

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    :Any      =			logging.get_logger(__name__)

__a    :int      =			{
    '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       __snake_case						( __UpperCamelCase	: List[str]				,__UpperCamelCase	: List[Any]				,__UpperCamelCase	: Dict				,__UpperCamelCase	: Any				,__UpperCamelCase	: Optional[int]    ):



      """simple docstring"""

      for attribute in key.split("."    ):
            A_						  =				getattr(__UpperCamelCase				,__UpperCamelCase    )

      if weight_type is not None:
            A_						  =				getattr(__UpperCamelCase				,__UpperCamelCase    ).shape
      else:
            A_						  =				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":
            A_						  =				value
      elif weight_type == "weight_g":
            A_						  =				value
      elif weight_type == "weight_v":
            A_						  =				value
      elif weight_type == "bias":
            A_						  =				value
      else:
            A_						  =				value

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

def       __snake_case						( __UpperCamelCase	: Optional[int]				,__UpperCamelCase	: Optional[Any]    ):



      """simple docstring"""

      A_						  =				[]
      A_						  =				fairseq_model.state_dict()

      A_						  =				hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            A_						  =				False
            if "conv_layers" in name:
                  load_conv_layer(
                      __UpperCamelCase				,__UpperCamelCase				,__UpperCamelCase				,__UpperCamelCase				,hf_model.config.feat_extract_norm == "group"				,)
                  A_						  =				True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("w2v_model."    )[-1] == name.split("."    )[0]:
                              A_						  =				True
                              if "*" in mapped_key:
                                    A_						  =				name.split(__UpperCamelCase    )[0].split("."    )[-2]
                                    A_						  =				mapped_key.replace("*"				,__UpperCamelCase    )
                              if "weight_g" in name:
                                    A_						  =				"weight_g"
                              elif "weight_v" in name:
                                    A_						  =				"weight_v"
                              elif "bias" in name and "relative_attention_bias" not in name:
                                    A_						  =				"bias"
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    A_						  =				"weight"
                              else:
                                    A_						  =				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       __snake_case						( __UpperCamelCase	: Optional[int]				,__UpperCamelCase	: Dict				,__UpperCamelCase	: List[str]				,__UpperCamelCase	: Union[str, Any]				,__UpperCamelCase	: Optional[int]    ):



      """simple docstring"""

      A_						  =				full_name.split("conv_layers."    )[-1]
      A_						  =				name.split("."    )
      A_						  =				int(items[0]    )
      A_						  =				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.'''
                  )
                  A_						  =				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.'''
                  )
                  A_						  =				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."
                  )
                  A_						  =				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.'''
                  )
                  A_						  =				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       __snake_case						( __UpperCamelCase	: Dict				,__UpperCamelCase	: str				,__UpperCamelCase	: int=None    ):



      """simple docstring"""

      A_						  =				torch.load(__UpperCamelCase    )
      A_						  =				WavLMConfigOrig(checkpoint["cfg"]    )
      A_						  =				WavLMOrig(__UpperCamelCase    )
      model.load_state_dict(checkpoint["model"]    )
      model.eval()

      if config_path is not None:
            A_						  =				WavLMConfig.from_pretrained(__UpperCamelCase    )
      else:
            A_						  =				WavLMConfig()

      A_						  =				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    :Optional[int]      =			parser.parse_args()
      convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





'''simple docstring'''


from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
				import torch


_snake_case : Tuple  				=						logging.get_logger(__name__)

class      A       (						_a       ):
							lowercase_							     =							['pixel_values']







							def __init__(    self			:    str				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Dict[str, int]] = None				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BILINEAR				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Union[int, float] = 1 / 2_55				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     **lowerCAmelCase_			:    Any				,     )     ->				None:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_   )
														_a      	=							size if size is not None else {'''shortest_edge''': 2_56}
														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														_a      	=							crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a      	=							get_size_dict(lowerCAmelCase_				,     param_name='''crop_size'''   )
														_a      	=							do_resize
														_a      	=							size
														_a      	=							resample
														_a      	=							do_center_crop
														_a      	=							crop_size
														_a      	=							do_rescale
														_a      	=							rescale_factor
														_a      	=							do_normalize
														_a      	=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_a      	=							image_std if image_std is not None else IMAGENET_STANDARD_STD







