
"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__     =						{
    """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
    """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase__     =						[
							    """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GPTSanJapaneseForConditionalGeneration""",
							    """GPTSanJapaneseModel""",
							    """GPTSanJapanesePreTrainedModel""",
							]
							lowerCamelCase__     =						[
							    """GPTSanJapaneseTokenizer""",
							]


if TYPE_CHECKING:
							from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
							from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptsan_japanese import (
														    GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTSanJapaneseForConditionalGeneration,
														    GPTSanJapaneseModel,
														    GPTSanJapanesePreTrainedModel,
														)
														from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
							import sys

							lowerCamelCase__     =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

"""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


lowerCamelCase__     =						logging.get_logger(__name__)




class 					A__			(      _lowerCamelCase):

		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			,						):
			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:
				__lowerCAmelCase     :		Tuple		     = self.load_generation_config(self.args.generation_config					)
				__lowerCAmelCase     :		int		     = gen_config

		@staticmethod
		def 				__lowerCamelCase				( _SCREAMING_SNAKE_CASE					):

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

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

			__lowerCAmelCase     :		Optional[int]		     = 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			,						):
			__lowerCAmelCase     :		Any		     = gen_kwargs.copy()
			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
				__lowerCAmelCase     :		Optional[Any]		     = self.args.generation_max_length
			__lowerCAmelCase     :		Tuple		     = (
			    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.args.generation_num_beams
			)
			__lowerCAmelCase     :		Optional[int]		     = 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			,						):
			__lowerCAmelCase     :		Dict		     = gen_kwargs.copy()
			if gen_kwargs.get('max_length'					) is None and gen_kwargs.get('max_new_tokens'					) is None:
				__lowerCAmelCase     :		Optional[Any]		     = self.args.generation_max_length
			__lowerCAmelCase     :		int		     = (
			    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.args.generation_num_beams
			)
			__lowerCAmelCase     :		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			,						):

			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					)

			__lowerCAmelCase     :		str		     = 'labels' in inputs
			__lowerCAmelCase     :		List[str]		     = 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'					):
				__lowerCAmelCase     :		Dict		     = self._gen_kwargs.copy()

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

			# Retrieves GenerationConfig from model.generation_config
			__lowerCAmelCase     :		List[Any]		     = 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     :		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:
				__lowerCAmelCase     :		Optional[Any]		     = 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():
						__lowerCAmelCase     :		List[Any]		     = model(**_SCREAMING_SNAKE_CASE					)
					if self.label_smoother is not None:
						__lowerCAmelCase     :		Optional[int]		     = self.label_smoother(_SCREAMING_SNAKE_CASE			,						inputs['labels']					).mean().detach()
					else:
						__lowerCAmelCase     :		Optional[int]		     = (outputs['loss'] if isinstance(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					) else outputs[0]).mean().detach()
				else:
					__lowerCAmelCase     :		Optional[int]		     = None

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

			if has_labels:
				__lowerCAmelCase     :		Tuple		     = inputs['labels']
				if labels.shape[-1] < gen_config.max_length:
					__lowerCAmelCase     :		List[str]		     = 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:
					__lowerCAmelCase     :		int		     = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE			,						gen_config.max_new_tokens + 1					)
			else:
				__lowerCAmelCase     :		Optional[int]		     = None

			return loss, generated_tokens, labels







		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			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     :		Any		     = (
				    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     :		List[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'					)

			__lowerCAmelCase     :		Dict		     = pad_token_id * torch.ones(
			    (tensor.shape[0], max_length)			,						dtype=tensor.dtype			,						device=tensor.device					)
			__lowerCAmelCase     :		Tuple		     = tensor
			return padded_tensor