


'''simple docstring'''




import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

__snake_case    						=16
__snake_case    						=32





def 	a_					( lowerCamelCase	:     Accelerator	, lowerCamelCase	:     int = 16				):
   lowerCAmelCase     =			AutoTokenizer.from_pretrained('bert-base-cased'				)
   lowerCAmelCase     =			load_dataset('glue'	, 'mrpc'				)

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

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      lowerCAmelCase     =			datasets.map(
          lowerCamelCase	, batched=lowerCamelCase	, remove_columns=['idx', 'sentence1', 'sentence2']	, )

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

   def collate_fn(lowerCamelCase	:     List[str]				):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      lowerCAmelCase     =			128 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         lowerCAmelCase     =			16
      elif accelerator.mixed_precision != "no":
         lowerCAmelCase     =			8
      else:
         lowerCAmelCase     =			None

      return tokenizer.pad(
          lowerCamelCase	, padding='longest'	, max_length=lowerCamelCase	, pad_to_multiple_of=lowerCamelCase	, return_tensors='pt'	, )

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

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       __snake_case    						=mocked_dataloaders  # noqa: F811





def 	a_					( lowerCamelCase	:     Any	, lowerCamelCase	:     Tuple				):
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS'	, lowerCamelCase				) == "1":
      lowerCAmelCase     =			2
   # Initialize Accelerator

   # New Code #
   # We pass in "all" to `log_with` to grab all available trackers in the environment
   # Note: If using a custom `Tracker` class, should be passed in here such as:
   # >>> log_with = ["all", MyCustomTrackerClassInstance()]
   if args.with_tracking:
      lowerCAmelCase     =			Accelerator(
          cpu=args.cpu	, mixed_precision=args.mixed_precision	, log_with='all'	, project_dir=args.project_dir				)
   else:
      lowerCAmelCase     =			Accelerator(cpu=args.cpu	, mixed_precision=args.mixed_precision				)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowerCAmelCase     =			config['lr']
   lowerCAmelCase     =			int(config['num_epochs']				)
   lowerCAmelCase     =			int(config['seed']				)
   lowerCAmelCase     =			int(config['batch_size']				)
   set_seed(lowerCamelCase				)

   lowerCAmelCase     ,  lowerCAmelCase     =			get_dataloaders(lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			evaluate.load('glue'	, 'mrpc'				)

   # If the batch size is too big we use gradient accumulation
   lowerCAmelCase     =			1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      lowerCAmelCase     =			batch_size // MAX_GPU_BATCH_SIZE
      lowerCAmelCase     =			MAX_GPU_BATCH_SIZE

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowerCAmelCase     =			AutoModelForSequenceClassification.from_pretrained('bert-base-cased'	, return_dict=lowerCamelCase				)

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   lowerCAmelCase     =			model.to(accelerator.device				)

   # Instantiate optimizer
   lowerCAmelCase     =			AdamW(params=model.parameters()	, lr=lowerCamelCase				)

   # Instantiate scheduler
   lowerCAmelCase     =			get_linear_schedule_with_warmup(
       optimizer=lowerCamelCase	, num_warmup_steps=100	, num_training_steps=(len(lowerCamelCase				) * num_epochs) // gradient_accumulation_steps	, )

   # 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.
   lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			accelerator.prepare(
       lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)

   # New Code #
   # We need to initialize the trackers we use. Overall configurations can also be stored
   if args.with_tracking:
      lowerCAmelCase     =			os.path.split(lowerCamelCase				)[-1].split('.'				)[0]
      accelerator.init_trackers(lowerCamelCase	, lowerCamelCase				)

