






import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				str									=							LongformerTokenizer
			__snake_case    :				Tuple									=							True
			__snake_case    :				Union[str, Any]									=							LongformerTokenizerFast
			__snake_case    :				Union[str, Any]									=							True







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									_SCREAMING_SNAKE_CASE     				=  [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """\u0120""",
									    """\u0120l""",
									    """\u0120n""",
									    """\u0120lo""",
									    """\u0120low""",
									    """er""",
									    """\u0120lowest""",
									    """\u0120newer""",
									    """\u0120wider""",
									    """<unk>""",
									]
									_SCREAMING_SNAKE_CASE     				=  dict(zip(UpperCAmelCase_				,     range(len(UpperCAmelCase_					)					)					)					)
									_SCREAMING_SNAKE_CASE     				=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
									_SCREAMING_SNAKE_CASE     				=  {"""unk_token""": """<unk>"""}

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







			def 							UpperCamelCase				(     self:  Optional[int]				,     **UpperCAmelCase_:  str					):







									'''simple docstring'''




									kwargs.update(self.special_tokens_map					)
									return self.tokenizer_class.from_pretrained(self.tmpdirname				,     **UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[str]				,     **UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									kwargs.update(self.special_tokens_map					)
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,     **UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """lower newer"""
									_SCREAMING_SNAKE_CASE     				=  """lower newer"""
									return input_text, output_text







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.tokenizer_class(self.vocab_file				,     self.merges_file				,     **self.special_tokens_map					)
									_SCREAMING_SNAKE_CASE     				=  """lower newer"""
									_SCREAMING_SNAKE_CASE     				=  ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
									_SCREAMING_SNAKE_CASE     				=  tokenizer.tokenize(UpperCAmelCase_					)  # , add_prefix_space=True)
									self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokens + [tokenizer.unk_token]
									_SCREAMING_SNAKE_CASE     				=  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_					)				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.get_tokenizer()

									self.assertListEqual(tokenizer.encode("""Hello world!"""				,     add_special_tokens=UpperCAmelCase_					)				,     [0, 31_414, 232, 328, 2]					)
									self.assertListEqual(
									    tokenizer.encode("""Hello world! cécé herlolip 418"""				,     add_special_tokens=UpperCAmelCase_					)				,     [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]				,     )







			@slow
			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""					)

									_SCREAMING_SNAKE_CASE     				=  tokenizer.encode("""sequence builders"""				,     add_special_tokens=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  tokenizer.encode("""multi-sequence build"""				,     add_special_tokens=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokenizer.encode(
									    """sequence builders"""				,     add_special_tokens=UpperCAmelCase_				,     add_prefix_space=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  tokenizer.encode(
									    """sequence builders"""				,     """multi-sequence build"""				,     add_special_tokens=UpperCAmelCase_				,     add_prefix_space=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_				,     UpperCAmelCase_					)

									assert encoded_sentence == encoded_text_from_decode
									assert encoded_pair == encoded_pair_from_decode







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.get_tokenizer()

									_SCREAMING_SNAKE_CASE     				=  """Encode this sequence."""
									_SCREAMING_SNAKE_CASE     				=  tokenizer.byte_encoder[""" """.encode("""utf-8"""					)[0]]

									# Testing encoder arguments
									_SCREAMING_SNAKE_CASE     				=  tokenizer.encode(UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_				,     add_prefix_space=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
									self.assertNotEqual(UpperCAmelCase_				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokenizer.encode(UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_				,     add_prefix_space=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
									self.assertEqual(UpperCAmelCase_				,     UpperCAmelCase_					)

									tokenizer.add_special_tokens({"""bos_token""": """<s>"""}					)
									_SCREAMING_SNAKE_CASE     				=  tokenizer.encode(UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
									self.assertNotEqual(UpperCAmelCase_				,     UpperCAmelCase_					)

									# Testing spaces after special tokens
									_SCREAMING_SNAKE_CASE     				=  """<mask>"""
									tokenizer.add_special_tokens(
									    {"""mask_token""": AddedToken(UpperCAmelCase_				,     lstrip=UpperCAmelCase_				,     rstrip=UpperCAmelCase_					)}					)  # mask token has a left space
									_SCREAMING_SNAKE_CASE     				=  tokenizer.convert_tokens_to_ids(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  """Encode <mask> sequence"""
									_SCREAMING_SNAKE_CASE     				=  """Encode <mask>sequence"""

									_SCREAMING_SNAKE_CASE     				=  tokenizer.encode(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  encoded.index(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
									self.assertEqual(UpperCAmelCase_				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokenizer.encode(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  encoded.index(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
									self.assertNotEqual(UpperCAmelCase_				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'					):
																					_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_				,     **UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  self.tokenizer_class.from_pretrained(UpperCAmelCase_				,     **UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  """A, <mask> AllenNLP sentence."""
																					_SCREAMING_SNAKE_CASE     				=  tokenizer_r.encode_plus(UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_				,     return_token_type_ids=UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  tokenizer_p.encode_plus(UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_				,     return_token_type_ids=UpperCAmelCase_					)

