code: 
  
  
  
  
  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     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = (None, None)					,       SCREAMING_SNAKE_CASE_ = None					,       )       ->   Dict:
            super().__init__(
                model=SCREAMING_SNAKE_CASE_					,       args=SCREAMING_SNAKE_CASE_					,       data_collator=SCREAMING_SNAKE_CASE_					,       train_dataset=SCREAMING_SNAKE_CASE_					,       eval_dataset=SCREAMING_SNAKE_CASE_					,       tokenizer=SCREAMING_SNAKE_CASE_					,       model_init=SCREAMING_SNAKE_CASE_					,       compute_metrics=SCREAMING_SNAKE_CASE_					,       callbacks=SCREAMING_SNAKE_CASE_					,       optimizers=SCREAMING_SNAKE_CASE_					,       preprocess_logits_for_metrics=SCREAMING_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:
                UpperCamelCase :Any										=     self.load_generation_config(self.args.generation_config				)
                UpperCamelCase :Optional[int]										=     gen_config
        @staticmethod
        def 					UpperCAmelCase		(       SCREAMING_SNAKE_CASE_				)       ->   GenerationConfig:
  
            # GenerationConfig provided, nothing to do
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                return deepcopy(SCREAMING_SNAKE_CASE_				)
  
            # str or Path
            UpperCamelCase :int										=     Path(SCREAMING_SNAKE_CASE_				) if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				) else gen_config_arg
            UpperCamelCase :Tuple										=     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 :List[str]										=     pretrained_model_name.name
                UpperCamelCase :int										=     pretrained_model_name.parent
            # dir path
            elif pretrained_model_name.is_dir():
                pass
            # model id or URL
            else:
                UpperCamelCase :Tuple										=     gen_config_arg
  
            UpperCamelCase :str										=     GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
            return gen_config
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = "eval"					,       **SCREAMING_SNAKE_CASE_					,       )       ->   Dict[str, float]:
            UpperCamelCase :str										=     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 :Tuple										=     (
                gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''				) is not None else self.args.generation_num_beams
            )
            UpperCamelCase :Optional[int]										=     gen_kwargs
  
            return super().evaluate(SCREAMING_SNAKE_CASE_					,       ignore_keys=SCREAMING_SNAKE_CASE_					,       metric_key_prefix=SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = "test"					,       **SCREAMING_SNAKE_CASE_					,       )       ->   "PredictionOutput":
            UpperCamelCase :Tuple										=     gen_kwargs.copy()
            if gen_kwargs.get('''max_length'''				) is None and gen_kwargs.get('''max_new_tokens'''				) is None:
                UpperCamelCase :int										=     self.args.generation_max_length
            UpperCamelCase :Any										=     (
                gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''				) is not None else self.args.generation_num_beams
            )
            UpperCamelCase :str										=     gen_kwargs
  
            return super().predict(SCREAMING_SNAKE_CASE_					,       ignore_keys=SCREAMING_SNAKE_CASE_					,       metric_key_prefix=SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None					,       **SCREAMING_SNAKE_CASE_					,       )       ->   Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
  
            if not self.args.predict_with_generate or prediction_loss_only:
                return super().prediction_step(
                    SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       prediction_loss_only=SCREAMING_SNAKE_CASE_					,       ignore_keys=SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :Tuple										=     '''labels''' in inputs
            UpperCamelCase :Any										=     self._prepare_inputs(SCREAMING_SNAKE_CASE_				)
  
            # XXX: adapt synced_gpus for fairscale as well
            # Priority (handled in generate):
            # gen_kwargs > model.generation_config > default GenerationConfig()
  
            if len(SCREAMING_SNAKE_CASE_				) == 0 and hasattr(self					,       '''_gen_kwargs'''				):
                UpperCamelCase :int										=     self._gen_kwargs.copy()
  
            if gen_kwargs.get('''max_length'''				) is None and gen_kwargs.get('''max_new_tokens'''				) is None:
                UpperCamelCase :Tuple										=     self.model.config.max_length
            UpperCamelCase :Optional[int]										=     (
                gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''				) is not None else self.model.config.num_beams
            )
            UpperCamelCase :Optional[int]										=     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 :Union[str, Any]										=     {k: v for k, v in inputs.items() if k != '''decoder_input_ids'''}
            UpperCamelCase :Dict										=     self.model.generate(**SCREAMING_SNAKE_CASE_					,       **SCREAMING_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:
                UpperCamelCase :Union[str, Any]										=     False
  
