
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def 						UpperCAmelCase      (     a_						)		->     Tuple:







				"""simple docstring"""



				__A             =			SwinConfig(image_size=1_9_2						)

				if "base" in model_name:
								__A             =			6
								__A             =			1_2_8
								__A             =			(2, 2, 1_8, 2)
								__A             =			(4, 8, 1_6, 3_2)
				elif "large" in model_name:
								__A             =			1_2
								__A             =			1_9_2
								__A             =			(2, 2, 1_8, 2)
								__A             =			(6, 1_2, 2_4, 4_8)
				else:
								raise ValueError("Model not supported, only supports base and large variants"						)

				__A             =			window_size
				__A             =			embed_dim
				__A             =			depths
				__A             =			num_heads

				return config


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



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

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

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

				return name


def 						UpperCAmelCase      (     a_		,       a_						)		->     Optional[int]:







				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__A             =			orig_state_dict.pop(_a						)

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

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

				return orig_state_dict


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_						)		->     Tuple:







				"""simple docstring"""



				__A             =			torch.load(_a		,       map_location="cpu"						)["model"]

				__A             =			get_swin_config(_a						)
				__A             =			SwinForMaskedImageModeling(_a						)
				model.eval()

				__A             =			convert_state_dict(_a		,       _a						)
				model.load_state_dict(_a						)

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

				__A             =			ViTImageProcessor(size={"height": 1_9_2, "width": 1_9_2}						)
				__A             =			Image.open(requests.get(_a		,       stream=_a						).raw						)
				__A             =			image_processor(images=_a		,       return_tensors="pt"						)

				with torch.no_grad():
								__A             =			model(**_a						).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(_a						)

								print(F'''Saving image processor to {pytorch_dump_folder_path}'''						)
								image_processor.save_pretrained(_a						)

				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__":
					SCREAMING_SNAKE_CASE							: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.'
					)

					SCREAMING_SNAKE_CASE							:Any										=    parser.parse_args()
					convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE							:int										=    {'configuration_encoder_decoder': ['EncoderDecoderConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Dict										=    ['EncoderDecoderModel']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:int										=    ['TFEncoderDecoderModel']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Optional[int]										=    ['FlaxEncoderDecoderModel']

if TYPE_CHECKING:
					from .configuration_encoder_decoder import EncoderDecoderConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_encoder_decoder import EncoderDecoderModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
					import sys

					SCREAMING_SNAKE_CASE							:int										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
