




'''simple docstring'''

import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english





@require_tokenizers
class __magic_name__(						__lowerCAmelCase    ,		unittest.TestCase      ):
						UpperCAmelCase_		:					List[str]    =  RealmTokenizer
						UpperCAmelCase_		:					str    =  RealmTokenizerFast
						UpperCAmelCase_		:					Dict    =  True
						UpperCAmelCase_		:					str    =  True
						UpperCAmelCase_		:					Union[str, Any]    =  filter_non_english






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									super().setUp()

									snake_case__          =						[
									    """[UNK]""",
									    """[CLS]""",
									    """[SEP]""",
									    """[PAD]""",
									    """[MASK]""",
									    """want""",
									    """##want""",
									    """##ed""",
									    """wa""",
									    """un""",
									    """runn""",
									    """##ing""",
									    """,""",
									    """low""",
									    """lowest""",
									]
									snake_case__          =						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]       )       )






						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						int       ):



									'''simple docstring'''






									snake_case__          =						"""UNwant\u00E9d,running"""
									snake_case__          =						"""unwanted, running"""
									return input_text, output_text






						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									snake_case__          =						self.tokenizer_class(self.vocab_file       )

									snake_case__          =						tokenizer.tokenize("""UNwant\u00E9d,running"""       )
									self.assertListEqual(__UpperCamelCase					,   ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]       )
									self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase       )					,   [9, 6, 7, 1_2, 1_0, 1_1]       )






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									if not self.test_rust_tokenizer:
												return

									snake_case__          =						self.get_tokenizer()
									snake_case__          =						self.get_rust_tokenizer()

									snake_case__          =						"""UNwant\u00E9d,running"""

									snake_case__          =						tokenizer.tokenize(__UpperCamelCase       )
									snake_case__          =						rust_tokenizer.tokenize(__UpperCamelCase       )
									self.assertListEqual(__UpperCamelCase					,   __UpperCamelCase       )

									snake_case__          =						tokenizer.encode(__UpperCamelCase					,   add_special_tokens=__UpperCamelCase       )
									snake_case__          =						rust_tokenizer.encode(__UpperCamelCase					,   add_special_tokens=__UpperCamelCase       )
									self.assertListEqual(__UpperCamelCase					,   __UpperCamelCase       )

									snake_case__          =						self.get_rust_tokenizer()
									snake_case__          =						tokenizer.encode(__UpperCamelCase       )
									snake_case__          =						rust_tokenizer.encode(__UpperCamelCase       )
									self.assertListEqual(__UpperCamelCase					,   __UpperCamelCase       )

									# With lower casing
									snake_case__          =						self.get_tokenizer(do_lower_case=__UpperCamelCase       )
									snake_case__          =						self.get_rust_tokenizer(do_lower_case=__UpperCamelCase       )

									snake_case__          =						"""UNwant\u00E9d,running"""

									snake_case__          =						tokenizer.tokenize(__UpperCamelCase       )
									snake_case__          =						rust_tokenizer.tokenize(__UpperCamelCase       )
									self.assertListEqual(__UpperCamelCase					,   __UpperCamelCase       )

									snake_case__          =						tokenizer.encode(__UpperCamelCase					,   add_special_tokens=__UpperCamelCase       )
									snake_case__          =						rust_tokenizer.encode(__UpperCamelCase					,   add_special_tokens=__UpperCamelCase       )
									self.assertListEqual(__UpperCamelCase					,   __UpperCamelCase       )

									snake_case__          =						self.get_rust_tokenizer()
									snake_case__          =						tokenizer.encode(__UpperCamelCase       )
									snake_case__          =						rust_tokenizer.encode(__UpperCamelCase       )
									self.assertListEqual(__UpperCamelCase					,   __UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									snake_case__          =						BasicTokenizer()

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






						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






									snake_case__          =						BasicTokenizer(do_lower_case=__UpperCamelCase       )

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






						def       __lowerCAmelCase(   self						:						List[str]       ):



									'''simple docstring'''






									snake_case__          =						BasicTokenizer(do_lower_case=__UpperCamelCase					,   strip_accents=__UpperCamelCase       )

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






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									snake_case__          =						BasicTokenizer(do_lower_case=__UpperCamelCase					,   strip_accents=__UpperCamelCase       )

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






						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									snake_case__          =						BasicTokenizer(do_lower_case=__UpperCamelCase       )

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






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									snake_case__          =						BasicTokenizer(do_lower_case=__UpperCamelCase       )

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






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									snake_case__          =						BasicTokenizer(do_lower_case=__UpperCamelCase					,   strip_accents=__UpperCamelCase       )

