
"""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


A__   :					List[str]							=   1_6
A__   :					Union[str, Any]							=   3_2




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase = 16 ,				_UpperCamelCase = "bert-base-cased"						):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     AutoTokenizer.from_pretrained(_lowerCamelCase						)
  _lowercase:			Optional[int]       =     load_dataset('''glue''' ,				'''mrpc'''						)

  def tokenize_function(_UpperCamelCase						):
    # max_length=None => use the model max length (it's actually the default)
    _lowercase:			Optional[Any]       =     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
  _lowercase:			str       =     datasets.map(
      _lowerCamelCase ,				batched=_lowerCamelCase ,				remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,				load_from_cache_file=_lowerCamelCase						)

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  _lowercase:			List[str]       =     tokenized_datasets.rename_column('''label''' ,				'''labels'''						)

  def collate_fn(_UpperCamelCase						):
    # 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(_lowerCamelCase ,				padding='''max_length''' ,				max_length=128 ,				return_tensors='''pt'''						)
    return tokenizer.pad(_lowerCamelCase ,				padding='''longest''' ,				return_tensors='''pt'''						)

  # Instantiate dataloaders.
  _lowercase:			Any       =     DataLoader(
      tokenized_datasets['''train'''] ,				shuffle=_lowerCamelCase ,				collate_fn=_lowerCamelCase ,				batch_size=_lowerCamelCase						)
  _lowercase:			int       =     DataLoader(
      tokenized_datasets['''validation'''] ,				shuffle=_lowerCamelCase ,				collate_fn=_lowerCamelCase ,				batch_size=_lowerCamelCase						)

  return train_dataloader, eval_dataloader




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Optional[int]       =     Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowercase:			List[Any]       =     config["lr"]
  _lowercase:			Tuple       =     int(config['''num_epochs''']						)
  _lowercase:			List[Any]       =     int(config['''seed''']						)
  _lowercase:			Union[str, Any]       =     int(config['''batch_size''']						)
  _lowercase:			Optional[int]       =     args.model_name_or_path

  set_seed(_lowerCamelCase						)
  _lowercase:			str       =     get_dataloaders(_lowerCamelCase ,				_lowerCamelCase ,				_lowerCamelCase						)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _lowercase:			str       =     AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase ,				return_dict=_lowerCamelCase						)

  # Instantiate optimizer
  _lowercase:			int       =     (
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  _lowercase:			Any       =     optimizer_cls(params=model.parameters() ,				lr=_lowerCamelCase						)

  if accelerator.state.deepspeed_plugin is not None:
    _lowercase:			Union[str, Any]       =     accelerator.state.deepspeed_plugin.deepspeed_config[
        "gradient_accumulation_steps"
    ]
  else:
    _lowercase:			int       =     1
  _lowercase:			List[Any]       =     (len(_lowerCamelCase						) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    _lowercase:			Optional[Any]       =     get_linear_schedule_with_warmup(
        optimizer=_lowerCamelCase ,				num_warmup_steps=0 ,				num_training_steps=_lowerCamelCase ,				)
  else:
    _lowercase:			Tuple       =     DummyScheduler(_lowerCamelCase ,				total_num_steps=_lowerCamelCase ,				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.
  _lowercase:			str       =     accelerator.prepare(
      _lowerCamelCase ,				_lowerCamelCase ,				_lowerCamelCase ,				_lowerCamelCase ,				_lowerCamelCase						)

  # We need to keep track of how many total steps we have iterated over
  _lowercase:			Optional[int]       =     0
  # We also need to keep track of the stating epoch so files are named properly
  _lowercase:			List[Any]       =     0

  # Now we train the model
  _lowercase:			Tuple       =     evaluate.load('''glue''' ,				'''mrpc'''						)
  _lowercase:			Dict       =     0
  _lowercase:			Optional[Any]       =     {}
  for epoch in range(_lowerCamelCase ,				_lowerCamelCase						):
    model.train()
    for step, batch in enumerate(_lowerCamelCase						):
      _lowercase:			Optional[Any]       =     model(**_lowerCamelCase						)
      _lowercase:			Optional[int]       =     outputs.loss
      _lowercase:			Tuple       =     loss / gradient_accumulation_steps
      accelerator.backward(_lowerCamelCase						)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1

