"""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							=logging.get_logger(__name__)






class    __UpperCAmelCase						(	__lowerCAmelCase	):

				def __init__(      self	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = (None, None)	, _lowerCamelCase = None	, ):
										super().__init__(
										    model=_lowerCamelCase	, args=_lowerCamelCase	, data_collator=_lowerCamelCase	, train_dataset=_lowerCamelCase	, eval_dataset=_lowerCamelCase	, tokenizer=_lowerCamelCase	, model_init=_lowerCamelCase	, compute_metrics=_lowerCamelCase	, callbacks=_lowerCamelCase	, optimizers=_lowerCamelCase	, preprocess_logits_for_metrics=_lowerCamelCase	, )

										# 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:
																lowerCamelCase__					=self.load_generation_config(self.args.generation_config      )
																lowerCamelCase__					=gen_config

				@staticmethod
				def 		_a	(      _lowerCamelCase      ):

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

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

										lowerCamelCase__					=GenerationConfig.from_pretrained(_lowerCamelCase	, _lowerCamelCase      )
										return gen_config

				def 		_a	(      self	, _lowerCamelCase = None	, _lowerCamelCase = None	, _lowerCamelCase = "eval"	, **_lowerCamelCase	, ):
										lowerCamelCase__					=gen_kwargs.copy()
										if gen_kwargs.get("max_length"      ) is None and gen_kwargs.get("max_new_tokens"      ) is None:
																lowerCamelCase__					=self.args.generation_max_length
										lowerCamelCase__					=(
										    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"      ) is not None else self.args.generation_num_beams
										)
										lowerCamelCase__					=gen_kwargs

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

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = None	, _lowerCamelCase = "test"	, **_lowerCamelCase	, ):
										lowerCamelCase__					=gen_kwargs.copy()
										if gen_kwargs.get("max_length"      ) is None and gen_kwargs.get("max_new_tokens"      ) is None:
																lowerCamelCase__					=self.args.generation_max_length
										lowerCamelCase__					=(
										    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"      ) is not None else self.args.generation_num_beams
										)
										lowerCamelCase__					=gen_kwargs

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

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = None	, **_lowerCamelCase	, ):

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

										lowerCamelCase__					="labels" in inputs
										lowerCamelCase__					=self._prepare_inputs(_lowerCamelCase      )

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

										if len(_lowerCamelCase      ) == 0 and hasattr(self	, "_gen_kwargs"      ):
																lowerCamelCase__					=self._gen_kwargs.copy()

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

										# 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:
																lowerCamelCase__					=False

										# Retrieves GenerationConfig from model.generation_config
										lowerCamelCase__					=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:
																lowerCamelCase__					=self._pad_tensors_to_max_len(_lowerCamelCase	, gen_config.max_length      )
										elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
																lowerCamelCase__					=self._pad_tensors_to_max_len(_lowerCamelCase	, gen_config.max_new_tokens + 1      )

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

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

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

										return loss, generated_tokens, labels






				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										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
																lowerCamelCase__					=(
																    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:
																						lowerCamelCase__					=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"      )

										lowerCamelCase__					=pad_token_id * torch.ones(
										    (tensor.shape[0], max_length)	, dtype=tensor.dtype	, device=tensor.device      )
										lowerCamelCase__					=tensor
										return padded_tensor





def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						str				):





    """simple docstring"""


    return "".join(chr(ord(_SCREAMING_SNAKE_CASE				) + 32				) if 'A' <= char <= 'Z' else char for char in word				)


if __name__ == "__main__":
       from doctest import testmod

       testmod()


