig_state_dict.pop(snake_case_	)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  _A				:	str     =		key.split("""."""	)
                  _A				:	Any     =		int(key_split[1]	)
                  _A				:	List[Any]     =		int(key_split[3]	)
                  _A				:	Union[str, Any]     =		model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        _A				:	List[Any]     =		val[:dim, :]
                        _A				:	Tuple     =		val[
                            dim : dim * 2, :
                        ]
                        _A				:	Optional[Any]     =		val[-dim:, :]
                  else:
                        _A				:	List[str]     =		val[
                            :dim
                        ]
                        _A				:	Optional[int]     =		val[
                            dim : dim * 2
                        ]
                        _A				:	Tuple     =		val[
                            -dim:
                        ]
            else:
                  _A				:	Any     =		val

      return orig_state_dict







def 							lowerCAmelCase_      (	snake_case_,snake_case_	):
      _A				:	str     =		timm.create_model(snake_case_,pretrained=snake_case_	)
      timm_model.eval()

      _A				:	Tuple     =		get_swin_config(snake_case_	)
      _A				:	Dict     =		SwinForImageClassification(snake_case_	)
      model.eval()

      _A				:	Union[str, Any]     =		convert_state_dict(timm_model.state_dict(),snake_case_	)
      model.load_state_dict(snake_case_	)

      _A				:	Optional[Any]     =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""

      _A				:	Optional[Any]     =		AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""","""-"""	)	)	)
      _A				:	str     =		Image.open(requests.get(snake_case_,stream=snake_case_	).raw	)
      _A				:	List[str]     =		image_processor(images=snake_case_,return_tensors="""pt"""	)

      _A				:	Union[str, Any]     =		timm_model(inputs["""pixel_values"""]	)
      _A				:	List[str]     =		model(**snake_case_	).logits

      assert torch.allclose(snake_case_,snake_case_,atol=1e-3	)

      print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''	)
      model.save_pretrained(snake_case_	)

      print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
      image_processor.save_pretrained(snake_case_	)


if __name__ == "__main__":
  _snake_case      =			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--swin_name",
      default="swin_tiny_patch4_window7_224",
      type=str,
      help="Name of the Swin timm model you\'d like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )

  _snake_case      =			parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)







# 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.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce





def 				A__ (snake_case   :		List[Any]				)     ->						Optional[Any]:
					return (torch.arange(state.num_processes				) + 1.0 + (state.num_processes * state.process_index)).to(state.device				)





def 				A__ (snake_case   :		int				)     ->						Any:
					__UpperCamelCase					:	List[str]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	List[Any]             =      gather(snake_case				)
					assert gathered_tensor.tolist() == list(range(1		,	state.num_processes**2 + 1				)				)





def 				A__ (snake_case   :		Optional[Any]				)     ->						Dict:
					__UpperCamelCase					:	Any             =      [state.process_index]
					__UpperCamelCase					:	int             =      gather_object(snake_case				)
					assert len(snake_case				) == state.num_processes, F'''{gathered_obj}, {len(snake_case				)} != {state.num_processes}'''
					assert gathered_obj == list(range(state.num_processes				)				), F'''{gathered_obj} != {list(range(state.num_processes				)				)}'''





def 				A__ (snake_case   :		Any				)     ->						Optional[Any]:
					__UpperCamelCase					:	Optional[Any]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	Any             =      broadcast(snake_case				)
					assert broadcasted_tensor.shape == torch.Size([state.num_processes]				)
					assert broadcasted_tensor.tolist() == list(range(1		,	state.num_processes + 1				)				)





def 				A__ (snake_case   :		Tuple				)     ->						Dict:
					# We need to pad the tensor with one more element if we are the main process
					# to ensure that we can pad
					if state.is_main_process:
										__UpperCamelCase					:	List[Any]             =      torch.arange(state.num_processes + 1				).to(state.device				)
					else:
										__UpperCamelCase					:	List[str]             =      torch.arange(state.num_processes				).to(state.device				)
					__UpperCamelCase					:	List[str]             =      pad_across_processes(snake_case				)
					assert padded_tensor.shape == torch.Size([state.num_processes + 1]				)
					if not state.is_main_process:
										assert padded_tensor.tolist() == list(range(0		,	state.num_processes				)				) + [0]





def 				A__ (snake_case   :		Tuple				)     ->						Dict:
					# For now runs on only two processes
					if state.num_processes != 2:
										return
					__UpperCamelCase					:	Optional[int]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	str             =      reduce(snake_case		,	"""sum"""				)
					__UpperCamelCase					:	Optional[int]             =      torch.tensor([4.0, 6]				).to(state.device				)
					assert torch.allclose(snake_case		,	snake_case				), F'''{reduced_tensor} != {truth_tensor}'''





def 				A__ (snake_case   :		Optional[Any]				)     ->						Optional[Any]:
					# For now runs on only two processes
					if state.num_processes != 2:
										return
					__UpperCamelCase					:	List[Any]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	str             =      reduce(snake_case		,	"""mean"""				)
					__UpperCamelCase					:	Union[str, Any]             =      torch.tensor([2.0, 3]				).to(state.device				)
					assert torch.allclose(snake_case		,	snake_case				), F'''{reduced_tensor} != {truth_tensor}'''





def 				A__ (snake_case   :		Tuple				)     ->						int:
					# For xla_spawn (TPUs)
					main()





def 				A__ ()     ->						List[Any]:
					__UpperCamelCase					:	List[Any]             =      PartialState()
					state.print(F'''State: {state}'''				)
					state.print("""testing gather"""				)
					test_gather(snake_case				)
					state.print("""testing gather_object"""				)
					test_gather_object(snake_case				)
					state.print("""testing broadcast"""				)
					test_broadcast(snake_case				)
					state.print("""testing pad_across_processes"""				)
					test_pad_across_processes(snake_case				)
					state.print("""testing reduce_sum"""				)
					test_reduce_sum(snake_case				)
					state.print("""testing reduce_mean"""				)
					test_reduce_mean(snake_case				)


if __name__ == "__main__":
	main()






