"""simple docstring"""







import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Union[str, Any]  )							->      List[Any]:

							'''simple docstring'''





							__snake_case      :							Tuple						=			SwinConfig(image_size=1_92  )

							if "base" in model_name:
														__snake_case      :							Optional[Any]						=			6
														__snake_case      :							Optional[int]						=			1_28
														__snake_case      :							Union[str, Any]						=			(2, 2, 18, 2)
														__snake_case      :							Any						=			(4, 8, 16, 32)
							elif "large" in model_name:
														__snake_case      :							Any						=			12
														__snake_case      :							Any						=			1_92
														__snake_case      :							Optional[Any]						=			(2, 2, 18, 2)
														__snake_case      :							Any						=			(6, 12, 24, 48)
							else:
														raise ValueError('Model not supported, only supports base and large variants'  )

							__snake_case      :							List[Any]						=			window_size
							__snake_case      :							int						=			embed_dim
							__snake_case      :							Union[str, Any]						=			depths
							__snake_case      :							Union[str, Any]						=			num_heads

							return config
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[int]  )							->      str:

							'''simple docstring'''





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

							if name == "encoder.norm.weight":
														__snake_case      :							List[str]						=			'layernorm.weight'
							if name == "encoder.norm.bias":
														__snake_case      :							List[str]						=			'layernorm.bias'

							if "decoder" in name:
														pass
							else:
														__snake_case      :							Any						=			'swin.' + name

							return name
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    int  )							->      int:

							'''simple docstring'''





							for key in orig_state_dict.copy().keys():
														__snake_case      :							List[Any]						=			orig_state_dict.pop(_lowerCAmelCase  )

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

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

							return orig_state_dict
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Any      ,							UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    List[str]  )							->      str:

							'''simple docstring'''





							__snake_case      :							Dict						=			torch.load(_lowerCAmelCase      ,							map_location='cpu'  )['model']

							__snake_case      :							Optional[int]						=			get_swin_config(_lowerCAmelCase  )
							__snake_case      :							str						=			SwinForMaskedImageModeling(_lowerCAmelCase  )
							model.eval()

							__snake_case      :							Tuple						=			convert_state_dict(_lowerCAmelCase      ,							_lowerCAmelCase  )
							model.load_state_dict(_lowerCAmelCase  )

							__snake_case      :							Dict						=			'http://images.cocodataset.org/val2017/000000039769.jpg'

							__snake_case      :							Union[str, Any]						=			ViTImageProcessor(size={'height': 1_92, 'width': 1_92}  )
							__snake_case      :							List[str]						=			Image.open(requests.get(_lowerCAmelCase      ,							stream=_lowerCAmelCase  ).raw  )
							__snake_case      :							List[str]						=			image_processor(images=_lowerCAmelCase      ,							return_tensors='pt'  )

							with torch.no_grad():
														__snake_case      :							Optional[Any]						=			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					:					str=			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 numpy

# List of input, output pairs
_a					:					Optional[int]=			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_a					:					int=			(((515, 22, 13), 555), ((61, 35, 49), 150))
_a					:					List[Any]=			[2, 4, 1, 5]
_a					:					Optional[int]=			len(train_data)
_a					:					str=			0.0_0_9
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    List[str]="train"  )							->      int:

	'''simple docstring'''





	return calculate_hypothesis_value(UpperCAmelCase_      ,							UpperCAmelCase_  ) - output(
	    UpperCAmelCase_      ,							UpperCAmelCase_  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Tuple  )							->      Dict:

	'''simple docstring'''





	__snake_case      :							int						=			0
	for i in range(len(UpperCAmelCase_  ) - 1  ):
		hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
	hyp_val += parameter_vector[0]
	return hyp_val
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    List[Any]  )							->      List[Any]:

	'''simple docstring'''





	if data_set == "train":
		return train_data[example_no][1]
	elif data_set == "test":
		return test_data[example_no][1]
	return None
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    Optional[int]  )							->      Any:

	'''simple docstring'''





	if data_set == "train":
		return _hypothesis_value(train_data[example_no][0]  )
	elif data_set == "test":
		return _hypothesis_value(test_data[example_no][0]  )
	return None
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Union[str, Any]      ,							UpperCAmelCase_   :    Any=m  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			0
	for i in range(UpperCAmelCase_  ):
		if index == -1:
			summation_value += _error(UpperCAmelCase_  )
		else:
			summation_value += _error(UpperCAmelCase_  ) * train_data[i][0][index]
	return summation_value
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      str:

	'''simple docstring'''





	__snake_case      :							Dict						=			summation_of_cost_derivative(UpperCAmelCase_      ,							UpperCAmelCase_  ) / m
	return cost_derivative_value
def   __UpperCAmelCase    (   )							->      List[str]:

	'''simple docstring'''





	global parameter_vector
	# Tune these values to set a tolerance value for predicted output
	__snake_case      :							Dict						=			0.000_002
	__snake_case      :							Optional[int]						=			0
	__snake_case      :							str						=			0
	while True:
		j += 1
		__snake_case      :							Optional[int]						=			[0, 0, 0, 0]
		for i in range(0      ,							len(UpperCAmelCase_  )  ):
			__snake_case      :							Union[str, Any]						=			get_cost_derivative(i - 1  )
			__snake_case      :							Any						=			(
			    parameter_vector[i] - LEARNING_RATE * cost_derivative
			)
		if numpy.allclose(
		    UpperCAmelCase_      ,							UpperCAmelCase_      ,							atol=UpperCAmelCase_      ,							rtol=UpperCAmelCase_      ,							):
			break
		__snake_case      :							Optional[Any]						=			temp_parameter_vector
	print(('Number of iterations:', j)  )
def   __UpperCAmelCase    (   )							->      int:

	'''simple docstring'''





	for i in range(len(UpperCAmelCase_  )  ):
		print(('Actual output value:', output(UpperCAmelCase_      ,							'test'  ))  )
		print(('Hypothesis output:', calculate_hypothesis_value(UpperCAmelCase_      ,							'test'  ))  )


if __name__ == "__main__":
	run_gradient_descent()
	print("\nTesting gradient descent for a linear hypothesis function.\n")
	test_gradient_descent()





