




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


__SCREAMING_SNAKE_CASE			:      int      			=logging.get_logger(__name__)






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Union["PreTrainedModel", nn.Module] = None		,     A__     :      "TrainingArguments" = None		,     A__     :      Optional["DataCollator"] = None		,     A__     :      Optional[Dataset] = None		,     A__     :      Optional[Union[Dataset, Dict[str, Dataset]]] = None		,     A__     :      Optional["PreTrainedTokenizerBase"] = None		,     A__     :      Optional[Callable[[], "PreTrainedModel"]] = None		,     A__     :      Optional[Callable[["EvalPrediction"], Dict]] = None		,     A__     :      Optional[List["TrainerCallback"]] = None		,     A__     :      Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)		,     A__     :      Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None		,     ) ->				Dict:
           super().__init__(
               model=A__		,     args=A__		,     data_collator=A__		,     train_dataset=A__		,     eval_dataset=A__		,     tokenizer=A__		,     model_init=A__		,     compute_metrics=A__		,     callbacks=A__		,     optimizers=A__		,     preprocess_logits_for_metrics=A__		,     )

           # 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        __snake_case      (			A__     :      Union[str, GenerationConfig]	) ->				GenerationConfig:

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

           # str or Path
           lowerCamelCase__					     = Path(A__	) if isinstance(A__		,     A__	) 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(A__		,     A__	)
           return gen_config




     def        __snake_case      (			self     :      Tuple		,     A__     :      Optional[Dataset] = None		,     A__     :      Optional[List[str]] = None		,     A__     :      str = "eval"		,     **A__     :      Tuple		,     ) ->				Dict[str, float]:
           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(A__		,     ignore_keys=A__		,     metric_key_prefix=A__	)




     def        __snake_case      (			self     :      Optional[int]		,     A__     :      Dataset		,     A__     :      Optional[List[str]] = None		,     A__     :      str = "test"		,     **A__     :      List[Any]		,     ) ->				"PredictionOutput":
           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(A__		,     ignore_keys=A__		,     metric_key_prefix=A__	)




     def        __snake_case      (			self     :      List[str]		,     A__     :      nn.Module		,     A__     :      Dict[str, Union[torch.Tensor, Any]]		,     A__     :      bool		,     A__     :      Optional[List[str]] = None		,     **A__     :      Optional[Any]		,     ) ->				Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

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

           lowerCamelCase__					     = 'labels' in inputs
           lowerCamelCase__					     = self._prepare_inputs(A__	)

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

           if len(A__	) == 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(**A__		,     **A__	)

           # 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(A__		,     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(A__		,     gen_config.max_new_tokens + 1	)

           with torch.no_grad():
                 if has_labels:
                       with self.compute_loss_context_manager():
                             lowerCamelCase__					     = model(**A__	)
                       if self.label_smoother is not None:
                             lowerCamelCase__					     = self.label_smoother(A__		,     inputs['labels']	).mean().detach()
                       else:
                             lowerCamelCase__					     = (outputs['loss'] if isinstance(A__		,     A__	) 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(A__		,     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(A__		,     gen_config.max_new_tokens + 1	)
           else:
                 lowerCamelCase__					     = None

           return loss, generated_tokens, labels




     def        __snake_case      (			self     :      str		,     A__     :      List[str]		,     A__     :      Optional[int]	) ->				Dict:
           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





import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @cached_property
     def        __snake_case      (			self     :      Union[str, Any]	) ->				str:
           return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'	)




     @cached_property
     def        __snake_case      (			self     :      Tuple	) ->				Dict:
           return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'	)




     def        __snake_case      (			self     :      List[str]	) ->				int:
           lowerCamelCase__					     = self.tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      List[str]	) ->				str:
           lowerCamelCase__					     = self.rust_tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      Any	) ->				str:
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]




     def        __snake_case      (			self     :      str	) ->				Union[str, Any]:
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]