



'''simple docstring'''





import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowercase_		       =  16
lowercase_		       =  32




def lowerCAmelCase  (__A  ,	__A = 16  ,	__A = "bert-base-cased"):



     """simple docstring"""



     _a      =  AutoTokenizer.from_pretrained(__A)
     _a      =  load_dataset('''glue'''  ,	'''mrpc''')

     def tokenize_function(__A):
          # max_length=None => use the model max length (it's actually the default)
          _a      =  tokenizer(examples['''sentence1''']  ,	examples['''sentence2''']  ,	truncation=__A  ,	max_length=__A)
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     _a      =  datasets.map(
         __A  ,	batched=__A  ,	remove_columns=['''idx''', '''sentence1''', '''sentence2''']  ,	load_from_cache_file=__A)

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     _a      =  tokenized_datasets.rename_column('''label'''  ,	'''labels''')

     def collate_fn(__A):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(__A  ,	padding='''max_length'''  ,	max_length=128  ,	return_tensors='''pt''')
          return tokenizer.pad(__A  ,	padding='''longest'''  ,	return_tensors='''pt''')

     # Instantiate dataloaders.
     _a      =  DataLoader(
         tokenized_datasets['''train''']  ,	shuffle=__A  ,	collate_fn=__A  ,	batch_size=__A)
     _a      =  DataLoader(
         tokenized_datasets['''validation''']  ,	shuffle=__A  ,	collate_fn=__A  ,	batch_size=__A)

     return train_dataloader, eval_dataloader




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     _a      =  config['''lr''']
     _a      =  int(config['''num_epochs'''])
     _a      =  int(config['''seed'''])
     _a      =  int(config['''batch_size'''])
     _a      =  args.model_name_or_path

     set_seed(__A)
     _a  ,    _a      =  get_dataloaders(__A  ,	__A  ,	__A)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     _a      =  AutoModelForSequenceClassification.from_pretrained(__A  ,	return_dict=__A)

     # Instantiate optimizer
     _a      =  (
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     _a      =  optimizer_cls(params=model.parameters()  ,	lr=__A)

     if accelerator.state.deepspeed_plugin is not None:
          _a      =  accelerator.state.deepspeed_plugin.deepspeed_config[
              '''gradient_accumulation_steps'''
          ]
     else:
          _a      =  1
     _a      =  (len(__A) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          _a      =  get_linear_schedule_with_warmup(
              optimizer=__A  ,	num_warmup_steps=0  ,	num_training_steps=__A  ,	)
     else:
          _a      =  DummyScheduler(__A  ,	total_num_steps=__A  ,	warmup_num_steps=0)

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     _a  ,    _a  ,    _a  ,    _a  ,    _a      =  accelerator.prepare(
         __A  ,	__A  ,	__A  ,	__A  ,	__A)

     # We need to keep track of how many total steps we have iterated over
     _a      =  0
     # We also need to keep track of the stating epoch so files are named properly
     _a      =  0

     # Now we train the model
     _a      =  evaluate.load('''glue'''  ,	'''mrpc''')
     _a      =  0
     _a      =  {}
     for epoch in range(__A  ,	__A):
          model.train()
          for step, batch in enumerate(__A):
               _a      =  model(**__A)
               _a      =  outputs.loss
               _a      =  loss / gradient_accumulation_steps
               accelerator.backward(__A)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

          model.eval()
          _a      =  0
          for step, batch in enumerate(__A):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device)
               with torch.no_grad():
                    _a      =  model(**__A)
               _a      =  outputs.logits.argmax(dim=-1)
               # It is slightly faster to call this once, than multiple times
               _a  ,    _a      =  accelerator.gather(
                   (predictions, batch['''labels''']))  # If we are in a multiprocess environment, the last batch has duplicates
               if accelerator.use_distributed:
                    if step == len(__A) - 1:
                         _a      =  predictions[: len(eval_dataloader.dataset) - samples_seen]
                         _a      =  references[: len(eval_dataloader.dataset) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=__A  ,	references=__A  ,	)