																					# token_type_ids should put 0 everywhere
																					self.assertEqual(sum(tokens_r["""token_type_ids"""]					)				,     sum(tokens_p["""token_type_ids"""]					)					)

																					# attention_mask should put 1 everywhere, so sum over length should be 1
																					self.assertEqual(
																					    sum(tokens_r["""attention_mask"""]					) / len(tokens_r["""attention_mask"""]					)				,     sum(tokens_p["""attention_mask"""]					) / len(tokens_p["""attention_mask"""]					)				,     )

																					_SCREAMING_SNAKE_CASE     				=  tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]					)
																					_SCREAMING_SNAKE_CASE     				=  tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]					)

																					# Rust correctly handles the space before the mask while python doesnt
																					self.assertSequenceEqual(tokens_p["""input_ids"""]				,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]					)
																					self.assertSequenceEqual(tokens_r["""input_ids"""]				,     [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]					)

																					self.assertSequenceEqual(
																					    UpperCAmelCase_				,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)
																					self.assertSequenceEqual(
																					    UpperCAmelCase_				,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]					)







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									for trim_offsets, add_prefix_space in itertools.product([True, False]				,     repeat=2					):
															_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(
															    self.tmpdirname				,     use_fast=UpperCAmelCase_				,     add_prefix_space=UpperCAmelCase_				,     trim_offsets=UpperCAmelCase_					)

															_SCREAMING_SNAKE_CASE     				=  json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
															_SCREAMING_SNAKE_CASE     				=  json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

															self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]				,     UpperCAmelCase_					)

															self.assertEqual(post_processor_state["""add_prefix_space"""]				,     UpperCAmelCase_					)
															self.assertEqual(post_processor_state["""trim_offsets"""]				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'					):
																					_SCREAMING_SNAKE_CASE     				=  """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																					_SCREAMING_SNAKE_CASE     				=  F'{text_of_1_token} {text_of_1_token}'

																					_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(
																					    UpperCAmelCase_				,     use_fast=UpperCAmelCase_				,     add_prefix_space=UpperCAmelCase_				,     trim_offsets=UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  tokenizer_r(UpperCAmelCase_				,     return_offsets_mapping=UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_					)
																					self.assertEqual(encoding.offset_mapping[0]				,     (0, len(UpperCAmelCase_					))					)
																					self.assertEqual(
																					    encoding.offset_mapping[1]				,     (len(UpperCAmelCase_					) + 1, len(UpperCAmelCase_					) + 1 + len(UpperCAmelCase_					))				,     )

																					_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(
																					    UpperCAmelCase_				,     use_fast=UpperCAmelCase_				,     add_prefix_space=UpperCAmelCase_				,     trim_offsets=UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  tokenizer_r(UpperCAmelCase_				,     return_offsets_mapping=UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_					)
																					self.assertEqual(encoding.offset_mapping[0]				,     (0, len(UpperCAmelCase_					))					)
																					self.assertEqual(
																					    encoding.offset_mapping[1]				,     (len(UpperCAmelCase_					) + 1, len(UpperCAmelCase_					) + 1 + len(UpperCAmelCase_					))				,     )

																					_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(
																					    UpperCAmelCase_				,     use_fast=UpperCAmelCase_				,     add_prefix_space=UpperCAmelCase_				,     trim_offsets=UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  tokenizer_r(UpperCAmelCase_				,     return_offsets_mapping=UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_					)
																					self.assertEqual(encoding.offset_mapping[0]				,     (0, len(UpperCAmelCase_					))					)
																					self.assertEqual(
																					    encoding.offset_mapping[1]				,     (len(UpperCAmelCase_					), len(UpperCAmelCase_					) + 1 + len(UpperCAmelCase_					))				,     )

																					_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(
																					    UpperCAmelCase_				,     use_fast=UpperCAmelCase_				,     add_prefix_space=UpperCAmelCase_				,     trim_offsets=UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  tokenizer_r(UpperCAmelCase_				,     return_offsets_mapping=UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_					)
																					self.assertEqual(encoding.offset_mapping[0]				,     (0, len(UpperCAmelCase_					))					)
																					self.assertEqual(
																					    encoding.offset_mapping[1]				,     (len(UpperCAmelCase_					), len(UpperCAmelCase_					) + 1 + len(UpperCAmelCase_					))				,     )

																					_SCREAMING_SNAKE_CASE     				=  F' {text}'

