



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 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  int, *_snake_case  :  int, _snake_case  :  Dict=None, _snake_case  :  Optional[Any]=None, **_snake_case  :  Dict					)   ->Optional[Any]:
            super().__init__(*_snake_case, **_snake_case					)
            snake_case__							:	Tuple   	=	eval_examples
            snake_case__							:	Optional[Any]   	=	post_process_function






     def    lowercase_   (       self  :  Dict, _snake_case  :  Optional[Dataset] = None, _snake_case  :  Optional[int]=None, _snake_case  :  Optional[List[str]] = None, _snake_case  :  str = "eval", **_snake_case  :  List[str], )   ->Dict[str, float]:
            snake_case__							:	Optional[int]   	=	gen_kwargs.copy()
            snake_case__							:	List[Any]   	=	(
                gen_kwargs['max_length'] if gen_kwargs.get('max_length'					) is not None else self.args.generation_max_length
            )
            snake_case__							:	Tuple   	=	(
                gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'					) is not None else self.args.generation_num_beams
            )
            snake_case__							:	Dict   	=	gen_kwargs

            snake_case__							:	int   	=	self.eval_dataset if eval_dataset is None else eval_dataset
            snake_case__							:	Union[str, Any]   	=	self.get_eval_dataloader(_snake_case					)
            snake_case__							:	Tuple   	=	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__							:	str   	=	self.compute_metrics
            snake_case__							:	Optional[int]   	=	None
            snake_case__							:	Optional[Any]   	=	time.time()
            snake_case__							:	List[Any]   	=	self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
            try:
                   snake_case__							:	Optional[Any]   	=	eval_loop(
                       _snake_case, description='Evaluation', prediction_loss_only=True if compute_metrics is None else None, ignore_keys=_snake_case, metric_key_prefix=_snake_case, )
            finally:
                   snake_case__							:	Any   	=	compute_metrics
            snake_case__							:	List[str]   	=	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(
                    _snake_case, _snake_case, 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__							:	List[str]   	=	self.post_process_function(_snake_case, _snake_case, _snake_case					)
                   snake_case__							:	List[Any]   	=	self.compute_metrics(_snake_case					)

                   # Prefix all keys with metric_key_prefix + '_'
                   for key in list(metrics.keys()					):
                          if not key.startswith(F'''{metric_key_prefix}_'''					):
                                 snake_case__							:	Union[str, Any]   	=	metrics.pop(_snake_case					)

                   metrics.update(output.metrics					)
            else:
                   snake_case__							:	List[str]   	=	output.metrics

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

            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__							:	str   	=	self.callback_handler.on_evaluate(self.args, self.state, self.control, _snake_case					)
            return metrics






     def    lowercase_   (       self  :  int, _snake_case  :  List[Any], _snake_case  :  Optional[Any], _snake_case  :  List[Any]=None, _snake_case  :  str = "test", **_snake_case  :  List[str]					)   ->Any:
            snake_case__							:	int   	=	gen_kwargs.copy()

            snake_case__							:	Any   	=	self.get_test_dataloader(_snake_case					)

            # Temporarily disable metric computation, we will do it in the loop here.
            snake_case__							:	Optional[Any]   	=	self.compute_metrics
            snake_case__							:	Optional[int]   	=	None
            snake_case__							:	Any   	=	time.time()
            snake_case__							:	int   	=	self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
            try:
                   snake_case__							:	str   	=	eval_loop(
                       _snake_case, description='Prediction', prediction_loss_only=True if compute_metrics is None else None, ignore_keys=_snake_case, metric_key_prefix=_snake_case, )
            finally:
                   snake_case__							:	Optional[Any]   	=	compute_metrics

            snake_case__							:	str   	=	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(
                    _snake_case, _snake_case, 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__							:	List[Any]   	=	self.post_process_function(_snake_case, _snake_case, _snake_case, 'predict'					)
            snake_case__							:	Optional[Any]   	=	self.compute_metrics(_snake_case					)

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











from typing import TYPE_CHECKING

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


a_   :int        =      {
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :List[str]        =      [
     "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongT5EncoderModel",
     "LongT5ForConditionalGeneration",
     "LongT5Model",
     "LongT5PreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :int        =      [
     "FlaxLongT5ForConditionalGeneration",
     "FlaxLongT5Model",
     "FlaxLongT5PreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longta import (
      LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongTaEncoderModel,
      LongTaForConditionalGeneration,
      LongTaModel,
      LongTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_longta import (
      FlaxLongTaForConditionalGeneration,
      FlaxLongTaModel,
      FlaxLongTaPreTrainedModel,
  )


else:
 import sys

 a_   :Optional[int]        =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






