


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


_snake_case				=			logging.get_logger(__name__)




class  A_ (			a_  ):







 """simple docstring"""





 def __init__(      self       :			int							,     _a       :			Union["PreTrainedModel", nn.Module] = None							,     _a       :			"TrainingArguments" = None							,     _a       :			Optional["DataCollator"] = None							,     _a       :			Optional[Dataset] = None							,     _a       :			Optional[Union[Dataset, Dict[str, Dataset]]] = None							,     _a       :			Optional["PreTrainedTokenizerBase"] = None							,     _a       :			Optional[Callable[[], "PreTrainedModel"]] = None							,     _a       :			Optional[Callable[["EvalPrediction"], Dict]] = None							,     _a       :			Optional[List["TrainerCallback"]] = None							,     _a       :			Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)							,     _a       :			Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None							,     )     ->      Dict:
      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:
           UpperCAmelCase    :					Optional[Any]			=      self.load_generation_config(self.args.generation_config					)
           UpperCAmelCase    :					Any			=      gen_config
 @staticmethod
 def 	_UpperCamelCase     (      _a       :			Union[str, GenerationConfig]					)     ->      GenerationConfig:

      # GenerationConfig provided, nothing to do
      if isinstance(_a							,     _a					):
           return deepcopy(_a					)

      # str or Path
      UpperCAmelCase    :					int			=      Path(_a					) if isinstance(_a							,     _a					) else gen_config_arg
      UpperCAmelCase    :					int			=      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():
           UpperCAmelCase    :					Dict			=      pretrained_model_name.name
           UpperCAmelCase    :					Any			=      pretrained_model_name.parent
      # dir path
      elif pretrained_model_name.is_dir():
           pass
      # model id or URL
      else:
           UpperCAmelCase    :					Optional[int]			=      gen_config_arg

      UpperCAmelCase    :					Tuple			=      GenerationConfig.from_pretrained(_a							,     _a					)
      return gen_config
 def 	_UpperCamelCase     (      self       :			int							,     _a       :			Optional[Dataset] = None							,     _a       :			Optional[List[str]] = None							,     _a       :			str = "eval"							,     **_a       :			Union[str, Any]							,     )     ->      Dict[str, float]:
      UpperCAmelCase    :					List[str]			=      gen_kwargs.copy()
      if gen_kwargs.get("max_length"					) is None and gen_kwargs.get("max_new_tokens"					) is None:
           UpperCAmelCase    :					Optional[Any]			=      self.args.generation_max_length
      UpperCAmelCase    :					List[str]			=      (
          gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"					) is not None else self.args.generation_num_beams
      )
      UpperCAmelCase    :					Any			=      gen_kwargs

      return super().evaluate(_a							,     ignore_keys=_a							,     metric_key_prefix=_a					)
 def 	_UpperCamelCase     (      self       :			Dict							,     _a       :			Dataset							,     _a       :			Optional[List[str]] = None							,     _a       :			str = "test"							,     **_a       :			Dict							,     )     ->      "PredictionOutput":
      UpperCAmelCase    :					List[Any]			=      gen_kwargs.copy()
      if gen_kwargs.get("max_length"					) is None and gen_kwargs.get("max_new_tokens"					) is None:
           UpperCAmelCase    :					List[Any]			=      self.args.generation_max_length
      UpperCAmelCase    :					List[Any]			=      (
          gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"					) is not None else self.args.generation_num_beams
      )
      UpperCAmelCase    :					Optional[Any]			=      gen_kwargs

      return super().predict(_a							,     ignore_keys=_a							,     metric_key_prefix=_a					)
 def 	_UpperCamelCase     (      self       :			Union[str, Any]							,     _a       :			nn.Module							,     _a       :			Dict[str, Union[torch.Tensor, Any]]							,     _a       :			bool							,     _a       :			Optional[List[str]] = None							,     **_a       :			Optional[int]							,     )     ->      Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

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

      UpperCAmelCase    :					str			=      "labels" in inputs
      UpperCAmelCase    :					List[Any]			=      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"					):
           UpperCAmelCase    :					List[Any]			=      self._gen_kwargs.copy()

      if gen_kwargs.get("max_length"					) is None and gen_kwargs.get("max_new_tokens"					) is None:
           UpperCAmelCase    :					int			=      self.model.config.max_length
      UpperCAmelCase    :					List[str]			=      (
          gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"					) is not None else self.model.config.num_beams
      )
      UpperCAmelCase    :					Union[str, Any]			=      True if is_deepspeed_zeroa_enabled() else False
      UpperCAmelCase    :					Any			=      (
          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
      ):
           UpperCAmelCase    :					str			=      {k: v for k, v in inputs.items() if k != "decoder_input_ids"}
      UpperCAmelCase    :					Tuple			=      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:
           UpperCAmelCase    :					int			=      False

      # Retrieves GenerationConfig from model.generation_config
      UpperCAmelCase    :					int			=      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:
           UpperCAmelCase    :					Tuple			=      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:
           UpperCAmelCase    :					Optional[int]			=      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():
                     UpperCAmelCase    :					List[Any]			=      model(**_a					)
                if self.label_smoother is not None:
                     UpperCAmelCase    :					List[Any]			=      self.label_smoother(_a							,     inputs["labels"]					).mean().detach()
                else:
                     UpperCAmelCase    :					Optional[Any]			=      (outputs["loss"] if isinstance(_a							,     _a					) else outputs[0]).mean().detach()
           else:
                UpperCAmelCase    :					Dict			=      None

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

      if has_labels:
           UpperCAmelCase    :					Tuple			=      inputs["labels"]
           if labels.shape[-1] < gen_config.max_length:
                UpperCAmelCase    :					Any			=      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:
                UpperCAmelCase    :					str			=      self._pad_tensors_to_max_len(_a							,     gen_config.max_new_tokens + 1					)
      else:
           UpperCAmelCase    :					int			=      None

      return loss, generated_tokens, labels

 def 	_UpperCamelCase     (      self       :			int							,     _a       :			Any							,     _a       :			Optional[int]					)     ->      str:
      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
           UpperCAmelCase    :					List[str]			=      (
               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:
                UpperCAmelCase    :					Any			=      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"					)

      UpperCAmelCase    :					Optional[int]			=      pad_token_id * torch.ones(
          (tensor.shape[0], max_length)							,     dtype=tensor.dtype							,     device=tensor.device					)
      UpperCAmelCase    :					Optional[int]			=      tensor
      return padded_tensor







def 	A (		A__			):
     UpperCAmelCase    :					Dict			=      9

     while num > 0:
          if num % 1_0 != digit:
               return False
          num //= 1_0_0
          digit -= 1

     return True







def 	A (		):
     UpperCAmelCase    :					Optional[Any]			=      1_3_8_9_0_2_6_6_3

     while not is_square_form(num * num			):
          if num % 1_0 == 3:
               num -= 6  # (3 - 6) % 10 = 7
          else:
               num -= 4  # (7 - 4) % 10 = 3

     return num * 1_0


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



