




'''simple docstring'''


import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class      A       (						_a	,unittest.TestCase       ):
							lowercase_							     =							GPTaTokenizer
							lowercase_							     =							GPTaTokenizerFast
							lowercase_							     =							True
							lowercase_							     =							{'add_prefix_space': True}
							lowercase_							     =							False







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				List[Any]:


														"""simple docstring"""


														super().setUp()

														# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
														_a      	=							[
														    '''l''',
														    '''o''',
														    '''w''',
														    '''e''',
														    '''r''',
														    '''s''',
														    '''t''',
														    '''i''',
														    '''d''',
														    '''n''',
														    '''\u0120''',
														    '''\u0120l''',
														    '''\u0120n''',
														    '''\u0120lo''',
														    '''\u0120low''',
														    '''er''',
														    '''\u0120lowest''',
														    '''\u0120newer''',
														    '''\u0120wider''',
														    '''<unk>''',
														    '''<|endoftext|>''',
														]
														_a      	=							dict(zip(lowerCAmelCase_				,     range(len(lowerCAmelCase_   )   )   )   )
														_a      	=							['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
														_a      	=							{'''unk_token''': '''<unk>'''}

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







							def   __lowerCAmelCase     (    self			:    List[str]				,     **lowerCAmelCase_			:    List[Any]   )     ->				List[str]:


														"""simple docstring"""


														kwargs.update(self.special_tokens_map   )
														return GPTaTokenizer.from_pretrained(self.tmpdirname				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[Any]				,     **lowerCAmelCase_			:    List[Any]   )     ->				List[Any]:


														"""simple docstring"""


														kwargs.update(self.special_tokens_map   )
														return GPTaTokenizerFast.from_pretrained(self.tmpdirname				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    List[str]   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							'''lower newer'''
														_a      	=							'''lower newer'''
														return input_text, output_text







							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							GPTaTokenizer(self.vocab_file				,     self.merges_file				,     **self.special_tokens_map   )
														_a      	=							'''lower newer'''
														_a      	=							['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
														_a      	=							tokenizer.tokenize(lowerCAmelCase_				,     add_prefix_space=lowerCAmelCase_   )
														self.assertListEqual(lowerCAmelCase_				,     lowerCAmelCase_   )

														_a      	=							tokens + [tokenizer.unk_token]
														_a      	=							[14, 15, 10, 9, 3, 2, 15, 19]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_   )				,     lowerCAmelCase_   )







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


														"""simple docstring"""


														if not self.test_rust_tokenizer:
																					return

														_a      	=							self.get_tokenizer()
														_a      	=							self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase_   )

														_a      	=							'''lower newer'''

														# Testing tokenization
														_a      	=							tokenizer.tokenize(lowerCAmelCase_				,     add_prefix_space=lowerCAmelCase_   )
														_a      	=							rust_tokenizer.tokenize(lowerCAmelCase_   )
														self.assertListEqual(lowerCAmelCase_				,     lowerCAmelCase_   )

														# Testing conversion to ids without special tokens
														_a      	=							tokenizer.encode(lowerCAmelCase_				,     add_special_tokens=lowerCAmelCase_				,     add_prefix_space=lowerCAmelCase_   )
														_a      	=							rust_tokenizer.encode(lowerCAmelCase_				,     add_special_tokens=lowerCAmelCase_   )
														self.assertListEqual(lowerCAmelCase_				,     lowerCAmelCase_   )

														# Testing conversion to ids with special tokens
														_a      	=							self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase_   )
														_a      	=							tokenizer.encode(lowerCAmelCase_				,     add_prefix_space=lowerCAmelCase_   )
														_a      	=							rust_tokenizer.encode(lowerCAmelCase_   )
														self.assertListEqual(lowerCAmelCase_				,     lowerCAmelCase_   )

														# Testing the unknown token
														_a      	=							tokens + [rust_tokenizer.unk_token]
														_a      	=							[14, 15, 10, 9, 3, 2, 15, 19]
														self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase_   )				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[Any]				,     *lowerCAmelCase_			:    Optional[int]				,     **lowerCAmelCase_			:    str   )     ->				Optional[Any]:


														"""simple docstring"""


														pass







							def   __lowerCAmelCase     (    self			:    Dict				,     lowerCAmelCase_			:    Any=15   )     ->				Any:


