




from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
							from .data.data_collator import DataCollator
							from .modeling_utils import PreTrainedModel
							from .tokenization_utils_base import PreTrainedTokenizerBase
							from .trainer_callback import TrainerCallback
							from .trainer_utils import EvalPrediction, PredictionOutput
							from .training_args import TrainingArguments


UpperCamelCase__						=		logging.get_logger(__name__)


class 							__SCREAMING_SNAKE_CASE				(						_lowerCamelCase    ):

					def __init__(							self					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = (None, None)					,			__lowercase = None					,			):







						'''simple docstring'''




						super().__init__(
						    model=_SCREAMING_SNAKE_CASE					,			args=_SCREAMING_SNAKE_CASE					,			data_collator=_SCREAMING_SNAKE_CASE					,			train_dataset=_SCREAMING_SNAKE_CASE					,			eval_dataset=_SCREAMING_SNAKE_CASE					,			tokenizer=_SCREAMING_SNAKE_CASE					,			model_init=_SCREAMING_SNAKE_CASE					,			compute_metrics=_SCREAMING_SNAKE_CASE					,			callbacks=_SCREAMING_SNAKE_CASE					,			optimizers=_SCREAMING_SNAKE_CASE					,			preprocess_logits_for_metrics=_SCREAMING_SNAKE_CASE					,			)

						# Override self.model.generation_config if a GenerationConfig is specified in args.
						# Priority: args.generation_config > model.generation_config > default GenerationConfig.
						if self.args.generation_config is not None:
							lowerCamelCase_				:						Tuple				  =				self.load_generation_config(self.args.generation_config   )
							lowerCamelCase_				:						int				  =				gen_config

					@staticmethod
					def       lowercase_				(							__lowercase   ):







						'''simple docstring'''




						if isinstance(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE   ):
							return deepcopy(_SCREAMING_SNAKE_CASE   )

						# str or Path
						lowerCamelCase_				:						Dict				  =				Path(_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE   ) else gen_config_arg
						lowerCamelCase_				:						Any				  =				None

						# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
						# This step is required in order to determine config_file_name
						if pretrained_model_name.is_file():
							lowerCamelCase_				:						List[Any]				  =				pretrained_model_name.name
							lowerCamelCase_				:						Tuple				  =				pretrained_model_name.parent
						# dir path
						elif pretrained_model_name.is_dir():
							pass
						# model id or URL
						else:
							lowerCamelCase_				:						List[Any]				  =				gen_config_arg

						lowerCamelCase_				:						Optional[int]				  =				GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE   )
						return gen_config

					def       lowercase_				(							self					,			__lowercase = None					,			__lowercase = None					,			__lowercase = "eval"					,			**__lowercase					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Any				  =				gen_kwargs.copy()
						if gen_kwargs.get('max_length'   ) is None and gen_kwargs.get('max_new_tokens'   ) is None:
							lowerCamelCase_				:						Optional[Any]				  =				self.args.generation_max_length
						lowerCamelCase_				:						Tuple				  =				(
						    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'   ) is not None else self.args.generation_num_beams
						)
						lowerCamelCase_				:						Optional[int]				  =				gen_kwargs

						return super().evaluate(_SCREAMING_SNAKE_CASE					,			ignore_keys=_SCREAMING_SNAKE_CASE					,			metric_key_prefix=_SCREAMING_SNAKE_CASE   )

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None					,			__lowercase = "test"					,			**__lowercase					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Dict				  =				gen_kwargs.copy()
						if gen_kwargs.get('max_length'   ) is None and gen_kwargs.get('max_new_tokens'   ) is None:
							lowerCamelCase_				:						Optional[Any]				  =				self.args.generation_max_length
						lowerCamelCase_				:						int				  =				(
						    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'   ) is not None else self.args.generation_num_beams
						)
						lowerCamelCase_				:						Tuple				  =				gen_kwargs

						return super().predict(_SCREAMING_SNAKE_CASE					,			ignore_keys=_SCREAMING_SNAKE_CASE					,			metric_key_prefix=_SCREAMING_SNAKE_CASE   )

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase = None					,			**__lowercase					,			):







						'''simple docstring'''




						if not self.args.predict_with_generate or prediction_loss_only:
							return super().prediction_step(
							    _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			prediction_loss_only=_SCREAMING_SNAKE_CASE					,			ignore_keys=_SCREAMING_SNAKE_CASE   )

						lowerCamelCase_				:						str				  =				'labels' in inputs
						lowerCamelCase_				:						List[str]				  =				self._prepare_inputs(_SCREAMING_SNAKE_CASE   )

