


"""simple docstring"""







import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				SwinConfig(image_size=192   )

					if "base" in model_name:
										__SCREAMING_SNAKE_CASE						   =				6
										__SCREAMING_SNAKE_CASE						   =				128
										__SCREAMING_SNAKE_CASE						   =				(2, 2, 18, 2)
										__SCREAMING_SNAKE_CASE						   =				(4, 8, 16, 32)
					elif "large" in model_name:
										__SCREAMING_SNAKE_CASE						   =				12
										__SCREAMING_SNAKE_CASE						   =				192
										__SCREAMING_SNAKE_CASE						   =				(2, 2, 18, 2)
										__SCREAMING_SNAKE_CASE						   =				(6, 12, 24, 48)
					else:
										raise ValueError("Model not supported, only supports base and large variants"   )

					__SCREAMING_SNAKE_CASE						   =				window_size
					__SCREAMING_SNAKE_CASE						   =				embed_dim
					__SCREAMING_SNAKE_CASE						   =				depths
					__SCREAMING_SNAKE_CASE						   =				num_heads

					return config




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					if "encoder.mask_token" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("encoder.mask_token"  ,						"embeddings.mask_token"   )
					if "encoder.patch_embed.proj" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("encoder.patch_embed.proj"  ,						"embeddings.patch_embeddings.projection"   )
					if "encoder.patch_embed.norm" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("encoder.patch_embed.norm"  ,						"embeddings.norm"   )
					if "attn.proj" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("attn.proj"  ,						"attention.output.dense"   )
					if "attn" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("attn"  ,						"attention.self"   )
					if "norm1" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("norm1"  ,						"layernorm_before"   )
					if "norm2" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("norm2"  ,						"layernorm_after"   )
					if "mlp.fc1" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("mlp.fc1"  ,						"intermediate.dense"   )
					if "mlp.fc2" in name:
										__SCREAMING_SNAKE_CASE						   =				name.replace("mlp.fc2"  ,						"output.dense"   )

					if name == "encoder.norm.weight":
										__SCREAMING_SNAKE_CASE						   =				"layernorm.weight"
					if name == "encoder.norm.bias":
										__SCREAMING_SNAKE_CASE						   =				"layernorm.bias"

					if "decoder" in name:
										pass
					else:
										__SCREAMING_SNAKE_CASE						   =				"swin." + name

					return name




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


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

										if "attn_mask" in key:
															pass
										elif "qkv" in key:
															__SCREAMING_SNAKE_CASE						   =				key.split("."   )
															__SCREAMING_SNAKE_CASE						   =				int(key_split[2]   )
															__SCREAMING_SNAKE_CASE						   =				int(key_split[4]   )
															__SCREAMING_SNAKE_CASE						   =				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

															if "weight" in key:
																				__SCREAMING_SNAKE_CASE						   =				val[:dim, :]
																				__SCREAMING_SNAKE_CASE						   =				val[
																				    dim : dim * 2, :
																				]
																				__SCREAMING_SNAKE_CASE						   =				val[-dim:, :]
															else:
																				__SCREAMING_SNAKE_CASE						   =				val[
																				    :dim
																				]
																				__SCREAMING_SNAKE_CASE						   =				val[
																				    dim : dim * 2
																				]
																				__SCREAMING_SNAKE_CASE						   =				val[
																				    -dim:
																				]
										else:
															__SCREAMING_SNAKE_CASE						   =				val

					return orig_state_dict




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				torch.load(lowerCAmelCase_  ,						map_location="cpu"   )["model"]

					__SCREAMING_SNAKE_CASE						   =				get_swin_config(lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				SwinForMaskedImageModeling(lowerCAmelCase_   )
					model.eval()

					__SCREAMING_SNAKE_CASE						   =				convert_state_dict(lowerCAmelCase_  ,						lowerCAmelCase_   )
					model.load_state_dict(lowerCAmelCase_   )

					__SCREAMING_SNAKE_CASE						   =				"http://images.cocodataset.org/val2017/000000039769.jpg"

					__SCREAMING_SNAKE_CASE						   =				ViTImageProcessor(size={"height": 192, "width": 192}   )
					__SCREAMING_SNAKE_CASE						   =				Image.open(requests.get(lowerCAmelCase_  ,						stream=lowerCAmelCase_   ).raw   )
					__SCREAMING_SNAKE_CASE						   =				image_processor(images=lowerCAmelCase_  ,						return_tensors="pt"   )

					with torch.no_grad():
										__SCREAMING_SNAKE_CASE						   =				model(**lowerCAmelCase_   ).logits

					print(outputs.keys()   )
					print("Looks ok!"   )

					if pytorch_dump_folder_path is not None:
										print(f"""Saving model {model_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 push_to_hub:
										print(f"""Pushing model and image processor for {model_name} to hub"""   )
										model.push_to_hub(f"""microsoft/{model_name}"""   )
										image_processor.push_to_hub(f"""microsoft/{model_name}"""   )


if __name__ == "__main__":
		a__				:							List[Any]						=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''swin-base-simmim-window6-192''',
		    type=str,
		    choices=['''swin-base-simmim-window6-192''', '''swin-large-simmim-window12-192'''],
		    help='''Name of the Swin SimMIM model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--checkpoint_path''',
		    default='''/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth''',
		    type=str,
		    help='''Path to the original PyTorch checkpoint (.pth file).''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)

		a__				:							List[Any]						=     parser.parse_args()
		convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




"""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_   )
