




"""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


__lowerCamelCase								=						logging.get_logger(__name__)



class  _snake_case				(							A__       ):

    '''simple docstring'''




    def __init__(							self      :  List[Any]					,							snake_case      :  Union["PreTrainedModel", nn.Module] = None					,							snake_case      :  "TrainingArguments" = None					,							snake_case      :  Optional["DataCollator"] = None					,							snake_case      :  Optional[Dataset] = None					,							snake_case      :  Optional[Union[Dataset, Dict[str, Dataset]]] = None					,							snake_case      :  Optional["PreTrainedTokenizerBase"] = None					,							snake_case      :  Optional[Callable[[], "PreTrainedModel"]] = None					,							snake_case      :  Optional[Callable[["EvalPrediction"], Dict]] = None					,							snake_case      :  Optional[List["TrainerCallback"]] = None					,							snake_case      :  Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)					,							snake_case      :  Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None					,							):
        super().__init__(
            model=snake_case					,							args=snake_case					,							data_collator=snake_case					,							train_dataset=snake_case					,							eval_dataset=snake_case					,							tokenizer=snake_case					,							model_init=snake_case					,							compute_metrics=snake_case					,							callbacks=snake_case					,							optimizers=snake_case					,							preprocess_logits_for_metrics=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_					:Optional[int]      			=      self.load_generation_config(self.args.generation_config     )
            UpperCAmelCase_					:Dict      			=      gen_config





    @staticmethod
    def 				snake_case_ (							snake_case      :  Union[str, GenerationConfig]     ):

        # GenerationConfig provided, nothing to do
        if isinstance(snake_case					,							snake_case     ):
            return deepcopy(snake_case     )

        # str or Path
        UpperCAmelCase_					:Optional[Any]      			=      Path(snake_case     ) if isinstance(snake_case					,							snake_case     ) else gen_config_arg
        UpperCAmelCase_					:List[Any]      			=      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[Any]      			=      pretrained_model_name.name
            UpperCAmelCase_					:Tuple      			=      pretrained_model_name.parent
        # dir path
        elif pretrained_model_name.is_dir():
            pass
        # model id or URL
        else:
            UpperCAmelCase_					:Dict      			=      gen_config_arg

        UpperCAmelCase_					:Optional[int]      			=      GenerationConfig.from_pretrained(snake_case					,							snake_case     )
        return gen_config





    def 				snake_case_ (							self      :  Tuple					,							snake_case      :  Optional[Dataset] = None					,							snake_case      :  Optional[List[str]] = None					,							snake_case      :  str = "eval"					,							**snake_case      :  Any					,							):
        UpperCAmelCase_					:Optional[int]      			=      gen_kwargs.copy()
        if gen_kwargs.get('''max_length'''     ) is None and gen_kwargs.get('''max_new_tokens'''     ) is None:
            UpperCAmelCase_					:str      			=      self.args.generation_max_length
        UpperCAmelCase_					:Dict      			=      (
            gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''     ) is not None else self.args.generation_num_beams
        )
        UpperCAmelCase_					:Tuple      			=      gen_kwargs

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





    def 				snake_case_ (							self      :  List[str]					,							snake_case      :  Dataset					,							snake_case      :  Optional[List[str]] = None					,							snake_case      :  str = "test"					,							**snake_case      :  Dict					,							):
        UpperCAmelCase_					:Optional[Any]      			=      gen_kwargs.copy()
        if gen_kwargs.get('''max_length'''     ) is None and gen_kwargs.get('''max_new_tokens'''     ) is None:
            UpperCAmelCase_					:Tuple      			=      self.args.generation_max_length
        UpperCAmelCase_					:Optional[Any]      			=      (
            gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''     ) is not None else self.args.generation_num_beams
        )
        UpperCAmelCase_					:Optional[Any]      			=      gen_kwargs

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





    def 				snake_case_ (							self      :  List[str]					,							snake_case      :  nn.Module					,							snake_case      :  Dict[str, Union[torch.Tensor, Any]]					,							snake_case      :  bool					,							snake_case      :  Optional[List[str]] = None					,							**snake_case      :  List[str]					,							):

        if not self.args.predict_with_generate or prediction_loss_only:
            return super().prediction_step(
                snake_case					,							snake_case					,							prediction_loss_only=snake_case					,							ignore_keys=snake_case     )

        UpperCAmelCase_					:int      			=      '''labels''' in inputs
        UpperCAmelCase_					:Dict      			=      self._prepare_inputs(snake_case     )

        # XXX: adapt synced_gpus for fairscale as well
        # Priority (handled in generate):
        # gen_kwargs > model.generation_config > default GenerationConfig()

        if len(snake_case     ) == 0 and hasattr(self					,							'''_gen_kwargs'''     ):
            UpperCAmelCase_					:List[str]      			=      self._gen_kwargs.copy()

        if gen_kwargs.get('''max_length'''     ) is None and gen_kwargs.get('''max_new_tokens'''     ) is None:
            UpperCAmelCase_					:List[Any]      			=      self.model.config.max_length
        UpperCAmelCase_					:Optional[Any]      			=      (
            gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''     ) is not None else self.model.config.num_beams
        )
        UpperCAmelCase_					:Union[str, Any]      			=      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_					:str      			=      self.model.generate(**snake_case					,							**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_					:Dict      			=      False

