






"""simple docstring"""


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 :    Union[str, Any]      =			logging.get_logger(__name__)






class __a ( snake_case_					):



  def __init__(self	:		Any					,      lowercase_	:		Union["PreTrainedModel", nn.Module] = None					,      lowercase_	:		"TrainingArguments" = None					,      lowercase_	:		Optional["DataCollator"] = None					,      lowercase_	:		Optional[Dataset] = None					,      lowercase_	:		Optional[Union[Dataset, Dict[str, Dataset]]] = None					,      lowercase_	:		Optional["PreTrainedTokenizerBase"] = None					,      lowercase_	:		Optional[Callable[[], "PreTrainedModel"]] = None					,      lowercase_	:		Optional[Callable[["EvalPrediction"], Dict]] = None					,      lowercase_	:		Optional[List["TrainerCallback"]] = None					,      lowercase_	:		Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)					,      lowercase_	:		Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None					,      )		->				Union[str, Any]:
   super().__init__(
       model=lowercase_					,      args=lowercase_					,      data_collator=lowercase_					,      train_dataset=lowercase_					,      eval_dataset=lowercase_					,      tokenizer=lowercase_					,      model_init=lowercase_					,      compute_metrics=lowercase_					,      callbacks=lowercase_					,      optimizers=lowercase_					,      preprocess_logits_for_metrics=lowercase_					,      )

   # 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:
    __A						:							Optional[int]      =						self.load_generation_config(self.args.generation_config )
    __A						:							Optional[Any]      =						gen_config



  @staticmethod
  def     A_					(lowercase_	:		Union[str, GenerationConfig] )		->				GenerationConfig:

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

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

   __A						:							Any      =						GenerationConfig.from_pretrained(lowercase_					,      lowercase_ )
   return gen_config



  def     A_					(self	:		Tuple					,      lowercase_	:		Optional[Dataset] = None					,      lowercase_	:		Optional[List[str]] = None					,      lowercase_	:		str = "eval"					,      **lowercase_	:		int					,      )		->				Dict[str, float]:
   __A						:							Optional[int]      =						gen_kwargs.copy()
   if gen_kwargs.get('max_length' ) is None and gen_kwargs.get('max_new_tokens' ) is None:
    __A						:							List[Any]      =						self.args.generation_max_length
   __A						:							Any      =						(
       gen_kwargs['num_beams'] if gen_kwargs.get('num_beams' ) is not None else self.args.generation_num_beams
   )
   __A						:							List[Any]      =						gen_kwargs

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



  def     A_					(self	:		Union[str, Any]					,      lowercase_	:		Dataset					,      lowercase_	:		Optional[List[str]] = None					,      lowercase_	:		str = "test"					,      **lowercase_	:		Union[str, Any]					,      )		->				"PredictionOutput":
   __A						:							Dict      =						gen_kwargs.copy()
   if gen_kwargs.get('max_length' ) is None and gen_kwargs.get('max_new_tokens' ) is None:
    __A						:							List[Any]      =						self.args.generation_max_length
   __A						:							Union[str, Any]      =						(
       gen_kwargs['num_beams'] if gen_kwargs.get('num_beams' ) is not None else self.args.generation_num_beams
   )
   __A						:							Optional[Any]      =						gen_kwargs

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



  def     A_					(self	:		str					,      lowercase_	:		nn.Module					,      lowercase_	:		Dict[str, Union[torch.Tensor, Any]]					,      lowercase_	:		bool					,      lowercase_	:		Optional[List[str]] = None					,      **lowercase_	:		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(
        lowercase_					,      lowercase_					,      prediction_loss_only=lowercase_					,      ignore_keys=lowercase_ )

   __A						:							Tuple      =						'labels' in inputs
   __A						:							Any      =						self._prepare_inputs(lowercase_ )

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

   if len(lowercase_ ) == 0 and hasattr(self					,      '_gen_kwargs' ):
    __A						:							str      =						self._gen_kwargs.copy()

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

   # 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:
    __A						:							Optional[int]      =						False

   # Retrieves GenerationConfig from model.generation_config
   __A						:							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:
    __A						:							Dict      =						self._pad_tensors_to_max_len(lowercase_					,      gen_config.max_length )
   elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
    __A						:							str      =						self._pad_tensors_to_max_len(lowercase_					,      gen_config.max_new_tokens + 1 )

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

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

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

   return loss, generated_tokens, labels



  def     A_					(self	:		Dict					,      lowercase_	:		Optional[Any]					,      lowercase_	:		Optional[int] )		->				List[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
    __A						:							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:
     __A						:							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' )

   __A						:							Union[str, Any]      =						pad_token_id * torch.ones(
       (tensor.shape[0], max_length)					,      dtype=tensor.dtype					,      device=tensor.device )
   __A						:							Tuple      =						tensor
   return padded_tensor













"""simple docstring"""


def 					a			(  UpperCamelCase_				:					float		)	->		int:
 return int(UpperCamelCase_		) if x - int(UpperCamelCase_		) <= 0 else int(UpperCamelCase_		) + 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()





