





"""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    lowercase_ (   __lowerCAmelCase							):

       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      ,    ):






        '''simple docstring'''

        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:
         snake_case__										= self.load_generation_config(self.args.generation_config   )
         snake_case__										= gen_config

       @staticmethod
       def 						_SCREAMING_SNAKE_CASE(	__SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

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

        # str or Path
        snake_case__										= Path(__SCREAMING_SNAKE_CASE   ) if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ) else gen_config_arg
        snake_case__										= 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():
         snake_case__										= pretrained_model_name.name
         snake_case__										= pretrained_model_name.parent
        # dir path
        elif pretrained_model_name.is_dir():
         pass
        # model id or URL
        else:
         snake_case__										= gen_config_arg

        snake_case__										= GenerationConfig.from_pretrained(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )
        return gen_config

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = "eval"      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

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

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

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = "test"      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

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

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

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    **__SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        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   )

        snake_case__										= "labels" in inputs
        snake_case__										= 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"   ):
         snake_case__										= self._gen_kwargs.copy()

        if gen_kwargs.get("max_length"   ) is None and gen_kwargs.get("max_new_tokens"   ) is None:
         snake_case__										= self.model.config.max_length
        snake_case__										= (
            gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"   ) is not None else self.model.config.num_beams
        )
        snake_case__										= True if is_deepspeed_zeroa_enabled() else False
        snake_case__										= (
            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
        ):
         snake_case__										= {k: v for k, v in inputs.items() if k != "decoder_input_ids"}
        snake_case__										= 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:
         snake_case__										= False

        # Retrieves GenerationConfig from model.generation_config
        snake_case__										= 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:
         snake_case__										= 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:
         snake_case__										= 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():
           snake_case__										= model(**__SCREAMING_SNAKE_CASE   )
          if self.label_smoother is not None:
           snake_case__										= self.label_smoother(__SCREAMING_SNAKE_CASE      ,    inputs["labels"]   ).mean().detach()
          else:
           snake_case__										= (outputs["loss"] if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ) else outputs[0]).mean().detach()
         else:
          snake_case__										= None

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

        if has_labels:
         snake_case__										= inputs["labels"]
         if labels.shape[-1] < gen_config.max_length:
          snake_case__										= 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:
          snake_case__										= self._pad_tensors_to_max_len(__SCREAMING_SNAKE_CASE      ,    gen_config.max_new_tokens + 1   )
        else:
         snake_case__										= None

        return loss, generated_tokens, labels

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''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
         snake_case__										= (
             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:
          snake_case__										= 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"   )

        snake_case__										= pad_token_id * torch.ones(
            (tensor.shape[0], max_length)      ,    dtype=tensor.dtype      ,    device=tensor.device   )
        snake_case__										= tensor
        return padded_tensor








"""simple docstring"""


from __future__ import annotations





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   )			->     list[float | int]:

 '''simple docstring'''






 if not all((nth_term, start_term_a, common_ratio_r)		):
  return []
 snake_case__										= []
 snake_case__										= 1
 snake_case__										= common_ratio_r
 for _ in range(int(_UpperCAmelCase		)		):
  if not series:
   series.append(_UpperCAmelCase		)
  else:
   power += 1
   series.append(float(start_term_a * multiple		)		)
   snake_case__										= pow(float(_UpperCAmelCase		)		,   _UpperCAmelCase		)
 return series


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   a__    :   int    						= float(input("""Enter the last number (n term) of the Geometric Series"""))
   a__    :   str    						= float(input("""Enter the starting term (a) of the Geometric Series"""))
   a__    :   str    						= float(
       input("""Enter the common ratio between two terms (r) of the Geometric Series""")
   )
   print("""Formula of Geometric Series => a + ar + ar^2 ... +ar^n""")
   print(geometric_series(nth_term, start_term_a, common_ratio_r))