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BICUBIC				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														if "shortest_edge" not in size:
																					raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'   )
														_a      	=							get_resize_output_image_size(lowerCAmelCase_				,     size=size['''shortest_edge''']				,     default_to_square=lowerCAmelCase_   )
														return resize(lowerCAmelCase_				,     size=lowerCAmelCase_				,     resample=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    List[Any]				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_   )
														if "height" not in size or "width" not in size:
																					raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'   )
														return center_crop(lowerCAmelCase_				,     size=(size['''height'''], size['''width'''])				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    Tuple   )     ->				np.ndarray:


														"""simple docstring"""


														return rescale(lowerCAmelCase_				,     scale=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				np.ndarray:


														"""simple docstring"""


														return normalize(lowerCAmelCase_				,     mean=lowerCAmelCase_				,     std=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    ImageInput				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    PILImageResampling = None				,     lowerCAmelCase_			:    bool = None				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Optional[float] = None				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[str, TensorType]] = None				,     lowerCAmelCase_			:    Union[str, ChannelDimension] = ChannelDimension.FIRST				,     **lowerCAmelCase_			:    Union[str, Any]				,     )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							do_resize if do_resize is not None else self.do_resize
														_a      	=							size if size is not None else self.size
														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														_a      	=							resample if resample is not None else self.resample
														_a      	=							do_center_crop if do_center_crop is not None else self.do_center_crop
														_a      	=							crop_size if crop_size is not None else self.crop_size
														_a      	=							get_size_dict(lowerCAmelCase_				,     param_name='''crop_size'''   )
														_a      	=							do_rescale if do_rescale is not None else self.do_rescale
														_a      	=							rescale_factor if rescale_factor is not None else self.rescale_factor
														_a      	=							do_normalize if do_normalize is not None else self.do_normalize
														_a      	=							image_mean if image_mean is not None else self.image_mean
														_a      	=							image_std if image_std is not None else self.image_std

														_a      	=							make_list_of_images(lowerCAmelCase_   )

														if not valid_images(lowerCAmelCase_   ):
																					raise ValueError(
																					    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																					    '''torch.Tensor, tf.Tensor or jax.ndarray.'''   )

														if do_resize and size is None:
																					raise ValueError('''Size must be specified if do_resize is True.'''   )

														if do_center_crop and crop_size is None:
																					raise ValueError('''Crop size must be specified if do_center_crop is True.'''   )

														if do_rescale and rescale_factor is None:
																					raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('''Image mean and std must be specified if do_normalize is True.'''   )

														# All transformations expect numpy arrays.
														_a      	=							[to_numpy_array(lowerCAmelCase_   ) for image in images]

														if do_resize:
																					_a      	=							[self.resize(image=lowerCAmelCase_				,     size=lowerCAmelCase_				,     resample=lowerCAmelCase_   ) for image in images]

														if do_center_crop:
																					_a      	=							[self.center_crop(image=lowerCAmelCase_				,     size=lowerCAmelCase_   ) for image in images]

														if do_rescale:
																					_a      	=							[self.rescale(image=lowerCAmelCase_				,     scale=lowerCAmelCase_   ) for image in images]

														if do_normalize:
																					_a      	=							[self.normalize(image=lowerCAmelCase_				,     mean=lowerCAmelCase_				,     std=lowerCAmelCase_   ) for image in images]

														_a      	=							[to_channel_dimension_format(lowerCAmelCase_				,     lowerCAmelCase_   ) for image in images]

														_a      	=							{'''pixel_values''': images}
														return BatchFeature(data=lowerCAmelCase_				,     tensor_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    Optional[int]				,     lowerCAmelCase_			:    List[Tuple] = None   )     ->				Any:


														"""simple docstring"""


														_a      	=							outputs.logits

														# Resize logits and compute semantic segmentation maps
														if target_sizes is not None:
																					if len(lowerCAmelCase_   ) != len(lowerCAmelCase_   ):
																												raise ValueError(
																												    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''   )

																					if is_torch_tensor(lowerCAmelCase_   ):
																												_a      	=							target_sizes.numpy()

																					_a      	=							[]

																					for idx in range(len(lowerCAmelCase_   )   ):
																												_a      	=							torch.nn.functional.interpolate(
																												    logits[idx].unsqueeze(dim=0   )				,     size=target_sizes[idx]				,     mode='''bilinear'''				,     align_corners=lowerCAmelCase_   )
																												_a      	=							resized_logits[0].argmax(dim=0   )
																												semantic_segmentation.append(lowerCAmelCase_   )
														else:
																					_a      	=							logits.argmax(dim=1   )
																					_a      	=							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

														return semantic_segmentation

