'''simple docstring'''




import math
import time

from transformers import Trainer, 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__(   lowerCAmelCase__      ):


			'''simple docstring'''






			def __init__(  self			,       *__lowerCAmelCase			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       **__lowerCAmelCase):




					"""simple docstring"""


					super().__init__(*__lowerCAmelCase			,       **__lowerCAmelCase)
					lowerCAmelCase     =    eval_examples
					lowerCAmelCase     =    post_process_function



			def 				a_			(  self			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       __lowerCAmelCase = "eval"):




					"""simple docstring"""


					lowerCAmelCase     =    self.eval_dataset if eval_dataset is None else eval_dataset
					lowerCAmelCase     =    self.get_eval_dataloader(__lowerCAmelCase)
					lowerCAmelCase     =    self.eval_examples if eval_examples is None else eval_examples

					# Temporarily disable metric computation, we will do it in the loop here.
					lowerCAmelCase     =    self.compute_metrics
					lowerCAmelCase     =    None
					lowerCAmelCase     =    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					lowerCAmelCase     =    time.time()
					try:
							lowerCAmelCase     =    eval_loop(
							    __lowerCAmelCase			,       description="""Evaluation"""			,       prediction_loss_only=True if compute_metrics is None else None			,       ignore_keys=__lowerCAmelCase			,       metric_key_prefix=__lowerCAmelCase			,       )
					finally:
							lowerCAmelCase     =    compute_metrics
					lowerCAmelCase     =    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
							lowerCAmelCase     =    self.post_process_function(__lowerCAmelCase			,       __lowerCAmelCase			,       output.predictions)
							lowerCAmelCase     =    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}_"):
											lowerCAmelCase     =    metrics.pop(__lowerCAmelCase)
							metrics.update(output.metrics)
					else:
							lowerCAmelCase     =    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())

					lowerCAmelCase     =    self.callback_handler.on_evaluate(self.args			,       self.state			,       self.control			,       __lowerCAmelCase)
					return metrics







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase=None			,       __lowerCAmelCase = "test"):




					"""simple docstring"""


					lowerCAmelCase     =    self.get_test_dataloader(__lowerCAmelCase)

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

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





'''simple docstring'''




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch

						from transformers.generation import DisjunctiveConstraint






@require_torch
class 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2, 4], [1, 2, 3, 4]]
					lowerCAmelCase     =    DisjunctiveConstraint(__lowerCAmelCase)
					self.assertTrue(isinstance(dc.token_ids			,       __lowerCAmelCase))

					with self.assertRaises(__lowerCAmelCase):
							DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]))

					with self.assertRaises(__lowerCAmelCase):
							DisjunctiveConstraint([torch.LongTensor([1, 2, 4]), torch.LongTensor([1, 2, 3, 4, 5])])



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2], [1, 2, 3, 4]]

					with self.assertRaises(__lowerCAmelCase):
							DisjunctiveConstraint(__lowerCAmelCase)  # fails here



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2, 3], [1, 2, 4]]

					lowerCAmelCase     =    DisjunctiveConstraint(__lowerCAmelCase)

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(1)
					lowerCAmelCase     =    stepped is True and completed is False and reset is False
					self.assertTrue(__lowerCAmelCase)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(2)
					lowerCAmelCase     =    stepped is True and completed is False and reset is False
					self.assertTrue(__lowerCAmelCase)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1, 2])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(3)
					lowerCAmelCase     =    stepped is True and completed is True and reset is False
					self.assertTrue(__lowerCAmelCase)
					self.assertTrue(dc.completed)  # Completed!
					self.assertTrue(dc.current_seq == [1, 2, 3])







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

					lowerCAmelCase     =    DisjunctiveConstraint(__lowerCAmelCase)

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(1)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(2)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1, 2])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(4)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.current_seq == [1, 2, 4])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(5)
					self.assertTrue(dc.completed)  # Completed!
					self.assertTrue(dc.current_seq == [1, 2, 4, 5])

					dc.reset()

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(1)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.remaining() == 3)
					self.assertTrue(dc.current_seq == [1])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(2)
					self.assertTrue(not dc.completed)
					self.assertTrue(dc.remaining() == 2)
					self.assertTrue(dc.current_seq == [1, 2])

					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    dc.update(5)
					self.assertTrue(dc.completed)  # Completed!
					self.assertTrue(dc.remaining() == 0)
					self.assertTrue(dc.current_seq == [1, 2, 5])




