






import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor

def        UpperCAmelCase__		(						lowerCamelCase_       :       Any				):
			__a						:  int									=     SwinaSRConfig()

			if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
						__a						:  Dict									=     4
			elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
						__a						:  Tuple									=     4
						__a						:  List[str]									=     4_8
						__a						:  Optional[Any]									=     'pixelshuffle_aux'
			elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
						__a						:  Optional[int]									=     [6, 6, 6, 6]
						__a						:  List[str]									=     6_0
						__a						:  Dict									=     [6, 6, 6, 6]
						__a						:  Tuple									=     'pixelshuffledirect'
			elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
						__a						:  List[str]									=     4
						__a						:  List[str]									=     'nearest+conv'
			elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
						__a						:  Tuple									=     1
						__a						:  List[str]									=     1
						__a						:  Dict									=     1_2_6
						__a						:  Optional[int]									=     7
						__a						:  Dict									=     255.0
						__a						:  Tuple									=     ''

			return config

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[str]   ,  lowerCamelCase_       :       List[Any]				):
			if "patch_embed.proj" in name and "layers" not in name:
						__a						:  str									=     name.replace('patch_embed.proj'   ,  'embeddings.patch_embeddings.projection'				)
			if "patch_embed.norm" in name:
						__a						:  str									=     name.replace('patch_embed.norm'   ,  'embeddings.patch_embeddings.layernorm'				)
			if "layers" in name:
						__a						:  Union[str, Any]									=     name.replace('layers'   ,  'encoder.stages'				)
			if "residual_group.blocks" in name:
						__a						:  int									=     name.replace('residual_group.blocks'   ,  'layers'				)
			if "attn.proj" in name:
						__a						:  str									=     name.replace('attn.proj'   ,  'attention.output.dense'				)
			if "attn" in name:
						__a						:  List[str]									=     name.replace('attn'   ,  'attention.self'				)
			if "norm1" in name:
						__a						:  Tuple									=     name.replace('norm1'   ,  'layernorm_before'				)
			if "norm2" in name:
						__a						:  Dict									=     name.replace('norm2'   ,  'layernorm_after'				)
			if "mlp.fc1" in name:
						__a						:  Any									=     name.replace('mlp.fc1'   ,  'intermediate.dense'				)
			if "mlp.fc2" in name:
						__a						:  Optional[int]									=     name.replace('mlp.fc2'   ,  'output.dense'				)
			if "q_bias" in name:
						__a						:  Union[str, Any]									=     name.replace('q_bias'   ,  'query.bias'				)
			if "k_bias" in name:
						__a						:  Union[str, Any]									=     name.replace('k_bias'   ,  'key.bias'				)
			if "v_bias" in name:
						__a						:  List[str]									=     name.replace('v_bias'   ,  'value.bias'				)
			if "cpb_mlp" in name:
						__a						:  Optional[Any]									=     name.replace('cpb_mlp'   ,  'continuous_position_bias_mlp'				)
			if "patch_embed.proj" in name:
						__a						:  Optional[int]									=     name.replace('patch_embed.proj'   ,  'patch_embed.projection'				)

			if name == "norm.weight":
						__a						:  List[Any]									=     'layernorm.weight'
			if name == "norm.bias":
						__a						:  Tuple									=     'layernorm.bias'

			if "conv_first" in name:
						__a						:  Optional[Any]									=     name.replace('conv_first'   ,  'first_convolution'				)

			if (
			    "upsample" in name
			    or "conv_before_upsample" in name
			    or "conv_bicubic" in name
			    or "conv_up" in name
			    or "conv_hr" in name
			    or "conv_last" in name
			    or "aux" in name
			):
						# heads
						if "conv_last" in name:
									__a						:  List[str]									=     name.replace('conv_last'   ,  'final_convolution'				)
						if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
									if "conv_before_upsample.0" in name:
												__a						:  List[str]									=     name.replace('conv_before_upsample.0'   ,  'conv_before_upsample'				)
									if "upsample.0" in name:
												__a						:  Tuple									=     name.replace('upsample.0'   ,  'upsample.convolution_0'				)
									if "upsample.2" in name:
												__a						:  Dict									=     name.replace('upsample.2'   ,  'upsample.convolution_1'				)
									__a						:  Optional[int]									=     'upsample.' + name
						elif config.upsampler == "pixelshuffledirect":
									__a						:  Optional[Any]									=     name.replace('upsample.0.weight'   ,  'upsample.conv.weight'				)
									__a						:  Dict									=     name.replace('upsample.0.bias'   ,  'upsample.conv.bias'				)
						else:
									pass
			else:
						__a						:  List[Any]									=     'swin2sr.' + name

