






'''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()
SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {
    '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',
}
SCREAMING_SNAKE_CASE__     =       [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Any:





      for attribute in key.split("""."""   ):
            UpperCamelCase      = getattr(__lowerCAmelCase		,						__lowerCAmelCase   )

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

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







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Tuple:
      UpperCamelCase      = []
      UpperCamelCase      = fairseq_model.state_dict()

      UpperCamelCase      = hf_model.feature_extractor

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

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

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







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			str:
      UpperCamelCase      = full_name.split("""conv_layers."""   )[-1]
      UpperCamelCase      = name.split("""."""   )
      UpperCamelCase      = int(items[0]   )
      UpperCamelCase      = 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."
                  )
                  UpperCamelCase      = 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."
                  )
                  UpperCamelCase      = 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."
                  )
                  UpperCamelCase      = 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."
                  )
                  UpperCamelCase      = value
                  logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
      else:
            unused_weights.append(__lowerCAmelCase   )







@torch.no_grad()
def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=None   )->			Any:
      UpperCamelCase      = torch.load(__lowerCAmelCase   )
      UpperCamelCase      = WavLMConfigOrig(checkpoint["""cfg"""]   )
      UpperCamelCase      = WavLMOrig(__lowerCAmelCase   )
      model.load_state_dict(checkpoint["""model"""]   )
      model.eval()

      if config_path is not None:
            UpperCamelCase      = WavLMConfig.from_pretrained(__lowerCAmelCase   )
      else:
            UpperCamelCase      = WavLMConfig()

      UpperCamelCase      = WavLMModel(__lowerCAmelCase   )

      recursively_load_weights(__lowerCAmelCase		,						__lowerCAmelCase   )

      hf_wavlm.save_pretrained(__lowerCAmelCase   )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__     =       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')
 SCREAMING_SNAKE_CASE__     =       parser.parse_args()
 convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)








'''simple docstring'''

from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors







def    lowercase__			(				__UpperCamelCase   )->			int:
      UpperCamelCase      = prime_factors(__UpperCamelCase   )
      if is_square_free(__UpperCamelCase   ):
            return -1 if len(__UpperCamelCase   ) % 2 else 1
      return 0


if __name__ == "__main__":
     import doctest

     doctest.testmod()
