





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

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

def    __UpperCamelCase	(	a    : Optional[Any]					,   a    : Tuple					,   a    : Dict					,   a    : Optional[int]					,   a    : List[str]     ) ->Dict:




 for attribute in key.split('''.'''     ):
  snake_case				=			getattr(__A					,   __A     )

 if weight_type is not None:
  snake_case				=			getattr(__A					,   __A     ).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    __UpperCamelCase	(	a    : int					,   a    : List[str]     ) ->Optional[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(
       __A					,   __A					,   __A					,   __A					,   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(__A     )[0].split('''.'''     )[-2]
      snake_case				=			mapped_key.replace('''*'''					,   __A     )
     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(__A					,   __A					,   __A					,   __A					,   __A     )
    continue
  if not is_used:
   unused_weights.append(__A     )

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

def    __UpperCamelCase	(	a    : Any					,   a    : Optional[Any]					,   a    : Dict					,   a    : Union[str, Any]					,   a    : Tuple     ) ->Optional[Any]:
 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(__A     )

@torch.no_grad()
def    __UpperCamelCase	(	a    : Any					,   a    : Tuple					,   a    : Dict=None     ) ->List[Any]:
 snake_case				=			torch.load(__A     )
 snake_case				=			WavLMConfigOrig(checkpoint['''cfg''']     )
 snake_case				=			WavLMOrig(__A     )
 model.load_state_dict(checkpoint['''model''']     )
 model.eval()

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

 snake_case				=			WavLMModel(__A     )

 recursively_load_weights(__A					,   __A     )

 hf_wavlm.save_pretrained(__A     )


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








'''simple docstring'''







import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


_lowercase       						=			[
    {'dataset': 'wikipedia', 'config_name': '20220301.de'},
    {'dataset': 'wikipedia', 'config_name': '20220301.en'},
    {'dataset': 'wikipedia', 'config_name': '20220301.fr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.frr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.it'},
    {'dataset': 'wikipedia', 'config_name': '20220301.simple'},
    {'dataset': 'snli', 'config_name': 'plain_text'},
    {'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
    {'dataset': 'wiki40b', 'config_name': 'en'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
    {'dataset': 'natural_questions', 'config_name': 'default'},
]

def    __UpperCamelCase	(	a    : Dict=True     ) ->str:
 if with_config:
  return [
      {
          "testcase_name": d["dataset"] + "/" + d["config_name"],
          "dataset": d["dataset"],
          "config_name": d["config_name"],
      }
      for d in DATASETS_ON_HF_GCP
  ]
 else:
  return [
      {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
  ]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__a					)					)
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					None
     _UpperCAmelCase										=					None







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	str:
      with TemporaryDirectory() as tmp_dir:
       snake_case				=			dataset_module_factory(A__    ,   cache_dir=A__    )

       snake_case				=			import_main_class(dataset_module.module_path    ,   dataset=A__    )

       snake_case				=			builder_cls(
           cache_dir=A__    ,   config_name=A__    ,   hash=dataset_module.hash    ,   )

       snake_case				=			'''/'''.join(
           [
               HF_GCP_BASE_URL,
               builder_instance._relative_data_dir(with_hash=A__    ).replace(os.sep    ,   '''/'''    ),
               config.DATASET_INFO_FILENAME,
           ]    )
       snake_case				=			cached_path(A__    ,   cache_dir=A__    )
       self.assertTrue(os.path.exists(A__    )    )




@pytest.mark.integration
def    __UpperCamelCase	(	a    : List[str]     ) ->Any:
 snake_case				=			tmp_path_factory.mktemp('''test_hf_gcp'''     ) / '''test_wikipedia_simple'''
 snake_case				=			dataset_module_factory('''wikipedia'''					,   cache_dir=a     )
 snake_case				=			import_main_class(dataset_module.module_path     )
 snake_case				=			builder_cls(
     cache_dir=a					,   config_name='''20220301.frr'''					,   hash=dataset_module.hash					,   )
 # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
 snake_case				=			None
 builder_instance.download_and_prepare()
 snake_case				=			builder_instance.as_dataset()
 assert ds






@pytest.mark.integration
def    __UpperCamelCase	(	a    : Any     ) ->Union[str, Any]:
 snake_case				=			dataset_module_factory('''wikipedia'''					,   cache_dir=a     )
 snake_case				=			import_main_class(dataset_module.module_path					,   dataset=a     )
 snake_case				=			builder_cls(
     cache_dir=a					,   config_name='''20220301.frr'''					,   hash=dataset_module.hash					,   )
 snake_case				=			builder_instance.as_streaming_dataset()
 assert ds
 assert isinstance(a					,   a     )
 assert "train" in ds
 assert isinstance(ds['''train''']					,   a     )
 assert next(iter(ds['''train''']     )     )

