


'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					UNetaDModel
    lowerCamelCase  :					KarrasVeScheduler







    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Dict:
         super().__init__()
         self.register_modules(unet=SCREAMING_SNAKE_CASE_			,     scheduler=SCREAMING_SNAKE_CASE_	)







    @torch.no_grad()
    def __call__(							self			,     SCREAMING_SNAKE_CASE_ = 1			,     SCREAMING_SNAKE_CASE_ = 50			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = "pil"			,     SCREAMING_SNAKE_CASE_ = True			,     **SCREAMING_SNAKE_CASE_			,     )				->     Union[Tuple, ImagePipelineOutput]:
         __lowerCamelCase   :	List[str]								=		self.unet.config.sample_size
         __lowerCamelCase   :	Any								=		(batch_size, 3, img_size, img_size)

         __lowerCamelCase   :	List[Any]								=		self.unet

         # sample x_0 ~ N(0, sigma_0^2 * I)
         __lowerCamelCase   :	str								=		randn_tensor(SCREAMING_SNAKE_CASE_			,     generator=SCREAMING_SNAKE_CASE_			,     device=self.device	) * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)

         for t in self.progress_bar(self.scheduler.timesteps	):
              # here sigma_t == t_i from the paper
              __lowerCamelCase   :	Optional[Any]								=		self.scheduler.schedule[t]
              __lowerCamelCase   :	Any								=		self.scheduler.schedule[t - 1] if t > 0 else 0

              # 1. Select temporarily increased noise level sigma_hat
              # 2. Add new noise to move from sample_i to sample_hat
              __lowerCamelCase				,       __lowerCamelCase   :	Optional[Any]								=		self.scheduler.add_noise_to_input(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     generator=SCREAMING_SNAKE_CASE_	)

              # 3. Predict the noise residual given the noise magnitude `sigma_hat`
              # The model inputs and output are adjusted by following eq. (213) in [1].
              __lowerCamelCase   :	Union[str, Any]								=		(sigma_hat / 2) * model((sample_hat + 1) / 2			,     sigma_hat / 2	).sample

              # 4. Evaluate dx/dt at sigma_hat
              # 5. Take Euler step from sigma to sigma_prev
              __lowerCamelCase   :	List[str]								=		self.scheduler.step(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)

              if sigma_prev != 0:
                   # 6. Apply 2nd order correction
                   # The model inputs and output are adjusted by following eq. (213) in [1].
                   __lowerCamelCase   :	Optional[Any]								=		(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2			,     sigma_prev / 2	).sample
                   __lowerCamelCase   :	Union[str, Any]								=		self.scheduler.step_correct(
                       SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     step_output.prev_sample			,     step_output['derivative']			,     )
              __lowerCamelCase   :	Optional[int]								=		step_output.prev_sample

         __lowerCamelCase   :	Any								=		(sample / 2 + 0.5).clamp(0			,     1	)
         __lowerCamelCase   :	Any								=		sample.cpu().permute(0			,     2			,     3			,     1	).numpy()
         if output_type == "pil":
              __lowerCamelCase   :	Optional[Any]								=		self.numpy_to_pil(SCREAMING_SNAKE_CASE_	)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_	)










'''simple docstring'''



import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
A__		:					Optional[int]												=  logging.get_logger(__name__)

A__		:					List[Any]												=  {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn.grep_linear""": """encoder.layers.*.attention.gru_rel_pos_linear""",
    """self_attn.relative_attention_bias""": """encoder.layers.*.attention.rel_attn_embed""",
    """self_attn.grep_a""": """encoder.layers.*.attention.gru_rel_pos_const""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
A__		:					Tuple												=  [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				List[str]      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				Optional[int]							)     ->		Tuple:
     for attribute in key.split('.'							):
          __lowerCamelCase   :	List[Any]								=		getattr(UpperCAmelCase_      ,    UpperCAmelCase_							)

     if weight_type is not None:
          __lowerCamelCase   :	Any								=		getattr(UpperCAmelCase_      ,    UpperCAmelCase_							).shape
     else:
          __lowerCamelCase   :	Any								=		hf_pointer.shape

     assert hf_shape == value.shape, (
         F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
         F' {value.shape} for {full_name}'
     )

     if weight_type == "weight":
          __lowerCamelCase   :	Tuple								=		value
     elif weight_type == "weight_g":
          __lowerCamelCase   :	Optional[int]								=		value
     elif weight_type == "weight_v":
          __lowerCamelCase   :	str								=		value
     elif weight_type == "bias":
          __lowerCamelCase   :	List[Any]								=		value
     else:
          __lowerCamelCase   :	List[str]								=		value

     logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				Union[str, Any]							)     ->		Optional[int]:
     __lowerCamelCase   :	Optional[Any]								=		[]
     __lowerCamelCase   :	Dict								=		fairseq_model.state_dict()

