



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
	import torch

	from transformers import XLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_torch
class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	@slow
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[Any]:
							UpperCAmelCase_=       XLMRobertaModel.from_pretrained("""xlm-roberta-base"""					)
							UpperCAmelCase_=       torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]					)
							# The dog is cute and lives in the garden house

							UpperCAmelCase_=       torch.Size((1, 12, 768)					)  # batch_size, sequence_length, embedding_vector_dim
							UpperCAmelCase_=       torch.tensor(
							    [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]					)
							#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
							#  xlmr.eval()
							#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
							with torch.no_grad():
													UpperCAmelCase_=       model(__UpperCAmelCase					)["""last_hidden_state"""].detach()
							self.assertEqual(output.shape						,  __UpperCAmelCase					)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]						,  __UpperCAmelCase						,  atol=1E-3					)					)




	@slow
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Optional[Any]:
							UpperCAmelCase_=       XLMRobertaModel.from_pretrained("""xlm-roberta-large"""					)
							UpperCAmelCase_=       torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]					)
							# The dog is cute and lives in the garden house

							UpperCAmelCase_=       torch.Size((1, 12, 1_024)					)  # batch_size, sequence_length, embedding_vector_dim
							UpperCAmelCase_=       torch.tensor(
							    [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]					)
							#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
							#  xlmr.eval()
							#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
							with torch.no_grad():
													UpperCAmelCase_=       model(__UpperCAmelCase					)["""last_hidden_state"""].detach()
							self.assertEqual(output.shape						,  __UpperCAmelCase					)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]						,  __UpperCAmelCase						,  atol=1E-3					)					)









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 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						Any						,  *__UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						List[str]=None						,  __UpperCAmelCase     :						Optional[int]=None						,  **__UpperCAmelCase     :						Dict					)      ->			Optional[int]:
							super().__init__(*__UpperCAmelCase						,  **__UpperCAmelCase					)
							UpperCAmelCase_=       eval_examples
							UpperCAmelCase_=       post_process_function




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						Optional[Dataset] = None						,  __UpperCAmelCase     :						Optional[Any]=None						,  __UpperCAmelCase     :						Optional[List[str]] = None						,  __UpperCAmelCase     :						str = "eval"						,  **__UpperCAmelCase     :						Any						,  )      ->			Dict[str, float]:
							UpperCAmelCase_=       gen_kwargs.copy()
							UpperCAmelCase_=       (
							    gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""					) is not None else self.args.generation_max_length
							)
							UpperCAmelCase_=       (
							    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""					) is not None else self.args.generation_num_beams
							)
							UpperCAmelCase_=       gen_kwargs

							UpperCAmelCase_=       self.eval_dataset if eval_dataset is None else eval_dataset
							UpperCAmelCase_=       self.get_eval_dataloader(__UpperCAmelCase					)
							UpperCAmelCase_=       self.eval_examples if eval_examples is None else eval_examples

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

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

							UpperCAmelCase_=       self.callback_handler.on_evaluate(self.args						,  self.state						,  self.control						,  __UpperCAmelCase					)
							return metrics




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						Dict						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						Optional[int]=None						,  __UpperCAmelCase     :						str = "test"						,  **__UpperCAmelCase     :						List[str]					)      ->			Tuple:
							UpperCAmelCase_=       gen_kwargs.copy()

							UpperCAmelCase_=       self.get_test_dataloader(__UpperCAmelCase					)

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

							UpperCAmelCase_=       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

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




