


import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        DebertaVaForMaskedLM,
        DebertaVaForMultipleChoice,
        DebertaVaForQuestionAnswering,
        DebertaVaForSequenceClassification,
        DebertaVaForTokenClassification,
        DebertaVaModel,
    )
    from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   def __init__(  self  : str       ,  _lowerCamelCase  : List[Any]       ,  _lowerCamelCase  : int=1_3       ,  _lowerCamelCase  : Any=7       ,  _lowerCamelCase  : List[Any]=True       ,  _lowerCamelCase  : Optional[int]=True       ,  _lowerCamelCase  : Optional[Any]=True       ,  _lowerCamelCase  : str=True       ,  _lowerCamelCase  : Tuple=9_9       ,  _lowerCamelCase  : List[str]=3_2       ,  _lowerCamelCase  : Tuple=5       ,  _lowerCamelCase  : List[Any]=4       ,  _lowerCamelCase  : Optional[int]=3_7       ,  _lowerCamelCase  : str="gelu"       ,  _lowerCamelCase  : Union[str, Any]=0.1       ,  _lowerCamelCase  : Union[str, Any]=0.1       ,  _lowerCamelCase  : int=5_1_2       ,  _lowerCamelCase  : List[str]=1_6       ,  _lowerCamelCase  : str=2       ,  _lowerCamelCase  : int=0.02       ,  _lowerCamelCase  : int=False       ,  _lowerCamelCase  : Optional[int]=True       ,  _lowerCamelCase  : str="None"       ,  _lowerCamelCase  : Optional[int]=3       ,  _lowerCamelCase  : Dict=4       ,  _lowerCamelCase  : Optional[int]=None       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						parent
         __lowerCamelCase     :      List[Any]       						=						batch_size
         __lowerCamelCase     :      Tuple       						=						seq_length
         __lowerCamelCase     :      str       						=						is_training
         __lowerCamelCase     :      Optional[int]       						=						use_input_mask
         __lowerCamelCase     :      str       						=						use_token_type_ids
         __lowerCamelCase     :      Dict       						=						use_labels
         __lowerCamelCase     :      Any       						=						vocab_size
         __lowerCamelCase     :      List[str]       						=						hidden_size
         __lowerCamelCase     :      List[Any]       						=						num_hidden_layers
         __lowerCamelCase     :      Any       						=						num_attention_heads
         __lowerCamelCase     :      int       						=						intermediate_size
         __lowerCamelCase     :      Optional[int]       						=						hidden_act
         __lowerCamelCase     :      Dict       						=						hidden_dropout_prob
         __lowerCamelCase     :      List[Any]       						=						attention_probs_dropout_prob
         __lowerCamelCase     :      Union[str, Any]       						=						max_position_embeddings
         __lowerCamelCase     :      Dict       						=						type_vocab_size
         __lowerCamelCase     :      List[Any]       						=						type_sequence_label_size
         __lowerCamelCase     :      Any       						=						initializer_range
         __lowerCamelCase     :      Any       						=						num_labels
         __lowerCamelCase     :      List[Any]       						=						num_choices
         __lowerCamelCase     :      Any       						=						relative_attention
         __lowerCamelCase     :      Dict       						=						position_biased_input
         __lowerCamelCase     :      Dict       						=						pos_att_type
         __lowerCamelCase     :      Optional[Any]       						=						scope

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size							)

         __lowerCamelCase     :      List[Any]       						=						None
         if self.use_input_mask:
               __lowerCamelCase     :      Union[str, Any]       						=						ids_tensor([self.batch_size, self.seq_length]       ,  vocab_size=2							)

         __lowerCamelCase     :      List[str]       						=						None
         if self.use_token_type_ids:
               __lowerCamelCase     :      int       						=						ids_tensor([self.batch_size, self.seq_length]       ,  self.type_vocab_size							)

         __lowerCamelCase     :      Union[str, Any]       						=						None
         __lowerCamelCase     :      Optional[Any]       						=						None
         __lowerCamelCase     :      Optional[int]       						=						None
         if self.use_labels:
               __lowerCamelCase     :      List[Any]       						=						ids_tensor([self.batch_size]       ,  self.type_sequence_label_size							)
               __lowerCamelCase     :      List[str]       						=						ids_tensor([self.batch_size, self.seq_length]       ,  self.num_labels							)
               __lowerCamelCase     :      str       						=						ids_tensor([self.batch_size]       ,  self.num_choices							)

         __lowerCamelCase     :      Tuple       						=						self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         return DebertaVaConfig(
             vocab_size=self.vocab_size       ,  hidden_size=self.hidden_size       ,  num_hidden_layers=self.num_hidden_layers       ,  num_attention_heads=self.num_attention_heads       ,  intermediate_size=self.intermediate_size       ,  hidden_act=self.hidden_act       ,  hidden_dropout_prob=self.hidden_dropout_prob       ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,  max_position_embeddings=self.max_position_embeddings       ,  type_vocab_size=self.type_vocab_size       ,  initializer_range=self.initializer_range       ,  relative_attention=self.relative_attention       ,  position_biased_input=self.position_biased_input       ,  pos_att_type=self.pos_att_type       ,  )

   def   _snake_case    (  self  : Tuple       ,  _lowerCamelCase  : List[str]							):






