





'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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_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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
_lowercase       						=			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]

def    __UpperCamelCase	(	a    : Any     ) ->List[str]:
 snake_case				=			{}
 with open(a					,   '''r'''     ) as file:
  for line_number, line in enumerate(a     ):
   snake_case				=			line.strip()
   if line:
    snake_case				=			line.split()
    snake_case				=			line_number
    snake_case				=			words[0]
    snake_case				=			value
 return result

def    __UpperCamelCase	(	a    : Tuple					,   a    : Tuple					,   a    : Optional[int]					,   a    : int					,   a    : str     ) ->Any:
 for attribute in key.split('''.'''     ):
  snake_case				=			getattr(a					,   a     )

 snake_case				=			None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(a     ):
   snake_case				=			PARAM_MAPPING[full_name.split('''.'''     )[-1]]
   snake_case				=			'''param'''

 if weight_type is not None and weight_type != "param":
  snake_case				=			getattr(a					,   a     ).shape
 elif weight_type is not None and weight_type == "param":
  snake_case				=			hf_pointer
  for attribute in hf_param_name.split('''.'''     ):
   snake_case				=			getattr(a					,   a     )
  snake_case				=			shape_pointer.shape

  # let's reduce dimension
  snake_case				=			value[0]
 else:
  snake_case				=			hf_pointer.shape

 if hf_shape != value.shape:
  raise ValueError(
      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
 elif weight_type == "param":
  for attribute in hf_param_name.split('''.'''     ):
   snake_case				=			getattr(a					,   a     )
  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    : Optional[int]					,   a    : Optional[Any]					,   a    : Optional[int]					,   a    : Any					,   a    : int     ) ->Dict:
 snake_case				=			None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(a     ):
   snake_case				=			PARAM_MAPPING[full_name.split('''.'''     )[-1]]
   snake_case				=			'''param'''

 if weight_type is not None and weight_type != "param":
  snake_case				=			'''.'''.join([key, weight_type]     )
 elif weight_type is not None and weight_type == "param":
  snake_case				=			'''.'''.join([key, hf_param_name]     )
 else:
  snake_case				=			key

 snake_case				=			value if '''lm_head''' in full_key else value[0]


_lowercase       						=			{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}

def    __UpperCamelCase	(	a    : List[Any]					,   a    : Optional[int]					,   a    : Optional[Any]=None					,   a    : Any=None     ) ->Any:
 snake_case				=			False
 for key, mapped_key in MAPPING.items():
  snake_case				=			'''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
  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:
    snake_case				=			'''bias'''
   elif "weight" in name:
    # TODO: don't match quantizer.weight_proj
    snake_case				=			'''weight'''
   else:
    snake_case				=			None
   if hf_dict is not None:
    rename_dict(a					,   a					,   a					,   a					,   a     )
   else:
    set_recursively(a					,   a					,   a					,   a					,   a     )
   return is_used
 return is_used

def    __UpperCamelCase	(	a    : List[Any]					,   a    : Union[str, Any]					,   a    : str     ) ->Any:
 snake_case				=			[]
 snake_case				=			fairseq_model.state_dict()

 snake_case				=			hf_model.wavaveca.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:
   snake_case				=			load_wavaveca_layer(a					,   a					,   a     )
  if not is_used:
   unused_weights.append(a     )

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

def    __UpperCamelCase	(	a    : Union[str, Any]					,   a    : List[str]					,   a    : Union[str, Any]					,   a    : Dict					,   a    : List[str]     ) ->Tuple:
 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:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
    raise ValueError(
        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:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
    raise ValueError(
        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:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
    raise ValueError(
        f"""{full_name} has size {value.shape}, but"""
        f""" {feature_extractor.conv_layers[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:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
    raise ValueError(
        f"""{full_name} has size {value.shape}, but"""
        f""" {feature_extractor.conv_layers[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    : Tuple					,   a    : Tuple					,   a    : List[str]=None					,   a    : Tuple=None					,   a    : str=True					,   a    : Tuple=False     ) ->Dict:
 if config_path is not None:
  snake_case				=			WavaVecaConfig.from_pretrained(a     )
 else:
  snake_case				=			WavaVecaConfig()

 if is_seq_class:
  snake_case				=			read_txt_into_dict(a     )
  snake_case				=			idalabel
  snake_case				=			WavaVecaForSequenceClassification(a     )
  snake_case				=			WavaVecaFeatureExtractor(
      feature_size=1					,   sampling_rate=1_6000					,   padding_value=0					,   do_normalize=a					,   return_attention_mask=a					,   )
  feature_extractor.save_pretrained(a     )

 elif is_finetuned:
  if dict_path:
   snake_case				=			Dictionary.load(a     )

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   snake_case				=			target_dict.pad_index
   snake_case				=			target_dict.bos_index
   snake_case				=			target_dict.eos_index
   snake_case				=			len(target_dict.symbols     )
   snake_case				=			os.path.join(a					,   '''vocab.json'''     )
   if not os.path.isdir(a     ):
    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(a     )     )
    return
   os.makedirs(a					,   exist_ok=a     )
   snake_case				=			target_dict.indices

   # fairseq has the <pad> and <s> switched
   snake_case				=			0
   snake_case				=			1
   with open(a					,   '''w'''					,   encoding='''utf-8'''     ) as vocab_handle:
    json.dump(a					,   a     )
   snake_case				=			WavaVecaCTCTokenizer(
       a					,   unk_token=target_dict.unk_word					,   pad_token=target_dict.pad_word					,   bos_token=target_dict.bos_word					,   eos_token=target_dict.eos_word					,   word_delimiter_token='''|'''					,   do_lower_case=a					,   )
   snake_case				=			True if config.feat_extract_norm == '''layer''' else False
   snake_case				=			WavaVecaFeatureExtractor(
       feature_size=1					,   sampling_rate=1_6000					,   padding_value=0					,   do_normalize=a					,   return_attention_mask=a					,   )
   snake_case				=			WavaVecaProcessor(feature_extractor=a					,   tokenizer=a     )
   processor.save_pretrained(a     )

  snake_case				=			WavaVecaForCTC(a     )
 else:
  snake_case				=			WavaVecaForPreTraining(a     )

 if is_finetuned or is_seq_class:
  snake_case				,		snake_case				,		snake_case				=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]					,   arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''     )[:-1]     )}     )
 else:
  snake_case				=			argparse.Namespace(task='''audio_pretraining'''     )
  snake_case				=			fairseq.tasks.setup_task(a     )

  snake_case				,		snake_case				,		snake_case				=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,   task=a     )

 snake_case				=			model[0].eval()

 recursively_load_weights(a					,   a					,   not is_finetuned     )

 hf_wavavec.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('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
   parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
   parser.add_argument(
       '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
   )
   parser.add_argument(
       '--is_seq_class',
       action='store_true',
       help='Whether the model to convert is a fine-tuned sequence classification model or not',
   )
   _lowercase       						=			parser.parse_args()

   _lowercase       						=			not args.not_finetuned and not args.is_seq_class
   convert_wavaveca_checkpoint(
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.dict_path,
       is_finetuned,
       args.is_seq_class,
   )








'''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''']     )     )

