'''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


__lowerCAmelCase	:       List[str]						       =	"<<<<<<< This should probably be modified because it mentions: "

__lowerCAmelCase	:       List[str]						       =	"=======\n>>>>>>>\n"

__lowerCAmelCase	:       Optional[int]						       =	[
    "TextEncoderConfig",
    "ByteTextEncoder",
    "SubwordTextEncoder",
    "encoder_config",
    "maybe_build_from_corpus",
    "manual_dir",
]

__lowerCAmelCase	:       Optional[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__				:   str       ):


	"""simple docstring"""
	return ConvertCommand(args.tfds_path    ,					args.datasets_directory       )






class 						A   (   snake_case__   ):





							@staticmethod
							def       snake_case__						(      __a				:							Any	)							->	List[str]:
								__UpperCAmelCase							=			parser.add_parser(
								    '''convert'''     ,		help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''     ,		)
								train_parser.add_argument(
								    '''--tfds_path'''     ,		type=lowercase_     ,		required=lowercase_     ,		help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''     ,		)
								train_parser.add_argument(
								    '''--datasets_directory'''     ,		type=lowercase_     ,		required=lowercase_     ,		help='''Path to the HuggingFace Datasets folder.'''	)
								train_parser.set_defaults(func=lowercase_	)





							def __init__(      self				:							int     ,		__a				:							List[str]     ,		__a				:							List[Any]     ,		*__a				:							Union[str, Any]	)							->	Union[str, Any]:
								__UpperCAmelCase							=			get_logger('''datasets-cli/converting'''	)

								__UpperCAmelCase							=			tfds_path
								__UpperCAmelCase							=			datasets_directory






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

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

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

								__UpperCAmelCase							=			[]
								__UpperCAmelCase							=			[]
								__UpperCAmelCase							=			{}

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

								for f_name in file_names:
									self._logger.info(f"""Looking at file {f_name}"""	)
									__UpperCAmelCase							=			os.path.join(lowercase_     ,		lowercase_	)
									__UpperCAmelCase							=			os.path.join(lowercase_     ,		lowercase_	)

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

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

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

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

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

									if needs_manual_update:
										with_manual_update.append(lowercase_	)

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

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

'''simple docstring'''


import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory





def       lowerCAmelCase					(		):


	"""simple docstring"""
	raise RuntimeError('''CUDA out of memory.'''       )






class 						A   (   nn.Module   ):





							def __init__(      self				:							Optional[Any]	)							->	int:
								super().__init__()
								__UpperCAmelCase							=			nn.Linear(3     ,		4	)
								__UpperCAmelCase							=			nn.BatchNormad(4	)
								__UpperCAmelCase							=			nn.Linear(4     ,		5	)






							def       snake_case__						(      self				:							List[str]     ,		__a				:							Optional[int]	)							->	Optional[int]:
								return self.lineara(self.batchnorm(self.lineara(__a	)	)	)






class 						A   (   unittest.TestCase   ):





							def       snake_case__						(      self				:							Optional[int]	)							->	Any:
								__UpperCAmelCase							=			[]

								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							Union[str, Any]	):
									nonlocal batch_sizes
									batch_sizes.append(__a	)
									if batch_size != 8:
										raise_fake_out_of_memory()

								mock_training_loop_function()
								self.assertListEqual(__a     ,		[1_2_8, 6_4, 3_2, 1_6, 8]	)





							def       snake_case__						(      self				:							str	)							->	int:
								__UpperCAmelCase							=			[]

								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							str     ,		__a				:							Optional[int]	):
									nonlocal batch_sizes
									batch_sizes.append(__a	)
									if batch_size != 8:
										raise_fake_out_of_memory()
									return batch_size, arga

								__UpperCAmelCase				,			__UpperCAmelCase							=			mock_training_loop_function('''hello'''	)
								self.assertListEqual(__a     ,		[1_2_8, 6_4, 3_2, 1_6, 8]	)
								self.assertListEqual([bs, arga]     ,		[8, '''hello''']	)





							def       snake_case__						(      self				:							Any	)							->	int:
								@find_executable_batch_size(starting_batch_size=0	)
								def mock_training_loop_function(__a				:							Optional[int]	):
									pass

								with self.assertRaises(__a	) as cm:
									mock_training_loop_function()
									self.assertIn('''No executable batch size found, reached zero.'''     ,		cm.exception.args[0]	)





							def       snake_case__						(      self				:							Any	)							->	List[Any]:
								@find_executable_batch_size(starting_batch_size=1_6	)
								def mock_training_loop_function(__a				:							Dict	):
									if batch_size > 0:
										raise_fake_out_of_memory()
									pass

								with self.assertRaises(__a	) as cm:
									mock_training_loop_function()
									self.assertIn('''No executable batch size found, reached zero.'''     ,		cm.exception.args[0]	)





							def       snake_case__						(      self				:							List[Any]	)							->	List[str]:
								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							str     ,		__a				:							Union[str, Any]     ,		__a				:							int	):
									if batch_size != 8:
										raise raise_fake_out_of_memory()

								with self.assertRaises(__a	) as cm:
									mock_training_loop_function(1_2_8     ,		'''hello'''     ,		'''world'''	)
									self.assertIn('''Batch size was passed into `f`'''     ,		cm.exception.args[0]	)
									self.assertIn('''`f(arg1=\'hello\', arg2=\'world\')'''     ,		cm.exception.args[0]	)





							def       snake_case__						(      self				:							Tuple	)							->	Optional[Any]:
								@find_executable_batch_size(starting_batch_size=1_6	)
								def mock_training_loop_function(__a				:							Tuple	):
									raise ValueError('''Oops, we had an error!'''	)

								with self.assertRaises(__a	) as cm:
									mock_training_loop_function()
									self.assertIn('''Oops, we had an error!'''     ,		cm.exception.args[0]	)






							@require_cuda
							def       snake_case__						(      self				:							Any	)							->	List[Any]:
								__UpperCAmelCase							=			torch.cuda.memory_allocated()
								__UpperCAmelCase							=			ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()     ,		__a	)
								__UpperCAmelCase							=			release_memory(__a	)
								self.assertEqual(torch.cuda.memory_allocated()     ,		__a	)