         '''simple docstring'''



         self.parent.assertListEqual(list(result.loss.size()							)       ,  []							)

   def   _snake_case    (  self  : Optional[Any]       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : List[Any]       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : Dict							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						DebertaVaModel(config=_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()
         __lowerCamelCase     :      Optional[Any]       						=						model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  token_type_ids=_lowerCamelCase							)[0]
         __lowerCamelCase     :      Union[str, Any]       						=						model(_lowerCamelCase       ,  token_type_ids=_lowerCamelCase							)[0]
         __lowerCamelCase     :      Dict       						=						model(_lowerCamelCase							)[0]

         self.parent.assertListEqual(list(sequence_output.size()							)       ,  [self.batch_size, self.seq_length, self.hidden_size]							)

   def   _snake_case    (  self  : Optional[int]       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						DebertaVaForMaskedLM(config=_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()
         __lowerCamelCase     :      Any       						=						model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  token_type_ids=_lowerCamelCase       ,  labels=_lowerCamelCase							)

         self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)							)

   def   _snake_case    (  self  : List[str]       ,  _lowerCamelCase  : Dict       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						self.num_labels
         __lowerCamelCase     :      str       						=						DebertaVaForSequenceClassification(_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()
         __lowerCamelCase     :      List[str]       						=						model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  token_type_ids=_lowerCamelCase       ,  labels=_lowerCamelCase							)
         self.parent.assertListEqual(list(result.logits.size()							)       ,  [self.batch_size, self.num_labels]							)
         self.check_loss_output(_lowerCamelCase							)

   def   _snake_case    (  self  : int       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : Dict       ,  _lowerCamelCase  : Dict       ,  _lowerCamelCase  : List[Any]       ,  _lowerCamelCase  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						self.num_labels
         __lowerCamelCase     :      Tuple       						=						DebertaVaForTokenClassification(config=_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()
         __lowerCamelCase     :      List[Any]       						=						model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  token_type_ids=_lowerCamelCase       ,  labels=_lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.num_labels)							)

   def   _snake_case    (  self  : List[Any]       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : Dict       ,  _lowerCamelCase  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						DebertaVaForQuestionAnswering(config=_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()
         __lowerCamelCase     :      Dict       						=						model(
             _lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  token_type_ids=_lowerCamelCase       ,  start_positions=_lowerCamelCase       ,  end_positions=_lowerCamelCase       ,  )
         self.parent.assertEqual(result.start_logits.shape       ,  (self.batch_size, self.seq_length)							)
         self.parent.assertEqual(result.end_logits.shape       ,  (self.batch_size, self.seq_length)							)

   def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : Dict       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						DebertaVaForMultipleChoice(config=_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()
         __lowerCamelCase     :      Any       						=						input_ids.unsqueeze(1							).expand(-1       ,  self.num_choices       ,  -1							).contiguous()
         __lowerCamelCase     :      str       						=						token_type_ids.unsqueeze(1							).expand(-1       ,  self.num_choices       ,  -1							).contiguous()
         __lowerCamelCase     :      Optional[int]       						=						input_mask.unsqueeze(1							).expand(-1       ,  self.num_choices       ,  -1							).contiguous()
         __lowerCamelCase     :      Any       						=						model(
             _lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  token_type_ids=_lowerCamelCase       ,  labels=_lowerCamelCase       ,  )
         self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_choices)							)


   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						self.prepare_config_and_inputs()
         (
             (
             __lowerCamelCase
         )						, (
             __lowerCamelCase
         )						, (
             __lowerCamelCase
         )						, (
             __lowerCamelCase
         )						, (
             __lowerCamelCase
         )						, (
             __lowerCamelCase
         )						, (
             __lowerCamelCase
         )						, 
         )     :      int       						=						config_and_inputs
         __lowerCamelCase     :      Tuple       						=						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
         return config, inputs_dict



@require_torch
class 							_UpperCamelCase (						A,A,unittest.TestCase						):

   '''simple docstring'''







   a_				:							Tuple    = (
       (
           DebertaVaModel,
           DebertaVaForMaskedLM,
           DebertaVaForSequenceClassification,
           DebertaVaForTokenClassification,
           DebertaVaForQuestionAnswering,
           DebertaVaForMultipleChoice,
       )
       if is_torch_available()
       else ()
   )
   a_				:							Tuple    = (
       {
           "feature-extraction": DebertaVaModel,
           "fill-mask": DebertaVaForMaskedLM,
           "question-answering": DebertaVaForQuestionAnswering,
           "text-classification": DebertaVaForSequenceClassification,
           "token-classification": DebertaVaForTokenClassification,
           "zero-shot": DebertaVaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )

   a_				:							Tuple    = True
   a_				:							Optional[Any]    = False
   a_				:							Tuple    = False
   a_				:							Any    = False
   a_				:							List[Any]    = False

