





import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english




@require_tokenizers
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	MobileBertTokenizer
 __SCREAMING_SNAKE_CASE							       =	MobileBertTokenizerFast
 __SCREAMING_SNAKE_CASE							       =	True
 __SCREAMING_SNAKE_CASE							       =	True
 __SCREAMING_SNAKE_CASE							       =	filter_non_english
 __SCREAMING_SNAKE_CASE							       =	'''google/mobilebert-uncased'''



 def 				UpperCamelCase			(							self ):
        super().setUp()

        A__											=						[
            '''[UNK]''',
            '''[CLS]''',
            '''[SEP]''',
            '''[PAD]''',
            '''[MASK]''',
            '''want''',
            '''##want''',
            '''##ed''',
            '''wa''',
            '''un''',
            '''runn''',
            '''##ing''',
            ''',''',
            '''low''',
            '''lowest''',
        ]
        A__											=						os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['''vocab_file'''] )
        with open(self.vocab_file,'''w''',encoding='''utf-8''' ) as vocab_writer:
               vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )

        A__											=						[
            (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
            for tokenizer_def in self.tokenizers_list
        ]



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						'''UNwant\u00E9d,running'''
        A__											=						'''unwanted, running'''
        return input_text, output_text



 def 				UpperCamelCase			(							self ):
        A__											=						self.tokenizer_class(self.vocab_file )

        A__											=						tokenizer.tokenize('''UNwant\u00E9d,running''' )
        self.assertListEqual(__lowerCamelCase,['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase ),[9, 6, 7, 12, 10, 11] )



 def 				UpperCamelCase			(							self ):
        if not self.test_rust_tokenizer:
               return

        A__											=						self.get_tokenizer()
        A__											=						self.get_rust_tokenizer()

        A__											=						'''UNwant\u00E9d,running'''

        A__											=						tokenizer.tokenize(__lowerCamelCase )
        A__											=						rust_tokenizer.tokenize(__lowerCamelCase )
        self.assertListEqual(__lowerCamelCase,__lowerCamelCase )

        A__											=						tokenizer.encode(__lowerCamelCase,add_special_tokens=__lowerCamelCase )
        A__											=						rust_tokenizer.encode(__lowerCamelCase,add_special_tokens=__lowerCamelCase )
        self.assertListEqual(__lowerCamelCase,__lowerCamelCase )

        A__											=						self.get_rust_tokenizer()
        A__											=						tokenizer.encode(__lowerCamelCase )
        A__											=						rust_tokenizer.encode(__lowerCamelCase )
        self.assertListEqual(__lowerCamelCase,__lowerCamelCase )

        # With lower casing
        A__											=						self.get_tokenizer(do_lower_case=__lowerCamelCase )
        A__											=						self.get_rust_tokenizer(do_lower_case=__lowerCamelCase )

        A__											=						'''UNwant\u00E9d,running'''

        A__											=						tokenizer.tokenize(__lowerCamelCase )
        A__											=						rust_tokenizer.tokenize(__lowerCamelCase )
        self.assertListEqual(__lowerCamelCase,__lowerCamelCase )

        A__											=						tokenizer.encode(__lowerCamelCase,add_special_tokens=__lowerCamelCase )
        A__											=						rust_tokenizer.encode(__lowerCamelCase,add_special_tokens=__lowerCamelCase )
        self.assertListEqual(__lowerCamelCase,__lowerCamelCase )

        A__											=						self.get_rust_tokenizer()
        A__											=						tokenizer.encode(__lowerCamelCase )
        A__											=						rust_tokenizer.encode(__lowerCamelCase )
        self.assertListEqual(__lowerCamelCase,__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						BasicTokenizer()

        self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''' ),['''ah''', '''\u535A''', '''\u63A8''', '''zz'''] )



 def 				UpperCamelCase			(							self ):
        A__											=						BasicTokenizer(do_lower_case=__lowerCamelCase )

        self.assertListEqual(
            tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''' ),['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ),['''hello'''] )



 def 				UpperCamelCase			(							self ):
        A__											=						BasicTokenizer(do_lower_case=__lowerCamelCase,strip_accents=__lowerCamelCase )

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' ),['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ),['''h\u00E9llo'''] )



 def 				UpperCamelCase			(							self ):
        A__											=						BasicTokenizer(do_lower_case=__lowerCamelCase,strip_accents=__lowerCamelCase )

