






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


lowercase     :							str	     =			logging.get_logger(__name__)




class 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						def __init__(		self						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = (None, None)						, _SCREAMING_SNAKE_CASE = None						, )       -> List[str]:
								super().__init__(
								    model=_SCREAMING_SNAKE_CASE						, args=_SCREAMING_SNAKE_CASE						, data_collator=_SCREAMING_SNAKE_CASE						, train_dataset=_SCREAMING_SNAKE_CASE						, eval_dataset=_SCREAMING_SNAKE_CASE						, tokenizer=_SCREAMING_SNAKE_CASE						, model_init=_SCREAMING_SNAKE_CASE						, compute_metrics=_SCREAMING_SNAKE_CASE						, callbacks=_SCREAMING_SNAKE_CASE						, optimizers=_SCREAMING_SNAKE_CASE						, preprocess_logits_for_metrics=_SCREAMING_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:
										snake_case_ :							Dict         =				self.load_generation_config(self.args.generation_config )
										snake_case_ :							List[str]         =				gen_config






						@staticmethod
						def 					_lowerCAmelCase				(		_SCREAMING_SNAKE_CASE )       -> GenerationConfig:

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

								# str or Path
								snake_case_ :							List[Any]         =				Path(_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE ) else gen_config_arg
								snake_case_ :							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():
										snake_case_ :							Union[str, Any]         =				pretrained_model_name.name
										snake_case_ :							int         =				pretrained_model_name.parent
								# dir path
								elif pretrained_model_name.is_dir():
										pass
								# model id or URL
								else:
										snake_case_ :							Tuple         =				gen_config_arg

								snake_case_ :							List[Any]         =				GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE )
								return gen_config






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = "eval"						, **_SCREAMING_SNAKE_CASE						, )       -> Dict[str, float]:
								snake_case_ :							Dict         =				gen_kwargs.copy()
								if gen_kwargs.get("max_length" ) is None and gen_kwargs.get("max_new_tokens" ) is None:
										snake_case_ :							Tuple         =				self.args.generation_max_length
								snake_case_ :							Any         =				(
								    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams" ) is not None else self.args.generation_num_beams
								)
								snake_case_ :							Tuple         =				gen_kwargs

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






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = None						, _SCREAMING_SNAKE_CASE = "test"						, **_SCREAMING_SNAKE_CASE						, )       -> "PredictionOutput":
								snake_case_ :							Tuple         =				gen_kwargs.copy()
								if gen_kwargs.get("max_length" ) is None and gen_kwargs.get("max_new_tokens" ) is None:
										snake_case_ :							Optional[int]         =				self.args.generation_max_length
								snake_case_ :							int         =				(
								    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams" ) is not None else self.args.generation_num_beams
								)
								snake_case_ :							Tuple         =				gen_kwargs

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






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE = None						, **_SCREAMING_SNAKE_CASE						, )       -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

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

								snake_case_ :							int         =				"labels" in inputs
								snake_case_ :							List[Any]         =				self._prepare_inputs(_SCREAMING_SNAKE_CASE )

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

								if len(_SCREAMING_SNAKE_CASE ) == 0 and hasattr(self						, "_gen_kwargs" ):
										snake_case_ :							Tuple         =				self._gen_kwargs.copy()

								if gen_kwargs.get("max_length" ) is None and gen_kwargs.get("max_new_tokens" ) is None:
										snake_case_ :							Optional[Any]         =				self.model.config.max_length
								snake_case_ :							Tuple         =				(
								    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams" ) is not None else self.model.config.num_beams
								)
								snake_case_ :							Optional[int]         =				True if is_deepspeed_zeroa_enabled() else False
								snake_case_ :							int         =				(
								    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
								):
										snake_case_ :							int         =				{k: v for k, v in inputs.items() if k != "decoder_input_ids"}
								snake_case_ :							Union[str, Any]         =				self.model.generate(**_SCREAMING_SNAKE_CASE						, **_SCREAMING_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:
										snake_case_ :							int         =				False

								# Retrieves GenerationConfig from model.generation_config
								snake_case_ :							Optional[int]         =				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:
										snake_case_ :							Optional[Any]         =				self._pad_tensors_to_max_len(_SCREAMING_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:
										snake_case_ :							Tuple         =				self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE						, gen_config.max_new_tokens + 1 )

								with torch.no_grad():
										if has_labels:
												with self.compute_loss_context_manager():
														snake_case_ :							Dict         =				model(**_SCREAMING_SNAKE_CASE )
												if self.label_smoother is not None:
														snake_case_ :							Optional[int]         =				self.label_smoother(_SCREAMING_SNAKE_CASE						, inputs["labels"] ).mean().detach()
												else:
														snake_case_ :							Tuple         =				(outputs["loss"] if isinstance(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE ) else outputs[0]).mean().detach()
										else:
												snake_case_ :							Any         =				None

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

								if has_labels:
										snake_case_ :							Any         =				inputs["labels"]
										if labels.shape[-1] < gen_config.max_length:
												snake_case_ :							Optional[int]         =				self._pad_tensors_to_max_len(_SCREAMING_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:
												snake_case_ :							Any         =				self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE						, gen_config.max_new_tokens + 1 )
								else:
										snake_case_ :							str         =				None

								return loss, generated_tokens, labels






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE )       -> str:
								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
										snake_case_ :							str         =				(
										    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:
												snake_case_ :							str         =				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" )

								snake_case_ :							List[Any]         =				pad_token_id * torch.ones(
								    (tensor.shape[0], max_length)						, dtype=tensor.dtype						, device=tensor.device )
								snake_case_ :							List[str]         =				tensor
								return padded_tensor








from __future__ import annotations

from collections.abc import Generator

def    lowerCAmelCase__		(			_a				:	int			):
		if not isinstance(_a     ,					_a			) or n < 1:
				raise Exception("Sequence only defined for positive integers"			)

		yield n
		while n != 1:
				if n % 2 == 0:
						n //= 2
				else:
						snake_case_ :							List[str]         =				3 * n + 1
				yield n

def    lowerCAmelCase__		(			):
		snake_case_ :							Optional[int]         =				43
		snake_case_ :							Union[str, Any]         =				tuple(collatz_sequence(_a			)			)
		print(_a			)
		print(F'''Collatz sequence from {n} took {len(_a			)} steps.'''			)


if __name__ == "__main__":
							main()
