


import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY






@is_pipeline_test
class    _UpperCamelCase     (       unittest.TestCase					):

				"""simple docstring"""







				lowerCAmelCase					=			MODEL_FOR_MASKED_LM_MAPPING
				lowerCAmelCase					=			TF_MODEL_FOR_MASKED_LM_MAPPING

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					super().tearDown()
					# clean-up as much as possible GPU memory occupied by PyTorch
					gc.collect()
					if is_torch_available():
						import torch

						torch.cuda.empty_cache()

				@require_tf
				def   _UpperCAmelCase       (    self )						->					List[Any]:
					A             =					pipeline(task="""fill-mask"""					, model="""sshleifer/tiny-distilroberta-base"""					, top_k=2					, framework="""tf""" )
					A             =					unmasker("""My name is <mask>""" )
					self.assertEqual(
					    nested_simplify(a__					, decimals=6 )					, [
					        {"""sequence""": """My name is grouped""", """score""": 2.1e-05, """token""": 3_8015, """token_str""": """ grouped"""},
					        {"""sequence""": """My name is accuser""", """score""": 2.1e-05, """token""": 2_5506, """token_str""": """ accuser"""},
					    ]					, )

					A             =					unmasker("""The largest city in France is <mask>""" )
					self.assertEqual(
					    nested_simplify(a__					, decimals=6 )					, [
					        {
					            """sequence""": """The largest city in France is grouped""",
					            """score""": 2.1e-05,
					            """token""": 3_8015,
					            """token_str""": """ grouped""",
					        },
					        {
					            """sequence""": """The largest city in France is accuser""",
					            """score""": 2.1e-05,
					            """token""": 2_5506,
					            """token_str""": """ accuser""",
					        },
					    ]					, )

					A             =					unmasker("""My name is <mask>"""					, targets=[""" Patrick""", """ Clara""", """ Teven"""]					, top_k=3 )
					self.assertEqual(
					    nested_simplify(a__					, decimals=6 )					, [
					        {"""sequence""": """My name is Clara""", """score""": 2e-05, """token""": 1_3606, """token_str""": """ Clara"""},
					        {"""sequence""": """My name is Patrick""", """score""": 2e-05, """token""": 3499, """token_str""": """ Patrick"""},
					        {"""sequence""": """My name is Te""", """score""": 1.9e-05, """token""": 2941, """token_str""": """ Te"""},
					    ]					, )

				@require_torch
				def   _UpperCAmelCase       (    self )						->					Optional[int]:
					A             =					pipeline(task="""fill-mask"""					, model="""sshleifer/tiny-distilroberta-base"""					, top_k=2					, framework="""pt""" )

					A             =					unmasker("""My name is <mask>""" )
					self.assertEqual(
					    nested_simplify(a__					, decimals=6 )					, [
					        {"""sequence""": """My name is Maul""", """score""": 2.2e-05, """token""": 3_5676, """token_str""": """ Maul"""},
					        {"""sequence""": """My name isELS""", """score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS"""},
					    ]					, )

					A             =					unmasker("""The largest city in France is <mask>""" )
					self.assertEqual(
					    nested_simplify(a__					, decimals=6 )					, [
					        {
					            """sequence""": """The largest city in France is Maul""",
					            """score""": 2.2e-05,
					            """token""": 3_5676,
					            """token_str""": """ Maul""",
					        },
					        {"""sequence""": """The largest city in France isELS""", """score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS"""},
					    ]					, )

					A             =					unmasker("""My name is <mask>"""					, targets=[""" Patrick""", """ Clara""", """ Teven"""]					, top_k=3 )
					self.assertEqual(
					    nested_simplify(a__					, decimals=6 )					, [
					        {"""sequence""": """My name is Patrick""", """score""": 2.1e-05, """token""": 3499, """token_str""": """ Patrick"""},
					        {"""sequence""": """My name is Te""", """score""": 2e-05, """token""": 2941, """token_str""": """ Te"""},
					        {"""sequence""": """My name is Clara""", """score""": 2e-05, """token""": 1_3606, """token_str""": """ Clara"""},
					    ]					, )

