


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:      int       ,		*UpperCamelCase_:      str       ,		UpperCamelCase_:      List[str]=None       ,		UpperCamelCase_:      int=None       ,		**UpperCamelCase_:      Optional[Any]				)     ->  List[str]:





									"""simple docstring"""







									super().__init__(*UpperCamelCase_       ,		**UpperCamelCase_				)
									lowercase__				= eval_examples
									lowercase__				= post_process_function

							def  lowerCamelCase_					(				self:      List[str]       ,		UpperCamelCase_:      Optional[Dataset] = None       ,		UpperCamelCase_:      List[Any]=None       ,		UpperCamelCase_:      Optional[List[str]] = None       ,		UpperCamelCase_:      str = "eval"       ,		**UpperCamelCase_:      int       ,		)     ->  Dict[str, float]:





									"""simple docstring"""







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

									lowercase__				= self.eval_dataset if eval_dataset is None else eval_dataset
									lowercase__				= self.get_eval_dataloader(UpperCamelCase_				)
									lowercase__				= self.eval_examples if eval_examples is None else eval_examples

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

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

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

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

									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()				)

									lowercase__				= self.callback_handler.on_evaluate(self.args       ,		self.state       ,		self.control       ,		UpperCamelCase_				)
									return metrics


							def  lowerCamelCase_					(				self:      Dict       ,		UpperCamelCase_:      Any       ,		UpperCamelCase_:      Tuple       ,		UpperCamelCase_:      List[str]=None       ,		UpperCamelCase_:      str = "test"       ,		**UpperCamelCase_:      Union[str, Any]				)     ->  Union[str, Any]:





									"""simple docstring"""







									lowercase__				= gen_kwargs.copy()

									lowercase__				= self.get_test_dataloader(UpperCamelCase_				)

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

									lowercase__				= 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(
									        UpperCamelCase_       ,		UpperCamelCase_       ,		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

									lowercase__				= self.post_process_function(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		'''predict'''				)
									lowercase__				= self.compute_metrics(UpperCamelCase_				)

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





A									=  [
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