						# XXX: adapt synced_gpus for fairscale as well
						# Priority (handled in generate):
						# gen_kwargs > model.generation_config > default GenerationConfig()

						if len(_SCREAMING_SNAKE_CASE   ) == 0 and hasattr(self					,			'_gen_kwargs'   ):
							lowerCamelCase_				:						Dict				  =				self._gen_kwargs.copy()

						if gen_kwargs.get('max_length'   ) is None and gen_kwargs.get('max_new_tokens'   ) is None:
							lowerCamelCase_				:						Tuple				  =				self.model.config.max_length
						lowerCamelCase_				:						Optional[Any]				  =				(
						    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'   ) is not None else self.model.config.num_beams
						)
						lowerCamelCase_				:						Union[str, Any]				  =				True if is_deepspeed_zeroa_enabled() else False
						lowerCamelCase_				:						int				  =				(
						    gen_kwargs['synced_gpus'] if gen_kwargs.get('synced_gpus'   ) is not None else default_synced_gpus
						)

						# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
						# (otherwise, it would continue generating from the padded `decoder_input_ids`)
						if (
						    "labels" in inputs
						    and "decoder_input_ids" in inputs
						    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
						):
							lowerCamelCase_				:						Optional[int]				  =				{k: v for k, v in inputs.items() if k != 'decoder_input_ids'}
						lowerCamelCase_				:						Union[str, Any]				  =				self.model.generate(**_SCREAMING_SNAKE_CASE					,			**_SCREAMING_SNAKE_CASE   )

						# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
						# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
						# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
						if self.model.generation_config._from_model_config:
							lowerCamelCase_				:						str				  =				False

						# Retrieves GenerationConfig from model.generation_config
						lowerCamelCase_				:						List[Any]				  =				self.model.generation_config
						# in case the batch is shorter than max length, the output should be padded
						if generated_tokens.shape[-1] < gen_config.max_length:
							lowerCamelCase_				:						Any				  =				self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE					,			gen_config.max_length   )
						elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
							lowerCamelCase_				:						Optional[Any]				  =				self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE					,			gen_config.max_new_tokens + 1   )

						with torch.no_grad():
							if has_labels:
								with self.compute_loss_context_manager():
									lowerCamelCase_				:						List[Any]				  =				model(**_SCREAMING_SNAKE_CASE   )
								if self.label_smoother is not None:
									lowerCamelCase_				:						Optional[int]				  =				self.label_smoother(_SCREAMING_SNAKE_CASE					,			inputs['labels']   ).mean().detach()
								else:
									lowerCamelCase_				:						Optional[int]				  =				(outputs['loss'] if isinstance(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE   ) else outputs[0]).mean().detach()
							else:
								lowerCamelCase_				:						Optional[int]				  =				None

						if self.args.prediction_loss_only:
							return loss, None, None

						if has_labels:
							lowerCamelCase_				:						Tuple				  =				inputs['labels']
							if labels.shape[-1] < gen_config.max_length:
								lowerCamelCase_				:						List[str]				  =				self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE					,			gen_config.max_length   )
							elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
								lowerCamelCase_				:						int				  =				self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE					,			gen_config.max_new_tokens + 1   )
						else:
							lowerCamelCase_				:						Optional[int]				  =				None

						return loss, generated_tokens, labels





					def       lowercase_				(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						if self.tokenizer is not None and hasattr(self.tokenizer					,			'pad_token_id'   ):
							# If PAD token is not defined at least EOS token has to be defined
							lowerCamelCase_				:						Any				  =				(
							    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
							)
						else:
							if self.model.config.pad_token_id is not None:
								lowerCamelCase_				:						List[str]				  =				self.model.config.pad_token_id
							else:
								raise ValueError('Pad_token_id must be set in the configuration of the model, in order to pad tensors'   )

						lowerCamelCase_				:						Dict				  =				pad_token_id * torch.ones(
						    (tensor.shape[0], max_length)					,			dtype=tensor.dtype					,			device=tensor.device   )
						lowerCamelCase_				:						Tuple				  =				tensor
						return padded_tensor


"""simple docstring"""







def        __lowerCAmelCase							(_UpperCamelCase = 10**6						):

	if n <= 0:
		raise ValueError('Please enter an integer greater than 0'						)

	__lowerCAmelCase     :		Optional[Any]		     = list(range(n + 1						)						)
	for number in range(2			,    n + 1						):
		if phi[number] == number:
			phi[number] -= 1
			for multiple in range(number * 2			,    n + 1			,    _UpperCamelCase						):
				__lowerCAmelCase     :		Tuple		     = (phi[multiple] // number) * (number - 1)

	__lowerCAmelCase     :		Any		     = 1
	for number in range(1			,    n + 1						):
		if (answer / phi[answer]) < (number / phi[number]):
			__lowerCAmelCase     :		List[Any]		     = number

	return answer


if __name__ == "__main__":
							print(solution())