






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()
UpperCamelCase							       =    logging.get_logger(__name__)

UpperCamelCase							       =    {
    """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""",
}
UpperCamelCase							       =    [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]






def        lowerCAmelCase (      UpperCamelCase_:							List[str]					)       ->							List[Any]:







  '''simple docstring'''







  _a							=	{}
  with open(UpperCamelCase_     , "r"					) as file:
    for line_number, line in enumerate(UpperCamelCase_					):
      _a							=	line.strip()
      if line:
        _a							=	line.split()
        _a							=	line_number
        _a							=	words[0]
        _a							=	value
  return result






def        lowerCAmelCase (      UpperCamelCase_:							List[str]     , UpperCamelCase_:							Dict     , UpperCamelCase_:							List[str]     , UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							Union[str, Any]					)       ->							Union[str, Any]:







  '''simple docstring'''







  for attribute in key.split("."					):
    _a							=	getattr(UpperCamelCase_     , UpperCamelCase_					)

  _a							=	None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(UpperCamelCase_					):
      _a							=	PARAM_MAPPING[full_name.split("."					)[-1]]
      _a							=	"param"

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

    # let's reduce dimension
    _a							=	value[0]
  else:
    _a							=	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":
    _a							=	value
  elif weight_type == "weight_g":
    _a							=	value
  elif weight_type == "weight_v":
    _a							=	value
  elif weight_type == "bias":
    _a							=	value
  elif weight_type == "param":
    for attribute in hf_param_name.split("."					):
      _a							=	getattr(UpperCamelCase_     , UpperCamelCase_					)
    _a							=	value
  else:
    _a							=	value

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






def        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							Dict     , UpperCamelCase_:							Tuple     , UpperCamelCase_:							Any     , UpperCamelCase_:							Union[str, Any]					)       ->							str:







  '''simple docstring'''







  _a							=	None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(UpperCamelCase_					):
      _a							=	PARAM_MAPPING[full_name.split("."					)[-1]]
      _a							=	"param"

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

  _a							=	value if "lm_head" in full_key else value[0]


UpperCamelCase							       =    {
    """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        lowerCAmelCase (      UpperCamelCase_:							str     , UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							int=None     , UpperCamelCase_:							Optional[int]=None					)       ->							Tuple:







  '''simple docstring'''







  _a							=	False
  for key, mapped_key in MAPPING.items():
    _a							=	"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]:
      _a							=	True
      if "*" in mapped_key:
        _a							=	name.split(UpperCamelCase_					)[0].split("."					)[-2]
        _a							=	mapped_key.replace("*"     , UpperCamelCase_					)
      if "weight_g" in name:
        _a							=	"weight_g"
      elif "weight_v" in name:
        _a							=	"weight_v"
      elif "bias" in name:
        _a							=	"bias"
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        _a							=	"weight"
      else:
        _a							=	None
      if hf_dict is not None:
        rename_dict(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
      else:
        set_recursively(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
      return is_used
  return is_used






def        lowerCAmelCase (      UpperCamelCase_:							Dict     , UpperCamelCase_:							Dict     , UpperCamelCase_:							Dict					)       ->							Dict:







  '''simple docstring'''







  _a							=	[]
  _a							=	fairseq_model.state_dict()

  _a							=	hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    _a							=	False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , hf_model.config.feat_extract_norm == "group"     , )
      _a							=	True
    else:
      _a							=	load_wavaveca_layer(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
    if not is_used:
      unused_weights.append(UpperCamelCase_					)

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






def        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							List[Any]     , UpperCamelCase_:							int     , UpperCamelCase_:							Optional[int]     , UpperCamelCase_:							Optional[int]					)       ->							Dict:







  '''simple docstring'''







  _a							=	full_name.split("conv_layers."					)[-1]
  _a							=	name.split("."					)
  _a							=	int(items[0]					)
  _a							=	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.'''					)
      _a							=	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.'''					)
      _a							=	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.'''					)
      _a							=	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.'''					)
      _a							=	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        lowerCAmelCase (      UpperCamelCase_:							Tuple     , UpperCamelCase_:							List[str]     , UpperCamelCase_:							int=None     , UpperCamelCase_:							int=None     , UpperCamelCase_:							Optional[Any]=True     , UpperCamelCase_:							Any=False					)       ->							Optional[Any]:







  '''simple docstring'''







  if config_path is not None:
    _a							=	WavaVecaConfig.from_pretrained(UpperCamelCase_					)
  else:
    _a							=	WavaVecaConfig()

  if is_seq_class:
    _a							=	read_txt_into_dict(UpperCamelCase_					)
    _a							=	idalabel
    _a							=	WavaVecaForSequenceClassification(UpperCamelCase_					)
    _a							=	WavaVecaFeatureExtractor(
        feature_size=1     , sampling_rate=16000     , padding_value=0     , do_normalize=UpperCamelCase_     , return_attention_mask=UpperCamelCase_     , )
    feature_extractor.save_pretrained(UpperCamelCase_					)

  elif is_finetuned:
    if dict_path:
      _a							=	Dictionary.load(UpperCamelCase_					)

