






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 (     __magic_name__					:							List[Any]					)       ->  Any:
		"""simple docstring"""




		lowercase__					      =		SwinaSRConfig()

		if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				lowercase__					      =		4
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				lowercase__					      =		4
				lowercase__					      =		48
				lowercase__					      =		"""pixelshuffle_aux"""
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				lowercase__					      =		[6, 6, 6, 6]
				lowercase__					      =		60
				lowercase__					      =		[6, 6, 6, 6]
				lowercase__					      =		"""pixelshuffledirect"""
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				lowercase__					      =		4
				lowercase__					      =		"""nearest+conv"""
		elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
				lowercase__					      =		1
				lowercase__					      =		1
				lowercase__					      =		126
				lowercase__					      =		7
				lowercase__					      =		255.0
				lowercase__					      =		""""""

		return config






def 			UpperCamelCase (     __magic_name__					:							Dict   ,   __magic_name__					:							Any					)       ->  List[str]:
		"""simple docstring"""




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

		if name == "norm.weight":
				lowercase__					      =		"""layernorm.weight"""
		if name == "norm.bias":
				lowercase__					      =		"""layernorm.bias"""

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

		return name






def 			UpperCamelCase (     __magic_name__					:							int   ,   __magic_name__					:							str					)       ->  List[str]:
		"""simple docstring"""




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

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

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

		return orig_state_dict






def 			UpperCamelCase (     __magic_name__					:							int   ,   __magic_name__					:							Optional[Any]   ,   __magic_name__					:							List[Any]					)       ->  int:
		"""simple docstring"""




		lowercase__					      =		get_config(_a					)
		lowercase__					      =		SwinaSRForImageSuperResolution(_a					)
		model.eval()

		lowercase__					      =		torch.hub.load_state_dict_from_url(_a   ,   map_location="""cpu"""					)
		lowercase__					      =		convert_state_dict(_a   ,   _a					)
		lowercase__      ,     lowercase__					      =		model.load_state_dict(_a   ,   strict=_a					)

		if len(_a					) > 0:
				raise ValueError("""Missing keys when converting: {}""".format(_a					)					)
		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
		lowercase__					      =		"""https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
		lowercase__					      =		Image.open(requests.get(_a   ,   stream=_a					).raw					).convert("""RGB"""					)
		lowercase__					      =		SwinaSRImageProcessor()
		# pixel_values = processor(image, return_tensors="pt").pixel_values

		lowercase__					      =		126 if """Jpeg""" in checkpoint_url else 256
		lowercase__					      =		Compose(
		    [
		        Resize((image_size, image_size)					),
		        ToTensor(),
		        Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]   ,   std=[0.2_2_9, 0.2_2_4, 0.2_2_5]					),
		    ]					)
		lowercase__					      =		transforms(_a					).unsqueeze(0					)

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

		lowercase__					      =		model(_a					)

		# assert values
		if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
				lowercase__					      =		torch.Size([1, 3, 512, 512]					)
				lowercase__					      =		torch.tensor(
				    [[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]]					)
		elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				lowercase__					      =		torch.Size([1, 3, 1024, 1024]					)
				lowercase__					      =		torch.tensor(
				    [[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]]					)
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				# TODO values didn't match exactly here
				lowercase__					      =		torch.Size([1, 3, 1024, 1024]					)
				lowercase__					      =		torch.tensor(
				    [[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]]					)
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				lowercase__					      =		torch.Size([1, 3, 512, 512]					)
				lowercase__					      =		torch.tensor(
				    [[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]]					)
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				lowercase__					      =		torch.Size([1, 3, 1024, 1024]					)
				lowercase__					      =		torch.tensor(
				    [[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]]					)

		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]   ,   _a   ,   atol=1E-3					)
		print("""Looks ok!"""					)

		lowercase__					      =		{
		    """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"""
		    ),
		}
		lowercase__					      =		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(_a					)
				print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
				processor.save_pretrained(_a					)

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


if __name__ == "__main__":
	A	:		List[str]    =       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.')

	A	:		Dict    =       parser.parse_args()
	convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
	import numpy as np
	import tensorflow as tf

	from transformers import TFXLMRobertaModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					@slow
					def   lowerCamelCase__					(self   :						Any		)						->       Union[str, Any]:




							"""simple docstring"""


							lowercase__					      =		TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base"""		)

							lowercase__					      =		{
							    """input_ids""": tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]				, dtype=tf.intaa		),  # "My dog is cute"
							    """attention_mask""": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]				, dtype=tf.intaa		),
							}

							lowercase__					      =		model(_UpperCAmelCase		)["""last_hidden_state"""]
							lowercase__					      =		tf.TensorShape((1, 6, 768)		)
							self.assertEqual(output.shape				, _UpperCAmelCase		)
							# compare the actual values for a slice.
							lowercase__					      =		tf.convert_to_tensor(
							    [
							        [
							            [0.0_681_762, 0.10_894_451, 0.06_772_504],
							            [-0.06_423_668, 0.02_366_615, 0.04_329_344],
							            [-0.06_057_295, 0.09_974_135, -0.00_070_584],
							        ]
							    ]				, dtype=tf.floataa				, )

							self.assertTrue(np.allclose(output[:, :3, :3].numpy()				, expected_slice.numpy()				, atol=1E-4		)		)

