





"""simple docstring"""





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__					(					_SCREAMING_SNAKE_CASE    ):




		"""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   =	48
				UpperCamelCase   =	"pixelshuffle_aux"
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				UpperCamelCase   =	[6, 6, 6, 6]
				UpperCamelCase   =	60
				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   =	126
				UpperCamelCase   =	7
				UpperCamelCase   =	255.0
				UpperCamelCase   =	""

		return config


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""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__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		for key in orig_state_dict.copy().keys():
				UpperCamelCase   =	orig_state_dict.pop(_SCREAMING_SNAKE_CASE    )

				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__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	get_config(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	SwinaSRForImageSuperResolution(_SCREAMING_SNAKE_CASE    )
		model.eval()

		UpperCamelCase   =	torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE  ,	map_location="cpu"    )
		UpperCamelCase   =	convert_state_dict(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   ,				UpperCamelCase   =	model.load_state_dict(_SCREAMING_SNAKE_CASE  ,	strict=_SCREAMING_SNAKE_CASE    )

		if len(_SCREAMING_SNAKE_CASE    ) > 0:
				raise ValueError("Missing keys when converting: {}".format(_SCREAMING_SNAKE_CASE    )    )
		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(_SCREAMING_SNAKE_CASE  ,	stream=_SCREAMING_SNAKE_CASE    ).raw    ).convert("RGB"    )
		UpperCamelCase   =	SwinaSRImageProcessor()
		# pixel_values = processor(image, return_tensors="pt").pixel_values

		UpperCamelCase   =	126 if "Jpeg" in checkpoint_url else 256
		UpperCamelCase   =	Compose(
		    [
		        Resize((image_size, image_size)    ),
		        ToTensor(),
		        Normalize(mean=[0.4_85, 0.4_56, 0.4_06]  ,	std=[0.2_29, 0.2_24, 0.2_25]    ),
		    ]    )
		UpperCamelCase   =	transforms(_SCREAMING_SNAKE_CASE    ).unsqueeze(0    )

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

		UpperCamelCase   =	model(_SCREAMING_SNAKE_CASE    )

		# assert values
		if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
				UpperCamelCase   =	torch.Size([1, 3, 512, 512]    )
				UpperCamelCase   =	torch.tensor(
				    [[-0.70_87, -0.71_38, -0.67_21], [-0.83_40, -0.80_95, -0.72_98], [-0.91_49, -0.84_14, -0.79_40]]    )
		elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
				UpperCamelCase   =	torch.Size([1, 3, 1_024, 1_024]    )
				UpperCamelCase   =	torch.tensor(
				    [[-0.77_75, -0.81_05, -0.89_33], [-0.77_64, -0.83_56, -0.92_25], [-0.79_76, -0.86_86, -0.95_79]]    )
		elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
				# TODO values didn't match exactly here
				UpperCamelCase   =	torch.Size([1, 3, 1_024, 1_024]    )
				UpperCamelCase   =	torch.tensor(
				    [[-0.80_35, -0.75_04, -0.74_91], [-0.85_38, -0.81_24, -0.77_82], [-0.88_04, -0.86_51, -0.84_93]]    )
		elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
				UpperCamelCase   =	torch.Size([1, 3, 512, 512]    )
				UpperCamelCase   =	torch.tensor(
				    [[-0.76_69, -0.86_62, -0.87_67], [-0.88_10, -0.99_62, -0.98_20], [-0.93_40, -1.03_22, -1.11_49]]    )
		elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
				UpperCamelCase   =	torch.Size([1, 3, 1_024, 1_024]    )
				UpperCamelCase   =	torch.tensor(
				    [[-0.52_38, -0.55_57, -0.63_21], [-0.60_16, -0.59_03, -0.63_91], [-0.62_44, -0.63_34, -0.68_89]]    )

		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]  ,	_SCREAMING_SNAKE_CASE  ,	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(_SCREAMING_SNAKE_CASE    )
				print(F"Saving image processor to {pytorch_dump_folder_path}"    )
				processor.save_pretrained(_SCREAMING_SNAKE_CASE    )

		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)













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__     			=			{
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			['''BlenderbotTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			[
						    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BlenderbotForCausalLM''',
						    '''BlenderbotForConditionalGeneration''',
						    '''BlenderbotModel''',
						    '''BlenderbotPreTrainedModel''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			[
						    '''TFBlenderbotForConditionalGeneration''',
						    '''TFBlenderbotModel''',
						    '''TFBlenderbotPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__     			=			[
						    '''FlaxBlenderbotForConditionalGeneration''',
						    '''FlaxBlenderbotModel''',
						    '''FlaxBlenderbotPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_blenderbot import (
						    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    BlenderbotConfig,
						    BlenderbotOnnxConfig,
						)
						from .tokenization_blenderbot import BlenderbotTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_blenderbot import (
												    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BlenderbotForCausalLM,
												    BlenderbotForConditionalGeneration,
												    BlenderbotModel,
												    BlenderbotPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_blenderbot import (
												    TFBlenderbotForConditionalGeneration,
												    TFBlenderbotModel,
												    TFBlenderbotPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_blenderbot import (
												    FlaxBlenderbotForConditionalGeneration,
												    FlaxBlenderbotModel,
												    FlaxBlenderbotPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase__     			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






