

'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase__											=	logging.get_logger(__name__)

lowerCAmelCase__											=	{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''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''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
lowerCAmelCase__											=	[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 for attribute in key.split('''.'''      ):
  __lowercase							  = getattr(A__     ,				A__      )

 if weight_type is not None:
  __lowercase							  = getattr(A__     ,				A__      ).shape
 else:
  __lowercase							  = 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":
  __lowercase							  = value
 elif weight_type == "weight_g":
  __lowercase							  = value
 elif weight_type == "weight_v":
  __lowercase							  = value
 elif weight_type == "bias":
  __lowercase							  = value
 elif weight_type == "running_mean":
  __lowercase							  = value
 elif weight_type == "running_var":
  __lowercase							  = value
 elif weight_type == "num_batches_tracked":
  __lowercase							  = value
 elif weight_type == "inv_freq":
  __lowercase							  = value
 else:
  __lowercase							  = value

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


def 		_A     (    A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = []
 __lowercase							  = fairseq_model.state_dict()

 __lowercase							  = hf_model.wavaveca_conformer.feature_extractor

 for name, value in fairseq_dict.items():
  __lowercase							  = False
  if "conv_layers" in name:
   load_conv_layer(
       A__     ,				A__     ,				A__     ,				A__     ,				hf_model.config.feat_extract_norm == '''group'''     ,				)
   __lowercase							  = True
  else:
   for key, mapped_key in MAPPING.items():
    __lowercase							  = '''wav2vec2_conformer.''' + 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]:
     __lowercase							  = True
     if "*" in mapped_key:
      __lowercase							  = name.split(A__      )[0].split('''.'''      )[-2]
      __lowercase							  = mapped_key.replace('''*'''     ,				A__      )
     if "pos_bias_u" in name:
      __lowercase							  = None
     elif "pos_bias_v" in name:
      __lowercase							  = None
     elif "weight_g" in name:
      __lowercase							  = '''weight_g'''
     elif "weight_v" in name:
      __lowercase							  = '''weight_v'''
     elif "bias" in name:
      __lowercase							  = '''bias'''
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      __lowercase							  = '''weight'''
     elif "running_mean" in name:
      __lowercase							  = '''running_mean'''
     elif "inv_freq" in name:
      __lowercase							  = '''inv_freq'''
     elif "running_var" in name:
      __lowercase							  = '''running_var'''
     elif "num_batches_tracked" in name:
      __lowercase							  = '''num_batches_tracked'''
     else:
      __lowercase							  = 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 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = full_name.split('''conv_layers.'''      )[-1]
 __lowercase							  = name.split('''.'''      )
 __lowercase							  = int(items[0]      )
 __lowercase							  = 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."      )
   __lowercase							  = 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."      )
   __lowercase							  = 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."      )
   __lowercase							  = 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."      )
   __lowercase							  = 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 		_A     (    A__     ,				A__     ,				A__=None     ,				A__=None     ,				A__=True      ):






 """simple docstring"""



 if config_path is not None:
  __lowercase							  = WavaVecaConformerConfig.from_pretrained(A__     ,				hidden_act='''swish'''      )
 else:
  __lowercase							  = WavaVecaConformerConfig()

 if "rope" in checkpoint_path:
  __lowercase							  = '''rotary'''

 if is_finetuned:
  if dict_path:
   __lowercase							  = Dictionary.load(A__      )

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   __lowercase							  = target_dict.pad_index
   __lowercase							  = target_dict.bos_index
   __lowercase							  = target_dict.eos_index
   __lowercase							  = len(target_dict.symbols      )
   __lowercase							  = 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__      )
   __lowercase							  = target_dict.indices

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

  __lowercase							  = WavaVecaConformerForCTC(A__      )
 else:
  __lowercase							  = WavaVecaConformerForPreTraining(A__      )

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

  __lowercase  ,	__lowercase  ,	__lowercase							  = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,				task=A__      )

 __lowercase							  = model[0].eval()

 recursively_load_weights(A__     ,				A__     ,				not is_finetuned      )

 hf_wavavec.save_pretrained(A__      )


if __name__ == "__main__":
 lowerCAmelCase__											=	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'''
 )
 lowerCAmelCase__											=	parser.parse_args()
 convert_wavaveca_conformer_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )




'''simple docstring'''