    model.eval()
    _lowercase:			str       =     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						)
      with torch.no_grad():
        _lowercase:			Optional[Any]       =     model(**_lowerCamelCase						)
      _lowercase:			Dict       =     outputs.logits.argmax(dim=-1						)
      # It is slightly faster to call this once, than multiple times
      _lowercase:			Optional[Any]       =     accelerator.gather(
          (predictions, batch['''labels'''])						)  # If we are in a multiprocess environment, the last batch has duplicates
      if accelerator.use_distributed:
        if step == len(_lowerCamelCase						) - 1:
          _lowercase:			Tuple       =     predictions[: len(eval_dataloader.dataset						) - samples_seen]
          _lowercase:			Optional[Any]       =     references[: len(eval_dataloader.dataset						) - samples_seen]
        else:
          samples_seen += references.shape[0]
      metric.add_batch(
          predictions=_lowerCamelCase ,				references=_lowerCamelCase ,				)

    _lowercase:			Any       =     metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:''' ,				_lowerCamelCase						)
    _lowercase:			List[str]       =     eval_metric["accuracy"]

    if best_performance < eval_metric["accuracy"]:
      _lowercase:			Union[str, Any]       =     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(_lowerCamelCase ,				_lowerCamelCase						)




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''						)
  parser.add_argument(
      '''--model_name_or_path''' ,				type=_lowerCamelCase ,				default='''bert-base-cased''' ,				help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,				required=_lowerCamelCase ,				)
  parser.add_argument(
      '''--output_dir''' ,				type=_lowerCamelCase ,				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=_lowerCamelCase ,				default=_lowerCamelCase ,				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=_lowerCamelCase ,				default=3 ,				help='''Number of train epochs.''' ,				)
  _lowercase:			Dict       =     parser.parse_args()
  _lowercase:			Tuple       =     {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
  training_function(_lowerCamelCase ,				_lowerCamelCase						)


if __name__ == "__main__":
   main()


"""simple docstring"""




import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


A__   :					Optional[Any]							=   '<<<<<<< This should probably be modified because it mentions: '

A__   :					Any							=   '=======\n>>>>>>>\n'

A__   :					int							=   [
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

A__   :					Union[str, Any]							=   [
    # (pattern, replacement)
    # Order is important here for some replacements
    (r'tfds\.core', r'datasets'),
    (r'tf\.io\.gfile\.GFile', r'open'),
    (r'tf\.([\w\d]+)', r'datasets.Value(\'\1\')'),
    (r'tfds\.features\.Text\(\)', r'datasets.Value(\'string\')'),
    (r'tfds\.features\.Text\(', r'datasets.Value(\'string\'),'),
    (r'features\s*=\s*tfds.features.FeaturesDict\(', r'features=datasets.Features('),
    (r'tfds\.features\.FeaturesDict\(', r'dict('),
    (r'The TensorFlow Datasets Authors', r'The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'),
    (r'tfds\.', r'datasets.'),
    (r'dl_manager\.manual_dir', r'self.config.data_dir'),
    (r'self\.builder_config', r'self.config'),
]




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  return ConvertCommand(args.tfds_path ,				args.datasets_directory						)







class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       @staticmethod
       def 							lowercase_							(     A_       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			int       =     parser.add_parser(
             '''convert''' ,				help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.''' ,				)
         train_parser.add_argument(
             '''--tfds_path''' ,				type=A_ ,				required=A_ ,				help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.''' ,				)
         train_parser.add_argument(
             '''--datasets_directory''' ,				type=A_ ,				required=A_ ,				help='''Path to the HuggingFace Datasets folder.'''       )
         train_parser.set_defaults(func=A_       )
       def __init__(     self ,				A_ ,				A_ ,				*A_       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     get_logger('''datasets-cli/converting'''       )

         _lowercase:			Optional[Any]       =     tfds_path
         _lowercase:			Dict       =     datasets_directory



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         if os.path.isdir(self._tfds_path       ):
           _lowercase:			Optional[Any]       =     os.path.abspath(self._tfds_path       )
         elif os.path.isfile(self._tfds_path       ):
           _lowercase:			Any       =     os.path.dirname(self._tfds_path       )
         else:
           raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.'''       )

         _lowercase:			Dict       =     os.path.abspath(self._datasets_directory       )

         self._logger.info(f'''Converting datasets from {abs_tfds_path} to {abs_datasets_path}'''       )