														"""simple docstring"""


														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_   )

																												# Simple input
																												_a      	=							'''This is a simple input'''
																												_a      	=							['''This is a simple input 1''', '''This is a simple input 2''']
																												_a      	=							('''This is a simple input''', '''This is a pair''')
																												_a      	=							[
																												    ('''This is a simple input 1''', '''This is a simple input 2'''),
																												    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																												]

																												# Simple input tests
																												self.assertRaises(lowerCAmelCase_				,     tokenizer_r.encode				,     lowerCAmelCase_				,     max_length=lowerCAmelCase_				,     padding='''max_length'''   )

																												# Simple input
																												self.assertRaises(lowerCAmelCase_				,     tokenizer_r.encode_plus				,     lowerCAmelCase_				,     max_length=lowerCAmelCase_				,     padding='''max_length'''   )

																												# Simple input
																												self.assertRaises(
																												    lowerCAmelCase_				,     tokenizer_r.batch_encode_plus				,     lowerCAmelCase_				,     max_length=lowerCAmelCase_				,     padding='''max_length'''				,     )

																												# Pair input
																												self.assertRaises(lowerCAmelCase_				,     tokenizer_r.encode				,     lowerCAmelCase_				,     max_length=lowerCAmelCase_				,     padding='''max_length'''   )

																												# Pair input
																												self.assertRaises(lowerCAmelCase_				,     tokenizer_r.encode_plus				,     lowerCAmelCase_				,     max_length=lowerCAmelCase_				,     padding='''max_length'''   )

																												# Pair input
																												self.assertRaises(
																												    lowerCAmelCase_				,     tokenizer_r.batch_encode_plus				,     lowerCAmelCase_				,     max_length=lowerCAmelCase_				,     padding='''max_length'''				,     )







							def   __lowerCAmelCase     (    self			:    int   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							GPTaTokenizer.from_pretrained(self.tmpdirname				,     pad_token='''<pad>'''   )

														# Simple input
														_a      	=							'''This is a simple input'''
														_a      	=							['''This is a simple input looooooooong''', '''This is a simple input''']
														_a      	=							('''This is a simple input''', '''This is a pair''')
														_a      	=							[
														    ('''This is a simple input loooooong''', '''This is a simple input'''),
														    ('''This is a simple pair loooooong''', '''This is a simple pair'''),
														]

														_a      	=							tokenizer.pad_token_id

														_a      	=							tokenizer(lowerCAmelCase_				,     padding='''max_length'''				,     max_length=30				,     return_tensors='''np'''   )
														_a      	=							tokenizer(lowerCAmelCase_				,     padding=lowerCAmelCase_				,     truncate=lowerCAmelCase_				,     return_tensors='''np'''   )
														_a      	=							tokenizer(*lowerCAmelCase_				,     padding='''max_length'''				,     max_length=60				,     return_tensors='''np'''   )
														_a      	=							tokenizer(lowerCAmelCase_				,     padding=lowerCAmelCase_				,     truncate=lowerCAmelCase_				,     return_tensors='''np'''   )

														# s
														# test single string max_length padding
														self.assertEqual(out_s['''input_ids'''].shape[-1]				,     30   )
														self.assertTrue(pad_token_id in out_s['''input_ids''']   )
														self.assertTrue(0 in out_s['''attention_mask''']   )

														# s2
														# test automatic padding
														self.assertEqual(out_sa['''input_ids'''].shape[-1]				,     33   )
														# long slice doesn't have padding
														self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]   )
														self.assertFalse(0 in out_sa['''attention_mask'''][0]   )
														# short slice does have padding
														self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]   )
														self.assertTrue(0 in out_sa['''attention_mask'''][1]   )

														# p
														# test single pair max_length padding
														self.assertEqual(out_p['''input_ids'''].shape[-1]				,     60   )
														self.assertTrue(pad_token_id in out_p['''input_ids''']   )
														self.assertTrue(0 in out_p['''attention_mask''']   )

														# p2
														# test automatic padding pair
														self.assertEqual(out_pa['''input_ids'''].shape[-1]				,     52   )
														# long slice pair doesn't have padding
														self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]   )
														self.assertFalse(0 in out_pa['''attention_mask'''][0]   )
														# short slice pair does have padding
														self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]   )
														self.assertTrue(0 in out_pa['''attention_mask'''][1]   )







