



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				:							List[Any]								=logging.get_logger(__name__)


class  _lowercase				(a_ ):
				'''simple docstring'''







				def __init__(	self       ,	snake_case__ = None       ,	snake_case__ = None       ,	snake_case__ = None       ,	snake_case__ = None       ,	snake_case__ = None       ,	snake_case__ = None       ,	snake_case__ = None       ,	snake_case__ = None       ,	snake_case__ = None       ,	snake_case__ = (None, None)       ,	snake_case__ = None       ,	):

									'''simple docstring'''


									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  _lowerCamelCase   (	snake_case__      ):

									'''simple docstring'''


									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  _lowerCamelCase   (	self       ,	snake_case__ = None       ,	snake_case__ = None       ,	snake_case__ = "eval"       ,	**snake_case__       ,	):

									'''simple docstring'''


									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  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__ = None       ,	snake_case__ = "test"       ,	**snake_case__       ,	):

									'''simple docstring'''


									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  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__ = None       ,	**snake_case__       ,	):

									'''simple docstring'''


									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  _lowerCamelCase   (	self       ,	snake_case__       ,	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
														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







from __future__ import annotations



def 				_lowerCAmelCase      (_lowerCAmelCase):
					UpperCamelCase_											=				{1}
					UpperCamelCase_											=				[1]
					for _ in range(2				,		depth + 1):
										UpperCamelCase_											=				[*previous_coefficients, 0]
										UpperCamelCase_											=				[0, *previous_coefficients]
										UpperCamelCase_											=				[]
										for x, y in zip(_lowerCAmelCase				,		_lowerCAmelCase):
															coefficients.add(x + y)
															previous_coefficients.append(x + y)
					return coefficients



def 				_lowerCAmelCase      (_lowerCAmelCase):
					UpperCamelCase_											=				set()
					for number in unique_coefficients:
										UpperCamelCase_											=				2
										UpperCamelCase_											=				number
										while divisor**2 <= copy_number:
															UpperCamelCase_											=				0
															while copy_number % divisor == 0:
																				copy_number //= divisor
																				multiplicity += 1
															if multiplicity >= 2:
																				non_squarefrees.add(_lowerCAmelCase)
																				break
															divisor += 1

					return unique_coefficients.difference(_lowerCAmelCase)



def 				_lowerCAmelCase      (_lowerCAmelCase = 51):
					UpperCamelCase_											=				get_pascal_triangle_unique_coefficients(_lowerCAmelCase)
					UpperCamelCase_											=				get_squarefrees(_lowerCAmelCase)
					return sum(_lowerCAmelCase)


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