     __lowerCamelCase   :	List[str]								=		hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          __lowerCamelCase   :	Union[str, Any]								=		False
          if "conv_layers" in name:
               load_conv_layer(
                   UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    hf_model.config.feat_extract_norm == 'group'      ,    )
               __lowerCamelCase   :	str								=		True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split('w2v_model.'							)[-1] == name.split('.'							)[0]:
                         __lowerCamelCase   :	int								=		True
                         if "*" in mapped_key:
                              __lowerCamelCase   :	Optional[int]								=		name.split(UpperCAmelCase_							)[0].split('.'							)[-2]
                              __lowerCamelCase   :	List[str]								=		mapped_key.replace('*'      ,    UpperCAmelCase_							)
                         if "weight_g" in name:
                              __lowerCamelCase   :	Dict								=		'weight_g'
                         elif "weight_v" in name:
                              __lowerCamelCase   :	Any								=		'weight_v'
                         elif "bias" in name and "relative_attention_bias" not in name:
                              __lowerCamelCase   :	Any								=		'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              __lowerCamelCase   :	List[Any]								=		'weight'
                         else:
                              __lowerCamelCase   :	str								=		None

                         set_recursively(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_							)
                    continue
          if not is_used:
               unused_weights.append(UpperCAmelCase_							)

     logger.warning(F'Unused weights: {unused_weights}'							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Any      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				List[Any]							)     ->		Tuple:
     __lowerCamelCase   :	List[str]								=		full_name.split('conv_layers.'							)[-1]
     __lowerCamelCase   :	List[Any]								=		name.split('.'							)
     __lowerCamelCase   :	Any								=		int(items[0]							)
     __lowerCamelCase   :	Optional[Any]								=		int(items[1]							)

     if type_id == 0:
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                   F'{full_name} has size {value.shape}, but'
                   F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
               )
               __lowerCamelCase   :	Union[str, Any]								=		value
               logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'							)
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                   F'{full_name} has size {value.shape}, but'
                   F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
               )
               __lowerCamelCase   :	Tuple								=		value
               logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'							)
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                   F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
                   " found."
               )
               __lowerCamelCase   :	str								=		value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                   F'{full_name} has size {value.shape}, but'
                   F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
               )
               __lowerCamelCase   :	List[Any]								=		value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
     else:
          unused_weights.append(UpperCAmelCase_							)



@torch.no_grad()
def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				Tuple      ,    UpperCAmelCase_    :				Optional[Any]=None							)     ->		Optional[int]:
     # load the pre-trained checkpoints
     __lowerCamelCase   :	Optional[Any]								=		torch.load(UpperCAmelCase_							)
     __lowerCamelCase   :	Optional[Any]								=		WavLMConfigOrig(checkpoint['cfg']							)
     __lowerCamelCase   :	Union[str, Any]								=		WavLMOrig(UpperCAmelCase_							)
     model.load_state_dict(checkpoint['model']							)
     model.eval()

     if config_path is not None:
          __lowerCamelCase   :	Optional[int]								=		WavLMConfig.from_pretrained(UpperCAmelCase_							)
     else:
          __lowerCamelCase   :	Any								=		WavLMConfig()

     __lowerCamelCase   :	Optional[int]								=		WavLMModel(UpperCAmelCase_							)

     recursively_load_weights(UpperCAmelCase_      ,    UpperCAmelCase_							)

     hf_wavlm.save_pretrained(UpperCAmelCase_							)


if __name__ == "__main__":
      A__		:					List[Any]												=  argparse.ArgumentParser()
      parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
      parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
      A__		:					List[str]												=  parser.parse_args()
      convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