            # Retrieves GenerationConfig from model.generation_config
            UpperCamelCase :Optional[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 :Optional[int]										=     self._pad_tensors_to_max_len(SCREAMING_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:
                UpperCamelCase :List[Any]										=     self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_					,       gen_config.max_new_tokens + 1				)
  
            with torch.no_grad():
                if has_labels:
                    with self.compute_loss_context_manager():
                        UpperCamelCase :int										=     model(**SCREAMING_SNAKE_CASE_				)
                    if self.label_smoother is not None:
                        UpperCamelCase :Union[str, Any]										=     self.label_smoother(SCREAMING_SNAKE_CASE_					,       inputs['''labels''']				).mean().detach()
                    else:
                        UpperCamelCase :Optional[int]										=     (outputs['''loss'''] if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				) else outputs[0]).mean().detach()
                else:
                    UpperCamelCase :Dict										=     None
  
            if self.args.prediction_loss_only:
                return loss, None, None
  
            if has_labels:
                UpperCamelCase :int										=     inputs['''labels''']
                if labels.shape[-1] < gen_config.max_length:
                    UpperCamelCase :List[str]										=     self._pad_tensors_to_max_len(SCREAMING_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:
                    UpperCamelCase :Optional[Any]										=     self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_					,       gen_config.max_new_tokens + 1				)
            else:
                UpperCamelCase :int										=     None
  
            return loss, generated_tokens, labels
  
  
  
  
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   List[Any]:
            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 :List[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[Any]										=     pad_token_id * torch.ones(
                (tensor.shape[0], max_length)					,       dtype=tensor.dtype					,       device=tensor.device				)
            UpperCamelCase :Any										=     tensor
            return padded_tensor
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  from typing import Generic, TypeVar
  
  __snake_case							      =						TypeVar("""T""")
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        pass
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        pass
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      Generic[T]				):
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_ = 10				)       ->   List[str]:
            UpperCamelCase :list[T]										=     []
            UpperCamelCase :Any										=     limit
        def __bool__(       self				)       ->   bool:
            return bool(self.stack				)
        def __str__(       self				)       ->   str:
            return str(self.stack				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
            if len(self.stack				) >= self.limit:
                raise StackOverflowError
            self.stack.append(SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   T:
            if not self.stack:
                raise StackUnderflowError
            return self.stack.pop()
        def 					UpperCAmelCase		(       self				)       ->   T:
            if not self.stack:
                raise StackUnderflowError
            return self.stack[-1]
        def 					UpperCAmelCase		(       self				)       ->   bool:
            return not bool(self.stack				)
        def 					UpperCAmelCase		(       self				)       ->   bool:
            return self.size() == self.limit
        def 					UpperCAmelCase		(       self				)       ->   int:
            return len(self.stack				)
  
  
  
  
        def __contains__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   bool:
            return item in self.stack
  
  
  
  
  
  
  
  def 						_A     (       ):
      UpperCamelCase :Stack[int]										=     Stack(10 )
      assert bool(SCREAMING_SNAKE_CASE__ ) is False
      assert stack.is_empty() is True
      assert stack.is_full() is False
      assert str(SCREAMING_SNAKE_CASE__ ) == "[]"
  
      try:
          UpperCamelCase :Optional[int]										=     stack.pop()
          raise AssertionError  # This should not happen
      except StackUnderflowError:
          assert True  # This should happen
  
      try:
          UpperCamelCase :Union[str, Any]										=     stack.peek()
          raise AssertionError  # This should not happen
      except StackUnderflowError:
          assert True  # This should happen
  
      for i in range(10 ):
          assert stack.size() == i
          stack.push(SCREAMING_SNAKE_CASE__ )
  
      assert bool(SCREAMING_SNAKE_CASE__ )
      assert not stack.is_empty()
      assert stack.is_full()
      assert str(SCREAMING_SNAKE_CASE__ ) == str(list(range(10 ) ) )
      assert stack.pop() == 9
      assert stack.peek() == 8
  
      stack.push(100 )
      assert str(SCREAMING_SNAKE_CASE__ ) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100] )
  
      try:
          stack.push(200 )
          raise AssertionError  # This should not happen
      except StackOverflowError:
          assert True  # This should happen
  
      assert not stack.is_empty()
      assert stack.size() == 10
  
      assert 5 in stack
      assert 55 not in stack
  
  
  if __name__ == "__main__":
   test_stack()
  
  
  
  
  
  