"""simple docstring"""




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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int	)       ->      Optional[Any]:
  _lowerCAmelCase						:       Optional[Any]  			=     SwinConfig()
  _lowerCAmelCase						:       Tuple  			=     swin_name.split("""_"""	)

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

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

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

  _lowerCAmelCase						:       Union[str, Any]  			=     img_size
  _lowerCAmelCase						:       Any  			=     num_classes
  _lowerCAmelCase						:       Tuple  			=     embed_dim
  _lowerCAmelCase						:       int  			=     depths
  _lowerCAmelCase						:       Union[str, Any]  			=     num_heads
  _lowerCAmelCase						:       int  			=     window_size

  return config





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[int]	)       ->      str:


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

  if name == "norm.weight":
    _lowerCAmelCase						:       Optional[Any]  			=     """layernorm.weight"""
  if name == "norm.bias":
    _lowerCAmelCase						:       str  			=     """layernorm.bias"""

  if "head" in name:
    _lowerCAmelCase						:       Optional[Any]  			=     name.replace("""head"""			,"""classifier"""	)
  else:
    _lowerCAmelCase						:       str  			=     """swin.""" + name

  return name





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       Union[str, Any]	)       ->      Optional[int]:


  for key in orig_state_dict.copy().keys():
    _lowerCAmelCase						:       Tuple  			=     orig_state_dict.pop(lowerCAmelCase_	)

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

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

  return orig_state_dict





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[int]			,_lowerCamelCase						:       Union[str, Any]	)       ->      Dict:
  _lowerCAmelCase						:       Any  			=     timm.create_model(lowerCAmelCase_			,pretrained=lowerCAmelCase_	)
  timm_model.eval()

  _lowerCAmelCase						:       Union[str, Any]  			=     get_swin_config(lowerCAmelCase_	)
  _lowerCAmelCase						:       str  			=     SwinForImageClassification(lowerCAmelCase_	)
  model.eval()

  _lowerCAmelCase						:       List[str]  			=     convert_state_dict(timm_model.state_dict()			,lowerCAmelCase_	)
  model.load_state_dict(lowerCAmelCase_	)

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

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

  _lowerCAmelCase						:       str  			=     timm_model(inputs["""pixel_values"""]	)
  _lowerCAmelCase						:       Optional[Any]  			=     model(**lowerCAmelCase_	).logits

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

  print(f"Saving model {swin_name} to {pytorch_dump_folder_path}"	)
  model.save_pretrained(lowerCAmelCase_	)

  print(f"Saving image processor to {pytorch_dump_folder_path}"	)
  image_processor.save_pretrained(lowerCAmelCase_	)


if __name__ == "__main__":
     _a      :		Dict           =   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.'
     )

     _a      :		Union[str, Any]           =   parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)








"""simple docstring"""







# Copyright 2022 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 argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


a__				:							Tuple						=     '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''




def    UpperCAmelCase__       (lowerCAmelCase_=None   ):




					'''simple docstring'''


					if subparsers is not None:
										__SCREAMING_SNAKE_CASE						   =				subparsers.add_parser("tpu-config"  ,						description=_description   )
					else:
										__SCREAMING_SNAKE_CASE						   =				argparse.ArgumentParser("Accelerate tpu-config command"  ,						description=_description   )
					# Core arguments
					__SCREAMING_SNAKE_CASE						   =				parser.add_argument_group(
					    "Config Arguments"  ,						"Arguments that can be configured through `accelerate config`."   )
					config_args.add_argument(
					    "--config_file"  ,						type=lowerCAmelCase_  ,						default=lowerCAmelCase_  ,						help="Path to the config file to use for accelerate."  ,						)
					config_args.add_argument(
					    "--tpu_name"  ,						default=lowerCAmelCase_  ,						help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."  ,						)
					config_args.add_argument(
					    "--tpu_zone"  ,						default=lowerCAmelCase_  ,						help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."  ,						)
					__SCREAMING_SNAKE_CASE						   =				parser.add_argument_group("TPU Arguments"  ,						"Arguments for options ran inside the TPU."   )
					pod_args.add_argument(
					    "--use_alpha"  ,						action="store_true"  ,						help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."  ,						)
					pod_args.add_argument(
					    "--command_file"  ,						default=lowerCAmelCase_  ,						help="The path to the file containing the commands to run on the pod on startup."  ,						)
					pod_args.add_argument(
					    "--command"  ,						action="append"  ,						nargs="+"  ,						help="A command to run on the pod. Can be passed multiple times."  ,						)
					pod_args.add_argument(
					    "--install_accelerate"  ,						action="store_true"  ,						help="Whether to install accelerate on the pod. Defaults to False."  ,						)
					pod_args.add_argument(
					    "--accelerate_version"  ,						default="latest"  ,						help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."  ,						)
					pod_args.add_argument(
					    "--debug"  ,						action="store_true"  ,						help="If set, will print the command that would be run instead of running it."   )

					if subparsers is not None:
										parser.set_defaults(func=lowerCAmelCase_   )
					return parser




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				None

					# Get the default from the config file if it exists.
					if args.config_file is not None or os.path.isfile(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				load_config_from_file(args.config_file   )
										if not args.command_file and defaults.command_file is not None and not args.command:
															__SCREAMING_SNAKE_CASE						   =				defaults.command_file
										if not args.command and defaults.commands is not None:
															__SCREAMING_SNAKE_CASE						   =				defaults.commands
										if not args.tpu_name:
															__SCREAMING_SNAKE_CASE						   =				defaults.tpu_name
										if not args.tpu_zone:
															__SCREAMING_SNAKE_CASE						   =				defaults.tpu_zone
					if args.accelerate_version == "dev":
										__SCREAMING_SNAKE_CASE						   =				"git+https://github.com/huggingface/accelerate.git"
					elif args.accelerate_version == "latest":
										__SCREAMING_SNAKE_CASE						   =				"accelerate -U"
					elif isinstance(parse(args.accelerate_version   )  ,						lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				f"""accelerate=={args.accelerate_version}"""

					if not args.command_file and not args.command:
										raise ValueError("You must specify either a command file or a command to run on the pod."   )

					if args.command_file:
										with open(args.command_file  ,						"r"   ) as f:
															__SCREAMING_SNAKE_CASE						   =				[f.read().splitlines()]

    # To turn list of lists into list of strings
					if isinstance(args.command[0]  ,						lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				[line for cmd in args.command for line in cmd]
					# Default to the shared folder and install accelerate
					__SCREAMING_SNAKE_CASE						   =				["cd /usr/share"]
					if args.install_accelerate:
										new_cmd += [f"""pip install {args.accelerate_version}"""]
					new_cmd += args.command
					__SCREAMING_SNAKE_CASE						   =				"; ".join(lowerCAmelCase_   )

					# Then send it to gcloud
					# Eventually try to use google-api-core to do this instead of subprocess
					__SCREAMING_SNAKE_CASE						   =				["gcloud"]
					if args.use_alpha:
										cmd += ["alpha"]
					cmd += [
					    "compute",
					    "tpus",
					    "tpu-vm",
					    "ssh",
					    args.tpu_name,
					    "--zone",
					    args.tpu_zone,
					    "--command",
					    args.command,
					    "--worker",
					    "all",
					]
					if args.debug:
										print(f"""Running {' '.join(lowerCAmelCase_   )}"""   )
										return
					subprocess.run(lowerCAmelCase_   )
					print("Successfully setup pod."   )




def    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				tpu_command_parser()
					__SCREAMING_SNAKE_CASE						   =				parser.parse_args()

					tpu_command_launcher(lowerCAmelCase_   )
