





"""simple docstring"""




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a						: List[str]     	=			logging.getLogger(__name__)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Union[str, Any]    ,       _lowercase						:    Union[str, Any]					) ->int:






	'''simple docstring'''






	return (preds == labels).mean()


@dataclass
class        __UpperCamelCase				:
							lowerCamelCase   :					str      					=field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}						)
							lowerCamelCase   :					Optional[str]      					=field(
							    default=a__		,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}						)
							lowerCamelCase   :					Optional[str]      					=field(
							    default=a__		,       metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}						)
							lowerCamelCase   :					Optional[str]      					=field(
							    default=a__		,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}		,       )


@dataclass
class        __UpperCamelCase				:
							lowerCamelCase   :					str      					=field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()						)}						)
							lowerCamelCase   :					str      					=field(metadata={"""help""": """Should contain the data files for the task."""}						)
							lowerCamelCase   :					int      					=field(
							    default=128		,       metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }		,       )
							lowerCamelCase   :					bool      					=field(
							    default=a__		,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}						)






def       _SCREAMING_SNAKE_CASE		(	) ->str:






	'''simple docstring'''






	a      :		Any	=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
	a,       a,       a      :		Union[str, Any]	=				parser.parse_args_into_dataclasses()

	if (
	    os.path.exists(training_args.output_dir					)
	    and os.listdir(training_args.output_dir					)
	    and training_args.do_train
	    and not training_args.overwrite_output_dir
	):
		raise ValueError(
		    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
		    " --overwrite_output_dir to overcome."					)

	# Setup logging
	logging.basicConfig(
	    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,       datefmt="%m/%d/%Y %H:%M:%S"    ,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN    ,       )
	logger.warning(
	    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"    ,       training_args.local_rank    ,       training_args.device    ,       training_args.n_gpu    ,       bool(training_args.local_rank != -1					)    ,       training_args.fpaa    ,       )
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank					):
		transformers.utils.logging.set_verbosity_info()
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
	logger.info("Training/evaluation parameters %s"    ,       _lowercase					)

	# Set seed
	set_seed(training_args.seed					)

	try:
		a      :		Union[str, Any]	=				processors[data_args.task_name]()
		a      :		Any	=				processor.get_labels()
		a      :		Optional[Any]	=				len(_lowercase					)
	except KeyError:
		raise ValueError("Task not found: %s" % (data_args.task_name)					)

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	a      :		List[Any]	=				AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,       num_labels=_lowercase    ,       finetuning_task=data_args.task_name    ,       cache_dir=model_args.cache_dir    ,       )
	a      :		Optional[int]	=				AutoTokenizer.from_pretrained(
	    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,       cache_dir=model_args.cache_dir    ,       )
	a      :		str	=				AutoModelForMultipleChoice.from_pretrained(
	    model_args.model_name_or_path    ,       from_tf=bool(".ckpt" in model_args.model_name_or_path					)    ,       config=_lowercase    ,       cache_dir=model_args.cache_dir    ,       )

	# Get datasets
	a      :		str	=				(
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir    ,       tokenizer=_lowercase    ,       task=data_args.task_name    ,       max_seq_length=data_args.max_seq_length    ,       overwrite_cache=data_args.overwrite_cache    ,       mode=Split.train    ,       )
	    if training_args.do_train
	    else None
	)
	a      :		Any	=				(
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir    ,       tokenizer=_lowercase    ,       task=data_args.task_name    ,       max_seq_length=data_args.max_seq_length    ,       overwrite_cache=data_args.overwrite_cache    ,       mode=Split.dev    ,       )
	    if training_args.do_eval
	    else None
	)

	def compute_metrics(_lowercase						:    EvalPrediction					) -> Dict:
		a      :		int	=				np.argmax(p.predictions    ,       axis=1					)
		return {"acc": simple_accuracy(_lowercase    ,       p.label_ids					)}

	# Data collator
	a      :		List[Any]	=				DataCollatorWithPadding(_lowercase    ,       pad_to_multiple_of=8					) if training_args.fpaa else None

	# Initialize our Trainer
	a      :		Dict	=				Trainer(
	    model=_lowercase    ,       args=_lowercase    ,       train_dataset=_lowercase    ,       eval_dataset=_lowercase    ,       compute_metrics=_lowercase    ,       data_collator=_lowercase    ,       )

	# Training
	if training_args.do_train:
		trainer.train(
		    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
		trainer.save_model()
		# For convenience, we also re-save the tokenizer to the same directory,
		# so that you can share your model easily on huggingface.co/models =)
		if trainer.is_world_master():
			tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
	a      :		Dict	=				{}
	if training_args.do_eval:
		logger.info("*** Evaluate ***"					)

		a      :		str	=				trainer.evaluate()

		a      :		List[str]	=				os.path.join(training_args.output_dir    ,       "eval_results.txt"					)
		if trainer.is_world_master():
			with open(_lowercase    ,       "w"					) as writer:
				logger.info("***** Eval results *****"					)
				for key, value in result.items():
					logger.info("  %s = %s"    ,       _lowercase    ,       _lowercase					)
					writer.write("%s = %s\n" % (key, value)					)

				results.update(_lowercase					)

	return results






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Union[str, Any]					) ->Any:






	'''simple docstring'''






	main()


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						: int     	=			'''<<<<<<< This should probably be modified because it mentions: '''

a						: Tuple     	=			'''=======
>>>>>>>
'''

a						: List[Any]     	=			[
    '''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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Namespace					) ->Any:






	'''simple docstring'''






	return ConvertCommand(args.tfds_path    ,       args.datasets_directory					)







class        __UpperCamelCase				(						a__						):






							@staticmethod
							def     __a   (   lowerCAmelCase__      )	-> int:
								a      :		Any	=				parser.add_parser(
								    "convert"      ,					help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."      ,					)
								train_parser.add_argument(
								    "--tfds_path"      ,					type=lowerCAmelCase__      ,					required=lowerCAmelCase__      ,					help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."      ,					)
								train_parser.add_argument(
								    "--datasets_directory"      ,					type=lowerCAmelCase__      ,					required=lowerCAmelCase__      ,					help="Path to the HuggingFace Datasets folder."      )
								train_parser.set_defaults(func=lowerCAmelCase__      )






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					*lowerCAmelCase__      )	-> Tuple:
								a      :		List[str]	=				get_logger("datasets-cli/converting"      )

								a      :		Optional[int]	=				tfds_path
								a      :		str	=				datasets_directory






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

								a      :		str	=				os.path.abspath(self._datasets_directory      )

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

								a      :		str	=				[]
								a      :		Union[str, Any]	=				[]
								a      :		str	=				{}

								if os.path.isdir(self._tfds_path      ):
									a      :		Dict	=				os.listdir(lowerCAmelCase__      )
								else:
									a      :		Dict	=				[os.path.basename(self._tfds_path      )]

								for f_name in file_names:
									self._logger.info(f"""Looking at file {f_name}"""      )
									a      :		Any	=				os.path.join(lowerCAmelCase__      ,					lowerCAmelCase__      )
									a      :		Dict	=				os.path.join(lowerCAmelCase__      ,					lowerCAmelCase__      )

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

									with open(lowerCAmelCase__      ,					encoding="utf-8"      ) as f:
										a      :		Any	=				f.readlines()

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

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

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

									if needs_manual_update:
										with_manual_update.append(lowerCAmelCase__      )

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

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


