





import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 					A__       (				UpperCamelCase__					,    unittest.TestCase      ):




   """simple docstring"""



   __A  :							Union[str, Any]			    =    LayoutLMTokenizer
   __A  :							Union[str, Any]			    =    LayoutLMTokenizerFast
   __A  :							Dict			    =    True
   __A  :							Tuple			    =    True

   def 				__lowercase						(   self)     ->		Union[str, Any]:







          '''simple docstring'''

          super().setUp()

          a__		:      Optional[int]					=							[
              '[UNK]',
              '[CLS]',
              '[SEP]',
              'want',
              '##want',
              '##ed',
              'wa',
              'un',
              'runn',
              '##ing',
              ',',
              'low',
              'lowest',
          ]
          a__		:      Union[str, Any]					=							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 				__lowercase						(   self   ,    **lowercase)     ->		List[Any]:







          '''simple docstring'''

          return LayoutLMTokenizer.from_pretrained(self.tmpdirname   ,    **lowercase_)

   def 				__lowercase						(   self   ,    lowercase)     ->		Dict:







          '''simple docstring'''

          a__		:      Union[str, Any]					=							'UNwant\u00E9d,running'
          a__		:      Dict					=							'unwanted, running'
          return input_text, output_text

   def 				__lowercase						(   self)     ->		List[Any]:







          '''simple docstring'''

          a__		:      Dict					=							self.tokenizer_class(self.vocab_file)

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

   def 				__lowercase						(   self)     ->		str:







          '''simple docstring'''

          pass









'''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 	UpperCAmelCase_				(							self :int		,			lowercase_ :str				)->				Any:
     A__							=     3
     A__							=     2_50

     A__							=     ids_tensor((batch_size, length)		,			lowercase_				)
     A__							=     torch.ones((batch_size, length)		,			device=lowercase_		,			dtype=torch.float				) / length
     return input_ids, scores
 def 	UpperCAmelCase_				(							self :str				)->				Dict:
     A__,      A__							=     self._get_tensors(5				)

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

     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

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

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				List[Any]:
     A__							=     MaxLengthCriteria(max_length=10				)

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

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

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				int:
     A__							=     MaxNewTokensCriteria(start_length=5		,			max_new_tokens=5				)

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

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

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

     A__							=     StoppingCriteriaList([criteria]				)
     self.assertEqual(criteria_list.max_length		,			10				)
 def 	UpperCAmelCase_				(							self :Dict				)->				int:
     A__,      A__							=     self._get_tensors(5				)

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

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



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

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

     A__							=     validate_stopping_criteria(StoppingCriteriaList()		,			11				)

     self.assertEqual(len(lowercase_				)		,			1				)