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






						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									snake_case__          =						BasicTokenizer(do_lower_case=__UpperCamelCase					,   strip_accents=__UpperCamelCase       )

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






						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






									snake_case__          =						BasicTokenizer(do_lower_case=__UpperCamelCase					,   never_split=["""[UNK]"""]       )

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






						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






									snake_case__          =						["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

									snake_case__          =						{}
									for i, token in enumerate(__UpperCamelCase       ):
												snake_case__          =						i
									snake_case__          =						WordpieceTokenizer(vocab=__UpperCamelCase					,   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       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									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       __lowerCAmelCase(   self						:						List[str]       ):



									'''simple docstring'''






									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       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									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       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									snake_case__          =						self.get_tokenizer()

									# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
									self.assertListEqual([tokenizer.tokenize(__UpperCamelCase       ) for t in ["""Test""", """\xad""", """test"""]]					,   [["""[UNK]"""], [], ["""[UNK]"""]]       )

									if self.test_rust_tokenizer:
												snake_case__          =						self.get_rust_tokenizer()
												self.assertListEqual(
												    [rust_tokenizer.tokenize(__UpperCamelCase       ) for t in ["""Test""", """\xad""", """test"""]]					,   [["""[UNK]"""], [], ["""[UNK]"""]]       )






						@slow
						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






									snake_case__          =						self.tokenizer_class.from_pretrained("""bert-base-uncased"""       )

									snake_case__          =						tokenizer.encode("""sequence builders"""					,   add_special_tokens=__UpperCamelCase       )
									snake_case__          =						tokenizer.encode("""multi-sequence build"""					,   add_special_tokens=__UpperCamelCase       )

									snake_case__          =						tokenizer.build_inputs_with_special_tokens(__UpperCamelCase       )
									snake_case__          =						tokenizer.build_inputs_with_special_tokens(__UpperCamelCase					,   __UpperCamelCase       )

									assert encoded_sentence == [1_0_1] + text + [1_0_2]
									assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]






						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






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

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

															snake_case__          =						tokenizer_r.do_lower_case if hasattr(__UpperCamelCase					,   """do_lower_case"""       ) else False
															snake_case__          =						(
															    [
															        ((0, 0), tokenizer_r.cls_token),
															        ((0, 1), """A"""),
															        ((1, 2), ""","""),
															        ((3, 5), """na"""),
															        ((5, 6), """##ï"""),
															        ((6, 8), """##ve"""),
															        ((9, 1_5), tokenizer_r.mask_token),
															        ((1_6, 2_1), """Allen"""),
															        ((2_1, 2_3), """##NL"""),
															        ((2_3, 2_4), """##P"""),
															        ((2_5, 3_3), """sentence"""),
															        ((3_3, 3_4), """."""),
															        ((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, 1_5), tokenizer_r.mask_token),
															        ((1_6, 2_1), """allen"""),
															        ((2_1, 2_3), """##nl"""),
															        ((2_3, 2_4), """##p"""),
															        ((2_5, 3_3), """sentence"""),
															        ((3_3, 3_4), """."""),
															        ((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"""]       )







						@slow
						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
															snake_case__          =						self.rust_tokenizer_class.from_pretrained(__UpperCamelCase					,   **__UpperCamelCase       )
															snake_case__          =						self.tokenizer_class.from_pretrained(__UpperCamelCase					,   **__UpperCamelCase       )
															snake_case__          =						[["""Hello world!""", """Nice to meet you!"""], ["""The cute cat.""", """The adorable dog."""]]

															snake_case__          =						tokenizer_r.batch_encode_candidates(__UpperCamelCase					,   max_length=1_0					,   return_tensors="""np"""       )
															snake_case__          =						tokenizer_p.batch_encode_candidates(__UpperCamelCase					,   max_length=1_0					,   return_tensors="""np"""       )

															snake_case__          =						(2, 2, 1_0)

															self.assertEqual(encoded_sentence_r["""input_ids"""].shape					,   __UpperCamelCase       )
															self.assertEqual(encoded_sentence_r["""attention_mask"""].shape					,   __UpperCamelCase       )
															self.assertEqual(encoded_sentence_r["""token_type_ids"""].shape					,   __UpperCamelCase       )

															self.assertEqual(encoded_sentence_p["""input_ids"""].shape					,   __UpperCamelCase       )
															self.assertEqual(encoded_sentence_p["""attention_mask"""].shape					,   __UpperCamelCase       )
															self.assertEqual(encoded_sentence_p["""token_type_ids"""].shape					,   __UpperCamelCase       )