        # Retrieves GenerationConfig from model.generation_config
        UpperCAmelCase_					:str      			=      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_					:Dict      			=      self._pad_tensors_to_max_len(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(snake_case					,							gen_config.max_new_tokens + 1     )

        with torch.no_grad():
            if has_labels:
                with self.compute_loss_context_manager():
                    UpperCAmelCase_					:str      			=      model(**snake_case     )
                if self.label_smoother is not None:
                    UpperCAmelCase_					:str      			=      self.label_smoother(snake_case					,							inputs['''labels''']     ).mean().detach()
                else:
                    UpperCAmelCase_					:Dict      			=      (outputs['''loss'''] if isinstance(snake_case					,							snake_case     ) else outputs[0]).mean().detach()
            else:
                UpperCAmelCase_					:List[Any]      			=      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_					:Optional[Any]      			=      self._pad_tensors_to_max_len(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(snake_case					,							gen_config.max_new_tokens + 1     )
        else:
            UpperCAmelCase_					:Optional[int]      			=      None

        return loss, generated_tokens, labels





    def 				snake_case_ (							self      :  Dict					,							snake_case      :  Optional[Any]					,							snake_case      :  Union[str, 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_					:Dict      			=      (
                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[int]      			=      pad_token_id * torch.ones(
            (tensor.shape[0], max_length)					,							dtype=tensor.dtype					,							device=tensor.device     )
        UpperCAmelCase_					:Union[str, Any]      			=      tensor
        return padded_tensor








"""simple docstring"""

def 				a (						__snake_case     :				list[list[int]]  ):



				'''simple docstring'''





				if len(__snake_case  ) == 0 or not all(len(__snake_case  ) == 2 for pair in coordinates  ):
								raise ValueError('''The program cannot work out a fitting polynomial.'''  )

				if len({tuple(__snake_case  ) for pair in coordinates}  ) != len(__snake_case  ):
								raise ValueError('''The program cannot work out a fitting polynomial.'''  )

				UpperCAmelCase_					:Optional[Any]      			=      {x for x, _ in coordinates}
				if len(__snake_case  ) == 1:
								return f'x={coordinates[0][0]}'

				if len(__snake_case  ) != len(__snake_case  ):
								raise ValueError('''The program cannot work out a fitting polynomial.'''  )

				UpperCAmelCase_					:List[Any]      			=      len(__snake_case  )

				UpperCAmelCase_					:str      			=      0
				UpperCAmelCase_					:list[list[float]]      			=      []
				# put the x and x to the power values in a matrix
				while count_of_line < x:
								UpperCAmelCase_					:str      			=      0
								UpperCAmelCase_					:Optional[int]      			=      coordinates[count_of_line][0]
								UpperCAmelCase_					:list[float]      			=      []
								while count_in_line < x:
												count_line.append(a ** (x - (count_in_line + 1))  )
												count_in_line += 1
								matrix.append(__snake_case  )
								count_of_line += 1

				UpperCAmelCase_					:Dict      			=      0
				# put the y values into a vector
				UpperCAmelCase_					:list[float]      			=      []
				while count_of_line < x:
								vector.append(coordinates[count_of_line][1]  )
								count_of_line += 1

				UpperCAmelCase_					:Dict      			=      0

				while count < x:
								UpperCAmelCase_					:Any      			=      0
								while zahlen < x:
												if count == zahlen:
																zahlen += 1
												if zahlen == x:
																break
												UpperCAmelCase_					:str      			=      matrix[zahlen][count] / matrix[count][count]
												for counting_columns, item in enumerate(matrix[count]  ):
																# manipulating all the values in the matrix
																matrix[zahlen][counting_columns] -= item * bruch
												# manipulating the values in the vector
												vector[zahlen] -= vector[count] * bruch
												zahlen += 1
								count += 1

				UpperCAmelCase_					:Tuple      			=      0
				# make solutions
				UpperCAmelCase_					:list[str]      			=      []
				while count < x:
								solution.append(str(vector[count] / matrix[count][count]  )  )
								count += 1

				UpperCAmelCase_					:Optional[int]      			=      0
				UpperCAmelCase_					:Optional[Any]      			=      '''f(x)='''

				while count < x:
								UpperCAmelCase_					:list[str]      			=      solution[count].split('''E'''  )
								if len(__snake_case  ) > 1:
												UpperCAmelCase_					:List[Any]      			=      f'{remove_e[0]}*10^{remove_e[1]}'
								solved += f'x^{x - (count + 1)}*{solution[count]}'
								if count + 1 != x:
												solved += "+"
								count += 1

				return solved


if __name__ == "__main__":
						print(points_to_polynomial([]))
						print(points_to_polynomial([[]]))
						print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
						print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
						print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
						print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
						print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
						print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))