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


														"""simple docstring"""


														_a      	=							'''$$$'''
														_a      	=							GPTaTokenizer.from_pretrained(self.tmpdirname				,     bos_token=lowerCAmelCase_				,     add_bos_token=lowerCAmelCase_   )

														_a      	=							'''This is a simple input'''
														_a      	=							['''This is a simple input 1''', '''This is a simple input 2''']

														_a      	=							tokenizer.bos_token_id

														_a      	=							tokenizer(lowerCAmelCase_   )
														_a      	=							tokenizer(lowerCAmelCase_   )

														self.assertEqual(out_s.input_ids[0]				,     lowerCAmelCase_   )
														self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids   )   )

														_a      	=							tokenizer.decode(out_s.input_ids   )
														_a      	=							tokenizer.batch_decode(out_sa.input_ids   )

														self.assertEqual(decode_s.split()[0]				,     lowerCAmelCase_   )
														self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa   )   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				Any:


														"""simple docstring"""


														pass







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Any:


														"""simple docstring"""


														_a      	=							[self.get_tokenizer(do_lower_case=lowerCAmelCase_				,     add_bos_token=lowerCAmelCase_   )]
														for tokenizer in tokenizers:
																					with self.subTest(F'{tokenizer.__class__.__name__}'   ):
																												_a      	=							'''Encode this.'''
																												_a      	=							'''This one too please.'''
																												_a      	=							tokenizer.encode(lowerCAmelCase_				,     add_special_tokens=lowerCAmelCase_   )
																												encoded_sequence += tokenizer.encode(lowerCAmelCase_				,     add_special_tokens=lowerCAmelCase_   )
																												_a      	=							tokenizer.encode_plus(
																												    lowerCAmelCase_				,     lowerCAmelCase_				,     add_special_tokens=lowerCAmelCase_				,     return_special_tokens_mask=lowerCAmelCase_				,     )
																												_a      	=							encoded_sequence_dict['''input_ids''']
																												_a      	=							encoded_sequence_dict['''special_tokens_mask''']
																												self.assertEqual(len(lowerCAmelCase_   )				,     len(lowerCAmelCase_   )   )

																												_a      	=							[
																												    (x if not special_tokens_mask[i] else None) for i, x in enumerate(lowerCAmelCase_   )
																												]
																												_a      	=							[x for x in filtered_sequence if x is not None]
																												self.assertEqual(lowerCAmelCase_				,     lowerCAmelCase_   )



@require_tokenizers
class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							AutoTokenizer.from_pretrained('''facebook/opt-350m'''				,     from_slow=lowerCAmelCase_   )
														_a      	=							'''A photo of a cat'''

														_a      	=							tokenizer.encode(
														    lowerCAmelCase_				,     )
														self.assertEqual(lowerCAmelCase_				,     [2, 2_50, 13_45, 9, 10, 47_58]   )
														tokenizer.save_pretrained('''test_opt'''   )

														_a      	=							AutoTokenizer.from_pretrained('''./test_opt'''   )
														_a      	=							tokenizer.encode(
														    lowerCAmelCase_				,     )
														self.assertEqual(lowerCAmelCase_				,     [2, 2_50, 13_45, 9, 10, 47_58]   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														_a      	=							AutoTokenizer.from_pretrained('''facebook/opt-350m'''				,     use_slow=lowerCAmelCase_   )
														_a      	=							'''A photo of a cat'''

														_a      	=							tokenizer.encode(
														    lowerCAmelCase_				,     )
														# Same as above
														self.assertEqual(lowerCAmelCase_				,     [2, 2_50, 13_45, 9, 10, 47_58]   )







							@unittest.skip('''This test is failing because of a bug in the fast tokenizer'''   )
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Dict:


														"""simple docstring"""


														_a      	=							AutoTokenizer.from_pretrained('''facebook/opt-350m'''				,     from_slow=lowerCAmelCase_   )

														_a      	=							'''bos'''
														_a      	=							tokenizer.get_vocab()['''bos''']

