





from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader







class 				__lowerCAmelCase       (    UpperCamelCase__):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = "arrow"		,      **lowerCAmelCase__		,      )   ->						Optional[int]:



        '''simple docstring'''




        super().__init__(
            split=lowerCAmelCase__		,      features=lowerCAmelCase__		,      cache_dir=lowerCAmelCase__		,      keep_in_memory=lowerCAmelCase__		,      streaming=lowerCAmelCase__		,      **lowerCAmelCase__		,      )
        a__	:					int										=load_from_cache_file
        a__	:					Tuple										=file_format
        a__	:					List[Any]										=Spark(
            df=lowerCAmelCase__		,      features=lowerCAmelCase__		,      cache_dir=lowerCAmelCase__		,      working_dir=lowerCAmelCase__		,      **lowerCAmelCase__		,      )







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        if self.streaming:
          return self.builder.as_streaming_dataset(split=self.split							)
        a__	:					str										=None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
        self.builder.download_and_prepare(
            download_mode=lowerCAmelCase__		,      file_format=self._file_format		,      )
        return self.builder.as_dataset(split=self.split							)











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     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     List[Any]     ,		*__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           super().__init__(*__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           __a						       =				eval_examples
           __a						       =				post_process_function



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Optional[Dataset] = None     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=None     ,		__SCREAMING_SNAKE_CASE							:     Optional[List[str]] = None     ,		__SCREAMING_SNAKE_CASE							:     str = "eval"     ,		**__SCREAMING_SNAKE_CASE							:     Any     ,		):


           '''simple docstring'''

           __a						       =				gen_kwargs.copy()
           __a						       =				(
               gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length''') is not None else self.args.generation_max_length
           )
           __a						       =				(
               gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams''') is not None else self.args.generation_num_beams
           )
           __a						       =				gen_kwargs

           __a						       =				self.eval_dataset if eval_dataset is None else eval_dataset
           __a						       =				self.get_eval_dataloader(__SCREAMING_SNAKE_CASE)
           __a						       =				self.eval_examples if eval_examples is None else eval_examples

           # Temporarily disable metric computation, we will do it in the loop here.
           __a						       =				self.compute_metrics
           __a						       =				None
           __a						       =				time.time()
           __a						       =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
               __a						       =				eval_loop(
                   __SCREAMING_SNAKE_CASE     ,		description='''Evaluation'''     ,		prediction_loss_only=True if compute_metrics is None else None     ,		ignore_keys=__SCREAMING_SNAKE_CASE     ,		metric_key_prefix=__SCREAMING_SNAKE_CASE     ,		)
           finally:
               __a						       =				compute_metrics
           __a						       =				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(
                   __SCREAMING_SNAKE_CASE     ,		__SCREAMING_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
               __a						       =				self.post_process_function(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
               __a						       =				self.compute_metrics(__SCREAMING_SNAKE_CASE)

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

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

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

           __a						       =				self.callback_handler.on_evaluate(self.args     ,		self.state     ,		self.control     ,		__SCREAMING_SNAKE_CASE)
           return metrics





       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		__SCREAMING_SNAKE_CASE							:     str = "test"     ,		**__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           __a						       =				gen_kwargs.copy()

           __a						       =				self.get_test_dataloader(__SCREAMING_SNAKE_CASE)

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

           __a						       =				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(
                   __SCREAMING_SNAKE_CASE     ,		__SCREAMING_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

           __a						       =				self.post_process_function(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		'''predict''')
           __a						       =				self.compute_metrics(__SCREAMING_SNAKE_CASE)

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





