




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


__magic_name__     :     List[str]  							=					logging.get_logger(__name__)





class 						SCREAMING_SNAKE_CASE__		(_a ):
     def __init__(	self  :		Tuple					,	__lowerCamelCase  :		Union["PreTrainedModel", nn.Module] = None					,	__lowerCamelCase  :		"TrainingArguments" = None					,	__lowerCamelCase  :		Optional["DataCollator"] = None					,	__lowerCamelCase  :		Optional[Dataset] = None					,	__lowerCamelCase  :		Optional[Union[Dataset, Dict[str, Dataset]]] = None					,	__lowerCamelCase  :		Optional["PreTrainedTokenizerBase"] = None					,	__lowerCamelCase  :		Optional[Callable[[], "PreTrainedModel"]] = None					,	__lowerCamelCase  :		Optional[Callable[["EvalPrediction"], Dict]] = None					,	__lowerCamelCase  :		Optional[List["TrainerCallback"]] = None					,	__lowerCamelCase  :		Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)					,	__lowerCamelCase  :		Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None					,	):



       """simple docstring"""
       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  :		Union[str, GenerationConfig]	):



       """simple docstring"""

       # 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  :		List[str]					,	__lowerCamelCase  :		Optional[Dataset] = None					,	__lowerCamelCase  :		Optional[List[str]] = None					,	__lowerCamelCase  :		str = "eval"					,	**__lowerCamelCase  :		str					,	):



       """simple docstring"""
       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  :		Any					,	__lowerCamelCase  :		Dataset					,	__lowerCamelCase  :		Optional[List[str]] = None					,	__lowerCamelCase  :		str = "test"					,	**__lowerCamelCase  :		Optional[Any]					,	):



       """simple docstring"""
       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  :		Tuple					,	__lowerCamelCase  :		nn.Module					,	__lowerCamelCase  :		Dict[str, Union[torch.Tensor, Any]]					,	__lowerCamelCase  :		bool					,	__lowerCamelCase  :		Optional[List[str]] = None					,	**__lowerCamelCase  :		Tuple					,	):



       """simple docstring"""

       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  :		List[str]					,	__lowerCamelCase  :		Dict					,	__lowerCamelCase  :		Optional[Any]	):



       """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
         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 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase							):
  lowerCAmelCase__								=			0
  for ele in arr:
    total_sum += product_sum(__lowerCAmelCase      ,   depth + 1							) if isinstance(__lowerCAmelCase      ,   __lowerCAmelCase							) else ele
  return total_sum * depth
def 		a_       (   __lowerCAmelCase							):
  return product_sum(__lowerCAmelCase      ,   1							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