# Copyright 2022 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 argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase__											=	'''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''


def 		_A     (    A__=None      ):






 """simple docstring"""



 if subparsers is not None:
  __lowercase							  = subparsers.add_parser('''tpu-config'''     ,				description=_description      )
 else:
  __lowercase							  = argparse.ArgumentParser('''Accelerate tpu-config command'''     ,				description=_description      )
 # Core arguments
 __lowercase							  = parser.add_argument_group(
     '''Config Arguments'''     ,				'''Arguments that can be configured through `accelerate config`.'''      )
 config_args.add_argument(
     '''--config_file'''     ,				type=A__     ,				default=A__     ,				help='''Path to the config file to use for accelerate.'''     ,				)
 config_args.add_argument(
     '''--tpu_name'''     ,				default=A__     ,				help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.'''     ,				)
 config_args.add_argument(
     '''--tpu_zone'''     ,				default=A__     ,				help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''     ,				)
 __lowercase							  = parser.add_argument_group('''TPU Arguments'''     ,				'''Arguments for options ran inside the TPU.'''      )
 pod_args.add_argument(
     '''--use_alpha'''     ,				action='''store_true'''     ,				help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'''     ,				)
 pod_args.add_argument(
     '''--command_file'''     ,				default=A__     ,				help='''The path to the file containing the commands to run on the pod on startup.'''     ,				)
 pod_args.add_argument(
     '''--command'''     ,				action='''append'''     ,				nargs='''+'''     ,				help='''A command to run on the pod. Can be passed multiple times.'''     ,				)
 pod_args.add_argument(
     '''--install_accelerate'''     ,				action='''store_true'''     ,				help='''Whether to install accelerate on the pod. Defaults to False.'''     ,				)
 pod_args.add_argument(
     '''--accelerate_version'''     ,				default='''latest'''     ,				help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'''     ,				)
 pod_args.add_argument(
     '''--debug'''     ,				action='''store_true'''     ,				help='''If set, will print the command that would be run instead of running it.'''      )

 if subparsers is not None:
  parser.set_defaults(func=A__      )
 return parser


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = None

 # Get the default from the config file if it exists.
 if args.config_file is not None or os.path.isfile(A__      ):
  __lowercase							  = load_config_from_file(args.config_file      )
  if not args.command_file and defaults.command_file is not None and not args.command:
   __lowercase							  = defaults.command_file
  if not args.command and defaults.commands is not None:
   __lowercase							  = defaults.commands
  if not args.tpu_name:
   __lowercase							  = defaults.tpu_name
  if not args.tpu_zone:
   __lowercase							  = defaults.tpu_zone
 if args.accelerate_version == "dev":
  __lowercase							  = '''git+https://github.com/huggingface/accelerate.git'''
 elif args.accelerate_version == "latest":
  __lowercase							  = '''accelerate -U'''
 elif isinstance(parse(args.accelerate_version      )     ,				A__      ):
  __lowercase							  = F"accelerate=={args.accelerate_version}"

 if not args.command_file and not args.command:
  raise ValueError('''You must specify either a command file or a command to run on the pod.'''      )

 if args.command_file:
  with open(args.command_file     ,				'''r'''      ) as f:
   __lowercase							  = [f.read().splitlines()]

    # To turn list of lists into list of strings
 if isinstance(args.command[0]     ,				A__      ):
  __lowercase							  = [line for cmd in args.command for line in cmd]
 # Default to the shared folder and install accelerate
 __lowercase							  = ['''cd /usr/share''']
 if args.install_accelerate:
  new_cmd += [F"pip install {args.accelerate_version}"]
 new_cmd += args.command
 __lowercase							  = '''; '''.join(A__      )

 # Then send it to gcloud
 # Eventually try to use google-api-core to do this instead of subprocess
 __lowercase							  = ['''gcloud''']
 if args.use_alpha:
  cmd += ["alpha"]
 cmd += [
     "compute",
     "tpus",
     "tpu-vm",
     "ssh",
     args.tpu_name,
     "--zone",
     args.tpu_zone,
     "--command",
     args.command,
     "--worker",
     "all",
 ]
 if args.debug:
  print(F"Running {' '.join(A__      )}"      )
  return
 subprocess.run(A__      )
 print('''Successfully setup pod.'''      )






def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = tpu_command_parser()
 __lowercase							  = parser.parse_args()

 tpu_command_launcher(A__      )