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					List[Any]    =     (DPMSolverMultistepScheduler,)
 A__    :					List[str]    =     (("""num_inference_steps""", 25),)

 def lowerCamelCase_					(   self  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							{
            """num_train_timesteps""": 1_0_0_0,
            """beta_start""": 0.0_001,
            """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""",
            """lower_order_final""": False,
            """lambda_min_clipped""": -float("""inf"""  ),
            """variance_type""": None,
        }

        config.update(**__UpperCamelCase  )
        return config

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )
        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.dummy_sample
        UpperCamelCase_											=							0.1 * sample
        UpperCamelCase_											=							[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        pass

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )
        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.dummy_sample
        UpperCamelCase_											=							0.1 * sample
        UpperCamelCase_											=							[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )
               scheduler.set_timesteps(__UpperCamelCase  )

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

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

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

               UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=None  ,						**__UpperCamelCase  ):




        """simple docstring"""


        if scheduler is None:
               UpperCamelCase_											=							self.scheduler_classes[0]
               UpperCamelCase_											=							self.get_scheduler_config(**__UpperCamelCase  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

        UpperCamelCase_											=							1_0
        UpperCamelCase_											=							self.dummy_model()
        UpperCamelCase_											=							self.dummy_sample_deter
        scheduler.set_timesteps(__UpperCamelCase  )

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

        return sample

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               if num_inference_steps is not None and hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      scheduler.set_timesteps(__UpperCamelCase  )
               elif num_inference_steps is not None and not hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      UpperCamelCase_											=							num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               UpperCamelCase_											=							[residual + 0.2, residual + 0.15, residual + 0.10]
               UpperCamelCase_											=							dummy_past_residuals[: scheduler.config.solver_order]

               UpperCamelCase_											=							scheduler.timesteps[5]
               UpperCamelCase_											=							scheduler.timesteps[6]

               UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

               self.assertEqual(output_a.shape  ,						sample.shape  )
               self.assertEqual(output_a.shape  ,						output_a.shape  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(thresholding=__UpperCamelCase  )
        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=__UpperCamelCase  ,						prediction_type=__UpperCamelCase  ,						sample_max_value=__UpperCamelCase  ,						algorithm_type="""dpmsolver++"""  ,						solver_order=__UpperCamelCase  ,						solver_type=__UpperCamelCase  ,						)

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


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

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                    if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                           if order == 3:
                                                  continue
                                    else:
                                           self.check_over_configs(
                                               solver_order=__UpperCamelCase  ,						solver_type=__UpperCamelCase  ,						prediction_type=__UpperCamelCase  ,						algorithm_type=__UpperCamelCase  ,						)
                                    UpperCamelCase_											=							self.full_loop(
                                        solver_order=__UpperCamelCase  ,						solver_type=__UpperCamelCase  ,						prediction_type=__UpperCamelCase  ,						algorithm_type=__UpperCamelCase  ,						)
                                    assert not torch.isnan(__UpperCamelCase  ).any(), "Samples have nan numbers"

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(lower_order_final=__UpperCamelCase  )
        self.check_over_configs(lower_order_final=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(lambda_min_clipped=-float("""inf"""  )  )
        self.check_over_configs(lambda_min_clipped=-5.1  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(variance_type=__UpperCamelCase  )
        self.check_over_configs(variance_type="""learned_range"""  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
               self.check_over_forward(num_inference_steps=__UpperCamelCase  ,						time_step=0  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop()
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.3_301  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop(thresholding=__UpperCamelCase  ,						dynamic_thresholding_ratio=0.87  ,						sample_max_value=0.5  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 1.1_364  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop(prediction_type="""v_prediction"""  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.2_251  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop(prediction_type="""v_prediction"""  ,						use_karras_sigmas=__UpperCamelCase  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.2_096  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							DPMSolverMultistepScheduler(**self.get_scheduler_config()  )
        UpperCamelCase_											=							self.full_loop(scheduler=__UpperCamelCase  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.3_301  ) < 1e-3

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

        UpperCamelCase_											=							self.full_loop(scheduler=__UpperCamelCase  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.3_301  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.scheduler_classes[0]
        UpperCamelCase_											=							self.get_scheduler_config(thresholding=__UpperCamelCase  ,						dynamic_thresholding_ratio=0  )
        UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

        UpperCamelCase_											=							1_0
        UpperCamelCase_											=							self.dummy_model()
        UpperCamelCase_											=							self.dummy_sample_deter.half()
        scheduler.set_timesteps(__UpperCamelCase  )

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

        assert sample.dtype == torch.floataa

 def lowerCamelCase_					(   self  ,						**__UpperCamelCase  ):




        """simple docstring"""


        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config(**__UpperCamelCase  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               scheduler.set_timesteps(scheduler.config.num_train_timesteps  )
               assert len(scheduler.timesteps.unique()  ) == scheduler.num_inference_steps