   # Now we train the model
   for epoch in range(lowerCamelCase				):
      model.train()
      # New Code #
      # For our tracking example, we will log the total loss of each epoch
      if args.with_tracking:
         lowerCAmelCase     =			0
      for step, batch in enumerate(lowerCamelCase				):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device				)
         lowerCAmelCase     =			model(**lowerCamelCase				)
         lowerCAmelCase     =			outputs.loss
         # New Code #
         if args.with_tracking:
            total_loss += loss.detach().float()
         lowerCAmelCase     =			loss / gradient_accumulation_steps
         accelerator.backward(lowerCamelCase				)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(lowerCamelCase				):
         # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
         batch.to(accelerator.device				)
         with torch.no_grad():
            lowerCAmelCase     =			model(**lowerCamelCase				)
         lowerCAmelCase     =			outputs.logits.argmax(dim=-1				)
         lowerCAmelCase     ,  lowerCAmelCase     =			accelerator.gather_for_metrics((predictions, batch['labels'])				)
         metric.add_batch(
             predictions=lowerCamelCase	, references=lowerCamelCase	, )

      lowerCAmelCase     =			metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''	, lowerCamelCase				)

      # New Code #
      # To actually log, we call `Accelerator.log`
      # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
      if args.with_tracking:
         accelerator.log(
             {
                 'accuracy': eval_metric['accuracy'],
                 'f1': eval_metric['f1'],
                 'train_loss': total_loss.item() / len(lowerCamelCase				),
                 'epoch': epoch,
             }	, step=lowerCamelCase	, )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
   if args.with_tracking:
      accelerator.end_training()





def 	a_					( ):
   lowerCAmelCase     =			argparse.ArgumentParser(description='Simple example of training script.'				)
   parser.add_argument(
       '--mixed_precision'	, type=lowerCamelCase	, default=lowerCamelCase	, choices=['no', 'fp16', 'bf16', 'fp8']	, help='Whether to use mixed precision. Choose'
       'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
       'and an Nvidia Ampere GPU.'	, )
   parser.add_argument('--cpu'	, action='store_true'	, help='If passed, will train on the CPU.'				)
   parser.add_argument(
       '--with_tracking'	, action='store_true'	, help='Whether to load in all available experiment trackers from the environment and use them for logging.'	, )
   parser.add_argument(
       '--project_dir'	, type=lowerCamelCase	, default='logs'	, help='Location on where to store experiment tracking logs` and relevent project information'	, )
   lowerCAmelCase     =			parser.parse_args()
   lowerCAmelCase     =			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(lowerCamelCase	, lowerCamelCase				)


if __name__ == "__main__":
       main()












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()
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',
    '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',
}
UpperCAmelCase_     							= [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			str		,   __UpperCAmelCase:			Any      )      ->	List[str]:
    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
                UpperCamelCase__							:				Tuple          =      '''lm_head'''

        UpperCamelCase__							:				Optional[int]          =      getattr(__UpperCAmelCase		,   __UpperCAmelCase      )

    if weight_type is not None:
        UpperCamelCase__							:				List[str]          =      getattr(__UpperCAmelCase		,   __UpperCAmelCase      ).shape
    else:
        UpperCamelCase__							:				Optional[Any]          =      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__							:				List[Any]          =      value
    elif weight_type == "weight_g":
        UpperCamelCase__							:				List[str]          =      value
    elif weight_type == "weight_v":
        UpperCamelCase__							:				str          =      value
    elif weight_type == "bias":
        UpperCamelCase__							:				Tuple          =      value
    else:
        UpperCamelCase__							:				int          =      value

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Dict		,   __UpperCAmelCase:			str      )      ->	List[Any]:
    UpperCamelCase__							:				Optional[Any]          =      []
    UpperCamelCase__							:				str          =      fairseq_model.state_dict()