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

      # fairseq has the <pad> and <s> switched
      _a							=	0
      _a							=	1
      with open(UpperCamelCase_     , "w"     , encoding="utf-8"					) as vocab_handle:
        json.dump(UpperCamelCase_     , UpperCamelCase_					)
      _a							=	WavaVecaCTCTokenizer(
          UpperCamelCase_     , 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=UpperCamelCase_     , )
      _a							=	True if config.feat_extract_norm == "layer" else False
      _a							=	WavaVecaFeatureExtractor(
          feature_size=1     , sampling_rate=16000     , padding_value=0     , do_normalize=UpperCamelCase_     , return_attention_mask=UpperCamelCase_     , )
      _a							=	WavaVecaProcessor(feature_extractor=UpperCamelCase_     , tokenizer=UpperCamelCase_					)
      processor.save_pretrained(UpperCamelCase_					)

    _a							=	WavaVecaForCTC(UpperCamelCase_					)
  else:
    _a							=	WavaVecaForPreTraining(UpperCamelCase_					)

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

    _a			,	_a			,	_a							=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     , task=UpperCamelCase_					)

  _a							=	model[0].eval()

  recursively_load_weights(UpperCamelCase_     , UpperCamelCase_     , not is_finetuned					)

  hf_wavavec.save_pretrained(UpperCamelCase_					)


if __name__ == "__main__":
     UpperCamelCase							       =    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""",
     )
     UpperCamelCase							       =    parser.parse_args()

     UpperCamelCase							       =    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"""




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType




class    lowercase_    (_UpperCAmelCase     ):






      def __init__(    self     ,     a_	)	->Tuple:







        '''simple docstring'''


        _a							=	data






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







        '''simple docstring'''


        for element in self.data:
          yield element







def        lowerCAmelCase (      UpperCamelCase_:							Optional[int]=True					)       ->							List[Any]:







  '''simple docstring'''







  _a							=	Accelerator(even_batches=UpperCamelCase_					)
  assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
  return accelerator






def        lowerCAmelCase (      UpperCamelCase_:							Accelerator     , UpperCamelCase_:							int     , UpperCamelCase_:							int     , UpperCamelCase_:							bool = False					)       ->							Tuple:







  '''simple docstring'''







  if iterable:
    _a							=	DummyIterableDataset(torch.as_tensor(range(UpperCamelCase_					)					)					)
  else:
    _a							=	TensorDataset(torch.as_tensor(range(UpperCamelCase_					)					)					)

  _a							=	DataLoader(UpperCamelCase_     , batch_size=UpperCamelCase_					)
  _a							=	accelerator.prepare(UpperCamelCase_					)

  return dl






def        lowerCAmelCase (      UpperCamelCase_:							Accelerator     , UpperCamelCase_:							int     , UpperCamelCase_:							int     , UpperCamelCase_:							List[int]     , UpperCamelCase_:							List[int]     , )       ->							List[Any]:







  '''simple docstring'''







  _a							=	create_dataloader(accelerator=UpperCamelCase_     , dataset_size=UpperCamelCase_     , batch_size=UpperCamelCase_					)

  _a							=	[len(batch[0]					) for batch in dl]

  if accelerator.process_index == 0:
    assert batch_sizes == process_0_expected_batch_sizes
  elif accelerator.process_index == 1:
    assert batch_sizes == process_1_expected_batch_sizes






def        lowerCAmelCase (      )       ->							Optional[Any]:







  '''simple docstring'''







  _a							=	create_accelerator()

  # without padding, we would expect a different number of batches
  verify_dataloader_batch_sizes(
      UpperCamelCase_     , dataset_size=3     , batch_size=1     , process_0_expected_batch_sizes=[1, 1]     , process_1_expected_batch_sizes=[1, 1]     , )

  # without padding, we would expect the same number of batches, but different sizes
  verify_dataloader_batch_sizes(
      UpperCamelCase_     , dataset_size=7     , batch_size=2     , process_0_expected_batch_sizes=[2, 2]     , process_1_expected_batch_sizes=[2, 2]     , )






def        lowerCAmelCase (      )       ->							str:







  '''simple docstring'''







  _a							=	create_accelerator(even_batches=UpperCamelCase_					)

  verify_dataloader_batch_sizes(
      UpperCamelCase_     , dataset_size=3     , batch_size=1     , process_0_expected_batch_sizes=[1, 1]     , process_1_expected_batch_sizes=[1]     , )

  verify_dataloader_batch_sizes(
      UpperCamelCase_     , dataset_size=7     , batch_size=2     , process_0_expected_batch_sizes=[2, 2]     , process_1_expected_batch_sizes=[2, 1]     , )






def        lowerCAmelCase (      )       ->							int:







  '''simple docstring'''







  _a							=	create_accelerator(even_batches=UpperCamelCase_					)

