
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


lowercase  : List[Any]						   =							16
lowercase  : Union[str, Any]						   =							32







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Dict:
       return int(x / 2**20    )

class __snake_case		:
    def __enter__(  self				):



           '''simple docstring'''







           gc.collect()
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
           lowercase							:					str	     =				torch.cuda.memory_allocated()
           return self
    def __exit__(  self		,*snake_case				):



           '''simple docstring'''







           gc.collect()
           torch.cuda.empty_cache()
           lowercase							:					Union[str, Any]	     =				torch.cuda.memory_allocated()
           lowercase							:					List[str]	     =				torch.cuda.max_memory_allocated()
           lowercase							:					List[str]	     =				bamb(self.end - self.begin				)
           lowercase							:					int	     =				bamb(self.peak - self.begin				)
           # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ = 16 ,     SCREAMING_SNAKE_CASE__ = "bert-base-cased" ,     SCREAMING_SNAKE_CASE__ = 320 ,     SCREAMING_SNAKE_CASE__ = 160 ,     )			->   List[str]:
       lowercase							:					str	     =				AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
       lowercase							:					Optional[Any]	     =				load_dataset(
           """glue""" ,     """mrpc""" ,     split={"""train""": f"train[:{n_train}]", """validation""": f"validation[:{n_val}]"}    )

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

       # Apply the method we just defined to all the examples in all the splits of the dataset
       lowercase							:					Optional[Any]	     =				datasets.map(
           SCREAMING_SNAKE_CASE__ ,     batched=SCREAMING_SNAKE_CASE__ ,     remove_columns=["""idx""", """sentence1""", """sentence2"""] ,     load_from_cache_file=SCREAMING_SNAKE_CASE__    )

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

       def collate_fn(SCREAMING_SNAKE_CASE__    ):
              # 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(SCREAMING_SNAKE_CASE__ ,     padding="""max_length""" ,     max_length=128 ,     return_tensors="""pt"""    )
              return tokenizer.pad(SCREAMING_SNAKE_CASE__ ,     padding="""longest""" ,     return_tensors="""pt"""    )

       # Instantiate dataloaders.
       lowercase							:					str	     =				DataLoader(
           tokenized_datasets["""train"""] ,     shuffle=SCREAMING_SNAKE_CASE__ ,     collate_fn=SCREAMING_SNAKE_CASE__ ,     batch_size=SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[str]	     =				DataLoader(
           tokenized_datasets["""validation"""] ,     shuffle=SCREAMING_SNAKE_CASE__ ,     collate_fn=SCREAMING_SNAKE_CASE__ ,     batch_size=SCREAMING_SNAKE_CASE__    )

       return train_dataloader, eval_dataloader







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Dict:
       # Initialize accelerator
       lowercase							:					Optional[int]	     =				Accelerator()

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

       set_seed(SCREAMING_SNAKE_CASE__    )
       lowercase     ,      lowercase							:					Union[str, Any]	     =				get_dataloaders(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     args.n_train ,     args.n_val    )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase							:					List[Any]	     =				AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__ ,     return_dict=SCREAMING_SNAKE_CASE__    )

       # Instantiate optimizer
       lowercase							:					Any	     =				(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       lowercase							:					List[str]	     =				optimizer_cls(params=model.parameters() ,     lr=SCREAMING_SNAKE_CASE__    )

       if accelerator.state.deepspeed_plugin is not None:
              lowercase							:					int	     =				accelerator.state.deepspeed_plugin.deepspeed_config[
                  """gradient_accumulation_steps"""
              ]
       else:
              lowercase							:					List[Any]	     =				1
       lowercase							:					Tuple	     =				(len(SCREAMING_SNAKE_CASE__    ) * 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							:					Any	     =				get_linear_schedule_with_warmup(
                  optimizer=SCREAMING_SNAKE_CASE__ ,     num_warmup_steps=0 ,     num_training_steps=SCREAMING_SNAKE_CASE__ ,     )
       else:
              lowercase							:					Union[str, Any]	     =				DummyScheduler(SCREAMING_SNAKE_CASE__ ,     total_num_steps=SCREAMING_SNAKE_CASE__ ,     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     ,      lowercase     ,      lowercase     ,      lowercase     ,      lowercase							:					Optional[int]	     =				accelerator.prepare(
           SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       # We need to keep track of how many total steps we have iterated over
       lowercase							:					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							:					str	     =				{}
       for epoch in range(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(SCREAMING_SNAKE_CASE__    ):
                            lowercase							:					List[Any]	     =				model(**SCREAMING_SNAKE_CASE__    )
                            lowercase							:					str	     =				outputs.loss
                            lowercase							:					List[str]	     =				loss / gradient_accumulation_steps
                            accelerator.backward(SCREAMING_SNAKE_CASE__    )
                            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    )    )    )
              lowercase							:					int	     =				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(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  )			->   List[str]:
       lowercase							:					Any	     =				argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
       parser.add_argument(
           """--model_name_or_path""" ,     type=SCREAMING_SNAKE_CASE__ ,     default="""bert-base-cased""" ,     help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,     required=SCREAMING_SNAKE_CASE__ ,     )
       parser.add_argument(
           """--output_dir""" ,     type=SCREAMING_SNAKE_CASE__ ,     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=SCREAMING_SNAKE_CASE__ ,     default=SCREAMING_SNAKE_CASE__ ,     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=SCREAMING_SNAKE_CASE__ ,     default=320 ,     help="""Number of training examples to use.""" ,     )
       parser.add_argument(
           """--n_val""" ,     type=SCREAMING_SNAKE_CASE__ ,     default=160 ,     help="""Number of validation examples to use.""" ,     )
       parser.add_argument(
           """--num_epochs""" ,     type=SCREAMING_SNAKE_CASE__ ,     default=1 ,     help="""Number of train epochs.""" ,     )
       lowercase							:					Optional[Any]	     =				parser.parse_args()
       lowercase							:					Union[str, Any]	     =				{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
       training_function(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
  main()


