






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 							__A(lowerCAmelCase )		->				List[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	SwinaSRConfig()

		if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				_UpperCamelCase						    =	4
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				_UpperCamelCase						    =	4
				_UpperCamelCase						    =	4_8
				_UpperCamelCase						    =	'''pixelshuffle_aux'''
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				_UpperCamelCase						    =	[6, 6, 6, 6]
				_UpperCamelCase						    =	6_0
				_UpperCamelCase						    =	[6, 6, 6, 6]
				_UpperCamelCase						    =	'''pixelshuffledirect'''
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				_UpperCamelCase						    =	4
				_UpperCamelCase						    =	'''nearest+conv'''
		elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
				_UpperCamelCase						    =	1
				_UpperCamelCase						    =	1
				_UpperCamelCase						    =	1_2_6
				_UpperCamelCase						    =	7
				_UpperCamelCase						    =	255.0
				_UpperCamelCase						    =	''''''

		return config


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				Any:

		"""simple docstring"""






		if "patch_embed.proj" in name and "layers" not in name:
				_UpperCamelCase						    =	name.replace("""patch_embed.proj"""						,  """embeddings.patch_embeddings.projection""" )
		if "patch_embed.norm" in name:
				_UpperCamelCase						    =	name.replace("""patch_embed.norm"""						,  """embeddings.patch_embeddings.layernorm""" )
		if "layers" in name:
				_UpperCamelCase						    =	name.replace("""layers"""						,  """encoder.stages""" )
		if "residual_group.blocks" in name:
				_UpperCamelCase						    =	name.replace("""residual_group.blocks"""						,  """layers""" )
		if "attn.proj" in name:
				_UpperCamelCase						    =	name.replace("""attn.proj"""						,  """attention.output.dense""" )
		if "attn" in name:
				_UpperCamelCase						    =	name.replace("""attn"""						,  """attention.self""" )
		if "norm1" in name:
				_UpperCamelCase						    =	name.replace("""norm1"""						,  """layernorm_before""" )
		if "norm2" in name:
				_UpperCamelCase						    =	name.replace("""norm2"""						,  """layernorm_after""" )
		if "mlp.fc1" in name:
				_UpperCamelCase						    =	name.replace("""mlp.fc1"""						,  """intermediate.dense""" )
		if "mlp.fc2" in name:
				_UpperCamelCase						    =	name.replace("""mlp.fc2"""						,  """output.dense""" )
		if "q_bias" in name:
				_UpperCamelCase						    =	name.replace("""q_bias"""						,  """query.bias""" )
		if "k_bias" in name:
				_UpperCamelCase						    =	name.replace("""k_bias"""						,  """key.bias""" )
		if "v_bias" in name:
				_UpperCamelCase						    =	name.replace("""v_bias"""						,  """value.bias""" )
		if "cpb_mlp" in name:
				_UpperCamelCase						    =	name.replace("""cpb_mlp"""						,  """continuous_position_bias_mlp""" )
		if "patch_embed.proj" in name:
				_UpperCamelCase						    =	name.replace("""patch_embed.proj"""						,  """patch_embed.projection""" )

		if name == "norm.weight":
				_UpperCamelCase						    =	'''layernorm.weight'''
		if name == "norm.bias":
				_UpperCamelCase						    =	'''layernorm.bias'''

		if "conv_first" in name:
				_UpperCamelCase						    =	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:
						_UpperCamelCase						    =	name.replace("""conv_last"""						,  """final_convolution""" )
				if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
						if "conv_before_upsample.0" in name:
								_UpperCamelCase						    =	name.replace("""conv_before_upsample.0"""						,  """conv_before_upsample""" )
						if "upsample.0" in name:
								_UpperCamelCase						    =	name.replace("""upsample.0"""						,  """upsample.convolution_0""" )
						if "upsample.2" in name:
								_UpperCamelCase						    =	name.replace("""upsample.2"""						,  """upsample.convolution_1""" )
						_UpperCamelCase						    =	'''upsample.''' + name
				elif config.upsampler == "pixelshuffledirect":
						_UpperCamelCase						    =	name.replace("""upsample.0.weight"""						,  """upsample.conv.weight""" )
						_UpperCamelCase						    =	name.replace("""upsample.0.bias"""						,  """upsample.conv.bias""" )
				else:
						pass
		else:
				_UpperCamelCase						    =	'''swin2sr.''' + name

		return name


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

		"""simple docstring"""






