





"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
		import numpy as np
		import tensorflow as tf

		from transformers import TFXLMRobertaModel


@require_tf
@require_sentencepiece
@require_tokenizers
class  __UpperCamelCase       (		unittest.TestCase		):






							@slow
							def 					__a     (     self   )			->				Tuple:
								a   :	str			=       TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base"   )

								a   :	Tuple			=       {
								    '''input_ids''': tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]   ,      dtype=tf.intaa   ),  # "My dog is cute"
								    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]   ,      dtype=tf.intaa   ),
								}

								a   :	Tuple			=       model(_UpperCAmelCase   )['''last_hidden_state''']
								a   :	int			=       tf.TensorShape((1, 6, 768)   )
								self.assertEqual(output.shape   ,      _UpperCAmelCase   )
								# compare the actual values for a slice.
								a   :	Tuple			=       tf.convert_to_tensor(
								    [
								        [
								            [0.0_681_762, 0.10_894_451, 0.06_772_504],
								            [-0.06_423_668, 0.02_366_615, 0.04_329_344],
								            [-0.06_057_295, 0.09_974_135, -0.00_070_584],
								        ]
								    ]   ,      dtype=tf.floataa   ,      )

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()   ,      expected_slice.numpy()   ,      atol=1E-4   )   )








"""simple docstring"""




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
		import torch

		from transformers.generation import (
		    MaxLengthCriteria,
		    MaxNewTokensCriteria,
		    MaxTimeCriteria,
		    StoppingCriteriaList,
		    validate_stopping_criteria,
		)


@require_torch
class  __UpperCamelCase       (		unittest.TestCase		):






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								a   :	str			=       3
								a   :	str			=       250

								a   :	List[Any]			=       ids_tensor((batch_size, length)   ,      lowerCAmelCase__   )
								a   :	Optional[Any]			=       torch.ones((batch_size, length)   ,      device=lowerCAmelCase__   ,      dtype=torch.float   ) / length
								return input_ids, scores






							def 					__a     (     self   )			->				List[Any]:
								a, a   :	str			=       self._get_tensors(5   )

								a   :	Any			=       StoppingCriteriaList(
								    [
								        MaxLengthCriteria(max_length=10   ),
								        MaxTimeCriteria(max_time=0.1   ),
								    ]   )

								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	str			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	Union[str, Any]			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[Any]:
								a   :	Optional[Any]			=       MaxLengthCriteria(max_length=10   )

								a, a   :	int			=       self._get_tensors(5   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	Union[str, Any]			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Tuple			=       MaxNewTokensCriteria(start_length=5   ,      max_new_tokens=5   )

								a, a   :	str			=       self._get_tensors(5   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a   :	List[Any]			=       StoppingCriteriaList([criteria]   )
								self.assertEqual(criteria_list.max_length   ,      10   )






							def 					__a     (     self   )			->				str:
								a, a   :	Tuple			=       self._get_tensors(5   )

								a   :	str			=       MaxTimeCriteria(max_time=0.1   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a   :	Optional[int]			=       MaxTimeCriteria(max_time=0.1   ,      initial_timestamp=time.time() - 0.2   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				str:
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,      10   )

								with self.assertWarns(lowerCAmelCase__   ):
									validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,      11   )

								a   :	Optional[int]			=       validate_stopping_criteria(StoppingCriteriaList()   ,      11   )

								self.assertEqual(len(lowerCAmelCase__   )   ,      1   )


