


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase__  :Optional[int]             =							logging.get_logger(__name__)

lowerCAmelCase__  :Tuple             =							{
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}

def 	lowerCAmelCase__			(      a__:		Optional[int]      ,  a__:		List[Any]      ,  a__:		Any      ,  a__:		Dict      ,  a__:		Optional[int]	)  ->		str:


       '''simple docstring'''





       for attribute in key.split('.'	):
              _UpperCAmelCase					=      getattr(a__      ,  a__	)

       if weight_type is not None:
              _UpperCAmelCase					=      getattr(a__      ,  a__	).shape
       else:
              _UpperCAmelCase					=      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":
              _UpperCAmelCase					=      value
       elif weight_type == "weight_g":
              _UpperCAmelCase					=      value
       elif weight_type == "weight_v":
              _UpperCAmelCase					=      value
       elif weight_type == "bias":
              _UpperCAmelCase					=      value
       else:
              _UpperCAmelCase					=      value

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

def 	lowerCAmelCase__			(      a__:		Tuple      ,  a__:		int      ,  a__:		Any	)  ->		str:


       '''simple docstring'''





       _UpperCAmelCase					=      []
       _UpperCAmelCase					=      fairseq_model.state_dict()

       _UpperCAmelCase					=      hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              _UpperCAmelCase					=      False
              if "conv_layers" in name:
                     load_conv_layer(
                         a__      ,  a__      ,  a__      ,  a__      ,  hf_model.config.feat_extract_norm == 'group'      ,  )
                     _UpperCAmelCase					=      True
              else:
                     for key, mapped_key in MAPPING.items():
                            _UpperCAmelCase					=      'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

                            if key in name or key.split('w2v_model.'	)[-1] == name.split('.'	)[0]:
                                   _UpperCAmelCase					=      True
                                   if "*" in mapped_key:
                                          _UpperCAmelCase					=      name.split(a__	)[0].split('.'	)[-2]
                                          _UpperCAmelCase					=      mapped_key.replace('*'      ,  a__	)
                                   if "weight_g" in name:
                                          _UpperCAmelCase					=      'weight_g'
                                   elif "weight_v" in name:
                                          _UpperCAmelCase					=      'weight_v'
                                   elif "weight" in name:
                                          _UpperCAmelCase					=      'weight'
                                   elif "bias" in name:
                                          _UpperCAmelCase					=      'bias'
                                   else:
                                          _UpperCAmelCase					=      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 	lowerCAmelCase__			(      a__:		Dict      ,  a__:		int      ,  a__:		Optional[Any]      ,  a__:		Optional[Any]      ,  a__:		List[Any]	)  ->		int:


       '''simple docstring'''





       _UpperCAmelCase					=      full_name.split('conv_layers.'	)[-1]
       _UpperCAmelCase					=      name.split('.'	)
       _UpperCAmelCase					=      int(items[0]	)
       _UpperCAmelCase					=      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.'''
                     )
                     _UpperCAmelCase					=      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.'''
                     )
                     _UpperCAmelCase					=      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."
                     )
                     _UpperCAmelCase					=      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.'''
                     )
                     _UpperCAmelCase					=      value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
       else:
              unused_weights.append(a__	)

def 	lowerCAmelCase__			(      a__:		List[Any]      ,  a__:		Optional[int]	)  ->		List[str]:


       '''simple docstring'''





       _UpperCAmelCase					=      SEWConfig()
       if is_finetuned:
              _UpperCAmelCase					=      model.wav_encoder.wav_model.cfg
       else:
              _UpperCAmelCase					=      model.cfg

       _UpperCAmelCase					=      fs_config.conv_bias
       _UpperCAmelCase					=      eval(fs_config.conv_feature_layers	)
       _UpperCAmelCase					=      [x[0] for x in conv_layers]
       _UpperCAmelCase					=      [x[1] for x in conv_layers]
       _UpperCAmelCase					=      [x[2] for x in conv_layers]
       _UpperCAmelCase					=      'gelu'
       _UpperCAmelCase					=      'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
       _UpperCAmelCase					=      0.0
       _UpperCAmelCase					=      fs_config.activation_fn.name
       _UpperCAmelCase					=      fs_config.encoder_embed_dim
       _UpperCAmelCase					=      0.02
       _UpperCAmelCase					=      fs_config.encoder_ffn_embed_dim
       _UpperCAmelCase					=      1e-5
       _UpperCAmelCase					=      fs_config.encoder_layerdrop
       _UpperCAmelCase					=      fs_config.encoder_attention_heads
       _UpperCAmelCase					=      fs_config.conv_pos_groups
       _UpperCAmelCase					=      fs_config.conv_pos
       _UpperCAmelCase					=      len(a__	)
       _UpperCAmelCase					=      fs_config.encoder_layers
       _UpperCAmelCase					=      fs_config.squeeze_factor