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' ),['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ),['''hello'''] )



 def 				UpperCamelCase			(							self ):
        A__											=						BasicTokenizer(do_lower_case=__lowerCamelCase )

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' ),['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ),['''hello'''] )



 def 				UpperCamelCase			(							self ):
        A__											=						BasicTokenizer(do_lower_case=__lowerCamelCase )

        self.assertListEqual(
            tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  ''' ),['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )



 def 				UpperCamelCase			(							self ):
        A__											=						BasicTokenizer(do_lower_case=__lowerCamelCase,strip_accents=__lowerCamelCase )

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' ),['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )



 def 				UpperCamelCase			(							self ):
        A__											=						BasicTokenizer(do_lower_case=__lowerCamelCase,strip_accents=__lowerCamelCase )

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  ''' ),['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )



 def 				UpperCamelCase			(							self ):
        A__											=						BasicTokenizer(do_lower_case=__lowerCamelCase,never_split=['''[UNK]'''] )

        self.assertListEqual(
            tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]''' ),['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''] )



 def 				UpperCamelCase			(							self ):
        A__											=						['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

        A__											=						{}
        for i, token in enumerate(__lowerCamelCase ):
               A__											=						i
        A__											=						WordpieceTokenizer(vocab=__lowerCamelCase,unk_token='''[UNK]''' )

        self.assertListEqual(tokenizer.tokenize('''''' ),[] )

        self.assertListEqual(tokenizer.tokenize('''unwanted running''' ),['''un''', '''##want''', '''##ed''', '''runn''', '''##ing'''] )

        self.assertListEqual(tokenizer.tokenize('''unwantedX running''' ),['''[UNK]''', '''runn''', '''##ing'''] )



 def 				UpperCamelCase			(							self ):
        self.assertTrue(_is_whitespace(''' ''' ) )
        self.assertTrue(_is_whitespace('''\t''' ) )
        self.assertTrue(_is_whitespace('''\r''' ) )
        self.assertTrue(_is_whitespace('''\n''' ) )
        self.assertTrue(_is_whitespace('''\u00A0''' ) )

        self.assertFalse(_is_whitespace('''A''' ) )
        self.assertFalse(_is_whitespace('''-''' ) )



 def 				UpperCamelCase			(							self ):
        self.assertTrue(_is_control('''\u0005''' ) )

        self.assertFalse(_is_control('''A''' ) )
        self.assertFalse(_is_control(''' ''' ) )
        self.assertFalse(_is_control('''\t''' ) )
        self.assertFalse(_is_control('''\r''' ) )



 def 				UpperCamelCase			(							self ):
        self.assertTrue(_is_punctuation('''-''' ) )
        self.assertTrue(_is_punctuation('''$''' ) )
        self.assertTrue(_is_punctuation('''`''' ) )
        self.assertTrue(_is_punctuation('''.''' ) )

        self.assertFalse(_is_punctuation('''A''' ) )
        self.assertFalse(_is_punctuation(''' ''' ) )



 def 				UpperCamelCase			(							self ):
        A__											=						self.get_tokenizer()
        A__											=						self.get_rust_tokenizer()

        # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
        self.assertListEqual([tokenizer.tokenize(__lowerCamelCase ) for t in ['''Test''', '''\xad''', '''test''']],[['''[UNK]'''], [], ['''[UNK]''']] )

        self.assertListEqual(
            [rust_tokenizer.tokenize(__lowerCamelCase ) for t in ['''Test''', '''\xad''', '''test''']],[['''[UNK]'''], [], ['''[UNK]''']] )



 @slow
 def 				UpperCamelCase			(							self ):
        A__											=						self.tokenizer_class.from_pretrained('''google/mobilebert-uncased''' )

        A__											=						tokenizer.encode('''sequence builders''',add_special_tokens=__lowerCamelCase )
        A__											=						tokenizer.encode('''multi-sequence build''',add_special_tokens=__lowerCamelCase )

        A__											=						tokenizer.build_inputs_with_special_tokens(__lowerCamelCase )
        A__											=						tokenizer.build_inputs_with_special_tokens(__lowerCamelCase,__lowerCamelCase )

        assert encoded_sentence == [101] + text + [102]
        assert encoded_pair == [101] + text + [102] + text_a + [102]



 def 				UpperCamelCase			(							self ):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
                      A__											=						self.rust_tokenizer_class.from_pretrained(__lowerCamelCase,**__lowerCamelCase )

