

'''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


_snake_case     						=						logging.get_logger(__name__)


class      a__						(							lowerCamelCase_						):


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





      """simple docstring"""





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

      # 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:
         _lowercase    :   Optional[Any]      =     self.load_generation_config(self.args.generation_config						)
         _lowercase    :   Union[str, Any]      =     gen_config


   @staticmethod
   def 		_lowerCamelCase     (				_UpperCamelCase						):





      """simple docstring"""





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

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

      _lowercase    :   Tuple      =     GenerationConfig.from_pretrained(_UpperCamelCase    ,       _UpperCamelCase						)
      return gen_config


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase = None    ,       _UpperCamelCase = None    ,       _UpperCamelCase = "eval"    ,       **_UpperCamelCase    ,       ):





      """simple docstring"""





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

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


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase = None    ,       _UpperCamelCase = "test"    ,       **_UpperCamelCase    ,       ):





      """simple docstring"""





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

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


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase = None    ,       **_UpperCamelCase    ,       ):





      """simple docstring"""





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

      _lowercase    :   Tuple      =     "labels" in inputs
      _lowercase    :   Tuple      =     self._prepare_inputs(_UpperCamelCase						)

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

      if len(_UpperCamelCase						) == 0 and hasattr(self    ,       "_gen_kwargs"						):
         _lowercase    :   int      =     self._gen_kwargs.copy()

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

      # 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:
         _lowercase    :   Optional[int]      =     False

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

      with torch.no_grad():
         if has_labels:
            with self.compute_loss_context_manager():
               _lowercase    :   List[str]      =     model(**_UpperCamelCase						)
            if self.label_smoother is not None:
               _lowercase    :   Union[str, Any]      =     self.label_smoother(_UpperCamelCase    ,       inputs["labels"]						).mean().detach()
            else:
               _lowercase    :   List[Any]      =     (outputs["loss"] if isinstance(_UpperCamelCase    ,       _UpperCamelCase						) else outputs[0]).mean().detach()
         else:
            _lowercase    :   Tuple      =     None

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

      if has_labels:
         _lowercase    :   str      =     inputs["labels"]
         if labels.shape[-1] < gen_config.max_length:
            _lowercase    :   Union[str, Any]      =     self._pad_tensors_to_max_len(_UpperCamelCase    ,       gen_config.max_length						)
         elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
            _lowercase    :   Optional[int]      =     self._pad_tensors_to_max_len(_UpperCamelCase    ,       gen_config.max_new_tokens + 1						)
      else:
         _lowercase    :   Optional[Any]      =     None

      return loss, generated_tokens, labels






   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase						):





      """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
         _lowercase    :   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:
            _lowercase    :   int      =     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"						)

      _lowercase    :   List[str]      =     pad_token_id * torch.ones(
          (tensor.shape[0], max_length)    ,       dtype=tensor.dtype    ,       device=tensor.device						)
      _lowercase    :   Optional[Any]      =     tensor
      return padded_tensor









'''simple docstring'''
from __future__ import annotations

from typing import Generic, TypeVar

_snake_case     						=						TypeVar('T')


class      a__						(							Generic[T]						):


   def __init__(				self    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   Any      =     data  # Assign data
      _lowercase    :   Node[T] | None      =     None  # Initialize next as null
      _lowercase    :   Node[T] | None      =     None  # Initialize prev as null




class      a__						(							Generic[T]						):


   def __init__(				self						):





      """simple docstring"""





      _lowercase    :   Node[T] | None      =     None


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase						):





      """simple docstring"""





      if self.head is None:
         _lowercase    :   Optional[int]      =     Node(_UpperCamelCase						)
      else:
         _lowercase    :   str      =     Node(_UpperCamelCase						)
         _lowercase    :   Optional[Any]      =     new_node
         _lowercase    :   List[str]      =     self.head
         _lowercase    :   Tuple      =     None
         _lowercase    :   Tuple      =     new_node


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      if self.head is None:
         return None
      else:
         assert self.head is not None
         _lowercase    :   str      =     self.head.data
         _lowercase    :   Optional[Any]      =     self.head.next
         if self.head is not None:
            _lowercase    :   Optional[int]      =     None
         return temp


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self.head.data if self.head is not None else None


   def __len__(				self						):





      """simple docstring"""





      _lowercase    :   Optional[Any]      =     self.head
      _lowercase    :   Tuple      =     0
      while temp is not None:
         count += 1
         _lowercase    :   Tuple      =     temp.next
      return count


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self.head is None






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      print("stack elements are:"						)
      _lowercase    :   str      =     self.head
      while temp is not None:
         print(temp.data    ,       end="->"						)
         _lowercase    :   Any      =     temp.next


# Code execution starts here
if __name__ == "__main__":
    # Start with the empty stack
    _snake_case     						=						Stack()

    # Insert 4 at the beginning. So stack becomes 4->None
    print('Stack operations using Doubly LinkedList')
    stack.push(4)

    # Insert 5 at the beginning. So stack becomes 4->5->None
    stack.push(5)

    # Insert 6 at the beginning. So stack becomes 4->5->6->None
    stack.push(6)

    # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
    stack.push(7)

    # Print the stack
    stack.print_stack()

    # Print the top element
    print('\nTop element is ', stack.top())

    # Print the stack size
    print('Size of the stack is ', len(stack))

    # pop the top element
    stack.pop()

    # pop the top element
    stack.pop()

    # two elements have now been popped off
    stack.print_stack()

    # Print True if the stack is empty else False
    print('\nstack is empty:', stack.is_empty())