					A             =					unmasker("""My name is <mask> <mask>"""					, top_k=2 )

					self.assertEqual(
					    nested_simplify(a__					, decimals=6 )					, [
					        [
					            {
					                """score""": 2.2e-05,
					                """token""": 3_5676,
					                """token_str""": """ Maul""",
					                """sequence""": """<s>My name is Maul<mask></s>""",
					            },
					            {"""score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
					        ],
					        [
					            {
					                """score""": 2.2e-05,
					                """token""": 3_5676,
					                """token_str""": """ Maul""",
					                """sequence""": """<s>My name is<mask> Maul</s>""",
					            },
					            {"""score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
					        ],
					    ]					, )

				@require_torch_gpu
				def   _UpperCAmelCase       (    self )						->					int:
					A             =					pipeline("""fill-mask"""					, model="""hf-internal-testing/tiny-random-distilbert"""					, device=0					, framework="""pt""" )

					# convert model to fp16
					pipe.model.half()

					A             =					pipe("""Paris is the [MASK] of France.""" )
					# We actually don't care about the result, we just want to make sure
					# it works, meaning the float16 tensor got casted back to float32
					# for postprocessing.
					self.assertIsInstance(a__					, a__ )

				@slow
				@require_torch
				def   _UpperCAmelCase       (    self )						->					Tuple:
					A             =					pipeline(task="""fill-mask"""					, model="""distilroberta-base"""					, top_k=2					, framework="""pt""" )
					self.run_large_test(a__ )

				@slow
				@require_tf
				def   _UpperCAmelCase       (    self )						->					List[Any]:
					A             =					pipeline(task="""fill-mask"""					, model="""distilroberta-base"""					, top_k=2					, framework="""tf""" )
					self.run_large_test(a__ )

				def   _UpperCAmelCase       (    self					, a__ )						->					List[Any]:
					A             =					unmasker("""My name is <mask>""" )
					self.assertEqual(
					    nested_simplify(a__ )					, [
					        {"""sequence""": """My name is John""", """score""": 0.0_08, """token""": 610, """token_str""": """ John"""},
					        {"""sequence""": """My name is Chris""", """score""": 0.0_07, """token""": 1573, """token_str""": """ Chris"""},
					    ]					, )
					A             =					unmasker("""The largest city in France is <mask>""" )
					self.assertEqual(
					    nested_simplify(a__ )					, [
					        {
					            """sequence""": """The largest city in France is Paris""",
					            """score""": 0.2_51,
					            """token""": 2201,
					            """token_str""": """ Paris""",
					        },
					        {
					            """sequence""": """The largest city in France is Lyon""",
					            """score""": 0.2_14,
					            """token""": 1_2790,
					            """token_str""": """ Lyon""",
					        },
					    ]					, )

					A             =					unmasker("""My name is <mask>"""					, targets=[""" Patrick""", """ Clara""", """ Teven"""]					, top_k=3 )
					self.assertEqual(
					    nested_simplify(a__ )					, [
					        {"""sequence""": """My name is Patrick""", """score""": 0.0_05, """token""": 3499, """token_str""": """ Patrick"""},
					        {"""sequence""": """My name is Clara""", """score""": 0.0_00, """token""": 1_3606, """token_str""": """ Clara"""},
					        {"""sequence""": """My name is Te""", """score""": 0.0_00, """token""": 2941, """token_str""": """ Te"""},
					    ]					, )

				@require_torch
				def   _UpperCAmelCase       (    self )						->					str:
					A             =					pipeline(task="""fill-mask"""					, model="""sshleifer/tiny-distilroberta-base"""					, framework="""pt""" )
					A             =					None
					A             =					None
					self.run_pipeline_test(a__					, [] )

				@require_tf
				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					A             =					pipeline(task="""fill-mask"""					, model="""sshleifer/tiny-distilroberta-base"""					, framework="""tf""" )
					A             =					None
					A             =					None
					self.run_pipeline_test(a__					, [] )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ )						->					Union[str, Any]:
					if tokenizer is None or tokenizer.mask_token_id is None:
						self.skipTest("""The provided tokenizer has no mask token, (probably reformer or wav2vec2)""" )

					A             =					FillMaskPipeline(model=a__					, tokenizer=a__ )
					A             =					[
					    f'This is another {tokenizer.mask_token} test',
					]
					return fill_masker, examples