														_a      	=							'''A photo of a cat'''
														_a      	=							tokenizer.encode(
														    lowerCAmelCase_				,     )
														# We changed the bos token
														self.assertEqual(lowerCAmelCase_				,     [3_19_57, 2_50, 13_45, 9, 10, 47_58]   )
														tokenizer.save_pretrained('''./tok'''   )
														_a      	=							AutoTokenizer.from_pretrained('''./tok'''   )
														self.assertTrue(tokenizer.is_fast   )
														_a      	=							tokenizer.encode(
														    lowerCAmelCase_				,     )
														self.assertEqual(lowerCAmelCase_				,     [3_19_57, 2_50, 13_45, 9, 10, 47_58]   )









"""simple docstring"""
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Optional[int] 			=					(UniPCMultistepScheduler,)
	_A							:					Optional[Any] 			=					(("""num_inference_steps""", 25),)





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		A_		:    str						     = {
		    """num_train_timesteps""": 1000,
		    """beta_start""": 0.0001,
		    """beta_end""": 0.02,
		    """beta_schedule""": """linear""",
		    """solver_order""": 2,
		    """solver_type""": """bh2""",
		}

		config.update(**lowerCAmelCase_				)
		return config





	def  lowerCamelCase(self  ,  lowerCAmelCase_=0  ,  **lowerCAmelCase_				):
		A_		:    Tuple						     = dict(self.forward_default_kwargs				)
		A_		:    Any						     = kwargs.pop("""num_inference_steps"""  ,  lowerCAmelCase_				)
		A_		:    str						     = self.dummy_sample
		A_		:    Dict						     = 0.1 * sample
		A_		:    int						     = [residual + 0.2, residual + 0.15, residual + 0.10]

		for scheduler_class in self.scheduler_classes:
			A_		:    List[Any]						     = self.get_scheduler_config(**lowerCAmelCase_				)
			A_		:    Dict						     = scheduler_class(**lowerCAmelCase_				)
			scheduler.set_timesteps(lowerCAmelCase_				)
			# copy over dummy past residuals
			A_		:    Tuple						     = dummy_past_residuals[: scheduler.config.solver_order]

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

			A_     ,  A_		:    Optional[int]						     = sample, sample
			for t in range(lowerCAmelCase_  ,  time_step + scheduler.config.solver_order + 1				):
				A_		:    Any						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample
				A_		:    Dict						     = 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  lowerCamelCase(self  ,  lowerCAmelCase_=0  ,  **lowerCAmelCase_				):
		A_		:    Optional[int]						     = dict(self.forward_default_kwargs				)
		A_		:    int						     = kwargs.pop("""num_inference_steps"""  ,  lowerCAmelCase_				)
		A_		:    Optional[Any]						     = self.dummy_sample
		A_		:    List[Any]						     = 0.1 * sample
		A_		:    List[Any]						     = [residual + 0.2, residual + 0.15, residual + 0.10]

		for scheduler_class in self.scheduler_classes:
			A_		:    Dict						     = self.get_scheduler_config()
			A_		:    str						     = scheduler_class(**lowerCAmelCase_				)
			scheduler.set_timesteps(lowerCAmelCase_				)

			# copy over dummy past residuals (must be after setting timesteps)
			A_		:    Union[str, Any]						     = dummy_past_residuals[: scheduler.config.solver_order]

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(lowerCAmelCase_				)
				A_		:    Tuple						     = 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_		:    Optional[int]						     = dummy_past_residuals[: new_scheduler.config.solver_order]

			A_		:    Tuple						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample
			A_		:    str						     = 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  lowerCamelCase(self  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_				):
		if scheduler is None:
			A_		:    int						     = self.scheduler_classes[0]
			A_		:    List[str]						     = self.get_scheduler_config(**lowerCAmelCase_				)
			A_		:    Any						     = scheduler_class(**lowerCAmelCase_				)

		A_		:    int						     = self.scheduler_classes[0]
		A_		:    str						     = self.get_scheduler_config(**lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = scheduler_class(**lowerCAmelCase_				)

		A_		:    Optional[int]						     = 10
		A_		:    Optional[Any]						     = self.dummy_model()
		A_		:    Optional[Any]						     = self.dummy_sample_deter
		scheduler.set_timesteps(lowerCAmelCase_				)

		for i, t in enumerate(scheduler.timesteps				):
			A_		:    Optional[int]						     = model(lowerCAmelCase_  ,  lowerCAmelCase_				)
			A_		:    Optional[int]						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				).prev_sample

		return sample





	def  lowerCamelCase(self				):
		A_		:    Any						     = dict(self.forward_default_kwargs				)