                      A__											=						f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
                      A__											=						tokenizer_r.encode_plus(
                          __lowerCamelCase,return_attention_mask=__lowerCamelCase,return_token_type_ids=__lowerCamelCase,return_offsets_mapping=__lowerCamelCase,add_special_tokens=__lowerCamelCase,)

                      A__											=						tokenizer_r.do_lower_case if hasattr(__lowerCamelCase,'''do_lower_case''' ) else False
                      A__											=						(
                          [
                              ((0, 0), tokenizer_r.cls_token),
                              ((0, 1), '''A'''),
                              ((1, 2), ''','''),
                              ((3, 5), '''na'''),
                              ((5, 6), '''##ï'''),
                              ((6, 8), '''##ve'''),
                              ((9, 15), tokenizer_r.mask_token),
                              ((16, 21), '''Allen'''),
                              ((21, 23), '''##NL'''),
                              ((23, 24), '''##P'''),
                              ((25, 33), '''sentence'''),
                              ((33, 34), '''.'''),
                              ((0, 0), tokenizer_r.sep_token),
                          ]
                          if not do_lower_case
                          else [
                              ((0, 0), tokenizer_r.cls_token),
                              ((0, 1), '''a'''),
                              ((1, 2), ''','''),
                              ((3, 8), '''naive'''),
                              ((9, 15), tokenizer_r.mask_token),
                              ((16, 21), '''allen'''),
                              ((21, 23), '''##nl'''),
                              ((23, 24), '''##p'''),
                              ((25, 33), '''sentence'''),
                              ((33, 34), '''.'''),
                              ((0, 0), tokenizer_r.sep_token),
                          ]
                      )

                      self.assertEqual(
                          [e[1] for e in expected_results],tokenizer_r.convert_ids_to_tokens(tokens['''input_ids'''] ) )
                      self.assertEqual([e[0] for e in expected_results],tokens['''offset_mapping'''] )




 def 				UpperCamelCase			(							self ):
        A__											=						['''的''', '''人''', '''有''']
        A__											=						''''''.join(__lowerCamelCase )
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
                      A__											=						True
                      A__											=						self.tokenizer_class.from_pretrained(__lowerCamelCase,**__lowerCamelCase )
                      A__											=						self.rust_tokenizer_class.from_pretrained(__lowerCamelCase,**__lowerCamelCase )

                      A__											=						tokenizer_p.encode(__lowerCamelCase,add_special_tokens=__lowerCamelCase )
                      A__											=						tokenizer_r.encode(__lowerCamelCase,add_special_tokens=__lowerCamelCase )

                      A__											=						tokenizer_r.convert_ids_to_tokens(__lowerCamelCase )
                      A__											=						tokenizer_p.convert_ids_to_tokens(__lowerCamelCase )

                      # it is expected that each Chinese character is not preceded by "##"
                      self.assertListEqual(__lowerCamelCase,__lowerCamelCase )
                      self.assertListEqual(__lowerCamelCase,__lowerCamelCase )

                      A__											=						False
                      A__											=						self.rust_tokenizer_class.from_pretrained(__lowerCamelCase,**__lowerCamelCase )
                      A__											=						self.tokenizer_class.from_pretrained(__lowerCamelCase,**__lowerCamelCase )

                      A__											=						tokenizer_r.encode(__lowerCamelCase,add_special_tokens=__lowerCamelCase )
                      A__											=						tokenizer_p.encode(__lowerCamelCase,add_special_tokens=__lowerCamelCase )

                      A__											=						tokenizer_r.convert_ids_to_tokens(__lowerCamelCase )
                      A__											=						tokenizer_p.convert_ids_to_tokens(__lowerCamelCase )

                      # it is expected that only the first Chinese character is not preceded by "##".
                      A__											=						[
                          f"##{token}" if idx != 0 else token for idx, token in enumerate(__lowerCamelCase )
                      ]
                      self.assertListEqual(__lowerCamelCase,__lowerCamelCase )
                      self.assertListEqual(__lowerCamelCase,__lowerCamelCase )












import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DPMSolverSinglestepScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''num_inference_steps''', 25),)



