
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						(   lowercase_ )		->						Any:


  '''simple docstring'''






  lowercase__	: Optional[Any]     		=   SwinaSRConfig()

  if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
    lowercase__	: List[str]     		=   4
  elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
    lowercase__	: Optional[int]     		=   4
    lowercase__	: Optional[Any]     		=   48
    lowercase__	: int     		=   """pixelshuffle_aux"""
  elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
    lowercase__	: List[str]     		=   [6, 6, 6, 6]
    lowercase__	: Any     		=   60
    lowercase__	: Tuple     		=   [6, 6, 6, 6]
    lowercase__	: Dict     		=   """pixelshuffledirect"""
  elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
    lowercase__	: Tuple     		=   4
    lowercase__	: Any     		=   """nearest+conv"""
  elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
    lowercase__	: str     		=   1
    lowercase__	: Optional[int]     		=   1
    lowercase__	: Optional[int]     		=   1_26
    lowercase__	: Any     		=   7
    lowercase__	: int     		=   255.0
    lowercase__	: List[Any]     		=   """"""

  return config

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






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

  if name == "norm.weight":
    lowercase__	: Union[str, Any]     		=   """layernorm.weight"""
  if name == "norm.bias":
    lowercase__	: List[str]     		=   """layernorm.bias"""

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

  return name

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






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

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

      if "weight" in key:
        lowercase__	: List[str]     		=   val[:dim, :]
        lowercase__	: List[str]     		=   val[dim : dim * 2, :]
        lowercase__	: Optional[Any]     		=   val[-dim:, :]
      else:
        lowercase__	: Optional[Any]     		=   val[:dim]
        lowercase__	: List[Any]     		=   val[dim : dim * 2]
        lowercase__	: Optional[int]     		=   val[-dim:]
      pass
    else:
      lowercase__	: Optional[Any]     		=   val

  return orig_state_dict

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






  lowercase__	: Dict     		=   get_config(lowercase_ )
  lowercase__	: Any     		=   SwinaSRForImageSuperResolution(lowercase_ )
  model.eval()

  lowercase__	: List[str]     		=   torch.hub.load_state_dict_from_url(lowercase_			,			map_location="""cpu""" )
  lowercase__	: Union[str, Any]     		=   convert_state_dict(lowercase_			,			lowercase_ )
  lowercase__		,     lowercase__	: Dict     		=   model.load_state_dict(lowercase_			,			strict=lowercase_ )

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

  lowercase__	: Optional[int]     		=   1_26 if """Jpeg""" in checkpoint_url else 2_56
  lowercase__	: Union[str, Any]     		=   Compose(
      [
          Resize((image_size, image_size) ),
          ToTensor(),
          Normalize(mean=[0.485, 0.456, 0.406]			,			std=[0.229, 0.224, 0.225] ),
      ] )
  lowercase__	: Dict     		=   transforms(lowercase_ ).unsqueeze(0 )

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

  lowercase__	: Union[str, Any]     		=   model(lowercase_ )

  # assert values
  if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
    lowercase__	: Optional[Any]     		=   torch.Size([1, 3, 5_12, 5_12] )
    lowercase__	: Optional[Any]     		=   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:
    lowercase__	: List[str]     		=   torch.Size([1, 3, 10_24, 10_24] )
    lowercase__	: int     		=   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
    lowercase__	: Optional[Any]     		=   torch.Size([1, 3, 10_24, 10_24] )
    lowercase__	: int     		=   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:
    lowercase__	: Tuple     		=   torch.Size([1, 3, 5_12, 5_12] )
    lowercase__	: int     		=   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:
    lowercase__	: Tuple     		=   torch.Size([1, 3, 10_24, 10_24] )
    lowercase__	: int     		=   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]			,			lowercase_			,			atol=1E-3 )
  print("""Looks ok!""" )

  lowercase__	: str     		=   {
      """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__	: str     		=   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(lowercase_ )
    print(F'Saving image processor to {pytorch_dump_folder_path}' )
    processor.save_pretrained(lowercase_ )

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


if __name__ == "__main__":
     lowerCamelCase__     :		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.""")

     lowerCamelCase__     :		Any        = parser.parse_args()
     convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)










import functools
import operator

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


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

SCREAMING_SNAKE_CASE						:Dict			=  {
    '''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'wav2vec2'



