


'''simple docstring'''


import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
						lowerCamelCase__					=   """pt"""
elif is_tf_available():
						lowerCamelCase__					=   """tf"""
else:
						lowerCamelCase__					=   """jax"""






class 		lowerCAmelCase__	(	_UpperCAmelCase			,				unittest.TestCase		):
			lowerCAmelCase       :		List[Any]				      =   ByTaTokenizer
			lowerCAmelCase       :		str				      =   False




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



					'''simple docstring'''







					super().setUp()
					_UpperCAmelCase						:				List[Any]	    =					ByTaTokenizer()
					tokenizer.save_pretrained(self.tmpdirname       )




			@cached_property
			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->Any:



					'''simple docstring'''







					return ByTaTokenizer.from_pretrained("google/byt5-small"       )




			def 							lowerCAmelCase__			(						self     :   str		,     **lowerCamelCase__     :   List[str]       )      ->ByTaTokenizer:



					'''simple docstring'''







					return self.tokenizer_class.from_pretrained(self.tmpdirname		,     **lowerCAmelCase__       )




			def 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   str=False		,     lowerCamelCase__     :   int=20		,     lowerCamelCase__     :   int=5       )      ->Tuple[str, list]:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					[]
					for i in range(len(lowerCAmelCase__       )       ):
							try:
									_UpperCAmelCase						:				Optional[int]	    =					tokenizer.decode([i]		,     clean_up_tokenization_spaces=lowerCAmelCase__       )
							except UnicodeDecodeError:
									pass
							toks.append((i, tok)       )

					_UpperCAmelCase						:				Any	    =					list(filter(lambda lowerCamelCase__       : re.match(R"^[ a-zA-Z]+$"		,     t[1]       )		,     lowerCAmelCase__       )       )
					_UpperCAmelCase						:				str	    =					list(filter(lambda lowerCamelCase__       : [t[0]] == tokenizer.encode(t[1]		,     add_special_tokens=lowerCAmelCase__       )		,     lowerCAmelCase__       )       )
					if max_length is not None and len(lowerCAmelCase__       ) > max_length:
							_UpperCAmelCase						:				Union[str, Any]	    =					toks[:max_length]
					if min_length is not None and len(lowerCAmelCase__       ) < min_length and len(lowerCAmelCase__       ) > 0:
							while len(lowerCAmelCase__       ) < min_length:
									_UpperCAmelCase						:				List[str]	    =					toks + toks
        # toks_str = [t[1] for t in toks]
					_UpperCAmelCase						:				str	    =					[t[0] for t in toks]

					# Ensure consistency
					_UpperCAmelCase						:				Dict	    =					tokenizer.decode(lowerCAmelCase__		,     clean_up_tokenization_spaces=lowerCAmelCase__       )
					if " " not in output_txt and len(lowerCAmelCase__       ) > 1:
							_UpperCAmelCase						:				Tuple	    =					(
							    tokenizer.decode([toks_ids[0]]		,     clean_up_tokenization_spaces=lowerCAmelCase__       )
							    + " "
							    + tokenizer.decode(toks_ids[1:]		,     clean_up_tokenization_spaces=lowerCAmelCase__       )
							)
					if with_prefix_space:
							_UpperCAmelCase						:				List[str]	    =					" " + output_txt
					_UpperCAmelCase						:				Dict	    =					tokenizer.encode(lowerCAmelCase__		,     add_special_tokens=lowerCAmelCase__       )
					return output_txt, output_ids




			def 							lowerCAmelCase__			(						self     :   Any       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					self.ta_base_tokenizer
					_UpperCAmelCase						:				List[Any]	    =					tokenizer(["hi</s>", "I went to the gym</s>", "</s>"]       )
					_UpperCAmelCase						:				Optional[int]	    =					tokenizer(["hi", "I went to the gym", ""]       )
					self.assertListEqual(batch_with_eos_added["input_ids"]		,     batch_without_eos_added["input_ids"]       )




			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					self.ta_base_tokenizer
					_UpperCAmelCase						:				Optional[Any]	    =					"Unicode €."
					_UpperCAmelCase						:				Optional[Any]	    =					tokenizer(lowerCAmelCase__       )
					_UpperCAmelCase						:				Optional[Any]	    =					[88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
					self.assertEqual(encoded["input_ids"]		,     lowerCAmelCase__       )

					# decoding
					_UpperCAmelCase						:				str	    =					tokenizer.decode(lowerCAmelCase__       )
					self.assertEqual(lowerCAmelCase__		,     "Unicode €.</s>"       )

