




"""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   _SCREAMING_SNAKE_CASE   (							lowercase_						)						->		str:
 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__       =				2_5_5.0
  A__       =				""

 return config







def   _SCREAMING_SNAKE_CASE   (							lowercase_			, lowercase_						)						->		Dict:
 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   (							lowercase_			, lowercase_						)						->		int:
 for key in orig_state_dict.copy().keys():
  A__       =				orig_state_dict.pop(snake_case__						)

  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   (							lowercase_			, lowercase_			, lowercase_						)						->		Optional[int]:
 A__       =				get_config(snake_case__						)
 A__       =				SwinaSRForImageSuperResolution(snake_case__						)
 model.eval()

 A__       =				torch.hub.load_state_dict_from_url(snake_case__			, map_location="cpu"						)
 A__       =				convert_state_dict(snake_case__			, snake_case__						)
 A__,      A__       =				model.load_state_dict(snake_case__			, strict=snake_case__						)

 if len(snake_case__						) > 0:
  raise ValueError("Missing keys when converting: {}".format(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
 A__       =				"https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
 A__       =				Image.open(requests.get(snake_case__			, stream=snake_case__						).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=[0.4_8_5, 0.4_5_6, 0.4_0_6]			, std=[0.2_2_9, 0.2_2_4, 0.2_2_5]						),
     ]						)
 A__       =				transforms(snake_case__						).unsqueeze(0						)

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

 A__       =				model(snake_case__						)

 # assert values
 if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
  A__       =				torch.Size([1, 3, 5_12, 5_12]						)
  A__       =				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:
  A__       =				torch.Size([1, 3, 10_24, 10_24]						)
  A__       =				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
  A__       =				torch.Size([1, 3, 10_24, 10_24]						)
  A__       =				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:
  A__       =				torch.Size([1, 3, 5_12, 5_12]						)
  A__       =				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:
  A__       =				torch.Size([1, 3, 10_24, 10_24]						)
  A__       =				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]			, snake_case__			, 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(snake_case__						)
  print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
  processor.save_pretrained(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__":
      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)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE										=						{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Any    			=		'''biogpt'''



 def __init__(					self		,				lowerCAmelCase_=4_23_84		,				lowerCAmelCase_=10_24		,				lowerCAmelCase_=24		,				lowerCAmelCase_=16		,				lowerCAmelCase_=40_96		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=10_24		,				lowerCAmelCase_=0.02		,				lowerCAmelCase_=1E-12		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=1		,				lowerCAmelCase_=0		,				lowerCAmelCase_=2		,				**lowerCAmelCase_		,				)  ->					Tuple:
        _A        =  vocab_size
        _A        =  max_position_embeddings
        _A        =  hidden_size
        _A        =  num_hidden_layers
        _A        =  num_attention_heads
        _A        =  intermediate_size
        _A        =  hidden_act
        _A        =  hidden_dropout_prob
        _A        =  attention_probs_dropout_prob
        _A        =  initializer_range
        _A        =  layer_norm_eps
        _A        =  scale_embedding
        _A        =  use_cache
        _A        =  layerdrop
        _A        =  activation_dropout
        super().__init__(pad_token_id=lowerCAmelCase_		,				bos_token_id=lowerCAmelCase_		,				eos_token_id=lowerCAmelCase_		,				**lowerCAmelCase_							)