																					# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																					#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																					# )
																					# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																					# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																					# self.assertEqual(
																					#     encoding.offset_mapping[1],
																					#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																					# )

																					_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(
																					    UpperCAmelCase_				,     use_fast=UpperCAmelCase_				,     add_prefix_space=UpperCAmelCase_				,     trim_offsets=UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  tokenizer_r(UpperCAmelCase_				,     return_offsets_mapping=UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_					)
																					self.assertEqual(encoding.offset_mapping[0]				,     (1, 1 + len(UpperCAmelCase_					))					)
																					self.assertEqual(
																					    encoding.offset_mapping[1]				,     (1 + len(UpperCAmelCase_					) + 1, 1 + len(UpperCAmelCase_					) + 1 + len(UpperCAmelCase_					))				,     )

																					_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(
																					    UpperCAmelCase_				,     use_fast=UpperCAmelCase_				,     add_prefix_space=UpperCAmelCase_				,     trim_offsets=UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  tokenizer_r(UpperCAmelCase_				,     return_offsets_mapping=UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_					)
																					self.assertEqual(encoding.offset_mapping[0]				,     (0, 1 + len(UpperCAmelCase_					))					)
																					self.assertEqual(
																					    encoding.offset_mapping[1]				,     (1 + len(UpperCAmelCase_					), 1 + len(UpperCAmelCase_					) + 1 + len(UpperCAmelCase_					))				,     )

																					_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(
																					    UpperCAmelCase_				,     use_fast=UpperCAmelCase_				,     add_prefix_space=UpperCAmelCase_				,     trim_offsets=UpperCAmelCase_					)
																					_SCREAMING_SNAKE_CASE     				=  tokenizer_r(UpperCAmelCase_				,     return_offsets_mapping=UpperCAmelCase_				,     add_special_tokens=UpperCAmelCase_					)
																					self.assertEqual(encoding.offset_mapping[0]				,     (0, 1 + len(UpperCAmelCase_					))					)
																					self.assertEqual(
																					    encoding.offset_mapping[1]				,     (1 + len(UpperCAmelCase_					), 1 + len(UpperCAmelCase_					) + 1 + len(UpperCAmelCase_					))				,     )









import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Optional[Any]									=							(UnCLIPScheduler,)







			def 							UpperCamelCase				(     self:  int				,     **UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  {
									    """num_train_timesteps""": 1_000,
									    """variance_type""": """fixed_small_log""",
									    """clip_sample""": True,
									    """clip_sample_range""": 1.0,
									    """prediction_type""": """epsilon""",
									}

									config.update(**UpperCAmelCase_					)
									return config







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									for timesteps in [1, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(prediction_type=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									for time_step in [0, 500, 999]:
															for prev_timestep in [None, 5, 100, 250, 500, 750]:
																					if prev_timestep is not None and prev_timestep >= time_step:
																											continue

																					self.check_over_forward(time_step=UpperCAmelCase_				,     prev_timestep=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""fixed_small_log"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0_000E-10					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.0_54_96_25					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.9_99_49_87					)					) < 1E-5







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""learned_range"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  0.5

									assert scheduler._get_variance(1				,     predicted_variance=UpperCAmelCase_					) - -10.1_71_27_90 < 1E-5
									assert scheduler._get_variance(487				,     predicted_variance=UpperCAmelCase_					) - -5.7_99_80_52 < 1E-5
									assert scheduler._get_variance(999				,     predicted_variance=UpperCAmelCase_					) - -0.0_01_00_11 < 1E-5







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

									_SCREAMING_SNAKE_CASE     				=  self.dummy_model()
									_SCREAMING_SNAKE_CASE     				=  self.dummy_sample_deter
									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

															# 2. predict previous mean of sample x_t-1
															_SCREAMING_SNAKE_CASE     				=  scheduler.step(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     generator=UpperCAmelCase_					).prev_sample

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_52.2_68_24_95					) < 1E-2
									assert abs(result_mean.item() - 0.3_28_47_43					) < 1E-3







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									scheduler.set_timesteps(25					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

									_SCREAMING_SNAKE_CASE     				=  self.dummy_model()
									_SCREAMING_SNAKE_CASE     				=  self.dummy_sample_deter
									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

															if i + 1 == timesteps.shape[0]:
																					_SCREAMING_SNAKE_CASE     				=  None
															else:
																					_SCREAMING_SNAKE_CASE     				=  timesteps[i + 1]

															# 2. predict previous mean of sample x_t-1
															_SCREAMING_SNAKE_CASE     				=  scheduler.step(
															    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     prev_timestep=UpperCAmelCase_				,     generator=UpperCAmelCase_					).prev_sample

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_58.2_04_49_83					) < 1E-2
									assert abs(result_mean.item() - 0.3_36_20_38					) < 1E-3







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									pass