			return name

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[Any]   ,  lowerCamelCase_       :       Optional[int]				):
			for key in orig_state_dict.copy().keys():
						__a						:  Dict									=     orig_state_dict.pop(lowerCamelCase_				)

						if "qkv" in key:
									__a						:  Any									=     key.split('.'				)
									__a						:  Optional[Any]									=     int(key_split[1]				)
									__a						:  List[str]									=     int(key_split[4]				)
									__a						:  Optional[Any]									=     config.embed_dim

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

			return orig_state_dict

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[Any]   ,  lowerCamelCase_       :       Optional[int]   ,  lowerCamelCase_       :       Tuple				):
			__a						:  int									=     get_config(lowerCamelCase_				)
			__a						:  List[Any]									=     SwinaSRForImageSuperResolution(lowerCamelCase_				)
			model.eval()

			__a						:  List[Any]									=     torch.hub.load_state_dict_from_url(lowerCamelCase_   ,  map_location='cpu'				)
			__a						:  Optional[Any]									=     convert_state_dict(lowerCamelCase_   ,  lowerCamelCase_				)
			__a						,					__a						:  Dict									=     model.load_state_dict(lowerCamelCase_   ,  strict=lowerCamelCase_				)

			if len(lowerCamelCase_				) > 0:
						raise ValueError('Missing keys when converting: {}'.format(lowerCamelCase_				)				)
			for key in unexpected_keys:
						if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
									raise ValueError(f'''Unexpected key {key} in state_dict'''				)

    # verify values
			__a						:  Union[str, Any]									=     'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
			__a						:  Any									=     Image.open(requests.get(lowerCamelCase_   ,  stream=lowerCamelCase_				).raw				).convert('RGB'				)
			__a						:  str									=     SwinaSRImageProcessor()
			# pixel_values = processor(image, return_tensors="pt").pixel_values

			__a						:  Union[str, Any]									=     1_2_6 if 'Jpeg' in checkpoint_url else 2_5_6
			__a						:  Tuple									=     Compose(
			    [
			        Resize((image_size, image_size)				),
			        ToTensor(),
			        Normalize(mean=[0.485, 0.456, 0.406]   ,  std=[0.229, 0.224, 0.225]				),
			    ]				)
			__a						:  Optional[int]									=     transforms(lowerCamelCase_				).unsqueeze(0				)

			if config.num_channels == 1:
						__a						:  List[Any]									=     pixel_values[:, 0, :, :].unsqueeze(1				)

			__a						:  str									=     model(lowerCamelCase_				)

			# assert values
			if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
						__a						:  Union[str, Any]									=     torch.Size([1, 3, 5_1_2, 5_1_2]				)
						__a						:  List[str]									=     torch.tensor(
						    [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]				)
			elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
						__a						:  Optional[int]									=     torch.Size([1, 3, 1_0_2_4, 1_0_2_4]				)
						__a						:  Union[str, Any]									=     torch.tensor(
						    [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]				)
			elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
						# TODO values didn't match exactly here
						__a						:  Optional[int]									=     torch.Size([1, 3, 1_0_2_4, 1_0_2_4]				)
						__a						:  List[str]									=     torch.tensor(
						    [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]				)
			elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
						__a						:  List[str]									=     torch.Size([1, 3, 5_1_2, 5_1_2]				)
						__a						:  Optional[int]									=     torch.tensor(
						    [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]				)
			elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
						__a						:  Dict									=     torch.Size([1, 3, 1_0_2_4, 1_0_2_4]				)
						__a						:  Optional[Any]									=     torch.tensor(
						    [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]				)

			assert (
			    outputs.reconstruction.shape == expected_shape
			), f'''Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'''
			assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]   ,  lowerCamelCase_   ,  atol=1e-3				)
			print('Looks ok!'				)

