
'''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 snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					Any				       =							SwinaSRConfig()

  if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
    UpperCAmelCase__      :					str				       =							4
  elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
    UpperCAmelCase__      :					Optional[Any]				       =							4
    UpperCAmelCase__      :					Union[str, Any]				       =							4_8
    UpperCAmelCase__      :					Dict				       =							"pixelshuffle_aux"
  elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
    UpperCAmelCase__      :					Dict				       =							[6, 6, 6, 6]
    UpperCAmelCase__      :					Optional[Any]				       =							6_0
    UpperCAmelCase__      :					Optional[Any]				       =							[6, 6, 6, 6]
    UpperCAmelCase__      :					str				       =							"pixelshuffledirect"
  elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
    UpperCAmelCase__      :					List[Any]				       =							4
    UpperCAmelCase__      :					List[Any]				       =							"nearest+conv"
  elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
    UpperCAmelCase__      :					int				       =							1
    UpperCAmelCase__      :					int				       =							1
    UpperCAmelCase__      :					Optional[Any]				       =							1_2_6
    UpperCAmelCase__      :					Union[str, Any]				       =							7
    UpperCAmelCase__      :					Optional[Any]				       =							255.0
    UpperCAmelCase__      :					int				       =							""

  return config







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

  if name == "norm.weight":
    UpperCAmelCase__      :					int				       =							"layernorm.weight"
  if name == "norm.bias":
    UpperCAmelCase__      :					Any				       =							"layernorm.bias"

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

  return name







def snake_case_     (					lowercase__   ,       lowercase__		):
  for key in orig_state_dict.copy().keys():
    UpperCAmelCase__      :					str				       =							orig_state_dict.pop(lowercase__		)

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

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

  return orig_state_dict







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__		):
  UpperCAmelCase__      :					Optional[int]				       =							get_config(lowercase__		)
  UpperCAmelCase__      :					int				       =							SwinaSRForImageSuperResolution(lowercase__		)
  model.eval()

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

  UpperCAmelCase__      :					Any				       =							1_2_6 if "Jpeg" in checkpoint_url else 2_5_6
  UpperCAmelCase__      :					Dict				       =							Compose(
      [
          Resize((image_size, image_size)		),
          ToTensor(),
          Normalize(mean=[0.485, 0.456, 0.406]   ,       std=[0.229, 0.224, 0.225]		),
      ]		)
  UpperCAmelCase__      :					Optional[int]				       =							transforms(lowercase__		).unsqueeze(0		)

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

  UpperCAmelCase__      :					List[str]				       =							model(lowercase__		)

  # assert values
  if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
    UpperCAmelCase__      :					Union[str, Any]				       =							torch.Size([1, 3, 5_1_2, 5_1_2]		)
    UpperCAmelCase__      :					Tuple				       =							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:
    UpperCAmelCase__      :					str				       =							torch.Size([1, 3, 1_0_2_4, 1_0_2_4]		)
    UpperCAmelCase__      :					Dict				       =							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
    UpperCAmelCase__      :					int				       =							torch.Size([1, 3, 1_0_2_4, 1_0_2_4]		)
    UpperCAmelCase__      :					List[str]				       =							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:
    UpperCAmelCase__      :					str				       =							torch.Size([1, 3, 5_1_2, 5_1_2]		)
    UpperCAmelCase__      :					Union[str, Any]				       =							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:
    UpperCAmelCase__      :					List[Any]				       =							torch.Size([1, 3, 1_0_2_4, 1_0_2_4]		)
    UpperCAmelCase__      :					str				       =							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!"		)

  UpperCAmelCase__      :					Optional[int]				       =							{
      "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__      :					List[Any]				       =							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__":
      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)





'''simple docstring'''





import functools
import operator

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


SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     =       {
    """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    List[str]				=							"wavlm"







 def __init__(					self		:  Optional[int]			,				snake_case__		:  Dict=32			,				snake_case__		:  Optional[Any]=7_68			,				snake_case__		:  Tuple=12			,				snake_case__		:  List[Any]=12			,				snake_case__		:  List[str]=30_72			,				snake_case__		:  Tuple="gelu"			,				snake_case__		:  Optional[Any]=0.1			,				snake_case__		:  List[str]=0.1			,				snake_case__		:  Any=0.1			,				snake_case__		:  List[Any]=0.0			,				snake_case__		:  Dict=0.1			,				snake_case__		:  List[Any]=0.1			,				snake_case__		:  Union[str, Any]=0.02			,				snake_case__		:  int=1e-5			,				snake_case__		:  Dict="group"			,				snake_case__		:  Any="gelu"			,				snake_case__		:  List[Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)			,				snake_case__		:  int=(5, 2, 2, 2, 2, 2, 2)			,				snake_case__		:  Dict=(10, 3, 3, 3, 3, 2, 2)			,				snake_case__		:  Tuple=False			,				snake_case__		:  int=1_28			,				snake_case__		:  Dict=16			,				snake_case__		:  int=3_20			,				snake_case__		:  Dict=8_00			,				snake_case__		:  List[str]=False			,				snake_case__		:  Union[str, Any]=True			,				snake_case__		:  str=0.05			,				snake_case__		:  List[Any]=10			,				snake_case__		:  Tuple=2			,				snake_case__		:  Optional[int]=0.0			,				snake_case__		:  int=10			,				snake_case__		:  Any=3_20			,				snake_case__		:  Dict=2			,				snake_case__		:  Optional[int]=0.1			,				snake_case__		:  Union[str, Any]=1_00			,				snake_case__		:  Dict=2_56			,				snake_case__		:  List[Any]=2_56			,				snake_case__		:  List[str]=0.1			,				snake_case__		:  List[Any]="mean"			,				snake_case__		:  int=False			,				snake_case__		:  Any=False			,				snake_case__		:  Any=2_56			,				snake_case__		:  Union[str, Any]=(5_12, 5_12, 5_12, 5_12, 15_00)			,				snake_case__		:  Union[str, Any]=(5, 3, 3, 1, 1)			,				snake_case__		:  Optional[Any]=(1, 2, 3, 1, 1)			,				snake_case__		:  List[Any]=5_12			,				snake_case__		:  str=80			,				snake_case__		:  Any=0			,				snake_case__		:  str=1			,				snake_case__		:  Union[str, Any]=2			,				snake_case__		:  int=False			,				snake_case__		:  Union[str, Any]=3			,				snake_case__		:  Union[str, Any]=2			,				snake_case__		:  List[str]=3			,				snake_case__		:  List[Any]=None			,				**snake_case__		:  int			,				):



