
"""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


lowercase		:    Tuple			=       logging.get_logger(__name__)




class  _snake_case	(    a_							):






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






							@staticmethod
							def 				__lowercase	(       snake_case__    :      Union[str, GenerationConfig]     ):






												"""simple docstring"""



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

												# str or Path
												__a:    int		     =       Path(_a     ) if isinstance(_a    ,			_a     ) else gen_config_arg
												__a:    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():
																	__a:    Dict		     =       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:    Optional[int]		     =       gen_config_arg

												__a:    Tuple		     =       GenerationConfig.from_pretrained(_a    ,			_a     )
												return gen_config






							def 				__lowercase	(       self    :      int    ,			snake_case__    :      Optional[Dataset] = None    ,			snake_case__    :      Optional[List[str]] = None    ,			snake_case__    :      str = "eval"    ,			**snake_case__    :      Union[str, Any]    ,			):






												"""simple docstring"""



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

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






							def 				__lowercase	(       self    :      Dict    ,			snake_case__    :      Dataset    ,			snake_case__    :      Optional[List[str]] = None    ,			snake_case__    :      str = "test"    ,			**snake_case__    :      Dict    ,			):






												"""simple docstring"""



												__a:    List[Any]		     =       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:    List[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(_a    ,			ignore_keys=_a    ,			metric_key_prefix=_a     )






							def 				__lowercase	(       self    :      Union[str, Any]    ,			snake_case__    :      nn.Module    ,			snake_case__    :      Dict[str, Union[torch.Tensor, Any]]    ,			snake_case__    :      bool    ,			snake_case__    :      Optional[List[str]] = None    ,			**snake_case__    :      Optional[int]    ,			):






												"""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     )

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

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

												# Retrieves GenerationConfig from model.generation_config
												__a:    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:
																	__a:    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:
																	__a:    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():
																											__a:    List[Any]		     =       model(**_a     )
																						if self.label_smoother is not None:
																											__a:    List[Any]		     =       self.label_smoother(_a    ,			inputs["""labels"""]     ).mean().detach()
																						else:
																											__a:    Optional[Any]		     =       (outputs["loss"] if isinstance(_a    ,			_a     ) else outputs[0]).mean().detach()
																	else:
																						__a:    Dict		     =       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:    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:
																						__a:    str		     =       self._pad_tensors_to_max_len(_a    ,			gen_config.max_new_tokens + 1     )
												else:
																	__a:    int		     =       None

												return loss, generated_tokens, labels






							def 				__lowercase	(       self    :      int    ,			snake_case__    :      Any    ,			snake_case__    :      Optional[int]     ):






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




from __future__ import annotations







def 	A (		A__		,		A__		,		A__		,		A__			):

     # Base Case
     if counter == 0 or capacity == 0:
          return 0

     # If weight of the nth item is more than Knapsack of capacity,
     #   then this item cannot be included in the optimal solution,
     # else return the maximum of two cases:
     #   (1) nth item included
     #   (2) not included
     if weights[counter - 1] > capacity:
          return knapsack(A__		,		A__		,		A__		,		counter - 1			)
     else:
          UpperCAmelCase    :					List[str]			=      capacity - weights[counter - 1]
          UpperCAmelCase    :					Tuple			=      values[counter - 1] + knapsack(
              A__		,		A__		,		A__		,		counter - 1			)
          UpperCAmelCase    :					Optional[int]			=      knapsack(A__		,		A__		,		A__		,		counter - 1			)
          return max(A__		,		A__			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



