




import importlib.metadata
from typing import Union

from packaging.version import Version, parse

from .constants import STR_OPERATION_TO_FUNC


__SCREAMING_SNAKE_CASE 						=parse(importlib.metadata.version("""torch"""))

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							if operation not in STR_OPERATION_TO_FUNC.keys():
														raise ValueError(F"`operation` must be one of {list(STR_OPERATION_TO_FUNC.keys()     )}, received {operation}"     )
							SCREAMING_SNAKE_CASE_   	=    STR_OPERATION_TO_FUNC[operation]
							if isinstance(_lowercase  ,    _lowercase     ):
														SCREAMING_SNAKE_CASE_   	=    parse(importlib.metadata.version(_lowercase     )     )
							return operation(_lowercase  ,    parse(_lowercase     )     )






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							return compare_versions(_lowercase  ,    _lowercase  ,    _lowercase     )





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  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def __init__(							self:   List[Any]		,					*_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Optional[Any]=None		,					_lowerCamelCase:   Dict=None		,					**_lowerCamelCase:   Dict		):
											super().__init__(*_lowerCamelCase		,					**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    eval_examples
											SCREAMING_SNAKE_CASE_   	=    post_process_function


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   Optional[Dataset] = None		,					_lowerCamelCase:   List[str]=None		,					_lowerCamelCase:   Optional[List[str]] = None		,					_lowerCamelCase:   str = "eval"		,					**_lowerCamelCase:   Union[str, Any]		,					):
											SCREAMING_SNAKE_CASE_   	=    gen_kwargs.copy()
											SCREAMING_SNAKE_CASE_   	=    (
											    gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''		) is not None else self.args.generation_max_length
											)
											SCREAMING_SNAKE_CASE_   	=    (
											    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''		) is not None else self.args.generation_num_beams
											)
											SCREAMING_SNAKE_CASE_   	=    gen_kwargs

											SCREAMING_SNAKE_CASE_   	=    self.eval_dataset if eval_dataset is None else eval_dataset
											SCREAMING_SNAKE_CASE_   	=    self.get_eval_dataloader(_lowerCamelCase		)
											SCREAMING_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.
											SCREAMING_SNAKE_CASE_   	=    self.compute_metrics
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    time.time()
											SCREAMING_SNAKE_CASE_   	=    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																		SCREAMING_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:
																		SCREAMING_SNAKE_CASE_   	=    compute_metrics
											SCREAMING_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
																		SCREAMING_SNAKE_CASE_   	=    self.post_process_function(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		)
																		SCREAMING_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}_"		):
																																SCREAMING_SNAKE_CASE_   	=    metrics.pop(_lowerCamelCase		)

																		metrics.update(output.metrics		)
											else:
																		SCREAMING_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()		)

											SCREAMING_SNAKE_CASE_   	=    self.callback_handler.on_evaluate(self.args		,					self.state		,					self.control		,					_lowerCamelCase		)
											return metrics





				def     _A							(							self:   List[str]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Optional[int]=None		,					_lowerCamelCase:   str = "test"		,					**_lowerCamelCase:   str		):
											SCREAMING_SNAKE_CASE_   	=    gen_kwargs.copy()

											SCREAMING_SNAKE_CASE_   	=    self.get_test_dataloader(_lowerCamelCase		)

											# Temporarily disable metric computation, we will do it in the loop here.
											SCREAMING_SNAKE_CASE_   	=    self.compute_metrics
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    time.time()
											SCREAMING_SNAKE_CASE_   	=    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																		SCREAMING_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:
																		SCREAMING_SNAKE_CASE_   	=    compute_metrics

											SCREAMING_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

											SCREAMING_SNAKE_CASE_   	=    self.post_process_function(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					'''predict'''		)
											SCREAMING_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}_"		):
																									SCREAMING_SNAKE_CASE_   	=    metrics.pop(_lowerCamelCase		)
											metrics.update(output.metrics		)
											return PredictionOutput(predictions=predictions.predictions		,					label_ids=predictions.label_ids		,					metrics=_lowerCamelCase		)