		A_		:    Any						     = kwargs.pop("""num_inference_steps"""  ,  lowerCAmelCase_				)

		for scheduler_class in self.scheduler_classes:
			A_		:    Dict						     = self.get_scheduler_config()
			A_		:    List[Any]						     = scheduler_class(**lowerCAmelCase_				)

			A_		:    int						     = self.dummy_sample
			A_		:    str						     = 0.1 * sample

			if num_inference_steps is not None and hasattr(lowerCAmelCase_  ,  """set_timesteps"""				):
				scheduler.set_timesteps(lowerCAmelCase_				)
			elif num_inference_steps is not None and not hasattr(lowerCAmelCase_  ,  """set_timesteps"""				):
				A_		:    Tuple						     = num_inference_steps

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

			A_		:    str						     = scheduler.timesteps[5]
			A_		:    Any						     = scheduler.timesteps[6]

			A_		:    Tuple						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample
			A_		:    Dict						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample

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





	def  lowerCamelCase(self				):
		# make sure that iterating over schedulers with same config names gives same results
		# for defaults
		A_		:    Tuple						     = UniPCMultistepScheduler(**self.get_scheduler_config()				)
		A_		:    List[str]						     = self.full_loop(scheduler=lowerCAmelCase_				)
		A_		:    int						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.2464				) < 1e-3

		A_		:    List[str]						     = DPMSolverSinglestepScheduler.from_config(scheduler.config				)
		A_		:    List[str]						     = DEISMultistepScheduler.from_config(scheduler.config				)
		A_		:    Optional[int]						     = DPMSolverMultistepScheduler.from_config(scheduler.config				)
		A_		:    str						     = UniPCMultistepScheduler.from_config(scheduler.config				)

		A_		:    Dict						     = self.full_loop(scheduler=lowerCAmelCase_				)
		A_		:    List[Any]						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.2464				) < 1e-3





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





	def  lowerCamelCase(self				):
		self.check_over_configs(thresholding=lowerCAmelCase_				)
		for order in [1, 2, 3]:
			for solver_type in ["bh1", "bh2"]:
				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_  ,  solver_order=lowerCAmelCase_  ,  solver_type=lowerCAmelCase_  ,  )





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





	def  lowerCamelCase(self				):
		for solver_type in ["bh1", "bh2"]:
			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_  ,  )
					A_		:    Union[str, Any]						     = self.full_loop(
					    solver_order=lowerCAmelCase_  ,  solver_type=lowerCAmelCase_  ,  prediction_type=lowerCAmelCase_  ,  )
					assert not torch.isnan(lowerCAmelCase_				).any(), "Samples have nan numbers"





	def  lowerCamelCase(self				):
		self.check_over_configs(lower_order_final=lowerCAmelCase_				)
		self.check_over_configs(lower_order_final=lowerCAmelCase_				)





	def  lowerCamelCase(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  lowerCamelCase(self				):
		A_		:    Optional[int]						     = self.full_loop()
		A_		:    Optional[int]						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.2464				) < 1e-3





	def  lowerCamelCase(self				):
		A_		:    str						     = self.full_loop(prediction_type="""v_prediction"""				)
		A_		:    Optional[Any]						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.1014				) < 1e-3





	def  lowerCamelCase(self				):
		A_		:    List[str]						     = self.scheduler_classes[0]
		A_		:    str						     = self.get_scheduler_config(thresholding=lowerCAmelCase_  ,  dynamic_thresholding_ratio=0				)
		A_		:    Union[str, Any]						     = scheduler_class(**lowerCAmelCase_				)

		A_		:    Any						     = 10
		A_		:    int						     = self.dummy_model()
		A_		:    Optional[Any]						     = self.dummy_sample_deter.half()
		scheduler.set_timesteps(lowerCAmelCase_				)

		for i, t in enumerate(scheduler.timesteps				):
			A_		:    Dict						     = model(lowerCAmelCase_  ,  lowerCAmelCase_				)
			A_		:    Tuple						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				).prev_sample

		assert sample.dtype == torch.floataa





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		for scheduler_class in self.scheduler_classes:
			A_		:    Tuple						     = self.get_scheduler_config(**lowerCAmelCase_				)
			A_		:    int						     = scheduler_class(**lowerCAmelCase_				)

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