					_UpperCAmelCase						:				int	    =					tokenizer("e è é ê ë"       )
					_UpperCAmelCase						:				List[str]	    =					[1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
					self.assertEqual(encoded["input_ids"]		,     lowerCAmelCase__       )
					# decoding
					_UpperCAmelCase						:				Union[str, Any]	    =					tokenizer.decode(lowerCAmelCase__       )
					self.assertEqual(lowerCAmelCase__		,     "e è é ê ë</s>"       )

					# encode/decode, but with `encode` instead of `__call__`
					self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"       )       )		,     "e è é ê ë</s>"       )




			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					self.ta_base_tokenizer
					_UpperCAmelCase						:				Tuple	    =					["A long paragraph for summarization.", "Another paragraph for summarization."]
					# fmt: off
					_UpperCAmelCase						:				Any	    =					[68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
					# fmt: on
					_UpperCAmelCase						:				List[Any]	    =					tokenizer(lowerCAmelCase__		,     padding=lowerCAmelCase__		,     return_tensors=lowerCAmelCase__       )
					self.assertIsInstance(lowerCAmelCase__		,     lowerCAmelCase__       )

					if FRAMEWORK != "jax":
							_UpperCAmelCase						:				Optional[int]	    =					list(batch.input_ids.numpy()[0]       )
					else:
							_UpperCAmelCase						:				Union[str, Any]	    =					list(batch.input_ids.tolist()[0]       )

					self.assertListEqual(lowerCAmelCase__		,     lowerCAmelCase__       )

					self.assertEqual((2, 37)		,     batch.input_ids.shape       )
					self.assertEqual((2, 37)		,     batch.attention_mask.shape       )




			def 							lowerCAmelCase__			(						self     :   str       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					self.ta_base_tokenizer
					_UpperCAmelCase						:				Dict	    =					["A long paragraph for summarization.", "Another paragraph for summarization."]
					_UpperCAmelCase						:				str	    =					tokenizer(lowerCAmelCase__		,     padding=lowerCAmelCase__		,     return_tensors=lowerCAmelCase__       )
					# check if input_ids are returned and no decoder_input_ids
					self.assertIn("input_ids"		,     lowerCAmelCase__       )
					self.assertIn("attention_mask"		,     lowerCAmelCase__       )
					self.assertNotIn("decoder_input_ids"		,     lowerCAmelCase__       )
					self.assertNotIn("decoder_attention_mask"		,     lowerCAmelCase__       )




			def 							lowerCAmelCase__			(						self     :   Any       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					self.ta_base_tokenizer
					_UpperCAmelCase						:				List[str]	    =					[
					    "Summary of the text.",
					    "Another summary.",
					]
					_UpperCAmelCase						:				Optional[int]	    =					tokenizer(
					    text_target=lowerCAmelCase__		,     max_length=32		,     padding="max_length"		,     truncation=lowerCAmelCase__		,     return_tensors=lowerCAmelCase__       )
					self.assertEqual(32		,     targets["input_ids"].shape[1]       )




			def 							lowerCAmelCase__			(						self     :   Dict       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					self.ta_base_tokenizer
					_UpperCAmelCase						:				Tuple	    =					["A long paragraph for summarization. </s>"]
					_UpperCAmelCase						:				Dict	    =					["Summary of the text. </s>"]
					# fmt: off
					_UpperCAmelCase						:				List[str]	    =					[68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
					_UpperCAmelCase						:				str	    =					[86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
					# fmt: on

					_UpperCAmelCase						:				Any	    =					tokenizer(lowerCAmelCase__		,     text_target=lowerCAmelCase__       )

					self.assertEqual(lowerCAmelCase__		,     batch["input_ids"][0]       )
					self.assertEqual(lowerCAmelCase__		,     batch["labels"][0]       )




			def 							lowerCAmelCase__			(						self     :   str       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self.get_tokenizers()
					for tokenizer in tokenizers:
							with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
									self.assertNotEqual(tokenizer.model_max_length		,     42       )

        # Now let's start the test
					_UpperCAmelCase						:				List[Any]	    =					self.get_tokenizers()
					for tokenizer in tokenizers:
							with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
									# Isolate this from the other tests because we save additional tokens/etc
									_UpperCAmelCase						:				Dict	    =					tempfile.mkdtemp()

									_UpperCAmelCase						:				Optional[int]	    =					" He is very happy, UNwant\u00E9d,running"
									_UpperCAmelCase						:				Dict	    =					tokenizer.encode(lowerCAmelCase__		,     add_special_tokens=lowerCAmelCase__       )
									tokenizer.save_pretrained(lowerCAmelCase__       )

