


"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			SwinConfig(image_size=192      )

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

			__lowerCAmelCase				    =			window_size
			__lowerCAmelCase				    =			embed_dim
			__lowerCAmelCase				    =			depths
			__lowerCAmelCase				    =			num_heads

			return config


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




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

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

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

			return name


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			for key in orig_state_dict.copy().keys():
						__lowerCAmelCase				    =			orig_state_dict.pop(_UpperCamelCase      )

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

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

			return orig_state_dict


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			torch.load(_UpperCamelCase    , map_location="cpu"      )["model"]

			__lowerCAmelCase				    =			get_swin_config(_UpperCamelCase      )
			__lowerCAmelCase				    =			SwinForMaskedImageModeling(_UpperCamelCase      )
			model.eval()

			__lowerCAmelCase				    =			convert_state_dict(_UpperCamelCase    , _UpperCamelCase      )
			model.load_state_dict(_UpperCamelCase      )

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

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

			with torch.no_grad():
						__lowerCAmelCase				    =			model(**_UpperCamelCase      ).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(_UpperCamelCase      )

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

			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     :   int			    =			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     :   Optional[int]			    =			parser.parse_args()
	convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""


import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							def __init__(     self    ,   __a    ,   __a=None    ,   __a=True    ,   __a=None    ,   **__a	):
										__lowerCAmelCase				    =			parent
										__lowerCAmelCase				    =			config_class
										__lowerCAmelCase				    =			has_text_modality
										__lowerCAmelCase				    =			kwargs
										__lowerCAmelCase				    =			common_properties

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)
										__lowerCAmelCase				    =			(
										    ["hidden_size", "num_attention_heads", "num_hidden_layers"]
										    if self.common_properties is None
										    else self.common_properties
										)

										# Add common fields for text models
										if self.has_text_modality:
													common_properties.extend(["vocab_size"]	)

										# Test that config has the common properties as getters
										for prop in common_properties:
													self.parent.assertTrue(hasattr(__a    ,   __a	)    ,   msg=f"`{prop}` does not exist"	)

										# Test that config has the common properties as setter
										for idx, name in enumerate(__a	):
													try:
																setattr(__a    ,   __a    ,   __a	)
																self.parent.assertEqual(
																    getattr(__a    ,   __a	)    ,   __a    ,   msg=f"`{name} value {idx} expected, but was {getattr(__a    ,   __a	)}"	)
													except NotImplementedError:
																# Some models might not be able to implement setters for common_properties
																# In that case, a NotImplementedError is raised
																pass

        # Test if config class can be called with Config(prop_name=..)
										for idx, name in enumerate(__a	):
													try:
																__lowerCAmelCase				    =			self.config_class(**{name: idx}	)
																self.parent.assertEqual(
																    getattr(__a    ,   __a	)    ,   __a    ,   msg=f"`{name} value {idx} expected, but was {getattr(__a    ,   __a	)}"	)
													except NotImplementedError:
																# Some models might not be able to implement setters for common_properties
																# In that case, a NotImplementedError is raised
																pass

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)
										__lowerCAmelCase				    =			json.loads(config.to_json_string()	)
										for key, value in self.inputs_dict.items():
													self.parent.assertEqual(obj[key]    ,   __a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)

										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCAmelCase				    =			os.path.join(__a    ,   "config.json"	)
													config_first.to_json_file(__a	)
													__lowerCAmelCase				    =			self.config_class.from_json_file(__a	)

										self.parent.assertEqual(config_second.to_dict()    ,   config_first.to_dict()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)

										with tempfile.TemporaryDirectory() as tmpdirname:
													config_first.save_pretrained(__a	)
													__lowerCAmelCase				    =			self.config_class.from_pretrained(__a	)

										self.parent.assertEqual(config_second.to_dict()    ,   config_first.to_dict()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)

										__lowerCAmelCase				    =			"test"
										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCAmelCase				    =			os.path.join(__a    ,   __a	)
													config_first.save_pretrained(__a	)
													__lowerCAmelCase				    =			self.config_class.from_pretrained(__a    ,   subfolder=__a	)

										self.parent.assertEqual(config_second.to_dict()    ,   config_first.to_dict()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict    ,   num_labels=5	)
										self.parent.assertEqual(len(config.idalabel	)    ,   5	)
										self.parent.assertEqual(len(config.labelaid	)    ,   5	)

										__lowerCAmelCase				    =			3
										self.parent.assertEqual(len(config.idalabel	)    ,   3	)
										self.parent.assertEqual(len(config.labelaid	)    ,   3	)

							def 			snake_case				(     self	):
										if self.config_class.is_composition:
													return
										__lowerCAmelCase				    =			self.config_class()
										self.parent.assertIsNotNone(__a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			copy.deepcopy(__a	)
										__lowerCAmelCase				    =			self.config_class(**__a	)
										__lowerCAmelCase				    =			[]
										for key, value in config_common_kwargs.items():
													if key == "torch_dtype":
																if not is_torch_available():
																			continue
																else:
																			import torch

																			if config.torch_dtype != torch.floataa:
																						wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa)	)
													elif getattr(__a    ,   __a	) != value:
																wrong_values.append((key, getattr(__a    ,   __a	), value)	)

										if len(__a	) > 0:
													__lowerCAmelCase				    =			"\n".join([f"- {v[0]}: got {v[1]} instead of {v[2]}" for v in wrong_values]	)
													raise ValueError(f"The following keys were not properly set in the config:\n{errors}"	)



							def 			snake_case				(     self	):
										self.create_and_test_config_common_properties()
										self.create_and_test_config_to_json_string()
										self.create_and_test_config_to_json_file()
										self.create_and_test_config_from_and_save_pretrained()
										self.create_and_test_config_from_and_save_pretrained_subfolder()
										self.create_and_test_config_with_num_labels()
										self.check_config_can_be_init_without_params()
										self.check_config_arguments_init()




