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		(      _A       ):



 """simple docstring"""


 __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							=	126
  __a							=	7
  __a							=	255.0
  __a							=	""

 return config



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 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 	__A		(      _A				,					_A       ):



 """simple docstring"""


 for key in orig_state_dict.copy().keys():
  __a							=	orig_state_dict.pop(_A       )

  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 	__A		(      _A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	get_config(_A       )
 __a							=	SwinaSRForImageSuperResolution(_A       )
 model.eval()

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

 __a							=	126 if "Jpeg" in checkpoint_url else 256
 __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(_A       ).unsqueeze(0       )

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

 __a							=	model(_A       )

 # assert values
 if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
  __a							=	torch.Size([1, 3, 512, 512]       )
  __a							=	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:
  __a							=	torch.Size([1, 3, 1024, 1024]       )
  __a							=	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
  __a							=	torch.Size([1, 3, 1024, 1024]       )
  __a							=	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:
  __a							=	torch.Size([1, 3, 512, 512]       )
  __a							=	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:
  __a							=	torch.Size([1, 3, 1024, 1024]       )
  __a							=	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]				,					_A				,					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(_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__":
  SCREAMING_SNAKE_CASE			:						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.""")

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



import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE			:						List[Any]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """Salesforce/blip-vqa-base""": """https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json""",
    """Salesforce/blip-vqa-capfit-large""": (
        """https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"""
    ),
    """Salesforce/blip-image-captioning-base""": (
        """https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"""
    ),
    """Salesforce/blip-image-captioning-large""": (
        """https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"""
    ),
    """Salesforce/blip-itm-base-coco""": """https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json""",
    """Salesforce/blip-itm-large-coco""": """https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json""",
    """Salesforce/blip-itm-base-flikr""": """https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json""",
    """Salesforce/blip-itm-large-flikr""": (
        """https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"""
    ),
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """blip_text_model"""

  def __init__(		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   str=3_05_24  ,				__SCREAMING_SNAKE_CASE			:   Any=7_68  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=7_68  ,				__SCREAMING_SNAKE_CASE			:   Tuple=30_72  ,				__SCREAMING_SNAKE_CASE			:   Any=7_68  ,				__SCREAMING_SNAKE_CASE			:   Tuple=12  ,				__SCREAMING_SNAKE_CASE			:   Dict=8  ,				__SCREAMING_SNAKE_CASE			:   int=5_12  ,				__SCREAMING_SNAKE_CASE			:   List[Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   int=1E-12  ,				__SCREAMING_SNAKE_CASE			:   Tuple=0.0  ,				__SCREAMING_SNAKE_CASE			:   Tuple=0.0  ,				__SCREAMING_SNAKE_CASE			:   Dict=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=3_05_22  ,				__SCREAMING_SNAKE_CASE			:   Any=2  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1_02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=True  ,				__SCREAMING_SNAKE_CASE			:   Dict=True  ,				**__SCREAMING_SNAKE_CASE			:   str  ,				):
   super().__init__(
       pad_token_id=__SCREAMING_SNAKE_CASE  ,				bos_token_id=__SCREAMING_SNAKE_CASE  ,				eos_token_id=__SCREAMING_SNAKE_CASE  ,				sep_token_id=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE  ,				)

   __a							=	vocab_size
   __a							=	hidden_size
   __a							=	encoder_hidden_size
   __a							=	intermediate_size
   __a							=	projection_dim
   __a							=	hidden_dropout_prob
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	max_position_embeddings
   __a							=	layer_norm_eps
   __a							=	hidden_act
   __a							=	initializer_range
   __a							=	attention_probs_dropout_prob
   __a							=	is_decoder
   __a							=	use_cache

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   int							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   # get the text config dict if we are loading from BlipConfig
   if config_dict.get("model_type"							) == "blip":
    __a							=	config_dict["text_config"]

   if "model_type" in config_dict and hasattr(cls  ,				"model_type"							) and config_dict["model_type"] != cls.model_type:
    logger.warning(
        f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
        f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""							)

   return cls.from_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """blip_vision_model"""

  def __init__(		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   str=7_68  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=30_72  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=5_12  ,				__SCREAMING_SNAKE_CASE			:   int=12  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=12  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=3_84  ,				__SCREAMING_SNAKE_CASE			:   str=16  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.0  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=1E-10  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	hidden_size
   __a							=	intermediate_size
   __a							=	projection_dim
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	patch_size
   __a							=	image_size
   __a							=	initializer_range
   __a							=	attention_dropout
   __a							=	layer_norm_eps
   __a							=	hidden_act

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   str							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   # get the vision config dict if we are loading from BlipConfig
   if config_dict.get("model_type"							) == "blip":
    __a							=	config_dict["vision_config"]

   if "model_type" in config_dict and hasattr(cls  ,				"model_type"							) and config_dict["model_type"] != cls.model_type:
    logger.warning(
        f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
        f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""							)

   return cls.from_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """blip"""
  _SCREAMING_SNAKE_CASE     					=      True

  def __init__(		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=None  ,				__SCREAMING_SNAKE_CASE			:   Any=None  ,				__SCREAMING_SNAKE_CASE			:   int=5_12  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=2.65_92  ,				__SCREAMING_SNAKE_CASE			:   Any=2_56  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   if text_config is None:
    __a							=	{}
    logger.info("`text_config` is `None`. Initializing the `BlipTextConfig` with default values."							)

   if vision_config is None:
    __a							=	{}
    logger.info("`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values."							)

   __a							=	BlipTextConfig(**__SCREAMING_SNAKE_CASE							)
   __a							=	BlipVisionConfig(**__SCREAMING_SNAKE_CASE							)

   __a							=	self.vision_config.hidden_size

   __a							=	projection_dim
   __a							=	logit_scale_init_value
   __a							=	1.0
   __a							=	0.02
   __a							=	image_text_hidden_size

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Any  ,				__SCREAMING_SNAKE_CASE			:   BlipTextConfig  ,				__SCREAMING_SNAKE_CASE			:   BlipVisionConfig  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):
   return cls(text_config=text_config.to_dict()  ,				vision_config=vision_config.to_dict()  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	copy.deepcopy(self.__dict__							)
   __a							=	self.text_config.to_dict()
   __a							=	self.vision_config.to_dict()
   __a							=	self.__class__.model_type
   return output


