




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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Tuple:
					a     =				SwinaSRConfig()

					if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
										a     =				4
					elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
										a     =				4
										a     =				48
										a     =				"pixelshuffle_aux"
					elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
										a     =				[6, 6, 6, 6]
										a     =				60
										a     =				[6, 6, 6, 6]
										a     =				"pixelshuffledirect"
					elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
										a     =				4
										a     =				"nearest+conv"
					elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
										a     =				1
										a     =				1
										a     =				1_26
										a     =				7
										a     =				255.0
										a     =				""

					return config

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

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

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

					return name

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					Optional[Any]:
					for key in orig_state_dict.copy().keys():
										a     =				orig_state_dict.pop(__UpperCamelCase)

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

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

					return orig_state_dict

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
					a     =				get_config(__UpperCamelCase)
					a     =				SwinaSRForImageSuperResolution(__UpperCamelCase)
					model.eval()

					a     =				torch.hub.load_state_dict_from_url(__UpperCamelCase		, map_location="cpu")
					a     =				convert_state_dict(__UpperCamelCase		, __UpperCamelCase)
					a  ,				a     =				model.load_state_dict(__UpperCamelCase		, strict=__UpperCamelCase)

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

					a     =				1_26 if "Jpeg" in checkpoint_url else 2_56
					a     =				Compose(
					    [
					        Resize((image_size, image_size)),
					        ToTensor(),
					        Normalize(mean=[0.485, 0.456, 0.406]		, std=[0.229, 0.224, 0.225]),
					    ])
					a     =				transforms(__UpperCamelCase).unsqueeze(0)

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

					a     =				model(__UpperCamelCase)

					# assert values
					if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
										a     =				torch.Size([1, 3, 5_12, 5_12])
										a     =				torch.tensor(
										    [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]])
					elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
										a     =				torch.Size([1, 3, 10_24, 10_24])
										a     =				torch.tensor(
										    [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]])
					elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
										# TODO values didn't match exactly here
										a     =				torch.Size([1, 3, 10_24, 10_24])
										a     =				torch.tensor(
										    [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]])
					elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
										a     =				torch.Size([1, 3, 5_12, 5_12])
										a     =				torch.tensor(
										    [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]])
					elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
										a     =				torch.Size([1, 3, 10_24, 10_24])
										a     =				torch.tensor(
										    [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]])

					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]		, __UpperCamelCase		, atol=1e-3)
					print("Looks ok!")

					a     =				{
					    "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     =				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(__UpperCamelCase)
										print(f'''Saving image processor to {pytorch_dump_folder_path}''')
										processor.save_pretrained(__UpperCamelCase)

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


if __name__ == "__main__":
			lowercase__				:      Optional[Any]	   = 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.")

			lowercase__				:      int	   = parser.parse_args()
			convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase__				:      Optional[Any]	   = logging.get_logger(__name__)

lowercase__				:      str	   = {
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}




class       a__	( UpperCamelCase__				):
     a		:      Optional[int]		 =     """table-transformer"""
     a		:      Tuple		 =     ["""past_key_values"""]
     a		:      int		 =     {
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }







     def __init__(     self  ,		A=True  ,		A=None  ,		A=3  ,		A=100  ,		A=6  ,		A=2048  ,		A=8  ,		A=6  ,		A=2048  ,		A=8  ,		A=0.0  ,		A=0.0  ,		A=True  ,		A="relu"  ,		A=256  ,		A=0.1  ,		A=0.0  ,		A=0.0  ,		A=0.0_2  ,		A=1.0  ,		A=False  ,		A="sine"  ,		A="resnet50"  ,		A=True  ,		A=False  ,		A=1  ,		A=5  ,		A=2  ,		A=1  ,		A=1  ,		A=5  ,		A=2  ,		A=0.1  ,		**A  ,		)  ->   List[str]:



          '''simple docstring'''



          if backbone_config is not None and use_timm_backbone:
               raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."			)

          if not use_timm_backbone:
               if backbone_config is None:
                    logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."			)
                    a     =				CONFIG_MAPPING["resnet"](out_features=["stage4"]			)
               elif isinstance(A  ,		A			):
                    a     =				backbone_config.get("model_type"			)
                    a     =				CONFIG_MAPPING[backbone_model_type]
                    a     =				config_class.from_dict(A			)
               # set timm attributes to None
               a  ,				a  ,				a     =				None, None, None

          a     =				use_timm_backbone
          a     =				backbone_config
          a     =				num_channels
          a     =				num_queries
          a     =				d_model
          a     =				encoder_ffn_dim
          a     =				encoder_layers
          a     =				encoder_attention_heads
          a     =				decoder_ffn_dim
          a     =				decoder_layers
          a     =				decoder_attention_heads
          a     =				dropout
          a     =				attention_dropout
          a     =				activation_dropout
          a     =				activation_function
          a     =				init_std
          a     =				init_xavier_std
          a     =				encoder_layerdrop
          a     =				decoder_layerdrop
          a     =				encoder_layers
          a     =				auxiliary_loss
          a     =				position_embedding_type
          a     =				backbone
          a     =				use_pretrained_backbone
          a     =				dilation
          # Hungarian matcher
          a     =				class_cost
          a     =				bbox_cost
          a     =				giou_cost
          # Loss coefficients
          a     =				mask_loss_coefficient
          a     =				dice_loss_coefficient
          a     =				bbox_loss_coefficient
          a     =				giou_loss_coefficient
          a     =				eos_coefficient
          super().__init__(is_encoder_decoder=A  ,		**A			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.encoder_attention_heads







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.d_model







class       a__	( UpperCamelCase__				):
     a		:      Any		 =     version.parse("""1.11"""				)







     @property
     def       lowerCAmelCase_    (     self			)  ->   Mapping[str, Mapping[int, str]]:



          '''simple docstring'''



          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                  ("pixel_mask", {0: "batch"}),
              ]			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   float:



          '''simple docstring'''



          return 1e-5







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return 12

