


"""simple docstring"""





import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 		snake_case_(       a__							,					unittest.TestCase			):
     __UpperCamelCase  =					BioGptTokenizer
     __UpperCamelCase  =					False






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           lowerCAmelCase					:	Any       =   [
               '''l''',
               '''o''',
               '''w''',
               '''e''',
               '''r''',
               '''s''',
               '''t''',
               '''i''',
               '''d''',
               '''n''',
               '''w</w>''',
               '''r</w>''',
               '''t</w>''',
               '''lo''',
               '''low''',
               '''er</w>''',
               '''low</w>''',
               '''lowest</w>''',
               '''newer</w>''',
               '''wider</w>''',
               '''<unk>''',
           ]
           lowerCAmelCase					:	Union[str, Any]       =   dict(zip(UpperCamelCase_					,				range(len(UpperCamelCase_				)				)				)				)
           lowerCAmelCase					:	List[str]       =   ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

           lowerCAmelCase					:	Tuple       =   os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''vocab_file''']				)
           lowerCAmelCase					:	Tuple       =   os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''merges_file''']				)
           with open(self.vocab_file					,				'''w'''				) as fp:
                 fp.write(json.dumps(UpperCamelCase_				)				)
           with open(self.merges_file					,				'''w'''				) as fp:
                 fp.write('''\n'''.join(UpperCamelCase_				)				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					Dict				):
           lowerCAmelCase					:	List[str]       =   '''lower newer'''
           lowerCAmelCase					:	List[str]       =   '''lower newer'''
           return input_text, output_text






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Dict       =   BioGptTokenizer(self.vocab_file					,				self.merges_file				)

           lowerCAmelCase					:	List[str]       =   '''lower'''
           lowerCAmelCase					:	Tuple       =   ['''low''', '''er</w>''']
           lowerCAmelCase					:	Dict       =   tokenizer.tokenize(UpperCamelCase_				)
           self.assertListEqual(UpperCamelCase_					,				UpperCamelCase_				)

           lowerCAmelCase					:	List[str]       =   tokens + ['''<unk>''']
           lowerCAmelCase					:	Tuple       =   [1_4, 1_5, 2_0]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_				)					,				UpperCamelCase_				)






     @slow
     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	List[Any]       =   BioGptTokenizer.from_pretrained('''microsoft/biogpt'''				)

           lowerCAmelCase					:	Dict       =   tokenizer.encode('''sequence builders'''					,				add_special_tokens=UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   tokenizer.encode('''multi-sequence build'''					,				add_special_tokens=UpperCamelCase_				)

           lowerCAmelCase					:	str       =   tokenizer.build_inputs_with_special_tokens(UpperCamelCase_				)
           lowerCAmelCase					:	Any       =   tokenizer.build_inputs_with_special_tokens(UpperCamelCase_					,				UpperCamelCase_				)

           self.assertTrue(encoded_sentence == [2] + text				)
           self.assertTrue(encoded_pair == [2] + text + [2] + text_a				)






"""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 		snake_case_(       unittest.TestCase			):






     def      lowerCamelCase__     (	self  :					int					,				UpperCamelCase_  :					int				):
           lowerCAmelCase					:	str       =   3
           lowerCAmelCase					:	Tuple       =   2_5_0

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






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   self._get_tensors(5				)

           lowerCAmelCase					:	Union[str, Any]       =   StoppingCriteriaList(
               [
                   MaxLengthCriteria(max_length=1_0				),
                   MaxTimeCriteria(max_time=0.1				),
               ]				)

           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   self._get_tensors(9				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	Any       =   self._get_tensors(1_0				)
           self.assertTrue(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase					:	Optional[Any]       =   MaxLengthCriteria(max_length=1_0				)

           lowerCAmelCase,      lowerCAmelCase					:	Optional[Any]       =   self._get_tensors(5				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   self._get_tensors(9				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase,      lowerCAmelCase					:	str       =   self._get_tensors(1_0				)
           self.assertTrue(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	Optional[Any]       =   MaxNewTokensCriteria(start_length=5					,				max_new_tokens=5				)

           lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   self._get_tensors(5				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

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

           lowerCAmelCase,      lowerCAmelCase					:	str       =   self._get_tensors(1_0				)
           self.assertTrue(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase					:	Dict       =   StoppingCriteriaList([criteria]				)
           self.assertEqual(criteria_list.max_length					,				1_0				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase,      lowerCAmelCase					:	Tuple       =   self._get_tensors(5				)

           lowerCAmelCase					:	List[str]       =   MaxTimeCriteria(max_time=0.1				)
           self.assertFalse(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)

           lowerCAmelCase					:	List[str]       =   MaxTimeCriteria(max_time=0.1					,				initial_timestamp=time.time() - 0.2				)
           self.assertTrue(criteria(UpperCamelCase_					,				UpperCamelCase_				)				)






     def      lowerCamelCase__     (	self  :					str				):
           validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0				)]				)					,				1_0				)

           with self.assertWarns(UpperCamelCase_				):
                 validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0				)]				)					,				1_1				)

           lowerCAmelCase					:	str       =   validate_stopping_criteria(StoppingCriteriaList()					,				1_1				)

           self.assertEqual(len(UpperCamelCase_				)					,				1				)