									_UpperCAmelCase						:				List[str]	    =					tokenizer.__class__.from_pretrained(lowerCAmelCase__       )
									_UpperCAmelCase						:				List[Any]	    =					after_tokenizer.encode(lowerCAmelCase__		,     add_special_tokens=lowerCAmelCase__       )
									self.assertListEqual(lowerCAmelCase__		,     lowerCAmelCase__       )

									shutil.rmtree(lowerCAmelCase__       )

					_UpperCAmelCase						:				Union[str, Any]	    =					self.get_tokenizers(model_max_length=42       )
					for tokenizer in tokenizers:
							with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
									# Isolate this from the other tests because we save additional tokens/etc
									_UpperCAmelCase						:				List[Any]	    =					tempfile.mkdtemp()

									_UpperCAmelCase						:				Optional[Any]	    =					" He is very happy, UNwant\u00E9d,running"
									tokenizer.add_tokens(["bim", "bambam"]       )
									_UpperCAmelCase						:				Union[str, Any]	    =					tokenizer.additional_special_tokens
									additional_special_tokens.append("new_additional_special_token"       )
									tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}       )
									_UpperCAmelCase						:				List[str]	    =					tokenizer.encode(lowerCAmelCase__		,     add_special_tokens=lowerCAmelCase__       )
									tokenizer.save_pretrained(lowerCAmelCase__       )

									_UpperCAmelCase						:				Optional[Any]	    =					tokenizer.__class__.from_pretrained(lowerCAmelCase__       )
									_UpperCAmelCase						:				str	    =					after_tokenizer.encode(lowerCAmelCase__		,     add_special_tokens=lowerCAmelCase__       )
									self.assertListEqual(lowerCAmelCase__		,     lowerCAmelCase__       )
									self.assertIn("new_additional_special_token"		,     after_tokenizer.additional_special_tokens       )
									self.assertEqual(after_tokenizer.model_max_length		,     42       )

									_UpperCAmelCase						:				str	    =					tokenizer.__class__.from_pretrained(lowerCAmelCase__		,     model_max_length=43       )
									self.assertEqual(tokenizer.model_max_length		,     43       )

									shutil.rmtree(lowerCAmelCase__       )




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



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					[]
					if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

					if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

					for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer_utils.save_pretrained(lowerCAmelCase__       )

									with open(os.path.join(lowerCAmelCase__		,     "special_tokens_map.json"       )		,     encoding="utf-8"       ) as json_file:
											_UpperCAmelCase						:				Optional[Any]	    =					json.load(lowerCAmelCase__       )

									with open(os.path.join(lowerCAmelCase__		,     "tokenizer_config.json"       )		,     encoding="utf-8"       ) as json_file:
											_UpperCAmelCase						:				List[str]	    =					json.load(lowerCAmelCase__       )

									_UpperCAmelCase						:				Optional[int]	    =					[F"""<extra_id_{i}>""" for i in range(1_25       )]

									_UpperCAmelCase						:				Optional[Any]	    =					added_tokens_extra_ids + [
									    "an_additional_special_token"
									]
									_UpperCAmelCase						:				List[str]	    =					added_tokens_extra_ids + [
									    "an_additional_special_token"
									]

									with open(os.path.join(lowerCAmelCase__		,     "special_tokens_map.json"       )		,     "w"		,     encoding="utf-8"       ) as outfile:
											json.dump(lowerCAmelCase__		,     lowerCAmelCase__       )
									with open(os.path.join(lowerCAmelCase__		,     "tokenizer_config.json"       )		,     "w"		,     encoding="utf-8"       ) as outfile:
											json.dump(lowerCAmelCase__		,     lowerCAmelCase__       )

									# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
									# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
									# "special_tokens_map.json" files
									_UpperCAmelCase						:				Any	    =					tokenizer_class.from_pretrained(
									    lowerCAmelCase__		,     )
									self.assertIn(
									    "an_additional_special_token"		,     tokenizer_without_change_in_init.additional_special_tokens       )
									# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
									self.assertEqual(
									    ["an_additional_special_token"]		,     tokenizer_without_change_in_init.convert_ids_to_tokens(
									        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]       )       )		,     )

									# Now we test that we can change the value of additional_special_tokens in the from_pretrained
									_UpperCAmelCase						:				int	    =					added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"		,     lstrip=lowerCAmelCase__       )]
									_UpperCAmelCase						:				Tuple	    =					tokenizer_class.from_pretrained(
									    lowerCAmelCase__		,     additional_special_tokens=lowerCAmelCase__		,     )

									self.assertIn("a_new_additional_special_token"		,     tokenizer.additional_special_tokens       )
									self.assertEqual(
									    ["a_new_additional_special_token"]		,     tokenizer.convert_ids_to_tokens(
									        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]       )       )		,     )