       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
              _UpperCAmelCase					=      model.cfg
              _UpperCAmelCase					=      fs_config.final_dropout
              _UpperCAmelCase					=      fs_config.layerdrop
       _UpperCAmelCase					=      fs_config.activation_dropout
       _UpperCAmelCase					=      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       _UpperCAmelCase					=      fs_config.attention_dropout
       _UpperCAmelCase					=      fs_config.dropout_input
       _UpperCAmelCase					=      fs_config.dropout
       _UpperCAmelCase					=      fs_config.mask_channel_length
       _UpperCAmelCase					=      fs_config.mask_channel_prob
       _UpperCAmelCase					=      fs_config.mask_length
       _UpperCAmelCase					=      fs_config.mask_prob

       _UpperCAmelCase					=      'Wav2Vec2FeatureExtractor'
       _UpperCAmelCase					=      'Wav2Vec2CTCTokenizer'

       return config

@torch.no_grad()
def 	lowerCAmelCase__			(      a__:		Optional[int]      ,  a__:		List[Any]      ,  a__:		int=None      ,  a__:		Optional[Any]=None      ,  a__:		List[str]=True	)  ->		Tuple:


       '''simple docstring'''





       if is_finetuned:
              _UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]      ,  arg_overrides={'data': '/'.join(dict_path.split('/'	)[:-1]	)}	)
       else:
              _UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

       if config_path is not None:
              _UpperCAmelCase					=      SEWConfig.from_pretrained(a__	)
       else:
              _UpperCAmelCase					=      convert_config(model[0]      ,  a__	)
       _UpperCAmelCase					=      model[0].eval()

       _UpperCAmelCase					=      True if config.feat_extract_norm == 'layer' else False
       _UpperCAmelCase					=      WavaVecaFeatureExtractor(
           feature_size=1      ,  sampling_rate=1_6_0_0_0      ,  padding_value=0      ,  do_normalize=a__      ,  return_attention_mask=a__      ,  )

       if is_finetuned:
              if dict_path:
                     _UpperCAmelCase					=      Dictionary.load(a__	)

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     _UpperCAmelCase					=      target_dict.pad_index
                     _UpperCAmelCase					=      target_dict.bos_index
                     _UpperCAmelCase					=      target_dict.pad_index
                     _UpperCAmelCase					=      target_dict.bos_index
                     _UpperCAmelCase					=      target_dict.eos_index
                     _UpperCAmelCase					=      len(target_dict.symbols	)
                     _UpperCAmelCase					=      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__	)
                     with open(a__      ,  'w'      ,  encoding='utf-8'	) as vocab_handle:
                            json.dump(target_dict.indices      ,  a__	)
                     _UpperCAmelCase					=      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__      ,  )
                     _UpperCAmelCase					=      WavaVecaProcessor(feature_extractor=a__      ,  tokenizer=a__	)
                     processor.save_pretrained(a__	)

              _UpperCAmelCase					=      SEWForCTC(a__	)
       else:
              _UpperCAmelCase					=      SEWModel(a__	)
              feature_extractor.save_pretrained(a__	)

       recursively_load_weights(a__      ,  a__      ,  a__	)

       hf_model.save_pretrained(a__	)


if __name__ == "__main__":
  lowerCAmelCase__  :Dict             =							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(
      '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
  )
  lowerCAmelCase__  :Tuple             =							parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  )




# 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__  :Dict             =							'''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''

def 	lowerCAmelCase__			(      a__:		Optional[Any]=None	)  ->		List[Any]:


							'''simple docstring'''





							if subparsers is not None:
														_UpperCAmelCase					=      subparsers.add_parser('tpu-config'      ,  description=_description	)
							else:
														_UpperCAmelCase					=      argparse.ArgumentParser('Accelerate tpu-config command'      ,  description=_description	)
							# Core arguments
							_UpperCAmelCase					=      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.'      ,  )
							_UpperCAmelCase					=      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 	lowerCAmelCase__			(      a__:		str	)  ->		Any:


							'''simple docstring'''





							_UpperCAmelCase					=      None

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

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

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							_UpperCAmelCase					=      ['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 	lowerCAmelCase__			(      )  ->		Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase					=      tpu_command_parser()
							_UpperCAmelCase					=      parser.parse_args()

							tpu_command_launcher(a__	)