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

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


lowercase  : Tuple						   =							"""<<<<<<< This should probably be modified because it mentions: """

lowercase  : Any						   =							"""=======
>>>>>>>
"""

lowercase  : List[str]						   =							[
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

lowercase  : 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 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   List[Any]:
       return ConvertCommand(args.tfds_path ,     args.datasets_directory    )




class __snake_case		(				lowerCAmelCase					):
    @staticmethod
    def 				_SCREAMING_SNAKE_CASE	(  snake_case				):



           '''simple docstring'''







           lowercase							:					str	     =				parser.add_parser(
               """convert"""		,help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."""		,)
           train_parser.add_argument(
               """--tfds_path"""		,type=snake_case		,required=snake_case		,help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."""		,)
           train_parser.add_argument(
               """--datasets_directory"""		,type=snake_case		,required=snake_case		,help="""Path to the HuggingFace Datasets folder."""				)
           train_parser.set_defaults(func=snake_case				)
    def __init__(  self		,snake_case		,snake_case		,*snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				get_logger("""datasets-cli/converting"""				)

           lowercase							:					Optional[int]	     =				tfds_path
           lowercase							:					Dict	     =				datasets_directory
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if os.path.isdir(self._tfds_path				):
                  lowercase							:					List[str]	     =				os.path.abspath(self._tfds_path				)
           elif os.path.isfile(self._tfds_path				):
                  lowercase							:					Tuple	     =				os.path.dirname(self._tfds_path				)
           else:
                  raise ValueError("""--tfds_path is neither a directory nor a file. Please check path."""				)

           lowercase							:					Optional[int]	     =				os.path.abspath(self._datasets_directory				)

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

           lowercase							:					List[Any]	     =				[]
           lowercase							:					Optional[int]	     =				[]
           lowercase							:					Dict	     =				{}

           if os.path.isdir(self._tfds_path				):
                  lowercase							:					int	     =				os.listdir(snake_case				)
           else:
                  lowercase							:					List[Any]	     =				[os.path.basename(self._tfds_path				)]

           for f_name in file_names:
                  self._logger.info(f"Looking at file {f_name}"				)
                  lowercase							:					List[Any]	     =				os.path.join(snake_case		,snake_case				)
                  lowercase							:					List[str]	     =				os.path.join(snake_case		,snake_case				)

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

                  with open(snake_case		,encoding="""utf-8"""				) as f:
                         lowercase							:					str	     =				f.readlines()

                  lowercase							:					Union[str, Any]	     =				[]
                  lowercase							:					Optional[Any]	     =				False
                  lowercase							:					Optional[Any]	     =				False
                  lowercase							:					Optional[int]	     =				[]
                  for line in lines:
                         lowercase							:					int	     =				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							:					Union[str, Any]	     =				"""import datasets\n"""
                         elif "import tensorflow" in out_line:
                                # order is important here
                                lowercase							:					List[Any]	     =				""""""
                                continue
                         elif "from absl import logging" in out_line:
                                lowercase							:					Optional[int]	     =				"""from datasets import logging\n"""
                         elif "getLogger" in out_line:
                                lowercase							:					Any	     =				out_line.replace("""getLogger"""		,"""get_logger"""				)
                         elif any(expression in out_line for expression in TO_HIGHLIGHT				):
                                lowercase							:					Optional[Any]	     =				True
                                lowercase							:					Optional[Any]	     =				list(filter(lambda snake_case				: e in out_line		,snake_case				)				)
                                out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(snake_case				) + """\n"""				)
                                out_lines.append(snake_case				)
                                out_lines.append(snake_case				)
                                continue
                         else:
                                for pattern, replacement in TO_CONVERT:
                                       lowercase							:					Union[str, Any]	     =				re.sub(snake_case		,snake_case		,snake_case				)

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

                  if is_builder or "wmt" in f_name:
                         # We create a new directory for each dataset
                         lowercase							:					Union[str, Any]	     =				f_name.replace(""".py"""		,""""""				)
                         lowercase							:					Optional[Any]	     =				os.path.join(snake_case		,snake_case				)
                         lowercase							:					List[str]	     =				os.path.join(snake_case		,snake_case				)
                         os.makedirs(snake_case		,exist_ok=snake_case				)
                         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(snake_case				)

                  if needs_manual_update:
                         with_manual_update.append(snake_case				)

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

           for utils_file in utils_files:
                  try:
                         lowercase							:					Optional[int]	     =				os.path.basename(snake_case				)
                         lowercase							:					int	     =				imports_to_builder_map[f_name.replace(""".py"""		,""""""				)]
                         self._logger.info(f"Moving {dest_folder} to {utils_file}"				)
                         shutil.copy(snake_case		,snake_case				)
                  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'."				)
