





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()
snake_case_           =      logging.get_logger(__name__)

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






def lowerCamelCase__   (							snake_case_     :  str	,				snake_case_     :  List[str]	,				snake_case_     :  int	,				snake_case_     :  Optional[int]	,				snake_case_     :  Dict	)				->    List[Any]:
   for attribute in key.split('''.'''	):
      __snake_case								=       getattr(snake_case_	,				snake_case_	)

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

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






def lowerCamelCase__   (							snake_case_     :  str	,				snake_case_     :  Dict	)				->    List[Any]:
   __snake_case								=       []
   __snake_case								=       fairseq_model.state_dict()

   __snake_case								=       hf_model.feature_extractor

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

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

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






def lowerCamelCase__   (							snake_case_     :  Union[str, Any]	,				snake_case_     :  Optional[Any]	,				snake_case_     :  Any	,				snake_case_     :  Optional[Any]	,				snake_case_     :  Optional[int]	)				->    List[str]:
   __snake_case								=       full_name.split('''conv_layers.'''	)[-1]
   __snake_case								=       name.split('''.'''	)
   __snake_case								=       int(items[0]	)
   __snake_case								=       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."""
         )
         __snake_case								=       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."""
         )
         __snake_case								=       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."
         )
         __snake_case								=       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."""
         )
         __snake_case								=       value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
   else:
      unused_weights.append(snake_case_	)






@torch.no_grad()
def lowerCamelCase__   (							snake_case_     :  str	,				snake_case_     :  Any	,				snake_case_     :  Tuple=None	)				->    Any:
   # load the pre-trained checkpoints
   __snake_case								=       torch.load(snake_case_	)
   __snake_case								=       WavLMConfigOrig(checkpoint['''cfg''']	)
   __snake_case								=       WavLMOrig(snake_case_	)
   model.load_state_dict(checkpoint['''model''']	)
   model.eval()

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

   __snake_case								=       WavLMModel(snake_case_	)

   recursively_load_weights(snake_case_	,				snake_case_	)

   hf_wavlm.save_pretrained(snake_case_	)


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








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    int							=   XLMTokenizer
      A_						:    Optional[Any]							=   False


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __snake_case								=       [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''w</w>''',
             '''r</w>''',
             '''t</w>''',
             '''lo''',
             '''low''',
             '''er</w>''',
             '''low</w>''',
             '''lowest</w>''',
             '''newer</w>''',
             '''wider</w>''',
             '''<unk>''',
         ]
         __snake_case								=       dict(zip(a__    ,      range(len(a__				)				)				)				)
         __snake_case								=       ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

         __snake_case								=       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''vocab_file''']				)
         __snake_case								=       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''merges_file''']				)
         with open(self.vocab_file    ,      '''w'''				) as fp:
            fp.write(json.dumps(a__				)				)
         with open(self.merges_file    ,      '''w'''				) as fp:
            fp.write('''\n'''.join(a__				)				)


      def        a			(self	:				int    ,      a__	:				Any				):





         """simple docstring"""



         __snake_case								=       '''lower newer'''
         __snake_case								=       '''lower newer'''
         return input_text, output_text


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       XLMTokenizer(self.vocab_file    ,      self.merges_file				)

         __snake_case								=       '''lower'''
         __snake_case								=       ['''low''', '''er</w>''']
         __snake_case								=       tokenizer.tokenize(a__				)
         self.assertListEqual(a__    ,      a__				)

         __snake_case								=       tokens + ['''<unk>''']
         __snake_case								=       [14, 15, 20]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a__				)    ,      a__				)


      @slow
      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''				)

         __snake_case								=       tokenizer.encode('''sequence builders'''    ,      add_special_tokens=a__				)
         __snake_case								=       tokenizer.encode('''multi-sequence build'''    ,      add_special_tokens=a__				)

         __snake_case								=       tokenizer.build_inputs_with_special_tokens(a__				)
         __snake_case								=       tokenizer.build_inputs_with_special_tokens(a__    ,      a__				)

         assert encoded_sentence == [0] + text + [1]
         assert encoded_pair == [0] + text + [1] + text_a + [1]

