'''simple docstring'''




import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class    a__				(       _UpperCAmelCase							,       unittest.TestCase			):
     lowerCamelCase  :					Dict							=FunnelTokenizer
     lowerCamelCase  :					Union[str, Any]							=FunnelTokenizerFast
     lowerCamelCase  :					Union[str, Any]							=True
     lowerCamelCase  :					Tuple							=True






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         super().setUp()

         __lowerCamelCase          =  [
             '''<unk>''',
             '''<cls>''',
             '''<sep>''',
             '''want''',
             '''##want''',
             '''##ed''',
             '''wa''',
             '''un''',
             '''runn''',
             '''##ing''',
             ''',''',
             '''low''',
             '''lowest''',
         ]
         __lowerCamelCase          =  os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES['''vocab_file''']   )
         with open(self.vocab_file  ,    '''w'''  ,    encoding='''utf-8'''   ) as vocab_writer:
             vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    **a		:		Optional[int]   ):





         """simple docstring"""



         return FunnelTokenizer.from_pretrained(self.tmpdirname  ,    **_UpperCAmelCase   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    **a		:		Tuple   ):





         """simple docstring"""



         return FunnelTokenizerFast.from_pretrained(self.tmpdirname  ,    **_UpperCAmelCase   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    a		:		Tuple   ):





         """simple docstring"""



         __lowerCamelCase          =  '''UNwant\u00E9d,running'''
         __lowerCamelCase          =  '''unwanted, running'''
         return input_text, output_text






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.tokenizer_class(self.vocab_file   )

         __lowerCamelCase          =  tokenizer.tokenize('''UNwant\u00E9d,running'''   )
         self.assertListEqual(_UpperCAmelCase  ,    ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']   )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase   )  ,    [7, 4, 5, 10, 8, 9]   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_tokenizers(do_lower_case=_UpperCAmelCase   )
         for tokenizer in tokenizers:
             __lowerCamelCase          =  tokenizer('''UNwant\u00E9d,running'''   )
             __lowerCamelCase          =  len(inputs['''input_ids''']   ) - 1
             self.assertListEqual(inputs['''token_type_ids''']  ,    [2] + [0] * sentence_len   )

             __lowerCamelCase          =  tokenizer('''UNwant\u00E9d,running'''  ,    '''UNwant\u00E9d,running'''   )
             self.assertListEqual(inputs['''token_type_ids''']  ,    [2] + [0] * sentence_len + [1] * sentence_len   )

'''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    a__				(       unittest.TestCase			):






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		int   ):





         """simple docstring"""



         __lowerCamelCase          =  3
         __lowerCamelCase          =  2_50

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )

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

         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(9   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(10   )
         self.assertTrue(criteria(a  ,    a   )   )






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





         """simple docstring"""



         __lowerCamelCase          =  MaxLengthCriteria(max_length=10   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(9   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(10   )
         self.assertTrue(criteria(a  ,    a   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  MaxNewTokensCriteria(start_length=5  ,    max_new_tokens=5   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(9   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(10   )
         self.assertTrue(criteria(a  ,    a   )   )

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )

         __lowerCamelCase          =  MaxTimeCriteria(max_time=0.1   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase          =  MaxTimeCriteria(max_time=0.1  ,    initial_timestamp=time.time() - 0.2   )
         self.assertTrue(criteria(a  ,    a   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )  ,    10   )

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

         __lowerCamelCase          =  validate_stopping_criteria(StoppingCriteriaList()  ,    11   )

         self.assertEqual(len(a   )  ,    1   )
