




'''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				: List[str]					      =			logging.get_logger(__name__)






class 			SCREAMING_SNAKE_CASE(							__A	):

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

       @staticmethod
       def        snake_case__     (       lowerCamelCase__	)       ->   GenerationConfig:

           """simple docstring"""



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

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

           __lowercase     							=		GenerationConfig.from_pretrained(lowerCamelCase__   ,						lowerCamelCase__	)
           return gen_config

       def        snake_case__     (       self   ,						lowerCamelCase__ = None   ,						lowerCamelCase__ = None   ,						lowerCamelCase__ = "eval"   ,						**lowerCamelCase__   ,						)       ->   Dict[str, float]:

           """simple docstring"""



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

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

       def        snake_case__     (       self   ,						lowerCamelCase__   ,						lowerCamelCase__ = None   ,						lowerCamelCase__ = "test"   ,						**lowerCamelCase__   ,						)       ->   "PredictionOutput":

           """simple docstring"""



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

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

       def        snake_case__     (       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__	)

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

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

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

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

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

           return loss, generated_tokens, labels




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










'''simple docstring'''
def 							snake_case_    (  a__						:      int     ):



    """simple docstring"""




    __lowercase     							=		1
    __lowercase     							=		0
    __lowercase     							=		1
    __lowercase     							=		1
    __lowercase     							=		3
    __lowercase     							=		3
    __lowercase     							=		limit
    __lowercase     							=		0

    __lowercase     							=		""""""
    while counter != decimal + 1:
        if 4 * q + r - t < n * t:
            result += str(a__     )
            if counter == 0:
                result += "."

            if decimal == counter:
                break

            counter += 1
            __lowercase     							=		10 * (r - n * t)
            __lowercase     							=		((10 * (3 * q + r)) // t) - 10 * n
            q *= 10
            __lowercase     							=		nr
        else:
            __lowercase     							=		(2 * q + r) * l
            __lowercase     							=		(q * (7 * k) + 2 + (r * l)) // (t * l)
            q *= k
            t *= l
            l += 2
            k += 1
            __lowercase     							=		nn
            __lowercase     							=		nr
    return result
def 							snake_case_    (  ):



    """simple docstring"""




    print(f'{calculate_pi(50     ) = }'     )
    import doctest

    doctest.testmod()


if __name__ == "__main__":
  main()




