





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_		:					Union[str, Any]      						=    {
    '''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:					Optional[Any]      						=    [
		    '''NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''NezhaForNextSentencePrediction''',
		    '''NezhaForMaskedLM''',
		    '''NezhaForPreTraining''',
		    '''NezhaForMultipleChoice''',
		    '''NezhaForQuestionAnswering''',
		    '''NezhaForSequenceClassification''',
		    '''NezhaForTokenClassification''',
		    '''NezhaModel''',
		    '''NezhaPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_nezha import (
				    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    NezhaForMaskedLM,
				    NezhaForMultipleChoice,
				    NezhaForNextSentencePrediction,
				    NezhaForPreTraining,
				    NezhaForQuestionAnswering,
				    NezhaForSequenceClassification,
				    NezhaForTokenClassification,
				    NezhaModel,
				    NezhaPreTrainedModel,
				)


else:
		import sys

		lowercase_		:					Optional[Any]      						=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm
   import torch_xla.debug.metrics as met


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					*_lowerCAmelCase							:							Dict     ,					_lowerCAmelCase							:							List[Any]=None     ,					_lowerCAmelCase							:							str=None     ,					**_lowerCAmelCase							:							str	) ->			Dict:




     """simple docstring"""
     super().__init__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)
     snake_case_            =       eval_examples
     snake_case_            =       post_process_function



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							Optional[Dataset] = None     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							Optional[List[str]] = None     ,					_lowerCAmelCase							:							str = "eval"     ,					**_lowerCAmelCase							:							Tuple     ,					) ->			Dict[str, float]:




     """simple docstring"""
     snake_case_            =       gen_kwargs.copy()
     snake_case_            =       (
         gen_kwargs["max_length"] if gen_kwargs.get("max_length"	) is not None else self.args.generation_max_length
     )
     snake_case_            =       (
         gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"	) is not None else self.args.generation_num_beams
     )
     snake_case_            =       gen_kwargs

     snake_case_            =       self.eval_dataset if eval_dataset is None else eval_dataset
     snake_case_            =       self.get_eval_dataloader(_lowerCAmelCase	)
     snake_case_            =       self.eval_examples if eval_examples is None else eval_examples

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_            =       self.compute_metrics
     snake_case_            =       None
     snake_case_            =       time.time()
     snake_case_            =       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
        snake_case_            =       eval_loop(
            _lowerCAmelCase     ,					description="Evaluation"     ,					prediction_loss_only=True if compute_metrics is None else None     ,					ignore_keys=_lowerCAmelCase     ,					metric_key_prefix=_lowerCAmelCase     ,					)
     finally:
        snake_case_            =       compute_metrics
     snake_case_            =       self.args.eval_batch_size * self.args.world_size
     if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
        start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
     output.metrics.update(
         speed_metrics(
             _lowerCAmelCase     ,					_lowerCAmelCase     ,					num_samples=output.num_samples     ,					num_steps=math.ceil(output.num_samples / total_batch_size	)     ,					)	)

     if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
        # Only the main node write the results by default
        snake_case_            =       self.post_process_function(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
        snake_case_            =       self.compute_metrics(_lowerCAmelCase	)

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()	):
           if not key.startswith(F'''{metric_key_prefix}_'''	):
              snake_case_            =       metrics.pop(_lowerCAmelCase	)

        metrics.update(output.metrics	)
     else:
        snake_case_            =       output.metrics

     if self.args.should_log:
        # Only the main node log the results by default
        self.log(_lowerCAmelCase	)

     if self.args.tpu_metrics_debug or self.args.debug:
        # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
        xm.master_print(met.metrics_report()	)

     snake_case_            =       self.callback_handler.on_evaluate(self.args     ,					self.state     ,					self.control     ,					_lowerCAmelCase	)
     return metrics



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[str]=None     ,					_lowerCAmelCase							:							str = "test"     ,					**_lowerCAmelCase							:							Optional[Any]	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       gen_kwargs.copy()

     snake_case_            =       self.get_test_dataloader(_lowerCAmelCase	)

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_            =       self.compute_metrics
     snake_case_            =       None
     snake_case_            =       time.time()
     snake_case_            =       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
        snake_case_            =       eval_loop(
            _lowerCAmelCase     ,					description="Prediction"     ,					prediction_loss_only=True if compute_metrics is None else None     ,					ignore_keys=_lowerCAmelCase     ,					metric_key_prefix=_lowerCAmelCase     ,					)
     finally:
        snake_case_            =       compute_metrics

     snake_case_            =       self.args.eval_batch_size * self.args.world_size
     if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
        start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
     output.metrics.update(
         speed_metrics(
             _lowerCAmelCase     ,					_lowerCAmelCase     ,					num_samples=output.num_samples     ,					num_steps=math.ceil(output.num_samples / total_batch_size	)     ,					)	)
     if self.post_process_function is None or self.compute_metrics is None:
        return output

     snake_case_            =       self.post_process_function(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					"predict"	)
     snake_case_            =       self.compute_metrics(_lowerCAmelCase	)

     # Prefix all keys with metric_key_prefix + '_'
     for key in list(metrics.keys()	):
        if not key.startswith(F'''{metric_key_prefix}_'''	):
           snake_case_            =       metrics.pop(_lowerCAmelCase	)
     metrics.update(output.metrics	)
     return PredictionOutput(predictions=predictions.predictions     ,					label_ids=predictions.label_ids     ,					metrics=_lowerCAmelCase	)




