



'''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__      :				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__      :				Dict   	=			[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__						,  A__	):
      for attribute in key.split('.'	):
            lowercase__     =  getattr(lowercase__						,  lowercase__	)

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

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

def   _lowerCAmelCase   (						A__						,  A__	):
      lowercase__     =  []
      lowercase__     =  fairseq_model.state_dict()

      lowercase__     =  hf_model.feature_extractor

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

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

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

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

@torch.no_grad()
def   _lowerCAmelCase   (						A__						,  A__						,  A__=None	):
      lowercase__     =  torch.load(lowercase__	)
      lowercase__     =  WavLMConfigOrig(checkpoint['cfg']	)
      lowercase__     =  WavLMOrig(lowercase__	)
      model.load_state_dict(checkpoint['model']	)
      model.eval()

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

      lowercase__     =  WavLMModel(lowercase__	)

      recursively_load_weights(lowercase__						,  lowercase__	)

      hf_wavlm.save_pretrained(lowercase__	)


if __name__ == "__main__":
       a__      :				Tuple   	=			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[Any]   	=			parser.parse_args()
       convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class       UpperCAmelCase__(							unittest.TestCase     ,							lowerCamelCase	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			List[str])	->				Any:




         """simple docstring"""

         lowercase__     =  load_tool('text-classification')
         self.tool.setup()
         lowercase__     =  load_tool('text-classification'			,				remote=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.tool('That\'s quite cool'			,				['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')


   def 			UpperCAmelCase		(						self  :			int)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.remote_tool('That\'s quite cool'			,				['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')


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




         """simple docstring"""

         lowercase__     =  self.tool(text='That\'s quite cool'			,				labels=['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')



   def 			UpperCAmelCase		(						self  :			Any)	->				Any:




         """simple docstring"""

         lowercase__     =  self.remote_tool(text='That\'s quite cool'			,				labels=['positive', 'negative'])
         self.assertEqual(lowerCAmelCase			,				'positive')