   '''simple docstring'''




   super().__init__(**snake_case__			,				pad_token_id=snake_case__			,				bos_token_id=snake_case__			,				eos_token_id=snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							hidden_size
   UpperCAmelCase__      :					List[str]				       =							feat_extract_norm
   UpperCAmelCase__      :					int				       =							feat_extract_activation
   UpperCAmelCase__      :					Optional[int]				       =							list(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							list(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							list(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							conv_bias
   UpperCAmelCase__      :					List[Any]				       =							num_buckets
   UpperCAmelCase__      :					Union[str, Any]				       =							max_bucket_distance
   UpperCAmelCase__      :					Tuple				       =							num_conv_pos_embeddings
   UpperCAmelCase__      :					Union[str, Any]				       =							num_conv_pos_embedding_groups
   UpperCAmelCase__      :					Optional[Any]				       =							len(self.conv_dim )
   UpperCAmelCase__      :					Optional[int]				       =							num_hidden_layers
   UpperCAmelCase__      :					Tuple				       =							intermediate_size
   UpperCAmelCase__      :					List[str]				       =							hidden_act
   UpperCAmelCase__      :					List[str]				       =							num_attention_heads
   UpperCAmelCase__      :					Any				       =							hidden_dropout
   UpperCAmelCase__      :					List[Any]				       =							attention_dropout
   UpperCAmelCase__      :					Optional[int]				       =							activation_dropout
   UpperCAmelCase__      :					Optional[Any]				       =							feat_proj_dropout
   UpperCAmelCase__      :					List[str]				       =							final_dropout
   UpperCAmelCase__      :					int				       =							layerdrop
   UpperCAmelCase__      :					int				       =							layer_norm_eps
   UpperCAmelCase__      :					Tuple				       =							initializer_range
   UpperCAmelCase__      :					Union[str, Any]				       =							num_ctc_classes
   UpperCAmelCase__      :					Any				       =							vocab_size
   UpperCAmelCase__      :					Dict				       =							do_stable_layer_norm
   UpperCAmelCase__      :					List[Any]				       =							use_weighted_layer_sum
   UpperCAmelCase__      :					Optional[Any]				       =							classifier_proj_size

   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
   UpperCAmelCase__      :					List[str]				       =							apply_spec_augment
   UpperCAmelCase__      :					Union[str, Any]				       =							mask_time_prob
   UpperCAmelCase__      :					List[Any]				       =							mask_time_length
   UpperCAmelCase__      :					List[str]				       =							mask_time_min_masks
   UpperCAmelCase__      :					List[str]				       =							mask_feature_prob
   UpperCAmelCase__      :					Dict				       =							mask_feature_length

   # parameters for pretraining with codevector quantized representations
   UpperCAmelCase__      :					Dict				       =							num_codevectors_per_group
   UpperCAmelCase__      :					Union[str, Any]				       =							num_codevector_groups
   UpperCAmelCase__      :					int				       =							contrastive_logits_temperature
   UpperCAmelCase__      :					List[str]				       =							num_negatives
   UpperCAmelCase__      :					List[str]				       =							codevector_dim
   UpperCAmelCase__      :					List[Any]				       =							proj_codevector_dim
   UpperCAmelCase__      :					List[Any]				       =							diversity_loss_weight

   # ctc loss
   UpperCAmelCase__      :					Tuple				       =							ctc_loss_reduction
   UpperCAmelCase__      :					Any				       =							ctc_zero_infinity

   # adapter
   UpperCAmelCase__      :					int				       =							add_adapter
   UpperCAmelCase__      :					List[Any]				       =							adapter_kernel_size
   UpperCAmelCase__      :					Dict				       =							adapter_stride
   UpperCAmelCase__      :					Dict				       =							num_adapter_layers
   UpperCAmelCase__      :					Union[str, Any]				       =							output_hidden_size or hidden_size

   # SequenceClassification-specific parameter. Feel free to ignore for other classes.
   UpperCAmelCase__      :					str				       =							classifier_proj_size

   # XVector-specific parameters. Feel free to ignore for other classes.
   UpperCAmelCase__      :					Any				       =							list(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							list(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							list(snake_case__ )
   UpperCAmelCase__      :					Dict				       =							xvector_output_dim







 @property
 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




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



