
'''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[Any]            =       logging.get_logger(__name__)


class  UpperCamelCase__  (    SCREAMING_SNAKE_CASE):






 def __init__(			self  :Dict	,				_A  :Union["PreTrainedModel", nn.Module] = None	,				_A  :"TrainingArguments" = None	,				_A  :Optional["DataCollator"] = None	,				_A  :Optional[Dataset] = None	,				_A  :Optional[Union[Dataset, Dict[str, Dataset]]] = None	,				_A  :Optional["PreTrainedTokenizerBase"] = None	,				_A  :Optional[Callable[[], "PreTrainedModel"]] = None	,				_A  :Optional[Callable[["EvalPrediction"], Dict]] = None	,				_A  :Optional[List["TrainerCallback"]] = None	,				_A  :Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)	,				_A  :Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None	,				)				->			Union[str, Any]:







       '''simple docstring'''



       super().__init__(
           model=_A	,				args=_A	,				data_collator=_A	,				train_dataset=_A	,				eval_dataset=_A	,				tokenizer=_A	,				model_init=_A	,				compute_metrics=_A	,				callbacks=_A	,				optimizers=_A	,				preprocess_logits_for_metrics=_A	,				)

       # 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       	=			self.load_generation_config(self.args.generation_config			)
             __A       	=			gen_config






 @staticmethod
 def 	lowercase_						(			_A  :Union[str, GenerationConfig]			)				->			GenerationConfig:







       '''simple docstring'''



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

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

       __A       	=			GenerationConfig.from_pretrained(_A	,				_A			)
       return gen_config






 def 	lowercase_						(			self  :Optional[int]	,				_A  :Optional[Dataset] = None	,				_A  :Optional[List[str]] = None	,				_A  :str = "eval"	,				**_A  :Tuple	,				)				->			Dict[str, float]:







       '''simple docstring'''



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

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






 def 	lowercase_						(			self  :Dict	,				_A  :Dataset	,				_A  :Optional[List[str]] = None	,				_A  :str = "test"	,				**_A  :Tuple	,				)				->			"PredictionOutput":







       '''simple docstring'''



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

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






 def 	lowercase_						(			self  :Optional[int]	,				_A  :nn.Module	,				_A  :Dict[str, Union[torch.Tensor, Any]]	,				_A  :bool	,				_A  :Optional[List[str]] = None	,				**_A  :Union[str, Any]	,				)				->			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(
                 _A	,				_A	,				prediction_loss_only=_A	,				ignore_keys=_A			)

       __A       	=			'labels' in inputs
       __A       	=			self._prepare_inputs(_A			)

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

       if len(_A			) == 0 and hasattr(self	,				'_gen_kwargs'			):
             __A       	=			self._gen_kwargs.copy()

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

       # 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       	=			False

       # Retrieves GenerationConfig from model.generation_config
       __A       	=			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       	=			self._pad_tensors_to_max_len(_A	,				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       	=			self._pad_tensors_to_max_len(_A	,				gen_config.max_new_tokens + 1			)

       with torch.no_grad():
             if has_labels:
                   with self.compute_loss_context_manager():
                         __A       	=			model(**_A			)
                   if self.label_smoother is not None:
                         __A       	=			self.label_smoother(_A	,				inputs['labels']			).mean().detach()
                   else:
                         __A       	=			(outputs['loss'] if isinstance(_A	,				_A			) else outputs[0]).mean().detach()
             else:
                   __A       	=			None

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

       if has_labels:
             __A       	=			inputs['labels']
             if labels.shape[-1] < gen_config.max_length:
                   __A       	=			self._pad_tensors_to_max_len(_A	,				gen_config.max_length			)
             elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
                   __A       	=			self._pad_tensors_to_max_len(_A	,				gen_config.max_new_tokens + 1			)
       else:
             __A       	=			None

       return loss, generated_tokens, labels






 def 	lowercase_						(			self  :Dict	,				_A  :Optional[int]	,				_A  :List[Any]			)				->			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       	=			(
                 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       	=			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       	=			pad_token_id * torch.ones(
           (tensor.shape[0], max_length)	,				dtype=tensor.dtype	,				device=tensor.device			)
       __A       	=			tensor
       return padded_tensor








'''simple docstring'''





import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

a__		:   List[Any]            =       pathlib.Path.cwd().joinpath("project_euler")
a__		:   Union[str, Any]            =       pathlib.Path.cwd().joinpath(
    "scripts", "project_euler_answers.json"
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
 a__		:   dict[str, str]            =       json.load(file_handle)





def        snake_case							(		UpperCAmelCase	)->			ModuleType:







      """simple docstring"""
      __A       	=			importlib.util.spec_from_file_location(file_path.name      ,  str(UpperCAmelCase	)	)
      __A       	=			importlib.util.module_from_spec(UpperCAmelCase	)  # type: ignore
      spec.loader.exec_module(UpperCAmelCase	)  # type: ignore
      return module





def        snake_case							(		)->			list[pathlib.Path]:







      """simple docstring"""
      __A       	=			[]
      for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
            if problem_dir_path.is_file() or problem_dir_path.name.startswith('_'	):
                  continue
            for file_path in problem_dir_path.iterdir():
                  if file_path.suffix != ".py" or file_path.name.startswith(('_', 'test')	):
                        continue
                  solution_file_paths.append(UpperCAmelCase	)
      return solution_file_paths





def        snake_case							(		)->			str:







      """simple docstring"""
      with open(os.environ['GITHUB_EVENT_PATH']	) as file:
            __A       	=			json.load(UpperCAmelCase	)
      return event["pull_request"]["url"] + "/files"





def        snake_case							(		)->			list[pathlib.Path]:







      """simple docstring"""
      __A       	=			[]
      __A       	=			{
          'Accept': 'application/vnd.github.v3+json',
          'Authorization': 'token ' + os.environ['GITHUB_TOKEN'],
      }
      __A       	=			requests.get(get_files_url()      ,  headers=UpperCAmelCase	).json()
      for file in files:
            __A       	=			pathlib.Path.cwd().joinpath(file['filename']	)
            if (
                filepath.suffix != ".py"
                or filepath.name.startswith(('_', 'test')	)
                or not filepath.name.startswith('sol'	)
            ):
                  continue
            solution_file_paths.append(UpperCAmelCase	)
      return solution_file_paths





def        snake_case							(		)->			list[pathlib.Path]:







      """simple docstring"""
      if os.environ.get('CI'	) and os.environ.get('GITHUB_EVENT_NAME'	) == "pull_request":
            # Return only if there are any, otherwise default to all solutions
            if filepaths := added_solution_file_path():
                  return filepaths
      return all_solution_file_paths()





@pytest.mark.parametrize(
    'solution_path'      ,  collect_solution_file_paths()      ,  ids=lambda UpperCAmelCase	: f'{path.parent.name}/{path.name}'      ,  )
def        snake_case							(		UpperCAmelCase	)->			None:







      """simple docstring"""
      __A       	=			solution_path.parent.name[8:].zfill(3	)
      __A       	=			PROBLEM_ANSWERS[problem_number]
      __A       	=			convert_path_to_module(UpperCAmelCase	)
      __A       	=			str(solution_module.solution()	)  # type: ignore
      __A       	=			hashlib.shaaaa(answer.encode()	).hexdigest()
      assert (
          answer == expected
      ), f'Expected solution to {problem_number} to have hash {expected}, got {answer}'






