


from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


lowerCAmelCase	=							logging.get_logger(__name__)


if is_torch_available():
			import torch
			from torch import nn
			from torch.utils.data import Dataset

			from transformers import Trainer


			class 	_a							(      _lowerCamelCase      ):

										def __init__(							self:     Tuple   ,						UpperCamelCase_:     int = 101							)      ->       Optional[int]:





												"""simple docstring"""







												lowercase__						=				length

										def __len__(							self:     int							)      ->       Optional[int]:





												"""simple docstring"""







												return self.length


										def __getitem__(							self:     Dict   ,						UpperCamelCase_:     Union[str, Any]							)      ->       Dict:





												"""simple docstring"""







												return i


			class 	_a							:

										def __call__(							self:     Optional[int]   ,						UpperCamelCase_:     Optional[Any]							)      ->       Tuple:





												"""simple docstring"""







												return {"input_ids": torch.tensor(lowercase_							), "labels": torch.tensor(lowercase_							)}


			class 	_a							(      nn.Module      ):

										def __init__(							self:     List[str]							)      ->       int:





												"""simple docstring"""







												super().__init__()
												# Add some (unused) params otherwise DDP will complain.
												lowercase__						=				nn.Linear(120   ,						80							)


										def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     List[str]=None							)      ->       int:





												"""simple docstring"""







												if labels is not None:
														return torch.tensor(0.0   ,						device=input_ids.device							), input_ids
												else:
														return input_ids


class 	_a							(      _lowerCamelCase      ):

							@require_torch_neuroncore
							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				f'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
									lowercase__						=				self.get_auto_remove_tmp_dir()
									lowercase__						=				f'--output_dir {output_dir}'.split()
									lowercase__						=				['''torchrun'''] + distributed_args + args
									execute_subprocess_async(lowercase_   ,						env=self.get_env()							)
									# successful return here == success - any errors would have caused an error in the sub-call


class 	_a							(      _lowerCamelCase      ):

							@require_torch_multi_gpu
							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				f'--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
									lowercase__						=				self.get_auto_remove_tmp_dir()
									lowercase__						=				f'--output_dir {output_dir}'.split()
									lowercase__						=				['''torchrun'''] + distributed_args + args
									execute_subprocess_async(lowercase_   ,						env=self.get_env()							)
									# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
			# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
			#
			# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

			lowerCAmelCase	=							HfArgumentParser((TrainingArguments,))
			lowerCAmelCase	=							parser.parse_args_into_dataclasses()[0]

			logger.warning(
			    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
			    f"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
			)

			# Essentially, what we want to verify in the distributed case is that we get all samples back,
			# in the right order. (this is crucial for prediction for instance)
			for dataset_length in [101, 40, 7]:
						lowerCAmelCase	=							DummyDataset(dataset_length)
						def       _a			(				SCREAMING_SNAKE_CASE					):


								"""simple docstring"""




								lowercase__						=				list(range(len(__UpperCAmelCase					)					)					)
								lowercase__						=				p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
								if not success and training_args.local_rank == 0:
										logger.warning(
										    '''Predictions and/or labels do not match expected results:\n  - predictions: '''
										    f'{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'					)
								return {"success": success}

						lowerCAmelCase	=							Trainer(
						    model=DummyModel(),
						    args=training_args,
						    data_collator=DummyDataCollator(),
						    eval_dataset=dataset,
						    compute_metrics=compute_metrics,
						)
						lowerCAmelCase	=							trainer.evaluate()
						logger.info(metrics)
						if metrics["eval_success"] is not True:
									logger.error(metrics)
									exit(1)

						lowerCAmelCase	=							trainer.predict(dataset)
						logger.info(p.metrics)
						if p.metrics["test_success"] is not True:
									logger.error(p.metrics)
									exit(1)

						lowerCAmelCase	=							2

						lowerCAmelCase	=							trainer.evaluate()
						logger.info(metrics)
						if metrics["eval_success"] is not True:
									logger.error(metrics)
									exit(1)

						lowerCAmelCase	=							trainer.predict(dataset)
						logger.info(p.metrics)
						if p.metrics["test_success"] is not True:
									logger.error(p.metrics)
									exit(1)
						lowerCAmelCase	=							None



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

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


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

lowerCAmelCase	=							'=======\n>>>>>>>\n'

lowerCAmelCase	=							[
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

lowerCAmelCase	=							[
    # (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       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		return ConvertCommand(args.tfds_path						,    args.datasets_directory					)


class 	_a							(      UpperCamelCase__      ):

							@staticmethod
							def 				lowerCamelCase_			(							UpperCamelCase_:     ArgumentParser							)      ->       int:





									"""simple docstring"""







									lowercase__						=				parser.add_parser(
									    '''convert'''   ,						help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''   ,						)
									train_parser.add_argument(
									    '''--tfds_path'''   ,						type=UpperCamelCase_   ,						required=UpperCamelCase_   ,						help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''   ,						)
									train_parser.add_argument(
									    '''--datasets_directory'''   ,						type=UpperCamelCase_   ,						required=UpperCamelCase_   ,						help='''Path to the HuggingFace Datasets folder.'''							)
									train_parser.set_defaults(func=UpperCamelCase_							)

							def __init__(							self:     Dict   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     str   ,						*UpperCamelCase_:     Union[str, Any]							)      ->       List[str]:





									"""simple docstring"""







									lowercase__						=				get_logger('''datasets-cli/converting'''							)

									lowercase__						=				tfds_path
									lowercase__						=				datasets_directory


							def 				lowerCamelCase_			(							self:     str							)      ->       Dict:





									"""simple docstring"""







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

									lowercase__						=				os.path.abspath(self._datasets_directory							)

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

									lowercase__						=				[]
									lowercase__						=				[]
									lowercase__						=				{}

									if os.path.isdir(self._tfds_path							):
											lowercase__						=				os.listdir(UpperCamelCase_							)
									else:
											lowercase__						=				[os.path.basename(self._tfds_path							)]

									for f_name in file_names:
											self._logger.info(f'Looking at file {f_name}'							)
											lowercase__						=				os.path.join(UpperCamelCase_   ,						UpperCamelCase_							)
											lowercase__						=				os.path.join(UpperCamelCase_   ,						UpperCamelCase_							)

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

											with open(UpperCamelCase_   ,						encoding='''utf-8'''							) as f:
													lowercase__						=				f.readlines()

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

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

											if is_builder or "wmt" in f_name:
													# We create a new directory for each dataset
													lowercase__						=				f_name.replace('''.py'''   ,						''''''							)
													lowercase__						=				os.path.join(UpperCamelCase_   ,						UpperCamelCase_							)
													lowercase__						=				os.path.join(UpperCamelCase_   ,						UpperCamelCase_							)
													os.makedirs(UpperCamelCase_   ,						exist_ok=UpperCamelCase_							)
													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(UpperCamelCase_							)

											if needs_manual_update:
													with_manual_update.append(UpperCamelCase_							)

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

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


