





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













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}""")






