






'''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 UpperCAmelCase_       (     __lowerCAmelCase					,							unittest.TestCase					):




      """simple docstring"""




      UpperCamelCase_             =      DebertaTokenizer
      UpperCamelCase_             =      True
      UpperCamelCase_             =      DebertaTokenizerFast






      def 					A__   (			self      :		int		)	->	Tuple:


       '''simple docstring'''

       super().setUp()

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

       lowercase       : Optional[int]						=os.path.join(self.tmpdirname  ,   VOCAB_FILES_NAMES['''vocab_file''']		)
       lowercase       : Optional[int]						=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 					A__   (			self      :		Optional[int]  ,   **UpperCAmelCase      :		Union[str, Any]		)	->	int:


       '''simple docstring'''

       kwargs.update(self.special_tokens_map		)
       return self.tokenizer_class.from_pretrained(self.tmpdirname  ,   **_UpperCAmelCase		)






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		Dict		)	->	Dict:


       '''simple docstring'''

       lowercase       : List[str]						='''lower newer'''
       lowercase       : Dict						='''lower newer'''
       return input_text, output_text






      def 					A__   (			self      :		Any		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : List[str]						=self.get_tokenizer()
       lowercase       : List[Any]						='''lower newer'''
       lowercase       : Any						=['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
       lowercase       : Dict						=tokenizer.tokenize(_UpperCAmelCase		)
       self.assertListEqual(_UpperCAmelCase  ,   _UpperCAmelCase		)

       lowercase       : Optional[int]						=tokens + [tokenizer.unk_token]
       lowercase       : List[str]						=[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)  ,   _UpperCAmelCase		)






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


       '''simple docstring'''

       lowercase       : Tuple						=self.get_tokenizer()
       lowercase       : List[str]						=tokenizer('''Hello'''  ,   '''World'''		)
       lowercase       : str						=[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd['''token_type_ids''']  ,   _UpperCAmelCase		)






      @slow
      def 					A__   (			self      :		int		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : List[str]						=self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''		)

       lowercase       : Any						=tokenizer.encode('''sequence builders'''  ,   add_special_tokens=_UpperCAmelCase		)
       lowercase       : str						=tokenizer.encode('''multi-sequence build'''  ,   add_special_tokens=_UpperCAmelCase		)

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

       lowercase       : Any						=tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase		)
       lowercase       : Union[str, Any]						=tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase  ,   _UpperCAmelCase		)

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode






      @slow
      def 					A__   (			self      :		List[Any]		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Tuple						=[self.tokenizer_class]
       if self.test_rust_tokenizer:
        tokenizer_classes.append(self.rust_tokenizer_class		)

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

        lowercase       : int						=[
            '''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.''',
        ]

        lowercase       : Tuple						=tokenizer(_UpperCAmelCase  ,   padding=_UpperCAmelCase		)
        lowercase       : Union[str, Any]						=[tokenizer.decode(_UpperCAmelCase  ,   skip_special_tokens=_UpperCAmelCase		) for seq in encoding['''input_ids''']]

        # fmt: off
        lowercase       : str						={
            '''input_ids''': [
                [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 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, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 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, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 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

        lowercase       : Dict						=[
            '''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 torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[int]     =      (KDPMaDiscreteScheduler,)
 UpperCAmelCase       :	Any     =      10

 def      lowerCAmelCase_				(					self			:			Dict       ,				**_UpperCAmelCase			:			Optional[Any]						):
       _A     =  {
           'num_train_timesteps': 1_100,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**_UpperCAmelCase						)
       return config

 def      lowerCAmelCase_				(					self			:			Any						):
       for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Dict						):
       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]       ,				[0.0002, 0.002, 0.02]						):
             self.check_over_configs(beta_start=_UpperCAmelCase       ,				beta_end=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config(prediction_type='v_prediction'						)
       _A     =  scheduler_class(**_UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter * scheduler.init_noise_sigma
       _A     =  sample.to(_UpperCAmelCase						)

       for i, t in enumerate(scheduler.timesteps						):
             _A     =  scheduler.scale_model_input(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  output.prev_sample

       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 4.6_9_3_4E-0_7						) < 1E-2
             assert abs(result_mean.item() - 6.1_1_1_2E-1_0						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7						) < 1E-2
             assert abs(result_mean.item() - 0.0002						) < 1E-3

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if torch_device == "mps":
             return
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config()
       _A     =  scheduler_class(**_UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter * scheduler.init_noise_sigma
       _A     =  sample.to(_UpperCAmelCase						)

       for i, t in enumerate(scheduler.timesteps						):
             _A     =  scheduler.scale_model_input(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  output.prev_sample

       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3



 def      lowerCAmelCase_				(					self			:			Any						):
       if torch_device == "mps":
             return
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config()
       _A     =  scheduler_class(**_UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps       ,				device=_UpperCAmelCase						)

       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter.to(_UpperCAmelCase						) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
             _A     =  scheduler.scale_model_input(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  output.prev_sample

       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       if str(_UpperCAmelCase						).startswith('cpu'						):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3