			__a						:  int									=     {
			    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth': (
			        'swin2SR-classical-sr-x2-64'
			    ),
			    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth': (
			        'swin2SR-classical-sr-x4-64'
			    ),
			    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth': (
			        'swin2SR-compressed-sr-x4-48'
			    ),
			    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth': (
			        'swin2SR-lightweight-x2-64'
			    ),
			    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth': (
			        'swin2SR-realworld-sr-x4-64-bsrgan-psnr'
			    ),
			}
			__a						:  int									=     url_to_name[checkpoint_url]

			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}'''				)
						processor.save_pretrained(lowerCamelCase_				)

			if push_to_hub:
						model.push_to_hub(f'''caidas/{model_name}'''				)
						processor.push_to_hub(f'''caidas/{model_name}'''				)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__				     =			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
							    type=str,
							    help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
							)
							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 to push the converted model to the hub.''')

							SCREAMING_SNAKE_CASE__				     =			parser.parse_args()
							convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean





def    lowerCAmelCase       (		UpperCamelCase__					: int							):


	"""simple docstring"""

	# A local function to see if a dot lands in the circle.
	def is_in_circle(UpperCamelCase__					: float     ,       UpperCamelCase__					: float							) -> bool:
		__UpperCAmelCase      					=      sqrt((x**2) + (y**2)							)
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	__UpperCAmelCase      					=      mean(
	    int(is_in_circle(uniform(-1.0     ,       1.0							)     ,       uniform(-1.0     ,       1.0							)							)							)
	    for _ in range(UpperCamelCase__							)							)
	# The ratio of the area for circle to square is pi/4.
	__UpperCAmelCase      					=      proportion * 4
	print(f"""The estimated value of pi is {pi_estimate}"""							)
	print(f"""The numpy value of pi is {pi}"""							)
	print(f"""The total error is {abs(pi - pi_estimate							)}"""							)





def    lowerCAmelCase       (		UpperCamelCase__					: int     ,       UpperCamelCase__					: Callable[[float], float]     ,       UpperCamelCase__					: float = 0.0     ,       UpperCamelCase__					: float = 1.0     ,       ):


	"""simple docstring"""
	return mean(
	    function_to_integrate(uniform(UpperCamelCase__     ,       UpperCamelCase__							)							) for _ in range(UpperCamelCase__							)							) * (max_value - min_value)





def    lowerCAmelCase       (		UpperCamelCase__					: int     ,       UpperCamelCase__					: float = 0.0     ,       UpperCamelCase__					: float = 1.0							):


	"""simple docstring"""

	def identity_function(UpperCamelCase__					: float							) -> float:
		return x

	__UpperCAmelCase      					=      area_under_curve_estimator(
	    UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
	__UpperCAmelCase      					=      (max_value * max_value - min_value * min_value) / 2

	print('''******************'''							)
	print(f"""Estimating area under y=x where x varies from {min_value} to {max_value}"""							)
	print(f"""Estimated value is {estimated_value}"""							)
	print(f"""Expected value is {expected_value}"""							)
	print(f"""Total error is {abs(estimated_value - expected_value							)}"""							)
	print('''******************'''							)





def    lowerCAmelCase       (		UpperCamelCase__					: int							):


	"""simple docstring"""

	def function_to_integrate(UpperCamelCase__					: float							) -> float:
		return sqrt(4.0 - x * x							)

	__UpperCAmelCase      					=      area_under_curve_estimator(
	    UpperCamelCase__     ,       UpperCamelCase__     ,       0.0     ,       2.0							)

	print('''******************'''							)
	print('''Estimating pi using area_under_curve_estimator'''							)
	print(f"""Estimated value is {estimated_value}"""							)
	print(f"""Expected value is {pi}"""							)
	print(f"""Total error is {abs(estimated_value - pi							)}"""							)
	print('''******************'''							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