  _a							=	torch.nn.Linear(1     , 1					)
  _a							=	accelerator.prepare(UpperCamelCase_					)

  _a							=	create_dataloader(UpperCamelCase_     , dataset_size=3     , batch_size=1					)

  _a							=	[]
  with accelerator.join_uneven_inputs([ddp_model]					):
    for batch_idx, batch in enumerate(UpperCamelCase_					):
      _a							=	ddp_model(batch[0].float()					)
      _a							=	output.sum()
      loss.backward()
      batch_idxs.append(UpperCamelCase_					)

  accelerator.wait_for_everyone()

  if accelerator.process_index == 0:
    assert batch_idxs == [0, 1]
  elif accelerator.process_index == 1:
    assert batch_idxs == [0]






def        lowerCAmelCase (      UpperCamelCase_:							int					)       ->							Tuple:







  '''simple docstring'''







  with warnings.catch_warnings(record=UpperCamelCase_					) as w:
    with accelerator.join_uneven_inputs([Mock()]					):
      pass

    assert issubclass(w[-1].category     , UpperCamelCase_					)
    assert "only supported for multi-GPU" in str(w[-1].message					)






def        lowerCAmelCase (      )       ->							List[str]:







  '''simple docstring'''







  _a							=	True
  _a							=	False
  _a							=	create_accelerator(even_batches=UpperCamelCase_					)
  _a							=	torch.nn.Linear(1     , 1					)
  _a							=	accelerator.prepare(UpperCamelCase_					)
  _a							=	create_dataloader(UpperCamelCase_     , dataset_size=3     , batch_size=1					)
  _a							=	create_dataloader(UpperCamelCase_     , dataset_size=3     , batch_size=1					)

  with accelerator.join_uneven_inputs([ddp_model]     , even_batches=UpperCamelCase_					):
    _a							=	train_dl.batch_sampler.even_batches
    _a							=	valid_dl.batch_sampler.even_batches

  assert train_dl_overridden_value == overridden_even_batches
  assert valid_dl_overridden_value == overridden_even_batches
  assert train_dl.batch_sampler.even_batches == default_even_batches
  assert valid_dl.batch_sampler.even_batches == default_even_batches






def        lowerCAmelCase (      )       ->							List[str]:







  '''simple docstring'''







  _a							=	True
  _a							=	False
  _a							=	create_accelerator(even_batches=UpperCamelCase_					)
  _a							=	torch.nn.Linear(1     , 1					)
  _a							=	accelerator.prepare(UpperCamelCase_					)
  create_dataloader(UpperCamelCase_     , dataset_size=3     , batch_size=1     , iterable=UpperCamelCase_					)
  _a							=	create_dataloader(UpperCamelCase_     , dataset_size=3     , batch_size=1					)

  with warnings.catch_warnings():
    warnings.filterwarnings("ignore"					)
    try:
      with accelerator.join_uneven_inputs([ddp_model]     , even_batches=UpperCamelCase_					):
        _a							=	batch_dl.batch_sampler.even_batches
    except AttributeError:
      # ensure attribute error is not raised when processing iterable dl
      raise AssertionError

  assert batch_dl_overridden_value == overridden_even_batches
  assert batch_dl.batch_sampler.even_batches == default_even_batches






def        lowerCAmelCase (      )       ->							List[str]:







  '''simple docstring'''







  _a							=	create_accelerator()
  _a							=	torch.nn.Linear(1     , 1					)
  _a							=	accelerator.prepare(UpperCamelCase_					)
  create_dataloader(UpperCamelCase_     , dataset_size=3     , batch_size=1     , iterable=UpperCamelCase_					)

  with warnings.catch_warnings(record=UpperCamelCase_					) as w:
    with accelerator.join_uneven_inputs([ddp_model]     , even_batches=UpperCamelCase_					):
      pass

    assert issubclass(w[-1].category     , UpperCamelCase_					)
    assert "only supported for map-style datasets" in str(w[-1].message					)






def        lowerCAmelCase (      )       ->							Optional[Any]:







  '''simple docstring'''







  _a							=	create_accelerator()

  accelerator.print("Test that even_batches variable ensures uniform batches across processes"					)
  test_default_ensures_even_batch_sizes()

  accelerator.print("Run tests with even_batches disabled"					)
  test_can_disable_even_batches()

  accelerator.print("Test joining uneven inputs"					)
  test_can_join_uneven_inputs()

  accelerator.print("Test overriding even_batches when joining uneven inputs"					)
  test_join_can_override_even_batches()

  accelerator.print("Test overriding even_batches for mixed dataloader types"					)
  test_join_can_override_for_mixed_type_dataloaders()

  accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders"					)
  test_join_raises_warning_for_iterable_when_overriding_even_batches()

  accelerator.print("Test join with non DDP distributed raises warning"					)
  _a							=	accelerator.state.distributed_type
  _a							=	DistributedType.FSDP
  test_join_raises_warning_for_non_ddp_distributed(UpperCamelCase_					)
  _a							=	original_state


if __name__ == "__main__":
     main()


