





'''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


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




class 				UpperCAmelCase  (    snake_case_     ):




      def __init__(    self    :				List[str]    ,    __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    ,    )       ->   Optional[Any]:
            super().__init__(
                model=__snake_case    ,    args=__snake_case    ,    data_collator=__snake_case    ,    train_dataset=__snake_case    ,    eval_dataset=__snake_case    ,    tokenizer=__snake_case    ,    model_init=__snake_case    ,    compute_metrics=__snake_case    ,    callbacks=__snake_case    ,    optimizers=__snake_case    ,    preprocess_logits_for_metrics=__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							  =							self.load_generation_config(self.args.generation_config						)
                  _lowerCAmelCase							  =							gen_config




      @staticmethod
      def        lowercase__					(    __snake_case    :				Union[str, GenerationConfig]						)       ->   GenerationConfig:

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

            # str or Path
            _lowerCAmelCase							  =							Path(__snake_case						) if isinstance(__snake_case    ,    __snake_case						) 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(__snake_case    ,    __snake_case						)
            return gen_config




      def        lowercase__					(    self    :				Tuple    ,    __snake_case    :				Optional[Dataset] = None    ,    __snake_case    :				Optional[List[str]] = None    ,    __snake_case    :				str = "eval"    ,    **__snake_case    :				int    ,    )       ->   Dict[str, float]:
            _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(__snake_case    ,    ignore_keys=__snake_case    ,    metric_key_prefix=__snake_case						)




      def        lowercase__					(    self    :				Optional[Any]    ,    __snake_case    :				Dataset    ,    __snake_case    :				Optional[List[str]] = None    ,    __snake_case    :				str = "test"    ,    **__snake_case    :				int    ,    )       ->   "PredictionOutput":
            _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(__snake_case    ,    ignore_keys=__snake_case    ,    metric_key_prefix=__snake_case						)




      def        lowercase__					(    self    :				Optional[Any]    ,    __snake_case    :				nn.Module    ,    __snake_case    :				Dict[str, Union[torch.Tensor, Any]]    ,    __snake_case    :				bool    ,    __snake_case    :				Optional[List[str]] = None    ,    **__snake_case    :				Any    ,    )       ->   Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

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

            _lowerCAmelCase							  =							"""labels""" in inputs
            _lowerCAmelCase							  =							self._prepare_inputs(__snake_case						)

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

            if len(__snake_case						) == 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(**__snake_case    ,    **__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							  =							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(__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							  =							self._pad_tensors_to_max_len(__snake_case    ,    gen_config.max_new_tokens + 1						)

            with torch.no_grad():
                  if has_labels:
                        with self.compute_loss_context_manager():
                              _lowerCAmelCase							  =							model(**__snake_case						)
                        if self.label_smoother is not None:
                              _lowerCAmelCase							  =							self.label_smoother(__snake_case    ,    inputs["""labels"""]						).mean().detach()
                        else:
                              _lowerCAmelCase							  =							(outputs["""loss"""] if isinstance(__snake_case    ,    __snake_case						) 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(__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							  =							self._pad_tensors_to_max_len(__snake_case    ,    gen_config.max_new_tokens + 1						)
            else:
                  _lowerCAmelCase							  =							None

            return loss, generated_tokens, labels




      def        lowercase__					(    self    :				str    ,    __snake_case    :				int    ,    __snake_case    :				Optional[int]						)       ->   Optional[int]:
            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













'''simple docstring'''






class 				UpperCAmelCase  :




      def __init__(    self    :				Optional[Any]						)       ->   List[str]:
            _lowerCAmelCase							  =							[0]
            _lowerCAmelCase							  =							0




      def        lowercase__					(    self    :				str    ,    __snake_case    :				int						)       ->   None:
            _lowerCAmelCase							  =							self.__heap[i]
            while i // 2 > 0:
                  if self.__heap[i] > self.__heap[i // 2]:
                        _lowerCAmelCase							  =							self.__heap[i // 2]
                        _lowerCAmelCase							  =							temporary
                  i //= 2




      def        lowercase__					(    self    :				Optional[Any]    ,    __snake_case    :				int						)       ->   None:
            self.__heap.append(__snake_case						)
            self.__size += 1
            self.__swap_up(self.__size						)




      def        lowercase__					(    self    :				str    ,    __snake_case    :				int						)       ->   None:
            while self.__size >= 2 * i:
                  if 2 * i + 1 > self.__size:
                        _lowerCAmelCase							  =							2 * i
                  else:
                        if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                              _lowerCAmelCase							  =							2 * i
                        else:
                              _lowerCAmelCase							  =							2 * i + 1
                  _lowerCAmelCase							  =							self.__heap[i]
                  if self.__heap[i] < self.__heap[bigger_child]:
                        _lowerCAmelCase							  =							self.__heap[bigger_child]
                        _lowerCAmelCase							  =							temporary
                  _lowerCAmelCase							  =							bigger_child




      def        lowercase__					(    self    :				Tuple						)       ->   int:
            _lowerCAmelCase							  =							self.__heap[1]
            _lowerCAmelCase							  =							self.__heap[self.__size]
            self.__size -= 1
            self.__heap.pop()
            self.__swap_down(1						)
            return max_value




      @property
      def        lowercase__					(    self    :				List[Any]						)       ->   str:
            return self.__heap[1:]




      def __len__(    self    :				Optional[Any]						)       ->   str:
            return self.__size


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      # create an instance of BinaryHeap
      A__       :     Tuple          =BinaryHeap()
      binary_heap.insert(6)
      binary_heap.insert(10)
      binary_heap.insert(15)
      binary_heap.insert(12)
      # pop root(max-values because it is max heap)
      print(binary_heap.pop())  # 15
      print(binary_heap.pop())  # 12
      # get the list and size after operations
      print(binary_heap.get_list)
      print(len(binary_heap))






