



'''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   UpperCAmelCase			(  a_						)					->    List[str]:



   """simple docstring"""






   A_   :						Dict		= SwinaSRConfig()

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

   return config



def   UpperCAmelCase			(  a_	,						a_						)					->    List[str]:



   """simple docstring"""






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

   if name == "norm.weight":
      A_   :						Optional[int]		= """layernorm.weight"""
   if name == "norm.bias":
      A_   :						List[str]		= """layernorm.bias"""

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

   return name



def   UpperCAmelCase			(  a_	,						a_						)					->    str:



   """simple docstring"""






   for key in orig_state_dict.copy().keys():
      A_   :						int		= orig_state_dict.pop(a_						)

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

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

   return orig_state_dict



def   UpperCAmelCase			(  a_	,						a_	,						a_						)					->    Union[str, Any]:



   """simple docstring"""






   A_   :						List[Any]		= get_config(a_						)
   A_   :						str		= SwinaSRForImageSuperResolution(a_						)
   model.eval()

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

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

   if config.num_channels == 1:
      A_   :						List[str]		= pixel_values[:, 0, :, :].unsqueeze(1						)

   A_   :						Optional[Any]		= model(a_						)

   # assert values
   if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
      A_   :						Tuple		= torch.Size([1, 3, 5_1_2, 5_1_2]						)
      A_   :						str		= 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_   :						str		= torch.Size([1, 3, 1_0_2_4, 1_0_2_4]						)
      A_   :						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
      A_   :						int		= torch.Size([1, 3, 1_0_2_4, 1_0_2_4]						)
      A_   :						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:
      A_   :						List[str]		= torch.Size([1, 3, 5_1_2, 5_1_2]						)
      A_   :						List[str]		= 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_   :						Optional[Any]		= torch.Size([1, 3, 1_0_2_4, 1_0_2_4]						)
      A_   :						List[Any]		= 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_   :						Union[str, Any]		= {
       """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_   :						int		= 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__":
  UpperCamelCase__  :			int          =					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.')

  UpperCamelCase__  :			List[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


__a  :     str       =  logging.get_logger(__name__)

__a  :     int       =  {
    """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      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Any			 =						'''wavlm'''





			def __init__(   self      ,   lowerCAmelCase__=32      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-5      ,   lowerCAmelCase__="group"      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)      ,   lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2)      ,   lowerCAmelCase__=(10, 3, 3, 3, 3, 2, 2)      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=1_28      ,   lowerCAmelCase__=16      ,   lowerCAmelCase__=3_20      ,   lowerCAmelCase__=8_00      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=0.05      ,   lowerCAmelCase__=10      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=10      ,   lowerCAmelCase__=3_20      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=1_00      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__="mean"      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=(5_12, 5_12, 5_12, 5_12, 15_00)      ,   lowerCAmelCase__=(5, 3, 3, 1, 1)      ,   lowerCAmelCase__=(1, 2, 3, 1, 1)      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=80      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Tuple:

							'''simple docstring'''


							super().__init__(**lowerCAmelCase__      ,   pad_token_id=lowerCAmelCase__      ,   bos_token_id=lowerCAmelCase__      ,   eos_token_id=lowerCAmelCase__						)
							__lowercase 			=							hidden_size
							__lowercase 			=							feat_extract_norm
							__lowercase 			=							feat_extract_activation
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							conv_bias
							__lowercase 			=							num_buckets
							__lowercase 			=							max_bucket_distance
							__lowercase 			=							num_conv_pos_embeddings
							__lowercase 			=							num_conv_pos_embedding_groups
							__lowercase 			=							len(self.conv_dim						)
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_act
							__lowercase 			=							num_attention_heads
							__lowercase 			=							hidden_dropout
							__lowercase 			=							attention_dropout
							__lowercase 			=							activation_dropout
							__lowercase 			=							feat_proj_dropout
							__lowercase 			=							final_dropout
							__lowercase 			=							layerdrop
							__lowercase 			=							layer_norm_eps
							__lowercase 			=							initializer_range
							__lowercase 			=							num_ctc_classes
							__lowercase 			=							vocab_size
							__lowercase 			=							do_stable_layer_norm
							__lowercase 			=							use_weighted_layer_sum
							__lowercase 			=							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
							__lowercase 			=							apply_spec_augment
							__lowercase 			=							mask_time_prob
							__lowercase 			=							mask_time_length
							__lowercase 			=							mask_time_min_masks
							__lowercase 			=							mask_feature_prob
							__lowercase 			=							mask_feature_length

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

							# ctc loss
							__lowercase 			=							ctc_loss_reduction
							__lowercase 			=							ctc_zero_infinity

							# adapter
							__lowercase 			=							add_adapter
							__lowercase 			=							adapter_kernel_size
							__lowercase 			=							adapter_stride
							__lowercase 			=							num_adapter_layers
							__lowercase 			=							output_hidden_size or hidden_size

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

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





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


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