





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    __lowerCAmelCase			(     _a,	unittest.TestCase	):
			lowerCamelCase_	:	Optional[int]			     = BlenderbotSmallTokenizer
			lowerCamelCase_	:	Dict			     = False






			def      lowerCamelCase    (self       )	->  List[str]:


							'''simple docstring'''



							super().setUp()

							snake_case_		:       Optional[int]           =    ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
							snake_case_		:       str           =    dict(zip(__magic_name__   ,				range(len(__magic_name__       )       )       )       )

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

							snake_case_		:       Optional[int]           =    os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file''']       )
							snake_case_		:       Optional[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(__magic_name__       ) + '''\n'''       )
							with open(self.merges_file   ,				'''w'''   ,				encoding='''utf-8'''       ) as fp:
											fp.write('''\n'''.join(__magic_name__       )       )






			def      lowerCamelCase    (self   ,				**__magic_name__       )	->  int:


							'''simple docstring'''



							kwargs.update(self.special_tokens_map       )
							return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Optional[Any]:


							'''simple docstring'''



							snake_case_		:       Optional[int]           =    '''adapt act apte'''
							snake_case_		:       int           =    '''adapt act apte'''
							return input_text, output_text






			def      lowerCamelCase    (self       )	->  Tuple:


							'''simple docstring'''



							snake_case_		:       Any           =    BlenderbotSmallTokenizer(self.vocab_file   ,				self.merges_file   ,				**self.special_tokens_map       )
							snake_case_		:       List[Any]           =    '''adapt act apte'''
							snake_case_		:       Dict           =    ['''adapt''', '''act''', '''ap@@''', '''te''']
							snake_case_		:       Any           =    tokenizer.tokenize(__magic_name__       )
							self.assertListEqual(__magic_name__   ,				__magic_name__       )

							snake_case_		:       Tuple           =    [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

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






			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							snake_case_		:       int           =    BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''       )
							assert tok('''sam'''       ).input_ids == [1384]
							snake_case_		:       int           =    '''I am a small frog.'''
							snake_case_		:       Dict           =    tok([src_text]   ,				padding=__magic_name__   ,				truncation=__magic_name__       )['''input_ids''']
							snake_case_		:       str           =    tok.batch_decode(__magic_name__   ,				skip_special_tokens=__magic_name__   ,				clean_up_tokenization_spaces=__magic_name__       )[0]
							assert src_text != decoded  # I wish it did!
							assert decoded == "i am a small frog ."






			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							snake_case_		:       str           =    BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''       )
							snake_case_		:       Tuple           =    '''I am a small frog .'''
							snake_case_		:       Optional[Any]           =    '''.'''
							snake_case_		:       int           =    tok(__magic_name__       )['''input_ids''']
							snake_case_		:       Any           =    tok(__magic_name__       )['''input_ids''']

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







import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						int							=   (KDPMaDiscreteScheduler,)
  __lowerCAmelCase   :						Union[str, Any]							=   10


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       A_          =       {
           '''num_train_timesteps''': 1_1_0_0,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**__snake_case )
       return config


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       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=__snake_case ,beta_end=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config(prediction_type='''v_prediction''' )
       A_          =       scheduler_class(**__snake_case )

       scheduler.set_timesteps(self.num_inference_steps )

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter * scheduler.init_noise_sigma
       A_          =       sample.to(__snake_case )

       for i, t in enumerate(scheduler.timesteps ):
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 4.69_34E-07 ) < 1E-2
            assert abs(result_mean.item() - 6.11_12E-10 ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07 ) < 1E-2
            assert abs(result_mean.item() - 0.0002 ) < 1E-3


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       if torch_device == "mps":
            return
       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config()
       A_          =       scheduler_class(**__snake_case )

       scheduler.set_timesteps(self.num_inference_steps )

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter * scheduler.init_noise_sigma
       A_          =       sample.to(__snake_case )

       for i, t in enumerate(scheduler.timesteps ):
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       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 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       if torch_device == "mps":
            return
       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config()
       A_          =       scheduler_class(**__snake_case )

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

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter.to(__snake_case ) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       if str(__snake_case ).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