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

				if "qkv" in key:
						_UpperCamelCase						    =	key.split(""".""" )
						_UpperCamelCase						    =	int(key_split[1] )
						_UpperCamelCase						    =	int(key_split[4] )
						_UpperCamelCase						    =	config.embed_dim

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

		return orig_state_dict


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Optional[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	get_config(lowerCAmelCase )
		_UpperCamelCase						    =	SwinaSRForImageSuperResolution(lowerCAmelCase )
		model.eval()

		_UpperCamelCase						    =	torch.hub.load_state_dict_from_url(lowerCAmelCase						,  map_location="""cpu""" )
		_UpperCamelCase						    =	convert_state_dict(lowerCAmelCase						,  lowerCAmelCase )
		_UpperCamelCase						    =	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
		_UpperCamelCase						    =	'''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
		_UpperCamelCase						    =	Image.open(requests.get(lowerCAmelCase						,  stream=lowerCAmelCase ).raw ).convert("""RGB""" )
		_UpperCamelCase						    =	SwinaSRImageProcessor()
		# pixel_values = processor(image, return_tensors="pt").pixel_values

		_UpperCamelCase						    =	1_2_6 if '''Jpeg''' in checkpoint_url else 2_5_6
		_UpperCamelCase						    =	Compose(
		    [
		        Resize((image_size, image_size) ),
		        ToTensor(),
		        Normalize(mean=[0.485, 0.456, 0.406]						,  std=[0.229, 0.224, 0.225] ),
		    ] )
		_UpperCamelCase						    =	transforms(lowerCAmelCase ).unsqueeze(0 )

		if config.num_channels == 1:
				_UpperCamelCase						    =	pixel_values[:, 0, :, :].unsqueeze(1 )

		_UpperCamelCase						    =	model(lowerCAmelCase )

		# assert values
		if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
				_UpperCamelCase						    =	torch.Size([1, 3, 5_1_2, 5_1_2] )
				_UpperCamelCase						    =	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:
				_UpperCamelCase						    =	torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
				_UpperCamelCase						    =	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
				_UpperCamelCase						    =	torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
				_UpperCamelCase						    =	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:
				_UpperCamelCase						    =	torch.Size([1, 3, 5_1_2, 5_1_2] )
				_UpperCamelCase						    =	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:
				_UpperCamelCase						    =	torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
				_UpperCamelCase						    =	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!""" )

		_UpperCamelCase						    =	{
		    '''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'''
		    ),
		}
		_UpperCamelCase						    =	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__":
		lowerCamelCase__	=							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.")

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





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 							__A(lowerCAmelCase )		->				Any:

		"""simple docstring"""






		_UpperCamelCase						    =	[
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase						,  lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase    ,	_UpperCamelCase						    =	emb.weight.shape
		_UpperCamelCase						    =	nn.Linear(lowerCAmelCase						,  lowerCAmelCase						,  bias=lowerCAmelCase )
		_UpperCamelCase						    =	emb.weight.data
		return lin_layer


def 							__A(lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )
		_UpperCamelCase						    =	Namespace(**checkpoint["""cfg"""]["""model"""] )
		_UpperCamelCase						    =	checkpoint["""model"""]
		remove_ignore_keys_(lowerCAmelCase )
		_UpperCamelCase						    =	state_dict["""decoder.embed_tokens.weight"""].shape[0]

		_UpperCamelCase						    =	{key.replace("""decoder"""						,  """model""" ): val for key, val in state_dict.items()}

		_UpperCamelCase						    =	XGLMConfig(
		    vocab_size=lowerCAmelCase						,  max_position_embeddings=args.max_target_positions						,  num_layers=args.decoder_layers						,  attention_heads=args.decoder_attention_heads						,  ffn_dim=args.decoder_ffn_embed_dim						,  d_model=args.decoder_embed_dim						,  layerdrop=args.decoder_layerdrop						,  dropout=args.dropout						,  attention_dropout=args.attention_dropout						,  activation_dropout=args.activation_dropout						,  activation_function="""gelu"""						,  scale_embedding=not args.no_scale_embedding						,  tie_word_embeddings=args.share_decoder_input_output_embed						,  )

		_UpperCamelCase						    =	XGLMForCausalLM(lowerCAmelCase )
		_UpperCamelCase						    =	model.load_state_dict(lowerCAmelCase						,  strict=lowerCAmelCase )
		print(lowerCAmelCase )
		_UpperCamelCase						    =	make_linear_from_emb(model.model.embed_tokens )

		return model


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
		parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		lowerCamelCase__	=							parser.parse_args()
		lowerCamelCase__	=							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)