  def __init__(  self							:							Any     ,					_lowerCAmelCase							:							Optional[Any]=3_2     ,					_lowerCAmelCase							:							List[Any]=7_6_8     ,					_lowerCAmelCase							:							Dict=1_2     ,					_lowerCAmelCase							:							Tuple=1_2     ,					_lowerCAmelCase							:							Optional[int]=3_0_7_2     ,					_lowerCAmelCase							:							Any="gelu"     ,					_lowerCAmelCase							:							List[str]=0.1     ,					_lowerCAmelCase							:							Dict=0.1     ,					_lowerCAmelCase							:							Union[str, Any]=0.1     ,					_lowerCAmelCase							:							Dict=0.0     ,					_lowerCAmelCase							:							Optional[int]=0.0     ,					_lowerCAmelCase							:							List[Any]=0.1     ,					_lowerCAmelCase							:							List[Any]=0.1     ,					_lowerCAmelCase							:							Dict=0.02     ,					_lowerCAmelCase							:							Any=1e-5     ,					_lowerCAmelCase							:							Any="group"     ,					_lowerCAmelCase							:							Union[str, Any]="gelu"     ,					_lowerCAmelCase							:							str=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)     ,					_lowerCAmelCase							:							Optional[Any]=(5, 2, 2, 2, 2, 2, 2)     ,					_lowerCAmelCase							:							Optional[Any]=(1_0, 3, 3, 3, 3, 2, 2)     ,					_lowerCAmelCase							:							int=False     ,					_lowerCAmelCase							:							int=1_2_8     ,					_lowerCAmelCase							:							Union[str, Any]=1_6     ,					_lowerCAmelCase							:							Union[str, Any]=False     ,					_lowerCAmelCase							:							List[Any]=True     ,					_lowerCAmelCase							:							Dict=0.05     ,					_lowerCAmelCase							:							List[str]=1_0     ,					_lowerCAmelCase							:							Tuple=2     ,					_lowerCAmelCase							:							str=0.0     ,					_lowerCAmelCase							:							Union[str, Any]=1_0     ,					_lowerCAmelCase							:							int=0     ,					_lowerCAmelCase							:							Optional[int]=3_2_0     ,					_lowerCAmelCase							:							Optional[Any]=2     ,					_lowerCAmelCase							:							str=0.1     ,					_lowerCAmelCase							:							Optional[Any]=1_0_0     ,					_lowerCAmelCase							:							Tuple=2_5_6     ,					_lowerCAmelCase							:							Union[str, Any]=2_5_6     ,					_lowerCAmelCase							:							Union[str, Any]=0.1     ,					_lowerCAmelCase							:							Optional[Any]="sum"     ,					_lowerCAmelCase							:							List[str]=False     ,					_lowerCAmelCase							:							List[Any]=False     ,					_lowerCAmelCase							:							List[str]=2_5_6     ,					_lowerCAmelCase							:							List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)     ,					_lowerCAmelCase							:							List[str]=(5, 3, 3, 1, 1)     ,					_lowerCAmelCase							:							Union[str, Any]=(1, 2, 3, 1, 1)     ,					_lowerCAmelCase							:							Optional[Any]=5_1_2     ,					_lowerCAmelCase							:							Any=0     ,					_lowerCAmelCase							:							Optional[int]=1     ,					_lowerCAmelCase							:							List[str]=2     ,					_lowerCAmelCase							:							Tuple=False     ,					_lowerCAmelCase							:							Any=3     ,					_lowerCAmelCase							:							List[Any]=2     ,					_lowerCAmelCase							:							Optional[Any]=3     ,					_lowerCAmelCase							:							List[str]=None     ,					_lowerCAmelCase							:							str=None     ,					**_lowerCAmelCase							:							Tuple     ,					) ->			Optional[int]:




     """simple docstring"""
     super().__init__(**_lowerCAmelCase     ,					pad_token_id=_lowerCAmelCase     ,					bos_token_id=_lowerCAmelCase     ,					eos_token_id=_lowerCAmelCase	)
     snake_case_            =       hidden_size
     snake_case_            =       feat_extract_norm
     snake_case_            =       feat_extract_activation
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       conv_bias
     snake_case_            =       num_conv_pos_embeddings
     snake_case_            =       num_conv_pos_embedding_groups
     snake_case_            =       len(self.conv_dim	)
     snake_case_            =       num_hidden_layers
     snake_case_            =       intermediate_size
     snake_case_            =       hidden_act
     snake_case_            =       num_attention_heads
     snake_case_            =       hidden_dropout
     snake_case_            =       attention_dropout
     snake_case_            =       activation_dropout
     snake_case_            =       feat_proj_dropout
     snake_case_            =       final_dropout
     snake_case_            =       layerdrop
     snake_case_            =       layer_norm_eps
     snake_case_            =       initializer_range
     snake_case_            =       vocab_size
     snake_case_            =       do_stable_layer_norm
     snake_case_            =       use_weighted_layer_sum

     if (
         (len(self.conv_stride	) != self.num_feat_extract_layers)
         or (len(self.conv_kernel	) != self.num_feat_extract_layers)
         or (len(self.conv_dim	) != self.num_feat_extract_layers)
     ):
        raise ValueError(
            "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
            " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
            F''' {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,'''
            F''' `len(config.conv_kernel) = {len(self.conv_kernel	)}`.'''	)

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     snake_case_            =       apply_spec_augment
     snake_case_            =       mask_time_prob
     snake_case_            =       mask_time_length
     snake_case_            =       mask_time_min_masks
     snake_case_            =       mask_feature_prob
     snake_case_            =       mask_feature_length
     snake_case_            =       mask_feature_min_masks

     # parameters for pretraining with codevector quantized representations
     snake_case_            =       num_codevectors_per_group
     snake_case_            =       num_codevector_groups
     snake_case_            =       contrastive_logits_temperature
     snake_case_            =       feat_quantizer_dropout
     snake_case_            =       num_negatives
     snake_case_            =       codevector_dim
     snake_case_            =       proj_codevector_dim
     snake_case_            =       diversity_loss_weight

     # ctc loss
     snake_case_            =       ctc_loss_reduction
     snake_case_            =       ctc_zero_infinity

     # adapter
     snake_case_            =       add_adapter
     snake_case_            =       adapter_kernel_size
     snake_case_            =       adapter_stride
     snake_case_            =       num_adapter_layers
     snake_case_            =       output_hidden_size or hidden_size
     snake_case_            =       adapter_attn_dim

     # SequenceClassification-specific parameter. Feel free to ignore for other classes.
     snake_case_            =       classifier_proj_size

     # XVector-specific parameters. Feel free to ignore for other classes.
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       list(_lowerCAmelCase	)
     snake_case_            =       xvector_output_dim



  @property
  def        lowerCAmelCase__	(  self							:							str	) ->			int:




     """simple docstring"""
     return functools.reduce(operator.mul     ,					self.conv_stride     ,					1	)