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



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					[]
					if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

					if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

					for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer_utils.save_pretrained(lowerCAmelCase__       )

									_UpperCAmelCase						:				int	    =					tokenizer_class.from_pretrained(lowerCAmelCase__       )

									self.assertTrue(tokenizer.decode([2_55]       ) == ""       )




			def 							lowerCAmelCase__			(						self     :   Dict       )      ->Optional[int]:



					'''simple docstring'''







					pass




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



					'''simple docstring'''







					pass




			def 							lowerCAmelCase__			(						self     :   Tuple       )      ->int:



					'''simple docstring'''







					pass




			def 							lowerCAmelCase__			(						self     :   str       )      ->str:



					'''simple docstring'''







					pass




			def 							lowerCAmelCase__			(						self     :   str       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					self.get_tokenizers(fast=lowerCAmelCase__		,     do_lower_case=lowerCAmelCase__       )
					for tokenizer in tokenizers:
							with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
									_UpperCAmelCase						:				Dict	    =					["t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "x", "t", "</s>"]
									_UpperCAmelCase						:				Optional[Any]	    =					tokenizer.convert_tokens_to_string(lowerCAmelCase__       )

									self.assertIsInstance(lowerCAmelCase__		,     lowerCAmelCase__       )






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



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					self.get_tokenizers()
					for tokenizer in tokenizers:
							with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
									_UpperCAmelCase						:				str	    =					[
									    "bos_token",
									    "eos_token",
									    "unk_token",
									    "sep_token",
									    "pad_token",
									    "cls_token",
									    "mask_token",
									]

									_UpperCAmelCase						:				List[str]	    =					0
									_UpperCAmelCase						:				int	    =					tokenizer.convert_ids_to_tokens(
									    lowerCAmelCase__		,     skip_special_tokens=lowerCAmelCase__       )

									for attr in attributes_list:
											setattr(lowerCAmelCase__		,     attr + "_id"		,     lowerCAmelCase__       )
											self.assertEqual(getattr(lowerCAmelCase__		,     lowerCAmelCase__       )		,     lowerCAmelCase__       )
											self.assertEqual(getattr(lowerCAmelCase__		,     attr + "_id"       )		,     lowerCAmelCase__       )

											setattr(lowerCAmelCase__		,     attr + "_id"		,     lowerCAmelCase__       )
											self.assertEqual(getattr(lowerCAmelCase__		,     lowerCAmelCase__       )		,     lowerCAmelCase__       )
											self.assertEqual(getattr(lowerCAmelCase__		,     attr + "_id"       )		,     lowerCAmelCase__       )

									setattr(lowerCAmelCase__		,     "additional_special_tokens_ids"		,     []       )
									self.assertListEqual(getattr(lowerCAmelCase__		,     "additional_special_tokens"       )		,     []       )
									self.assertListEqual(getattr(lowerCAmelCase__		,     "additional_special_tokens_ids"       )		,     []       )

									setattr(lowerCAmelCase__		,     "additional_special_tokens_ids"		,     [token_id_to_test_setters]       )
									self.assertListEqual(getattr(lowerCAmelCase__		,     "additional_special_tokens"       )		,     [token_to_test_setters]       )
									self.assertListEqual(getattr(lowerCAmelCase__		,     "additional_special_tokens_ids"       )		,     [token_id_to_test_setters]       )




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Optional[Any]			 =						(KDPMaDiscreteScheduler,)
			__a  : Dict			 =						10





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							{
							    '''num_train_timesteps''': 11_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							}

							config.update(**lowerCAmelCase__						)
							return config





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							for timesteps in [10, 50, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]      ,   [0.0002, 0.002, 0.02]						):
											self.check_over_configs(beta_start=lowerCAmelCase__      ,   beta_end=lowerCAmelCase__						)





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

							'''simple docstring'''


							for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=lowerCAmelCase__						)





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

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config(prediction_type='''v_prediction'''						)
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase 			=							sample.to(lowerCAmelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

							__lowercase 			=							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase 			=							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6934E-07						) < 1E-2
											assert abs(result_mean.item() - 6.1112E-10						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 4.693_4286_5017_0972E-07						) < 1E-2
											assert abs(result_mean.item() - 0.0002						) < 1E-3





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							if torch_device == "mps":
											return
							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config()
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase 			=							sample.to(lowerCAmelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

							__lowercase 			=							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase 			=							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							if torch_device == "mps":
											return
							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config()
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps      ,   device=lowerCAmelCase__						)

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter.to(lowerCAmelCase__						) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

							__lowercase 			=							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase 			=							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if str(lowerCAmelCase__						).startswith('''cpu'''						):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3