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
  
  
  UpperCAmelCase			:			Optional[int]	  = logging.get_logger(__name__)
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					SCREAMING_SNAKE_CASE__):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = (None, None)	,   lowerCAmelCase__ = None	,   )     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().__init__(
              model=lowerCAmelCase__	,   args=lowerCAmelCase__	,   data_collator=lowerCAmelCase__	,   train_dataset=lowerCAmelCase__	,   eval_dataset=lowerCAmelCase__	,   tokenizer=lowerCAmelCase__	,   model_init=lowerCAmelCase__	,   compute_metrics=lowerCAmelCase__	,   callbacks=lowerCAmelCase__	,   optimizers=lowerCAmelCase__	,   preprocess_logits_for_metrics=lowerCAmelCase__	,   )
  
          # 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__			:      Dict            =gen_config
  
  
  
  
  
  
  
        @staticmethod
        def      _lowercase						(					lowerCAmelCase__							)     ->		GenerationConfig:
  
  
  
          '''simple docstring'''
  
  
  
  
          if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							):
            return deepcopy(lowerCAmelCase__							)
  
          # str or Path
          a__			:      str            =Path(lowerCAmelCase__							) if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else gen_config_arg
          a__			:      List[str]            =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__			:      Tuple            =pretrained_model_name.name
            a__			:      str            =pretrained_model_name.parent
          # dir path
          elif pretrained_model_name.is_dir():
            pass
          # model id or URL
          else:
            a__			:      List[Any]            =gen_config_arg
  
          a__			:      List[str]            =GenerationConfig.from_pretrained(lowerCAmelCase__	,   lowerCAmelCase__							)
          return gen_config
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = "eval"	,   **lowerCAmelCase__	,   )     ->		Dict[str, float]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Dict            =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__			:      Dict            =(
              gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"							) is not None else self.args.generation_num_beams
          )
          a__			:      int            =gen_kwargs
  
          return super().evaluate(lowerCAmelCase__	,   ignore_keys=lowerCAmelCase__	,   metric_key_prefix=lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = "test"	,   **lowerCAmelCase__	,   )     ->		"PredictionOutput":
  
  
  
          '''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__			:      int            =(
              gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"							) is not None else self.args.generation_num_beams
          )
          a__			:      Dict            =gen_kwargs
  
          return super().predict(lowerCAmelCase__	,   ignore_keys=lowerCAmelCase__	,   metric_key_prefix=lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__ = None	,   **lowerCAmelCase__	,   )     ->		Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if not self.args.predict_with_generate or prediction_loss_only:
            return super().prediction_step(
                lowerCAmelCase__	,   lowerCAmelCase__	,   prediction_loss_only=lowerCAmelCase__	,   ignore_keys=lowerCAmelCase__							)
  
          a__			:      Union[str, Any]            ="labels" in inputs
          a__			:      List[Any]            =self._prepare_inputs(lowerCAmelCase__							)
  
          # XXX: adapt synced_gpus for fairscale as well
          # Priority (handled in generate):
          # gen_kwargs > model.generation_config > default GenerationConfig()
  
          if len(lowerCAmelCase__							) == 0 and hasattr(self	,   "_gen_kwargs"							):
            a__			:      List[str]            =self._gen_kwargs.copy()
  
          if gen_kwargs.get("max_length"							) is None and gen_kwargs.get("max_new_tokens"							) is None:
            a__			:      str            =self.model.config.max_length
          a__			:      Dict            =(
              gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"							) is not None else self.model.config.num_beams
          )
          a__			:      Tuple            =True if is_deepspeed_zeroa_enabled() else False
          a__			:      List[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__			:      Dict            =self.model.generate(**lowerCAmelCase__	,   **lowerCAmelCase__							)
  
          # 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__			:      Union[str, Any]            =False
  
          # Retrieves GenerationConfig from model.generation_config
          a__			:      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:
            a__			:      Tuple            =self._pad_tensors_to_max_len(lowerCAmelCase__	,   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__			:      Any            =self._pad_tensors_to_max_len(lowerCAmelCase__	,   gen_config.max_new_tokens + 1							)
  
          with torch.no_grad():
            if has_labels:
              with self.compute_loss_context_manager():
                a__			:      int            =model(**lowerCAmelCase__							)
              if self.label_smoother is not None:
                a__			:      Dict            =self.label_smoother(lowerCAmelCase__	,   inputs["labels"]							).mean().detach()
              else:
                a__			:      int            =(outputs["loss"] if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else outputs[0]).mean().detach()
            else:
              a__			:      Optional[int]            =None
  
          if self.args.prediction_loss_only:
            return loss, None, None
  
          if has_labels:
            a__			:      int            =inputs["labels"]
            if labels.shape[-1] < gen_config.max_length:
              a__			:      Optional[Any]            =self._pad_tensors_to_max_len(lowerCAmelCase__	,   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(lowerCAmelCase__	,   gen_config.max_new_tokens + 1							)
          else:
            a__			:      Union[str, Any]            =None
  
          return loss, generated_tokens, labels
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		List[str]:
  
