

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   lowercase_   (  unittest.TestCase					):



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    int		)   ->					Dict:
        _snake_case						=							3
        _snake_case						=							250

        _snake_case						=							ids_tensor((batch_size, length)      , A__		)
        _snake_case						=							torch.ones((batch_size, length)      , device=A__      , dtype=torch.float		) / length
        return input_ids, scores



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					int:
        _snake_case,					_snake_case						=							self._get_tensors(5		)

        _snake_case						=							StoppingCriteriaList(
            [
                MaxLengthCriteria(max_length=10		),
                MaxTimeCriteria(max_time=0.1		),
            ]		)

        self.assertFalse(criteria(A__      , A__		)		)

        _snake_case,					_snake_case						=							self._get_tensors(9		)
        self.assertFalse(criteria(A__      , A__		)		)

        _snake_case,					_snake_case						=							self._get_tensors(10		)
        self.assertTrue(criteria(A__      , A__		)		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					str:
        _snake_case						=							MaxLengthCriteria(max_length=10		)

        _snake_case,					_snake_case						=							self._get_tensors(5		)
        self.assertFalse(criteria(A__      , A__		)		)

        _snake_case,					_snake_case						=							self._get_tensors(9		)
        self.assertFalse(criteria(A__      , A__		)		)

        _snake_case,					_snake_case						=							self._get_tensors(10		)
        self.assertTrue(criteria(A__      , A__		)		)



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Any:
        _snake_case						=							MaxNewTokensCriteria(start_length=5      , max_new_tokens=5		)

        _snake_case,					_snake_case						=							self._get_tensors(5		)
        self.assertFalse(criteria(A__      , A__		)		)

        _snake_case,					_snake_case						=							self._get_tensors(9		)
        self.assertFalse(criteria(A__      , A__		)		)

        _snake_case,					_snake_case						=							self._get_tensors(10		)
        self.assertTrue(criteria(A__      , A__		)		)

        _snake_case						=							StoppingCriteriaList([criteria]		)
        self.assertEqual(criteria_list.max_length      , 10		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					Optional[Any]:
        _snake_case,					_snake_case						=							self._get_tensors(5		)

        _snake_case						=							MaxTimeCriteria(max_time=0.1		)
        self.assertFalse(criteria(A__      , A__		)		)

        _snake_case						=							MaxTimeCriteria(max_time=0.1      , initial_timestamp=time.time() - 0.2		)
        self.assertTrue(criteria(A__      , A__		)		)







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

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

        _snake_case						=							validate_stopping_criteria(StoppingCriteriaList()      , 11		)

        self.assertEqual(len(A__		)      , 1		)




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A      		=     {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A      		=     [
     '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MraForMaskedLM''',
     '''MraForMultipleChoice''',
     '''MraForQuestionAnswering''',
     '''MraForSequenceClassification''',
     '''MraForTokenClassification''',
     '''MraLayer''',
     '''MraModel''',
     '''MraPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mra import (
      MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraLayer,
      MraModel,
      MraPreTrainedModel,
  )
else:
 import sys

 __A      		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure)

