import argparse
import gc
import json
import os

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


SCREAMING_SNAKE_CASE :	Optional[int]							     =		16
SCREAMING_SNAKE_CASE :	Any							     =		32



def   __A		(			_A					):



 """simple docstring"""


 return int(x / 2**20					)
class    A_ :

  def __enter__(		self :      List[Any]       ):
   gc.collect()
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
   __a       =  torch.cuda.memory_allocated()
   return self

  def __exit__(		self :      int     ,							*__SCREAMING_SNAKE_CASE :      int       ):
   gc.collect()
   torch.cuda.empty_cache()
   __a       =  torch.cuda.memory_allocated()
   __a       =  torch.cuda.max_memory_allocated()
   __a       =  bamb(self.end - self.begin       )
   __a       =  bamb(self.peak - self.begin       )
   # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")



def   __A		(			_A							,				_A = 16							,				_A = "bert-base-cased"							,				_A = 320							,				_A = 160							,				):



 """simple docstring"""


 __a       =  AutoTokenizer.from_pretrained(_A					)
 __a       =  load_dataset(
     "glue"							,				"mrpc"							,				split={"train": f"""train[:{n_train}]""", "validation": f"""validation[:{n_val}]"""}					)

 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   __A		(			_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							,				args.n_train							,				args.n_val					)

 # 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       =  {}
 for epoch in range(_A							,				_A					):
  with TorchTracemalloc() as tracemalloc:
   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

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin					)					)					)
  accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used					)					)
  accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked					)					)
  accelerator.print(
      "Total Peak Memory consumed during the train (max): {}".format(
          tracemalloc.peaked + bamb(tracemalloc.begin					)					)					)
  __a       =  tracemalloc.peaked + bamb(tracemalloc.begin					)
  if args.peak_memory_upper_bound is not None:
   assert (
       train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
   ), "Peak memory usage exceeded the upper bound"

 accelerator.wait_for_everyone()
 if accelerator.is_main_process:
  with open(os.path.join(args.output_dir							,				"peak_memory_utilization.json"					)							,				"w"					) as f:
   json.dump(_A							,				_A					)



def   __A		(			):



 """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(
     "--peak_memory_upper_bound"							,				type=_A							,				default=_A							,				help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."							,				)
 parser.add_argument(
     "--n_train"							,				type=_A							,				default=320							,				help="Number of training examples to use."							,				)
 parser.add_argument(
     "--n_val"							,				type=_A							,				default=160							,				help="Number of validation examples to use."							,				)
 parser.add_argument(
     "--num_epochs"							,				type=_A							,				default=1							,				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()



import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  args.log_outputs
 __a       =  "_".join(args.dataset.split("/"					) + [args.config, args.split]					)

 # load metric
 __a       =  load_metric("wer"					)
 __a       =  load_metric("cer"					)

 # compute metrics
 __a       =  wer.compute(references=result["target"]							,				predictions=result["prediction"]					)
 __a       =  cer.compute(references=result["target"]							,				predictions=result["prediction"]					)

 # print & log results
 __a       =  f"""WER: {wer_result}\nCER: {cer_result}"""
 print(_A					)

 with open(f"""{dataset_id}_eval_results.txt"""							,				"w"					) as f:
  f.write(_A					)

 # log all results in text file. Possibly interesting for analysis
 if log_outputs is not None:
  __a       =  f"""log_{dataset_id}_predictions.txt"""
  __a       =  f"""log_{dataset_id}_targets.txt"""

  with open(_A							,				"w"					) as p, open(_A							,				"w"					) as t:
   # mapping function to write output
   def write_to_file(_A							,				_A					):
    p.write(f"""{i}""" + "\n"					)
    p.write(batch["prediction"] + "\n"					)
    t.write(f"""{i}""" + "\n"					)
    t.write(batch["target"] + "\n"					)

   result.map(_A							,				with_indices=_A					)



def   __A		(			_A					):



 """simple docstring"""


 __a       =  "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

 __a       =  re.sub(_A							,				""							,				text.lower()					)

 # In addition, we can normalize the target text, e.g. removing new lines characters etc...
 # note that order is important here!
 __a       =  ["\n\n", "\n", "   ", "  "]

 for t in token_sequences_to_ignore:
  __a       =  " ".join(text.split(_A					)					)

 return text



def   __A		(			_A					):



 """simple docstring"""


 __a       =  load_dataset(args.dataset							,				args.config							,				split=args.split							,				use_auth_token=_A					)

 # for testing: only process the first two examples as a test
 # dataset = dataset.select(range(10))

 # load processor
 __a       =  AutoFeatureExtractor.from_pretrained(args.model_id					)
 __a       =  feature_extractor.sampling_rate

 # resample audio
 __a       =  dataset.cast_column("audio"							,				Audio(sampling_rate=_A					)					)

 # load eval pipeline
 if args.device is None:
  __a       =  0 if torch.cuda.is_available() else -1
 __a       =  pipeline("automatic-speech-recognition"							,				model=args.model_id							,				device=args.device					)

 # map function to decode audio
 def map_to_pred(_A					):
  __a       =  asr(
      batch["audio"]["array"]							,				chunk_length_s=args.chunk_length_s							,				stride_length_s=args.stride_length_s					)

  __a       =  prediction["text"]
  __a       =  normalize_text(batch["sentence"]					)
  return batch

 # run inference on all examples
 __a       =  dataset.map(_A							,				remove_columns=dataset.column_names					)

 # compute and log_results
 # do not change function below
 log_results(_A							,				_A					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	Any							     =		argparse.ArgumentParser()

  parser.add_argument(
      """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
  )
  parser.add_argument(
      """--dataset""",
      type=str,
      required=True,
      help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
  )
  parser.add_argument(
      """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
  )
  parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
  parser.add_argument(
      """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
  )
  parser.add_argument(
      """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
  )
  parser.add_argument(
      """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
  )
  parser.add_argument(
      """--device""",
      type=int,
      default=None,
      help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
  )
  SCREAMING_SNAKE_CASE :	Optional[Any]							     =		parser.parse_args()

  main(args)