         _lowercase:			Tuple       =     []
         _lowercase:			List[str]       =     []
         _lowercase:			int       =     {}

         if os.path.isdir(self._tfds_path       ):
           _lowercase:			str       =     os.listdir(A_       )
         else:
           _lowercase:			Tuple       =     [os.path.basename(self._tfds_path       )]

         for f_name in file_names:
           self._logger.info(f'''Looking at file {f_name}'''       )
           _lowercase:			Optional[int]       =     os.path.join(A_ ,				A_       )
           _lowercase:			Optional[Any]       =     os.path.join(A_ ,				A_       )

           if not os.path.isfile(A_       ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
             self._logger.info('''Skipping file'''       )
             continue

           with open(A_ ,				encoding='''utf-8'''       ) as f:
             _lowercase:			Any       =     f.readlines()

           _lowercase:			str       =     []
           _lowercase:			Dict       =     False
           _lowercase:			Any       =     False
           _lowercase:			Tuple       =     []
           for line in lines:
             _lowercase:			str       =     line

             # Convert imports
             if "import tensorflow.compat.v2 as tf" in out_line:
               continue
             elif "@tfds.core" in out_line:
               continue
             elif "builder=self" in out_line:
               continue
             elif "import tensorflow_datasets.public_api as tfds" in out_line:
               _lowercase:			Optional[Any]       =     '''import datasets\n'''
             elif "import tensorflow" in out_line:
               # order is important here
               _lowercase:			Any       =     ''''''
               continue
             elif "from absl import logging" in out_line:
               _lowercase:			Dict       =     '''from datasets import logging\n'''
             elif "getLogger" in out_line:
               _lowercase:			Optional[Any]       =     out_line.replace('''getLogger''' ,				'''get_logger'''       )
             elif any(expression in out_line for expression in TO_HIGHLIGHT       ):
               _lowercase:			int       =     True
               _lowercase:			List[str]       =     list(filter(lambda A_       : e in out_line ,				A_       )       )
               out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(A_       ) + '''\n'''       )
               out_lines.append(A_       )
               out_lines.append(A_       )
               continue
             else:
               for pattern, replacement in TO_CONVERT:
                 _lowercase:			Tuple       =     re.sub(A_ ,				A_ ,				A_       )

                # Take care of saving utilities (to later move them together with main script)
             if "tensorflow_datasets" in out_line:
               _lowercase:			Optional[int]       =     re.match(R'''from\stensorflow_datasets.*import\s([^\.\r\n]+)''' ,				A_       )
               tfds_imports.extend(imp.strip() for imp in match.group(1       ).split(''','''       )       )
               _lowercase:			Dict       =     '''from . import ''' + match.group(1       )

             # Check we have not forget anything
             if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
               raise ValueError(f'''Error converting {out_line.strip()}'''       )

             if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
               _lowercase:			Tuple       =     True
             out_lines.append(A_       )

           if is_builder or "wmt" in f_name:
             # We create a new directory for each dataset
             _lowercase:			List[str]       =     f_name.replace('''.py''' ,				''''''       )
             _lowercase:			Dict       =     os.path.join(A_ ,				A_       )
             _lowercase:			Dict       =     os.path.join(A_ ,				A_       )
             os.makedirs(A_ ,				exist_ok=A_       )
             self._logger.info(f'''Adding directory {output_dir}'''       )
             imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}       )
           else:
             # Utilities will be moved at the end
             utils_files.append(A_       )

           if needs_manual_update:
             with_manual_update.append(A_       )

           with open(A_ ,				'''w''' ,				encoding='''utf-8'''       ) as f:
             f.writelines(A_       )
           self._logger.info(f'''Converted in {output_file}'''       )

         for utils_file in utils_files:
           try:
             _lowercase:			Optional[int]       =     os.path.basename(A_       )
             _lowercase:			List[Any]       =     imports_to_builder_map[f_name.replace('''.py''' ,				''''''       )]
             self._logger.info(f'''Moving {dest_folder} to {utils_file}'''       )
             shutil.copy(A_ ,				A_       )
           except KeyError:
             self._logger.error(f'''Cannot find destination folder for {utils_file}. Please copy manually.'''       )

         if with_manual_update:
           for file_path in with_manual_update:
             self._logger.warning(
                 f'''You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.'''       )