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''solver_order''': 2,
            '''prediction_type''': '''epsilon''',
            '''thresholding''': False,
            '''sample_max_value''': 1.0,
            '''algorithm_type''': '''dpmsolver++''',
            '''solver_type''': '''midpoint''',
            '''lambda_min_clipped''': -float('''inf''' ),
            '''variance_type''': None,
        }

        config.update(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowerCamelCase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               A__											=						self.get_scheduler_config(**__lowerCamelCase )
               A__											=						scheduler_class(**__lowerCamelCase )
               scheduler.set_timesteps(__lowerCamelCase )
               # copy over dummy past residuals
               A__											=						dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__lowerCamelCase )
                      A__											=						scheduler_class.from_pretrained(__lowerCamelCase )
                      new_scheduler.set_timesteps(__lowerCamelCase )
                      # copy over dummy past residuals
                      A__											=						dummy_past_residuals[: new_scheduler.config.solver_order]

               A__					,	A__											=						sample, sample
               for t in range(__lowerCamelCase,time_step + scheduler.config.solver_order + 1 ):
                      A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample
                      A__											=						new_scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample

                      assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"



 def 				UpperCamelCase			(							self ):
        pass



 def 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowerCamelCase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               A__											=						self.get_scheduler_config()
               A__											=						scheduler_class(**__lowerCamelCase )
               scheduler.set_timesteps(__lowerCamelCase )

               # copy over dummy past residuals (must be after setting timesteps)
               A__											=						dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__lowerCamelCase )
                      A__											=						scheduler_class.from_pretrained(__lowerCamelCase )
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(__lowerCamelCase )

                      # copy over dummy past residual (must be after setting timesteps)
                      A__											=						dummy_past_residuals[: new_scheduler.config.solver_order]

               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample
               A__											=						new_scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample

               assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"



 def 				UpperCamelCase			(							self,__lowerCamelCase=None,**__lowerCamelCase ):
        if scheduler is None:
               A__											=						self.scheduler_classes[0]
               A__											=						self.get_scheduler_config(**__lowerCamelCase )
               A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowerCamelCase )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        scheduler.set_timesteps(__lowerCamelCase )

        for i, t in enumerate(scheduler.timesteps ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        return sample



 def 				UpperCamelCase			(							self ):
        A__											=						DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
        A__											=						50
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        scheduler.set_timesteps(__lowerCamelCase )

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:] ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2574 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        for timesteps in [25, 50, 100, 999, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        A__											=						DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
        A__											=						self.full_loop(scheduler=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2791 ) < 1E-3

        A__											=						DEISMultistepScheduler.from_config(scheduler.config )
        A__											=						DPMSolverMultistepScheduler.from_config(scheduler.config )
        A__											=						UniPCMultistepScheduler.from_config(scheduler.config )
        A__											=						DPMSolverSinglestepScheduler.from_config(scheduler.config )

        A__											=						self.full_loop(scheduler=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2791 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(thresholding=__lowerCamelCase )
        for order in [1, 2, 3]:
               for solver_type in ["midpoint", "heun"]:
                      for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        thresholding=__lowerCamelCase,prediction_type=__lowerCamelCase,sample_max_value=__lowerCamelCase,algorithm_type='''dpmsolver++''',solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,)



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



 def 				UpperCamelCase			(							self ):
        for algorithm_type in ["dpmsolver", "dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,prediction_type=__lowerCamelCase,algorithm_type=__lowerCamelCase,)
                                    A__											=						self.full_loop(
                                        solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,prediction_type=__lowerCamelCase,algorithm_type=__lowerCamelCase,)
                                    assert not torch.isnan(__lowerCamelCase ).any(), "Samples have nan numbers"



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(lower_order_final=__lowerCamelCase )
        self.check_over_configs(lower_order_final=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(lambda_min_clipped=-float('''inf''' ) )
        self.check_over_configs(lambda_min_clipped=-5.1 )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(variance_type=__lowerCamelCase )
        self.check_over_configs(variance_type='''learned_range''' )



 def 				UpperCamelCase			(							self ):
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
               self.check_over_forward(num_inference_steps=__lowerCamelCase,time_step=0 )



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2791 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(use_karras_sigmas=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2248 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.1453 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''',use_karras_sigmas=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.0649 ) < 1E-3




 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(thresholding=__lowerCamelCase,dynamic_thresholding_ratio=0 )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter.half()
        scheduler.set_timesteps(__lowerCamelCase )

        for i, t in enumerate(scheduler.timesteps ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        assert sample.dtype == torch.floataa





