



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_	       =   logging.get_logger(__name__)







class 							a_  (		__SCREAMING_SNAKE_CASE				):

						def __init__(    self	:Optional[Any]   ,						_lowercase	:str = None   ,						_lowercase	:Tuple = None   ,						_lowercase	:Tuple = None   ,						_lowercase	:Union[str, Any] = None   ,						_lowercase	:Any = None   ,						_lowercase	:str = None   ,						_lowercase	:Union[str, Any] = None   ,						_lowercase	:List[Any] = None   ,						_lowercase	:Optional[Any] = None   ,						_lowercase	:Optional[int] = (None, None)   ,						_lowercase	:Any = None   ,						)		->					Dict:
												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_     							=     self.load_generation_config(self.args.generation_config)
																		UpperCAmelCase_     							=     gen_config

						@staticmethod
						def 	__a       (    _lowercase	:Tuple)		->					Any:

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

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

												UpperCAmelCase_     							=     GenerationConfig.from_pretrained(_snake_case   ,						_snake_case)
												return gen_config

						def 	__a       (    self	:Optional[int]   ,						_lowercase	:Tuple = None   ,						_lowercase	:Optional[int] = None   ,						_lowercase	:str = "eval"   ,						**_lowercase	:Any   ,						)		->					int:
												UpperCAmelCase_     							=     gen_kwargs.copy()
												if gen_kwargs.get('''max_length''') is None and gen_kwargs.get('''max_new_tokens''') is None:
																		UpperCAmelCase_     							=     self.args.generation_max_length
												UpperCAmelCase_     							=     (
												    gen_kwargs["num_beams"] if gen_kwargs.get('''num_beams''') is not None else self.args.generation_num_beams
												)
												UpperCAmelCase_     							=     gen_kwargs

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

						def 	__a       (    self	:Union[str, Any]   ,						_lowercase	:Tuple   ,						_lowercase	:int = None   ,						_lowercase	:Optional[Any] = "test"   ,						**_lowercase	:Any   ,						)		->					Union[str, Any]:
												UpperCAmelCase_     							=     gen_kwargs.copy()
												if gen_kwargs.get('''max_length''') is None and gen_kwargs.get('''max_new_tokens''') is None:
																		UpperCAmelCase_     							=     self.args.generation_max_length
												UpperCAmelCase_     							=     (
												    gen_kwargs["num_beams"] if gen_kwargs.get('''num_beams''') is not None else self.args.generation_num_beams
												)
												UpperCAmelCase_     							=     gen_kwargs

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

						def 	__a       (    self	:Optional[int]   ,						_lowercase	:int   ,						_lowercase	:Dict   ,						_lowercase	:Optional[Any]   ,						_lowercase	:List[str] = None   ,						**_lowercase	:Tuple   ,						)		->					Tuple:

												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_     							=     "labels" in inputs
												UpperCAmelCase_     							=     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_     							=     self._gen_kwargs.copy()

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

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

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

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

												return loss, generated_tokens, labels




						def 	__a       (    self	:int   ,						_lowercase	:List[Any]   ,						_lowercase	:Tuple)		->					int:
												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_     							=     (
																		    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_     							=     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_     							=     pad_token_id * torch.ones(
												    (tensor.shape[0], max_length)   ,						dtype=tensor.dtype   ,						device=tensor.device)
												UpperCAmelCase_     							=     tensor
												return padded_tensor








'''simple docstring'''
from __future__ import annotations


def       a__    (  _SCREAMING_SNAKE_CASE					:    int							)			-> set[int]:


	"""simple docstring"""





	UpperCAmelCase_			:	Dict      =   {1}
	UpperCAmelCase_			:	int      =   [1]
	for _ in range(2     ,							depth + 1							):
		UpperCAmelCase_			:	Dict      =   [*previous_coefficients, 0]
		UpperCAmelCase_			:	Tuple      =   [0, *previous_coefficients]
		UpperCAmelCase_			:	Dict      =   []
		for x, y in zip(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE							):
			coefficients.add(x + y							)
			previous_coefficients.append(x + y							)
	return coefficients


def       a__    (  _SCREAMING_SNAKE_CASE					:    set[int]							)			-> set[int]:


	"""simple docstring"""





	UpperCAmelCase_			:	List[Any]      =   set()
	for number in unique_coefficients:
		UpperCAmelCase_			:	Tuple      =   2
		UpperCAmelCase_			:	Tuple      =   number
		while divisor**2 <= copy_number:
			UpperCAmelCase_			:	List[Any]      =   0
			while copy_number % divisor == 0:
				copy_number //= divisor
				multiplicity += 1
			if multiplicity >= 2:
				non_squarefrees.add(_SCREAMING_SNAKE_CASE							)
				break
			divisor += 1

	return unique_coefficients.difference(_SCREAMING_SNAKE_CASE							)


def       a__    (  _SCREAMING_SNAKE_CASE					:    int = 51							)			-> int:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[Any]      =   get_pascal_triangle_unique_coefficients(_SCREAMING_SNAKE_CASE							)
	UpperCAmelCase_			:	Union[str, Any]      =   get_squarefrees(_SCREAMING_SNAKE_CASE							)
	return sum(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
					print(f"""{solution() = }""")