  
  
          '''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__			:      str            =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[Any]            =pad_token_id * torch.ones(
              (tensor.shape[0], max_length)	,   dtype=tensor.dtype	,   device=tensor.device							)
          a__			:      List[str]            =tensor
          return padded_tensor
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     dict[str, str]		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__      ,							a__			:      Optional[int]            ="", "", ""
    a__			:      str            =[]
  
    for character in message_part:
      tmp.append(character_to_number[character]		)
  
    for each in tmp:
      one += each[0]
      two += each[1]
      three += each[2]
  
    return one + two + three
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     dict[str, str]		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__			:      Any            ="", ""
    a__			:      Optional[int]            =[]
  
    for character in message_part:
      this_part += character_to_number[character]
  
    for digit in this_part:
      tmp += digit
      if len(SCREAMING_SNAKE_CASE		) == len(SCREAMING_SNAKE_CASE		):
        result.append(SCREAMING_SNAKE_CASE		)
        a__			:      str            =""
  
    return result[0], result[1], result[2]
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[Any]            =alphabet.replace(" " , ""		).upper()
    a__			:      List[str]            =message.replace(" " , ""		).upper()
  
    # Check length and characters
    if len(SCREAMING_SNAKE_CASE		) != 27:
      raise KeyError("Length of alphabet has to be 27."		)
    for each in message:
      if each not in alphabet:
        raise ValueError("Each message character has to be included in alphabet!"		)
  
      # Generate dictionares
    a__			:      Any            =(
        "111",
        "112",
        "113",
        "121",
        "122",
        "123",
        "131",
        "132",
        "133",
        "211",
        "212",
        "213",
        "221",
        "222",
        "223",
        "231",
        "232",
        "233",
        "311",
        "312",
        "313",
        "321",
        "322",
        "323",
        "331",
        "332",
        "333",
    )
    a__			:      Tuple            ={}
    a__			:      List[str]            ={}
    for letter, number in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		):
      a__			:      Dict            =number
      a__			:      Union[str, Any]            =letter
  
    return message, alphabet, character_to_number, number_to_character
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." , SCREAMING_SNAKE_CASE  :     int = 5		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__      ,							a__      ,							a__			:      Union[str, Any]            =__prepare(
        SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
    a__      ,							a__			:      Union[str, Any]            ="", ""
  
    for i in range(0 , len(SCREAMING_SNAKE_CASE		) + 1 , SCREAMING_SNAKE_CASE		):
      encrypted_numeric += __encrypt_part(
          message[i : i + period] , SCREAMING_SNAKE_CASE		)
  
    for i in range(0 , len(SCREAMING_SNAKE_CASE		) , 3		):
      encrypted += number_to_character[encrypted_numeric[i : i + 3]]
  
    return encrypted
  def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." , SCREAMING_SNAKE_CASE  :     int = 5		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__      ,							a__      ,							a__      ,							a__			:      Dict            =__prepare(
        SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
    a__			:      Optional[Any]            =[]
    a__			:      Any            =""
  
    for i in range(0 , len(SCREAMING_SNAKE_CASE		) + 1 , SCREAMING_SNAKE_CASE		):
      a__      ,							a__      ,							a__			:      Union[str, Any]            =__decrypt_part(message[i : i + period] , SCREAMING_SNAKE_CASE		)
  
      for j in range(0 , len(SCREAMING_SNAKE_CASE		)		):
        decrypted_numeric.append(a[j] + b[j] + c[j]		)
  
    for each in decrypted_numeric:
      decrypted += number_to_character[each]
  
    return decrypted
  
  
  if __name__ == "__main__":
       UpperCAmelCase			:			List[str]	  = """DEFEND THE EAST WALL OF THE CASTLE."""
       UpperCAmelCase			:			Optional[Any]	  = encrypt_message(msg, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
       UpperCAmelCase			:			Optional[int]	  = decrypt_message(encrypted, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
       print(F"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")
  
  
  
  
  
  
  