    UpperCamelCase__							:				Optional[int]          =      hf_model.unispeech.feature_extractor

    for name, value in fairseq_dict.items():
        UpperCamelCase__							:				Dict          =      False
        if "conv_layers" in name:
            load_conv_layer(
                __UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase		,   hf_model.config.feat_extract_norm == '''group'''		,   )
            UpperCamelCase__							:				Union[str, Any]          =      True
        else:
            for key, mapped_key in MAPPING.items():
                UpperCamelCase__							:				int          =      '''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]:
                    UpperCamelCase__							:				Tuple          =      True
                    if "*" in mapped_key:
                        UpperCamelCase__							:				Any          =      name.split(__UpperCAmelCase      )[0].split('''.'''      )[-2]
                        UpperCamelCase__							:				Optional[Any]          =      mapped_key.replace('''*'''		,   __UpperCAmelCase      )
                    if "weight_g" in name:
                        UpperCamelCase__							:				List[str]          =      '''weight_g'''
                    elif "weight_v" in name:
                        UpperCamelCase__							:				Dict          =      '''weight_v'''
                    elif "bias" in name:
                        UpperCamelCase__							:				List[Any]          =      '''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        UpperCamelCase__							:				Optional[Any]          =      '''weight'''
                    else:
                        UpperCamelCase__							:				List[Any]          =      None
                    set_recursively(__UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase      )
                continue
        if not is_used:
            unused_weights.append(__UpperCAmelCase      )

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			Any		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			List[Any]		,   __UpperCAmelCase:			Tuple      )      ->	Optional[int]:
    UpperCamelCase__							:				List[str]          =      full_name.split('''conv_layers.'''      )[-1]
    UpperCamelCase__							:				List[str]          =      name.split('''.'''      )
    UpperCamelCase__							:				str          =      int(items[0]      )
    UpperCamelCase__							:				Union[str, Any]          =      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__							:				int          =      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__							:				Tuple          =      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__							:				int          =      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__							:				List[str]          =      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:			Optional[Any]		,   __UpperCAmelCase:			Dict=None		,   __UpperCAmelCase:			Optional[Any]=None		,   __UpperCAmelCase:			Optional[int]=True      )      ->	Union[str, Any]:
    if config_path is not None:
        UpperCamelCase__							:				str          =      UniSpeechConfig.from_pretrained(__UpperCAmelCase      )
    else:
        UpperCamelCase__							:				List[Any]          =      UniSpeechConfig()

    if is_finetuned:
        if dict_path:
            UpperCamelCase__							:				str          =      Dictionary.load_from_json(__UpperCAmelCase      )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            UpperCamelCase__							:				Any          =      target_dict.pad_index
            UpperCamelCase__							:				str          =      target_dict.bos_index
            UpperCamelCase__							:				Any          =      target_dict.eos_index
            UpperCamelCase__							:				Tuple          =      len(target_dict.symbols      )
            UpperCamelCase__							:				List[str]          =      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      )
            UpperCamelCase__							:				Dict          =      target_dict.indices

            # fairseq has the <pad> and <s> switched
            UpperCamelCase__							:				Optional[Any]          =      42
            UpperCamelCase__							:				List[str]          =      43
            with open(__UpperCAmelCase		,   '''w'''		,   encoding='''utf-8'''      ) as vocab_handle:
                json.dump(__UpperCAmelCase		,   __UpperCAmelCase      )
            UpperCamelCase__							:				Dict          =      WavaVecaPhonemeCTCTokenizer(
                __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		,   )
            UpperCamelCase__							:				List[Any]          =      True if config.feat_extract_norm == '''layer''' else False
            UpperCamelCase__							:				str          =      WavaVecaFeatureExtractor(
                feature_size=1		,   sampling_rate=1_6000		,   padding_value=0		,   do_normalize=__UpperCAmelCase		,   return_attention_mask=__UpperCAmelCase		,   )
            UpperCamelCase__							:				str          =      WavaVecaProcessor(feature_extractor=__UpperCAmelCase		,   tokenizer=__UpperCAmelCase      )
            processor.save_pretrained(__UpperCAmelCase      )

        UpperCamelCase__							:				Optional[int]          =      UniSpeechForCTC(__UpperCAmelCase      )
    else:
        UpperCamelCase__							:				Any          =      UniSpeechForPreTraining(__UpperCAmelCase      )

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

    UpperCamelCase__							:				Tuple          =      model[0].eval()

    recursively_load_weights(__UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase      )

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

