





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   a_ (			unittest.TestCase      ):

			def      lowerCAmelCase(   self   :  List[Any]     , UpperCAmelCase__   :  List[Any]						):






							"""simple docstring"""
							snake_case   :					Tuple    						=				3
							snake_case   :					Union[str, Any]    						=				250

							snake_case   :					Union[str, Any]    						=				ids_tensor((batch_size, length)     , a_						)
							snake_case   :					Optional[int]    						=				torch.ones((batch_size, length)     , device=a_     , dtype=torch.float						) / length
							return input_ids, scores

			def      lowerCAmelCase(   self   :  Union[str, Any]						):






							"""simple docstring"""
							snake_case						,						snake_case   :					Any    						=				self._get_tensors(5						)

							snake_case   :					Tuple    						=				StoppingCriteriaList(
							    [
							        MaxLengthCriteria(max_length=10						),
							        MaxTimeCriteria(max_time=0.1						),
							    ]						)

							self.assertFalse(criteria(a_     , a_						)						)

							snake_case						,						snake_case   :					str    						=				self._get_tensors(9						)
							self.assertFalse(criteria(a_     , a_						)						)

							snake_case						,						snake_case   :					Tuple    						=				self._get_tensors(10						)
							self.assertTrue(criteria(a_     , a_						)						)

			def      lowerCAmelCase(   self   :  int						):






							"""simple docstring"""
							snake_case   :					Any    						=				MaxLengthCriteria(max_length=10						)

							snake_case						,						snake_case   :					Optional[Any]    						=				self._get_tensors(5						)
							self.assertFalse(criteria(a_     , a_						)						)

							snake_case						,						snake_case   :					Optional[int]    						=				self._get_tensors(9						)
							self.assertFalse(criteria(a_     , a_						)						)

							snake_case						,						snake_case   :					List[Any]    						=				self._get_tensors(10						)
							self.assertTrue(criteria(a_     , a_						)						)

			def      lowerCAmelCase(   self   :  str						):






							"""simple docstring"""
							snake_case   :					Dict    						=				MaxNewTokensCriteria(start_length=5     , max_new_tokens=5						)

							snake_case						,						snake_case   :					Dict    						=				self._get_tensors(5						)
							self.assertFalse(criteria(a_     , a_						)						)

							snake_case						,						snake_case   :					Union[str, Any]    						=				self._get_tensors(9						)
							self.assertFalse(criteria(a_     , a_						)						)

							snake_case						,						snake_case   :					Dict    						=				self._get_tensors(10						)
							self.assertTrue(criteria(a_     , a_						)						)

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

			def      lowerCAmelCase(   self   :  Dict						):






							"""simple docstring"""
							snake_case						,						snake_case   :					Any    						=				self._get_tensors(5						)

							snake_case   :					Any    						=				MaxTimeCriteria(max_time=0.1						)
							self.assertFalse(criteria(a_     , a_						)						)

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



			def      lowerCAmelCase(   self   :  Dict						):






							"""simple docstring"""
							validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10						)]						)     , 10						)

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

							snake_case   :					Tuple    						=				validate_stopping_criteria(StoppingCriteriaList()     , 11						)

							self.assertEqual(len(a_						)     , 1						)



'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_a					:			Dict							=    {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Optional[Any]							=    [
      'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'XLMForMultipleChoice',
      'XLMForQuestionAnswering',
      'XLMForQuestionAnsweringSimple',
      'XLMForSequenceClassification',
      'XLMForTokenClassification',
      'XLMModel',
      'XLMPreTrainedModel',
      'XLMWithLMHeadModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			int							=    [
      'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFXLMForMultipleChoice',
      'TFXLMForQuestionAnsweringSimple',
      'TFXLMForSequenceClassification',
      'TFXLMForTokenClassification',
      'TFXLMMainLayer',
      'TFXLMModel',
      'TFXLMPreTrainedModel',
      'TFXLMWithLMHeadModel',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

  _a					:			Union[str, Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