          _a      =  metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''  ,	__A)
          _a      =  eval_metric['''accuracy''']

          if best_performance < eval_metric["accuracy"]:
               _a      =  eval_metric['''accuracy''']

     if args.performance_lower_bound is not None:
          assert (
              args.performance_lower_bound <= best_performance
          ), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
          with open(os.path.join(args.output_dir  ,	'''all_results.json''')  ,	'''w''') as f:
               json.dump(__A  ,	__A)




def lowerCAmelCase  ():



     """simple docstring"""



     _a      =  argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''')
     parser.add_argument(
         '''--model_name_or_path'''  ,	type=__A  ,	default='''bert-base-cased'''  ,	help='''Path to pretrained model or model identifier from huggingface.co/models.'''  ,	required=__A  ,	)
     parser.add_argument(
         '''--output_dir'''  ,	type=__A  ,	default='''.'''  ,	help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,	)
     parser.add_argument(
         '''--performance_lower_bound'''  ,	type=__A  ,	default=__A  ,	help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''  ,	)
     parser.add_argument(
         '''--num_epochs'''  ,	type=__A  ,	default=3  ,	help='''Number of train epochs.'''  ,	)
     _a      =  parser.parse_args()
     _a      =  {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
     training_function(__A  ,	__A)


if __name__ == "__main__":
  main()








'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    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_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 lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     for attribute in key.split('''.'''):
          if is_finetuned:
               if attribute in ["quantizer", "project_q", "project_hid"]:
                    # those layers are only relevant for pretraining and should be dropped
                    return

               if attribute == "ctc_proj":
                    # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                    _a      =  '''lm_head'''

          _a      =  getattr(__A  ,	__A)

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



     """simple docstring"""



     _a      =  []
     _a      =  fairseq_model.state_dict()

     _a      =  hf_model.unispeech.feature_extractor

     for name, value in fairseq_dict.items():
          _a      =  False
          if "conv_layers" in name:
               load_conv_layer(
                   __A  ,	__A  ,	__A  ,	__A  ,	hf_model.config.feat_extract_norm == '''group'''  ,	)
               _a      =  True
          else:
               for key, mapped_key in MAPPING.items():
                    _a      =  '''unispeech.''' + 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(__A)[0].split('''.''')[-2]
                              _a      =  mapped_key.replace('''*'''  ,	__A)
                         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
                         set_recursively(__A  ,	__A  ,	__A  ,	__A  ,	__A  ,	__A)
                    continue
          if not is_used:
               unused_weights.append(__A)

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




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A):



     """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:
               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.'''
               )
               _a      =  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.'''
               )
               _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:
               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."
               )
               _a      =  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.'''
               )
               _a      =  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 lowerCAmelCase  (__A  ,	__A  ,	__A=None  ,	__A=None  ,	__A=True):



     """simple docstring"""



     if config_path is not None:
          _a      =  UniSpeechConfig.from_pretrained(__A)
     else:
          _a      =  UniSpeechConfig()

     if is_finetuned:
          if dict_path:
               _a      =  Dictionary.load_from_json(__A)

               # 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(__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)
               _a      =  target_dict.indices

               # fairseq has the <pad> and <s> switched
               _a      =  42
               _a      =  43
               with open(__A  ,	'''w'''  ,	encoding='''utf-8''') as vocab_handle:
                    json.dump(__A  ,	__A)
               _a      =  WavaVecaPhonemeCTCTokenizer(
                   __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  ,	)
               _a      =  True if config.feat_extract_norm == '''layer''' else False
               _a      =  WavaVecaFeatureExtractor(
                   feature_size=1  ,	sampling_rate=16_000  ,	padding_value=0  ,	do_normalize=__A  ,	return_attention_mask=__A  ,	)
               _a      =  WavaVecaProcessor(feature_extractor=__A  ,	tokenizer=__A)
               processor.save_pretrained(__A)

          _a      =  UniSpeechForCTC(__A)
     else:
          _a      =  UniSpeechForPreTraining(__A)

     if is_finetuned:
          _a  ,    _a  ,    _a      =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]  ,	arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''')[:-1]), '''w2v_path''': checkpoint_path})
     else:
          _a  ,    _a  ,    _a      =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

     _a      =  model[0].eval()

     recursively_load_weights(__A  ,	__A  ,	__A)

     hf_unispeech.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"
  )
  lowercase_		       =  parser.parse_args()
  convert_unispeech_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )



