


"""simple docstring"""





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 		snake_case_(       a__							,					unittest.TestCase			):
     __UpperCamelCase  =					DebertaTokenizer
     __UpperCamelCase  =					True
     __UpperCamelCase  =					DebertaTokenizerFast






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

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           lowerCAmelCase					:	List[Any]       =   [
               '''l''',
               '''o''',
               '''w''',
               '''e''',
               '''r''',
               '''s''',
               '''t''',
               '''i''',
               '''d''',
               '''n''',
               '''\u0120''',
               '''\u0120l''',
               '''\u0120n''',
               '''\u0120lo''',
               '''\u0120low''',
               '''er''',
               '''\u0120lowest''',
               '''\u0120newer''',
               '''\u0120wider''',
               '''[UNK]''',
           ]
           lowerCAmelCase					:	Optional[Any]       =   dict(zip(UpperCamelCase_					,				range(len(UpperCamelCase_				)				)				)				)
           lowerCAmelCase					:	Any       =   ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
           lowerCAmelCase					:	Union[str, Any]       =   {'''unk_token''': '''[UNK]'''}

           lowerCAmelCase					:	Optional[Any]       =   os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''vocab_file''']				)
           lowerCAmelCase					:	Union[str, Any]       =   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  :					Optional[int]					,				**UpperCamelCase_  :					Optional[Any]				):
           kwargs.update(self.special_tokens_map				)
           return self.tokenizer_class.from_pretrained(self.tmpdirname					,				**UpperCamelCase_				)






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






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase					:	Optional[Any]       =   self.get_tokenizer()
           lowerCAmelCase					:	str       =   '''lower newer'''
           lowerCAmelCase					:	Optional[int]       =   ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
           lowerCAmelCase					:	Optional[int]       =   tokenizer.tokenize(UpperCamelCase_				)
           self.assertListEqual(UpperCamelCase_					,				UpperCamelCase_				)

           lowerCAmelCase					:	Optional[Any]       =   tokens + [tokenizer.unk_token]
           lowerCAmelCase					:	List[str]       =   [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_				)					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase					:	Any       =   self.get_tokenizer()
           lowerCAmelCase					:	List[Any]       =   tokenizer('''Hello'''					,				'''World'''				)
           lowerCAmelCase					:	str       =   [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
           self.assertListEqual(tokd['''token_type_ids''']					,				UpperCamelCase_				)






     @slow
     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase					:	Tuple       =   self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''				)

           lowerCAmelCase					:	str       =   tokenizer.encode('''sequence builders'''					,				add_special_tokens=UpperCamelCase_				)
           lowerCAmelCase					:	Optional[int]       =   tokenizer.encode('''multi-sequence build'''					,				add_special_tokens=UpperCamelCase_				)

           lowerCAmelCase					:	Optional[int]       =   tokenizer.encode(
               '''sequence builders'''					,				add_special_tokens=UpperCamelCase_					,				add_prefix_space=UpperCamelCase_				)
           lowerCAmelCase					:	int       =   tokenizer.encode(
               '''sequence builders'''					,				'''multi-sequence build'''					,				add_special_tokens=UpperCamelCase_					,				add_prefix_space=UpperCamelCase_				)

           lowerCAmelCase					:	int       =   tokenizer.build_inputs_with_special_tokens(UpperCamelCase_				)
           lowerCAmelCase					:	List[str]       =   tokenizer.build_inputs_with_special_tokens(UpperCamelCase_					,				UpperCamelCase_				)

           assert encoded_sentence == encoded_text_from_decode
           assert encoded_pair == encoded_pair_from_decode






     @slow
     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Union[str, Any]       =   [self.tokenizer_class]
           if self.test_rust_tokenizer:
                 tokenizer_classes.append(self.rust_tokenizer_class				)

           for tokenizer_class in tokenizer_classes:
                 lowerCAmelCase					:	Tuple       =   tokenizer_class.from_pretrained('''microsoft/deberta-base'''				)

                 lowerCAmelCase					:	Tuple       =   [
                     '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                     '''ALBERT incorporates two parameter reduction techniques''',
                     '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                     ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                     ''' vocabulary embedding.''',
                 ]

                 lowerCAmelCase					:	Dict       =   tokenizer(UpperCamelCase_					,				padding=UpperCamelCase_				)
                 lowerCAmelCase					:	Optional[int]       =   [tokenizer.decode(UpperCamelCase_					,				skip_special_tokens=UpperCamelCase_				) for seq in encoding['''input_ids''']]

                 # fmt: off
                 lowerCAmelCase					:	int       =   {
                     '''input_ids''': [
                         [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [1, 1_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 4, 2]
                     ],
                     '''token_type_ids''': [
                         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                     ],
                     '''attention_mask''': [
                         [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                         [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                     ]
                 }
                 # fmt: on

                 lowerCAmelCase					:	Any       =   [
                     '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                     '''ALBERT incorporates two parameter reduction techniques''',
                     '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                     ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                     ''' vocabulary embedding.''',
                 ]

                 self.assertDictEqual(encoding.data					,				UpperCamelCase_				)

                 for expected, decoded in zip(UpperCamelCase_					,				UpperCamelCase_				):
                       self.assertEqual(UpperCamelCase_					,				UpperCamelCase_				)






"""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				)