				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					Any:
					A             =					fill_masker.tokenizer
					A             =					fill_masker.model

					A             =					fill_masker(
					    f'This is a {tokenizer.mask_token}'					, )
					self.assertEqual(
					    a__					, [
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					    ]					, )

					A             =					fill_masker([f'This is a {tokenizer.mask_token}'] )
					self.assertEqual(
					    a__					, [
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					    ]					, )

					A             =					fill_masker([f'This is a {tokenizer.mask_token}', f'Another {tokenizer.mask_token} great test.'] )
					self.assertEqual(
					    a__					, [
					        [
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        ],
					        [
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        ],
					    ]					, )

					with self.assertRaises(a__ ):
						fill_masker([None] )
					# No mask_token is not supported
					with self.assertRaises(a__ ):
						fill_masker("""This is""" )

					self.run_test_top_k(a__					, a__ )
					self.run_test_targets(a__					, a__ )
					self.run_test_top_k_targets(a__					, a__ )
					self.fill_mask_with_duplicate_targets_and_top_k(a__					, a__ )
					self.fill_mask_with_multiple_masks(a__					, a__ )

				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					Optional[Any]:
					A             =					tokenizer.get_vocab()
					A             =					sorted(vocab.keys() )[:2]
					# Pipeline argument
					A             =					FillMaskPipeline(model=a__					, tokenizer=a__					, targets=a__ )
					A             =					fill_masker(f'This is a {tokenizer.mask_token}' )
					self.assertEqual(
					    a__					, [
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					    ]					, )
					A             =					{vocab[el] for el in targets}
					self.assertEqual({el["""token"""] for el in outputs}					, a__ )
					A             =					[tokenizer.decode([x] ) for x in target_ids]
					self.assertEqual({el["""token_str"""] for el in outputs}					, set(a__ ) )

					# Call argument
					A             =					FillMaskPipeline(model=a__					, tokenizer=a__ )
					A             =					fill_masker(f'This is a {tokenizer.mask_token}'					, targets=a__ )
					self.assertEqual(
					    a__					, [
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					    ]					, )
					A             =					{vocab[el] for el in targets}
					self.assertEqual({el["""token"""] for el in outputs}					, a__ )
					A             =					[tokenizer.decode([x] ) for x in target_ids]
					self.assertEqual({el["""token_str"""] for el in outputs}					, set(a__ ) )

					# Score equivalence
					A             =					fill_masker(f'This is a {tokenizer.mask_token}'					, targets=a__ )
					A             =					[top_mask["""token_str"""] for top_mask in outputs]
					A             =					[top_mask["""score"""] for top_mask in outputs]

					# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
					if set(a__ ) == set(a__ ):
						A             =					fill_masker(f'This is a {tokenizer.mask_token}'					, targets=a__ )
						A             =					[top_mask["""score"""] for top_mask in unmasked_targets]
						self.assertEqual(nested_simplify(a__ )					, nested_simplify(a__ ) )

					# Raises with invalid
					with self.assertRaises(a__ ):
						A             =					fill_masker(f'This is a {tokenizer.mask_token}'					, targets=[] )
					# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
					if "" not in tokenizer.get_vocab():
						with self.assertRaises(a__ ):
							A             =					fill_masker(f'This is a {tokenizer.mask_token}'					, targets=[""""""] )
						with self.assertRaises(a__ ):
							A             =					fill_masker(f'This is a {tokenizer.mask_token}'					, targets="""""" )

				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					int:
					A             =					FillMaskPipeline(model=a__					, tokenizer=a__					, top_k=2 )
					A             =					fill_masker(f'This is a {tokenizer.mask_token}' )
					self.assertEqual(
					    a__					, [
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					    ]					, )

					A             =					FillMaskPipeline(model=a__					, tokenizer=a__ )
					A             =					fill_masker(f'This is a {tokenizer.mask_token}'					, top_k=2 )
					self.assertEqual(
					    a__					, [
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					    ]					, )
					self.assertEqual(nested_simplify(a__ )					, nested_simplify(a__ ) )

				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					Optional[int]:
					A             =					tokenizer.get_vocab()
					A             =					FillMaskPipeline(model=a__					, tokenizer=a__ )

