






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


a_		:						Optional[int]             =    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 		_snake_case  (  A__					):






							def __init__(       self		,					a = 101)      ->       str:
										SCREAMING_SNAKE_CASE						= length






							def __len__(       self)      ->       List[Any]:
										return self.length







							def __getitem__(       self		,					a)      ->       int:
										return i



				class 		_snake_case  :






							def __call__(       self		,					a)      ->       Optional[int]:
										return {"input_ids": torch.tensor(a), "labels": torch.tensor(a)}



				class 		_snake_case  (  nn.Module					):






							def __init__(       self)      ->       Union[str, Any]:
										super().__init__()
										# Add some (unused) params otherwise DDP will complain.
										SCREAMING_SNAKE_CASE						= nn.Linear(120		,					80)







							def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a=None)      ->       List[str]:
										if labels is not None:
													return torch.tensor(0.0		,					device=input_ids.device), input_ids
										else:
													return input_ids

class 		_snake_case  (  A__					):






			@require_torch_neuroncore
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= f'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
						SCREAMING_SNAKE_CASE						= self.get_auto_remove_tmp_dir()
						SCREAMING_SNAKE_CASE						= f'''--output_dir {output_dir}'''.split()
						SCREAMING_SNAKE_CASE						= ['torchrun'] + distributed_args + args
						execute_subprocess_async(a		,					env=self.get_env())
						# successful return here == success - any errors would have caused an error in the sub-call



class 		_snake_case  (  A__					):






			@require_torch_multi_gpu
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= f'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
						SCREAMING_SNAKE_CASE						= self.get_auto_remove_tmp_dir()
						SCREAMING_SNAKE_CASE						= f'''--output_dir {output_dir}'''.split()
						SCREAMING_SNAKE_CASE						= ['torchrun'] + distributed_args + args
						execute_subprocess_async(a		,					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

				a_		:						Any             =    HfArgumentParser((TrainingArguments,))
				a_		:						Dict             =    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 [1_01, 40, 7]:
								a_		:						int             =    DummyDataset(dataset_length)






								def 				lowerCamelCase__				(_UpperCAmelCase):
											SCREAMING_SNAKE_CASE						= list(range(len(_UpperCAmelCase)))
											SCREAMING_SNAKE_CASE						= 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}

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

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

								a_		:						Any             =    2

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

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







								a_		:						Union[str, Any]             =    None












from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

a_		:						str             =    (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a_		:						list[int]             =    [ord(letter) for letter in string.ascii_lowercase]
a_		:						set[int]             =    {ord(char) for char in VALID_CHARS}

a_		:						list[str]             =    ["the", "be", "to", "of", "and", "in", "that", "have"]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= ""
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42

			for keychar, cipherchar in zip(cycle(_UpperCAmelCase)							,       _UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= cipherchar ^ keychar
						if decodedchar not in VALID_INTS:
									return None
						decoded += chr(_UpperCAmelCase)

			return decoded






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= []
			for key in product(_UpperCAmelCase							,       repeat=3):
						SCREAMING_SNAKE_CASE						= try_key(_UpperCAmelCase							,       _UpperCAmelCase)
						if encoded is not None:
									possibles.append(_UpperCAmelCase)
			return possibles






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			return [possible for possible in possibles if common_word in possible.lower()]






def 				lowerCamelCase__				(_UpperCAmelCase = "p059_cipher.txt"):
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= Path(_UpperCAmelCase).parent.joinpath(_UpperCAmelCase).read_text(encoding='utf-8')

			SCREAMING_SNAKE_CASE						= [int(_UpperCAmelCase) for number in data.strip().split(',')]

			SCREAMING_SNAKE_CASE						= filter_valid_chars(_UpperCAmelCase)
			for common_word in COMMON_WORDS:
						SCREAMING_SNAKE_CASE						= filter_common_word(_UpperCAmelCase							,       _UpperCAmelCase)
						if len(_UpperCAmelCase) == 1:
									break

			SCREAMING_SNAKE_CASE						= possibles[0]
			return sum(ord(_UpperCAmelCase) for char in decoded_text)


if __name__ == "__main__":
				print(f"""{solution() = }""")




