


"""simple docstring"""





import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin



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






     def      lowerCamelCase__     (	self  :					List[str]				):
           super().setUp()

           lowerCAmelCase					:	int       =   ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
           lowerCAmelCase					:	int       =   dict(zip(UpperCamelCase_					,				range(len(UpperCamelCase_				)				)				)				)

           lowerCAmelCase					:	str       =   ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
           lowerCAmelCase					:	Union[str, Any]       =   {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

           lowerCAmelCase					:	str       =   os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''vocab_file''']				)
           lowerCAmelCase					:	Dict       =   os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''merges_file''']				)
           with open(self.vocab_file					,				'''w'''					,				encoding='''utf-8'''				) as fp:
                 fp.write(json.dumps(UpperCamelCase_				) + '''\n'''				)
           with open(self.merges_file					,				'''w'''					,				encoding='''utf-8'''				) as fp:
                 fp.write('''\n'''.join(UpperCamelCase_				)				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				**UpperCamelCase_  :					Tuple				):
           kwargs.update(self.special_tokens_map				)
           return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname					,				**UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					Union[str, Any]				):
           lowerCAmelCase					:	List[Any]       =   '''adapt act apte'''
           lowerCAmelCase					:	str       =   '''adapt act apte'''
           return input_text, output_text






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	int       =   BlenderbotSmallTokenizer(self.vocab_file					,				self.merges_file					,				**self.special_tokens_map				)
           lowerCAmelCase					:	List[str]       =   '''adapt act apte'''
           lowerCAmelCase					:	List[Any]       =   ['''adapt''', '''act''', '''ap@@''', '''te''']
           lowerCAmelCase					:	List[str]       =   tokenizer.tokenize(UpperCamelCase_				)
           self.assertListEqual(UpperCamelCase_					,				UpperCamelCase_				)

           lowerCAmelCase					:	List[str]       =   [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

           lowerCAmelCase					:	str       =   [0, 1, 2, 3, 4, 5]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_				)					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Dict       =   BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''				)
           assert tok('''sam'''				).input_ids == [1_3_8_4]
           lowerCAmelCase					:	Optional[int]       =   '''I am a small frog.'''
           lowerCAmelCase					:	List[str]       =   tok([src_text]					,				padding=UpperCamelCase_					,				truncation=UpperCamelCase_				)['''input_ids''']
           lowerCAmelCase					:	int       =   tok.batch_decode(UpperCamelCase_					,				skip_special_tokens=UpperCamelCase_					,				clean_up_tokenization_spaces=UpperCamelCase_				)[0]
           assert src_text != decoded  # I wish it did!
           assert decoded == "i am a small frog ."






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase					:	List[str]       =   BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''				)
           lowerCAmelCase					:	Dict       =   '''I am a small frog .'''
           lowerCAmelCase					:	Tuple       =   '''.'''
           lowerCAmelCase					:	Any       =   tok(UpperCamelCase_				)['''input_ids''']
           lowerCAmelCase					:	Tuple       =   tok(UpperCamelCase_				)['''input_ids''']

           assert encoded[-1] == encoded_dot[0]






"""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				)