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						DebertaVaModelTester(self							)
         __lowerCamelCase     :      Optional[Any]       						=						ConfigTester(self       ,  config_class=_lowerCamelCase       ,  hidden_size=3_7							)

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         self.config_tester.run_common_tests()

   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_model(*_lowerCamelCase							)

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_sequence_classification(*_lowerCamelCase							)

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_masked_lm(*_lowerCamelCase							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_question_answering(*_lowerCamelCase							)

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_token_classification(*_lowerCamelCase							)

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_multiple_choice(*_lowerCamelCase							)


   @slow
   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __lowerCamelCase     :      Any       						=						DebertaVaModel.from_pretrained(_lowerCamelCase							)
               self.assertIsNotNone(_lowerCamelCase							)



@require_torch
@require_sentencepiece
@require_tokenizers
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   @unittest.skip(reason="""Model not available yet"""							)
   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         pass


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






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge"""							)

         __lowerCamelCase     :      Union[str, Any]       						=						torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]							)
         __lowerCamelCase     :      str       						=						torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
         with torch.no_grad():
               __lowerCamelCase     :      Optional[Any]       						=						model(_lowerCamelCase       ,  attention_mask=_lowerCamelCase							)[0]
         # compare the actual values for a slice.
         __lowerCamelCase     :      Dict       						=						torch.tensor(
             [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]							)
         self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,  _lowerCamelCase       ,  atol=1E-4							)       ,  F"""{output[:, 1:4, 1:4]}"""							)







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()
__UpperCamelCase    :						Any     =							logging.get_logger(__name__)

__UpperCamelCase    :						str     =							{
    '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',
}
__UpperCamelCase    :						Optional[int]     =							[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Tuple      ):
      """simple docstring"""




      for attribute in key.split("""."""      ):
            __lowerCamelCase     :      Union[str, Any]       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      )

      if weight_type is not None:
            __lowerCamelCase     :      Union[str, 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     :      List[str]       						=						value
      elif weight_type == "weight_g":
            __lowerCamelCase     :      Dict       						=						value
      elif weight_type == "weight_v":
            __lowerCamelCase     :      str       						=						value
      elif weight_type == "bias":
            __lowerCamelCase     :      Optional[Any]       						=						value
      else:
            __lowerCamelCase     :      str       						=						value

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						[]
      __lowerCamelCase     :      int       						=						fairseq_model.state_dict()

      __lowerCamelCase     :      List[str]       						=						hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            __lowerCamelCase     :      str       						=						False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	hf_model.config.feat_extract_norm == """group"""						,	)
                  __lowerCamelCase     :      Optional[int]       						=						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     :      Optional[int]       						=						mapped_key.replace("""*"""						,	UpperCAmelCase      )
                              if "weight_g" in name:
                                    __lowerCamelCase     :      Optional[Any]       						=						"""weight_g"""
                              elif "weight_v" in name:
                                    __lowerCamelCase     :      Dict       						=						"""weight_v"""
                              elif "bias" in name and "relative_attention_bias" not in name:
                                    __lowerCamelCase     :      List[Any]       						=						"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    __lowerCamelCase     :      List[str]       						=						"""weight"""
                              else:
                                    __lowerCamelCase     :      List[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      :			Optional[int]						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Optional[int]      ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						full_name.split("""conv_layers."""      )[-1]
      __lowerCamelCase     :      List[Any]       						=						name.split("""."""      )
      __lowerCamelCase     :      Tuple       						=						int(items[0]      )
      __lowerCamelCase     :      List[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     :      List[str]       						=						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     :      str       						=						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     :      Any       						=						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     :      Tuple       						=						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      :			List[str]						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Optional[int]=None      ):
      """simple docstring"""




      __lowerCamelCase     :      Any       						=						torch.load(UpperCAmelCase      )
      __lowerCamelCase     :      Optional[int]       						=						WavLMConfigOrig(checkpoint["""cfg"""]      )
      __lowerCamelCase     :      Any       						=						WavLMOrig(UpperCAmelCase      )
      model.load_state_dict(checkpoint["""model"""]      )
      model.eval()

      if config_path is not None:
            __lowerCamelCase     :      Union[str, Any]       						=						WavLMConfig.from_pretrained(UpperCAmelCase      )
      else:
            __lowerCamelCase     :      str       						=						WavLMConfig()

      __lowerCamelCase     :      Dict       						=						WavLMModel(UpperCAmelCase      )

      recursively_load_weights(UpperCAmelCase						,	UpperCAmelCase      )

      hf_wavlm.save_pretrained(UpperCAmelCase      )


if __name__ == "__main__":
    __UpperCamelCase    :						List[str]     =							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')
    __UpperCamelCase    :						Tuple     =							parser.parse_args()
    convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



