






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     a_   (__A      )				->						str:



					"""simple docstring"""
					__a					:						List[Any]      =					SwinConfig()
					__a					:						Dict      =					swin_name.split("_"      )

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

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

					if "in22k" in swin_name:
										__a					:						int      =					21_841
					else:
										__a					:						Dict      =					1_000
										__a					:						List[Any]      =					"huggingface/label-files"
										__a					:						str      =					"imagenet-1k-id2label.json"
										__a					:						Any      =					json.load(open(hf_hub_download(__A				,       __A				,       repo_type="dataset"      )				,       "r"      )      )
										__a					:						str      =					{int(__A      ): v for k, v in idalabel.items()}
										__a					:						Dict      =					idalabel
										__a					:						Optional[int]      =					{v: k for k, v in idalabel.items()}

					__a					:						str      =					img_size
					__a					:						Union[str, Any]      =					num_classes
					__a					:						str      =					embed_dim
					__a					:						List[str]      =					depths
					__a					:						List[str]      =					num_heads
					__a					:						Tuple      =					window_size

					return config







def     a_   (__A      )				->						Any:



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

					if name == "norm.weight":
										__a					:						int      =					"layernorm.weight"
					if name == "norm.bias":
										__a					:						Union[str, Any]      =					"layernorm.bias"

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

					return name







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					for key in orig_state_dict.copy().keys():
										__a					:						Optional[Any]      =					orig_state_dict.pop(__A      )

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

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

					return orig_state_dict







def     a_   (__A				,       __A      )				->						Union[str, Any]:



					"""simple docstring"""
					__a					:						List[Any]      =					timm.create_model(__A				,       pretrained=__A      )
					timm_model.eval()

					__a					:						Optional[Any]      =					get_swin_config(__A      )
					__a					:						Optional[int]      =					SwinForImageClassification(__A      )
					model.eval()

					__a					:						Dict      =					convert_state_dict(timm_model.state_dict()				,       __A      )
					model.load_state_dict(__A      )

					__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(__A				,       stream=__A      ).raw      )
					__a					:						Optional[int]      =					image_processor(images=__A				,       return_tensors="pt"      )

					__a					:						Union[str, Any]      =					timm_model(inputs["pixel_values"]      )
					__a					:						Union[str, Any]      =					model(**__A      ).logits

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

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

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


if __name__ == "__main__":
			UpperCAmelCase__      			=						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.'''
			)

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













# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCAmelCase__      			=						'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
			import torch

			print('''Torch version:''', torch.__version__)
			print('''Cuda available:''', torch.cuda.is_available())
			print('''Cuda version:''', torch.version.cuda)
			print('''CuDNN version:''', torch.backends.cudnn.version())
			print('''Number of GPUs available:''', torch.cuda.device_count())
			print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
			print('''Torch version:''', None)

try:
			import deepspeed

			print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
			print('''DeepSpeed version:''', None)

try:
			import tensorflow as tf

			print('''TensorFlow version:''', tf.__version__)
			print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
			print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
			print('''TensorFlow version:''', None)





