




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      a__       (   snake_case__ ):






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




											"""simple docstring"""



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

											# 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							  =			self.load_generation_config(self.args.generation_config     )
																__lowerCAmelCase							  =			gen_config






						@staticmethod
						def 							__SCREAMING_SNAKE_CASE(  _A     ):




											"""simple docstring"""



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

											# str or Path
											__lowerCAmelCase							  =			Path(_A     ) if isinstance(_A						,     _A     ) else gen_config_arg
											__lowerCAmelCase							  =			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							  =			pretrained_model_name.name
																__lowerCAmelCase							  =			pretrained_model_name.parent
											# dir path
											elif pretrained_model_name.is_dir():
																pass
											# model id or URL
											else:
																__lowerCAmelCase							  =			gen_config_arg

											__lowerCAmelCase							  =			GenerationConfig.from_pretrained(_A						,     _A     )
											return gen_config






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A = None						,     _A = None						,     _A = "eval"						,     **_A						,     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			gen_kwargs.copy()
											if gen_kwargs.get("max_length"     ) is None and gen_kwargs.get("max_new_tokens"     ) is None:
																__lowerCAmelCase							  =			self.args.generation_max_length
											__lowerCAmelCase							  =			(
											    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"     ) is not None else self.args.generation_num_beams
											)
											__lowerCAmelCase							  =			gen_kwargs

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






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = None						,     _A = "test"						,     **_A						,     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			gen_kwargs.copy()
											if gen_kwargs.get("max_length"     ) is None and gen_kwargs.get("max_new_tokens"     ) is None:
																__lowerCAmelCase							  =			self.args.generation_max_length
											__lowerCAmelCase							  =			(
											    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"     ) is not None else self.args.generation_num_beams
											)
											__lowerCAmelCase							  =			gen_kwargs

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






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A						,     _A = None						,     **_A						,     ):




											"""simple docstring"""



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

											__lowerCAmelCase							  =			"labels" in inputs
											__lowerCAmelCase							  =			self._prepare_inputs(_A     )

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

											if len(_A     ) == 0 and hasattr(self						,     "_gen_kwargs"     ):
																__lowerCAmelCase							  =			self._gen_kwargs.copy()

											if gen_kwargs.get("max_length"     ) is None and gen_kwargs.get("max_new_tokens"     ) is None:
																__lowerCAmelCase							  =			self.model.config.max_length
											__lowerCAmelCase							  =			(
											    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"     ) is not None else self.model.config.num_beams
											)
											__lowerCAmelCase							  =			True if is_deepspeed_zeroa_enabled() else False
											__lowerCAmelCase							  =			(
											    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							  =			{k: v for k, v in inputs.items() if k != "decoder_input_ids"}
											__lowerCAmelCase							  =			self.model.generate(**_A						,     **_A     )

											# 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							  =			False

											# Retrieves GenerationConfig from model.generation_config
											__lowerCAmelCase							  =			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							  =			self._pad_tensors_to_max_len(_A						,     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							  =			self._pad_tensors_to_max_len(_A						,     gen_config.max_new_tokens + 1     )

											with torch.no_grad():
																if has_labels:
																					with self.compute_loss_context_manager():
																										__lowerCAmelCase							  =			model(**_A     )
																					if self.label_smoother is not None:
																										__lowerCAmelCase							  =			self.label_smoother(_A						,     inputs["labels"]     ).mean().detach()
																					else:
																										__lowerCAmelCase							  =			(outputs["loss"] if isinstance(_A						,     _A     ) else outputs[0]).mean().detach()
																else:
																					__lowerCAmelCase							  =			None

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

											if has_labels:
																__lowerCAmelCase							  =			inputs["labels"]
																if labels.shape[-1] < gen_config.max_length:
																					__lowerCAmelCase							  =			self._pad_tensors_to_max_len(_A						,     gen_config.max_length     )
																elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
																					__lowerCAmelCase							  =			self._pad_tensors_to_max_len(_A						,     gen_config.max_new_tokens + 1     )
											else:
																__lowerCAmelCase							  =			None

											return loss, generated_tokens, labels






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A     ):




											"""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							  =			(
																    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							  =			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							  =			pad_token_id * torch.ones(
											    (tensor.shape[0], max_length)						,     dtype=tensor.dtype						,     device=tensor.device     )
											__lowerCAmelCase							  =			tensor
											return padded_tensor










import decimal







def       _a (		):
					__lowerCAmelCase							  =			0
					__lowerCAmelCase							  =			decimal.Context(prec=1_05	)
					for i in range(2 , 1_00	):
										__lowerCAmelCase							  =			decimal.Decimal(SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			number.sqrt(SCREAMING_SNAKE_CASE_	)
										if len(str(SCREAMING_SNAKE_CASE_	)	) > 1:
															answer += int(str(SCREAMING_SNAKE_CASE_	)[0]	)
															__lowerCAmelCase							  =			str(SCREAMING_SNAKE_CASE_	)[2:1_01]
															answer += sum(int(SCREAMING_SNAKE_CASE_	) for x in sqrt_number_str	)
					return answer


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(f'''{solution() = }''')