					# top_k=2, ntargets=3
					A             =					sorted(vocab.keys() )[:3]
					A             =					fill_masker(f'This is a {tokenizer.mask_token}'					, top_k=2					, targets=a__ )

					# If we use the most probably targets, and filter differently, we should still
					# have the same results
					A             =					[el["""token_str"""] for el in sorted(a__					, key=lambda a__ : x["score"]					, reverse=a__ )]
					# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
					if set(a__ ).issubset(a__ ):
						A             =					fill_masker(f'This is a {tokenizer.mask_token}'					, top_k=3					, targets=a__ )
						# They should yield exactly the same result
						self.assertEqual(nested_simplify(a__ )					, nested_simplify(a__ ) )

				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					Union[str, Any]:
					A             =					FillMaskPipeline(model=a__					, tokenizer=a__ )
					A             =					tokenizer.get_vocab()
					# String duplicates + id duplicates
					A             =					sorted(vocab.keys() )[:3]
					A             =					[targets[0], targets[1], targets[0], targets[2], targets[1]]
					A             =					fill_masker(f'My name is {tokenizer.mask_token}'					, targets=a__					, top_k=10 )

					# The target list contains duplicates, so we can't output more
					# than them
					self.assertEqual(len(a__ )					, 3 )

				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					Optional[Any]:
					A             =					FillMaskPipeline(model=a__					, tokenizer=a__ )

					A             =					fill_masker(
					    f'This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}'					, top_k=2 )
					self.assertEqual(
					    a__					, [
					        [
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        ],
					        [
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        ],
					        [
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					            {"""sequence""": ANY(a__ ), """score""": ANY(a__ ), """token""": ANY(a__ ), """token_str""": ANY(a__ )},
					        ],
					    ]					, )







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			(IPNDMScheduler,)
				lowerCAmelCase					=			(('num_inference_steps', 5_0),)

				def   _UpperCAmelCase       (    self					, **a__ )						->					List[str]:
					A             =					{"""num_train_timesteps""": 1000}
					config.update(**a__ )
					return config

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					Tuple:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config(**a__ )
						A             =					scheduler_class(**a__ )
						scheduler.set_timesteps(a__ )
						# copy over dummy past residuals
						A             =					dummy_past_residuals[:]

						if time_step is None:
							A             =					scheduler.timesteps[len(scheduler.timesteps ) // 2]

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

				def   _UpperCAmelCase       (    self )						->					Any:
					pass

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					int:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config()
						A             =					scheduler_class(**a__ )
						scheduler.set_timesteps(a__ )

						# copy over dummy past residuals (must be after setting timesteps)
						A             =					dummy_past_residuals[:]

						if time_step is None:
							A             =					scheduler.timesteps[len(scheduler.timesteps ) // 2]

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

							# copy over dummy past residual (must be after setting timesteps)
							A             =					dummy_past_residuals[:]

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

				def   _UpperCAmelCase       (    self					, **a__ )						->					List[Any]:
					A             =					self.scheduler_classes[0]
					A             =					self.get_scheduler_config(**a__ )
					A             =					scheduler_class(**a__ )

					A             =					10
					A             =					self.dummy_model()
					A             =					self.dummy_sample_deter
					scheduler.set_timesteps(a__ )

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

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

					return sample

				def   _UpperCAmelCase       (    self )						->					Any:
					A             =					dict(self.forward_default_kwargs )

					A             =					kwargs.pop("""num_inference_steps"""					, a__ )

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config()
						A             =					scheduler_class(**a__ )

						A             =					self.dummy_sample
						A             =					0.1 * sample

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

						# copy over dummy past residuals (must be done after set_timesteps)
						A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
						A             =					dummy_past_residuals[:]

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

				def   _UpperCAmelCase       (    self )						->					Tuple:
					for timesteps in [100, 1000]:
						self.check_over_configs(num_train_timesteps=a__					, time_step=a__ )

				def   _UpperCAmelCase       (    self )						->					Any:
					for t, num_inference_steps in zip([1, 5, 10]					, [10, 50, 100] ):
						self.check_over_forward(num_inference_steps=a__					, time_step=a__ )

				def   _UpperCAmelCase       (    self )						->					int:
					A             =					self.full_loop()
					A             =					torch.mean(torch.abs(a__ ) )

					assert abs(result_mean.item() - 254_0529 ) < 10



