


'''simple docstring'''





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__					(					a_   ):





			'''simple docstring'''

			def __init__(  self							:			Optional[int]			,    *lowerCAmelCase_							:			Any			,    lowerCAmelCase_							:			Any=None			,    lowerCAmelCase_							:			Optional[Any]=None			,    **lowerCAmelCase_							:			List[str]	)      ->		Union[str, Any]:
						super().__init__(*lowerCAmelCase_			,    **lowerCAmelCase_	)
						__A=	eval_examples
						__A=	post_process_function
			def 					lowerCAmelCase     (  self							:			List[str]			,    lowerCAmelCase_							:			Optional[Dataset] = None			,    lowerCAmelCase_							:			Union[str, Any]=None			,    lowerCAmelCase_							:			Optional[List[str]] = None			,    lowerCAmelCase_							:			str = "eval"			,    **lowerCAmelCase_							:			int			,    )      ->		Dict[str, float]:
						__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(lowerCAmelCase_	)
						__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(
									    lowerCAmelCase_			,    description='Evaluation'			,    prediction_loss_only=True if compute_metrics is None else None			,    ignore_keys=lowerCAmelCase_			,    metric_key_prefix=lowerCAmelCase_			,    )
						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(
						        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
									__A=	self.post_process_function(lowerCAmelCase_			,    lowerCAmelCase_			,    lowerCAmelCase_	)
									__A=	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}_"""	):
															__A=	metrics.pop(lowerCAmelCase_	)

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

						__A=	self.callback_handler.on_evaluate(self.args			,    self.state			,    self.control			,    lowerCAmelCase_	)
						return metrics

			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			Dict			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Optional[Any]=None			,    lowerCAmelCase_							:			str = "test"			,    **lowerCAmelCase_							:			List[str]	)      ->		Dict:
						__A=	gen_kwargs.copy()

						__A=	self.get_test_dataloader(lowerCAmelCase_	)

						# 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(
									    lowerCAmelCase_			,    description='Prediction'			,    prediction_loss_only=True if compute_metrics is None else None			,    ignore_keys=lowerCAmelCase_			,    metric_key_prefix=lowerCAmelCase_			,    )
						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(
						        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

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







'''simple docstring'''





# Lint as: python3
import itertools
import os
import re


UpperCAmelCase__  			=				re.compile(r'''([A-Z]+)([A-Z][a-z])''')
UpperCAmelCase__  			=				re.compile(r'''([a-z\d])([A-Z])''')

UpperCAmelCase__  			=				re.compile(r'''(?<!_)_(?!_)''')
UpperCAmelCase__  			=				re.compile(r'''(_{2,})''')

UpperCAmelCase__  			=				r'''^\w+(\.\w+)*$'''

UpperCAmelCase__  			=				r'''<>:/\|?*'''






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any] ):






			"""simple docstring"""




			__A=	_uppercase_uppercase_re.sub(r'\1_\2',_SCREAMING_SNAKE_CASE )
			__A=	_lowercase_uppercase_re.sub(r'\1_\2',_SCREAMING_SNAKE_CASE )
			return name.lower()






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			__A=	_single_underscore_re.split(_SCREAMING_SNAKE_CASE )
			__A=	[_multiple_underscores_re.split(_SCREAMING_SNAKE_CASE ) for n in name]
			return "".join(n.capitalize() for n in itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE ) if n != '' )






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			if os.path.basename(_SCREAMING_SNAKE_CASE ) != name:
						raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
			return camelcase_to_snakecase(_SCREAMING_SNAKE_CASE )






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any],_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			if os.path.basename(_SCREAMING_SNAKE_CASE ) != name:
						raise ValueError(f"""Should be a dataset name, not a path: {name}""" )
			if not re.match(_split_re,_SCREAMING_SNAKE_CASE ):
						raise ValueError(f"""Split name should match '{_split_re}'' but got '{split}'.""" )
			return f"""{filename_prefix_for_name(_SCREAMING_SNAKE_CASE )}-{split}"""






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any],_SCREAMING_SNAKE_CASE					: Optional[Any],_SCREAMING_SNAKE_CASE					: Optional[int],_SCREAMING_SNAKE_CASE					: Union[str, Any]=None ):






			"""simple docstring"""




			__A=	filename_prefix_for_split(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			if filetype_suffix:
						prefix += f""".{filetype_suffix}"""
			__A=	os.path.join(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			return f"""{filepath}*"""






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: List[str],_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: List[Any]=None,_SCREAMING_SNAKE_CASE					: List[str]=None ):






			"""simple docstring"""




			__A=	filename_prefix_for_split(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			__A=	os.path.join(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )

			if shard_lengths:
						__A=	len(_SCREAMING_SNAKE_CASE )
						__A=	[f"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(_SCREAMING_SNAKE_CASE )]
						if filetype_suffix:
									__A=	[filename + f""".{filetype_suffix}""" for filename in filenames]
						return filenames
			else:
						__A=	prefix
						if filetype_suffix:
									filename += f""".{filetype_suffix}"""
						return [filename]



