




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

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

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
	_lowerCamelCase      :  List[Any]      						=						"pt"
elif is_tf_available():
	_lowerCamelCase      :  int      						=						"tf"
else:
	_lowerCamelCase      :  List[Any]      						=						"jax"
class        __snake_case  (_a						,	unittest.TestCase		):
				lowerCAmelCase__				=     PerceiverTokenizer
				lowerCAmelCase__				=     False



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





								'''simple docstring'''




								super().setUp()
								_lowerCAmelCase    :    int										=      PerceiverTokenizer()
								tokenizer.save_pretrained(self.tmpdirname      )



				@cached_property
				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Optional[int]:





								'''simple docstring'''




								return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]		,	**_UpperCAmelCase      :		Tuple      )  ->    PerceiverTokenizer:





								'''simple docstring'''




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



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





								'''simple docstring'''




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

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

								# Ensure consistency
								_lowerCAmelCase    :    Optional[int]										=      tokenizer.decode(_UpperCAmelCase		,	clean_up_tokenization_spaces=_UpperCAmelCase      )
								if " " not in output_txt and len(_UpperCAmelCase      ) > 1:
												_lowerCAmelCase    :    Optional[Any]										=      (
												    tokenizer.decode([toks_ids[0]]		,	clean_up_tokenization_spaces=_UpperCAmelCase      )
												    + """ """
												    + tokenizer.decode(toks_ids[1:]		,	clean_up_tokenization_spaces=_UpperCAmelCase      )
												)
								if with_prefix_space:
												_lowerCAmelCase    :    Optional[Any]										=      """ """ + output_txt
								_lowerCAmelCase    :    Union[str, Any]										=      tokenizer.encode(_UpperCAmelCase		,	add_special_tokens=_UpperCAmelCase      )
								return output_txt, output_ids



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





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.perceiver_tokenizer
								_lowerCAmelCase    :    List[Any]										=      """Unicode €."""
								_lowerCAmelCase    :    Tuple										=      tokenizer(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
								self.assertEqual(encoded["""input_ids"""]		,	_UpperCAmelCase      )

								# decoding
								_lowerCAmelCase    :    Optional[int]										=      tokenizer.decode(_UpperCAmelCase      )
								self.assertEqual(_UpperCAmelCase		,	"""[CLS]Unicode €.[SEP]"""      )

								_lowerCAmelCase    :    Union[str, Any]										=      tokenizer("""e è é ê ë"""      )
								_lowerCAmelCase    :    List[str]										=      [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
								self.assertEqual(encoded["""input_ids"""]		,	_UpperCAmelCase      )
								# decoding
								_lowerCAmelCase    :    Tuple										=      tokenizer.decode(_UpperCAmelCase      )
								self.assertEqual(_UpperCAmelCase		,	"""[CLS]e è é ê ë[SEP]"""      )

								# encode/decode, but with `encode` instead of `__call__`
								self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""      )      )		,	"""[CLS]e è é ê ë[SEP]"""      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.perceiver_tokenizer
								_lowerCAmelCase    :    Optional[int]										=      ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
								# fmt: off
								_lowerCAmelCase    :    Optional[int]										=      [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
								# fmt: on
								_lowerCAmelCase    :    Dict										=      tokenizer(_UpperCAmelCase		,	padding=_UpperCAmelCase		,	return_tensors=_UpperCAmelCase      )
								self.assertIsInstance(_UpperCAmelCase		,	_UpperCAmelCase      )

								if FRAMEWORK != "jax":
												_lowerCAmelCase    :    Any										=      list(batch.input_ids.numpy()[0]      )
								else:
												_lowerCAmelCase    :    List[Any]										=      list(batch.input_ids.tolist()[0]      )

								self.assertListEqual(_UpperCAmelCase		,	_UpperCAmelCase      )

								self.assertEqual((2, 38)		,	batch.input_ids.shape      )
								self.assertEqual((2, 38)		,	batch.attention_mask.shape      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    Dict:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Tuple:





								'''simple docstring'''




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



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





								'''simple docstring'''




								_lowerCAmelCase    :    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
								_lowerCAmelCase    :    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
																_lowerCAmelCase    :    List[str]										=      tempfile.mkdtemp()

																_lowerCAmelCase    :    int										=      """ He is very happy, UNwant\u00E9d,running"""
																_lowerCAmelCase    :    List[str]										=      tokenizer.encode(_UpperCAmelCase		,	add_special_tokens=_UpperCAmelCase      )
																tokenizer.save_pretrained(_UpperCAmelCase      )

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

																shutil.rmtree(_UpperCAmelCase      )

								_lowerCAmelCase    :    List[str]										=      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
																_lowerCAmelCase    :    Dict										=      tempfile.mkdtemp()

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

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

																_lowerCAmelCase    :    Tuple										=      tokenizer.__class__.from_pretrained(_UpperCAmelCase		,	model_max_length=43      )
																self.assertEqual(tokenizer.model_max_length		,	43      )

																shutil.rmtree(_UpperCAmelCase      )



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





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      []
								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(_UpperCAmelCase      )

																with open(os.path.join(_UpperCAmelCase		,	"""special_tokens_map.json"""      )		,	encoding="""utf-8"""      ) as json_file:
																				_lowerCAmelCase    :    Tuple										=      json.load(_UpperCAmelCase      )

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

																_lowerCAmelCase    :    List[str]										=      [f"<extra_id_{i}>" for i in range(125      )]

																_lowerCAmelCase    :    str										=      added_tokens_extra_ids + [
																    """an_additional_special_token"""
																]
																_lowerCAmelCase    :    List[Any]										=      added_tokens_extra_ids + [
																    """an_additional_special_token"""
																]

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

																# 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
																_lowerCAmelCase    :    Tuple										=      tokenizer_class.from_pretrained(
																    _UpperCAmelCase		,	)
																self.assertIn(
																    """an_additional_special_token"""		,	tokenizer_without_change_in_init.additional_special_tokens      )
																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
																_lowerCAmelCase    :    str										=      added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""		,	lstrip=_UpperCAmelCase      )]
																_lowerCAmelCase    :    Optional[int]										=      tokenizer_class.from_pretrained(
																    _UpperCAmelCase		,	additional_special_tokens=_UpperCAmelCase		,	)

																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       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      self.perceiver_tokenizer
								self.assertEqual(tokenizer.decode([178]      )		,	"""�"""      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Optional[int]:





								'''simple docstring'''




								pass



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





								'''simple docstring'''




								pass



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





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Optional[Any]:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      self.get_tokenizers(fast=_UpperCAmelCase		,	do_lower_case=_UpperCAmelCase      )
								for tokenizer in tokenizers:
												with self.subTest(f"{tokenizer.__class__.__name__}"      ):
																_lowerCAmelCase    :    List[str]										=      ["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
																_lowerCAmelCase    :    Optional[Any]										=      tokenizer.convert_tokens_to_string(_UpperCAmelCase      )

																self.assertIsInstance(_UpperCAmelCase		,	_UpperCAmelCase      )







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class        __snake_case  (_a		):
				lowerCAmelCase__				=     (PNDMScheduler,)
				lowerCAmelCase__				=     (("num_inference_steps", 5_0),)



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	**_UpperCAmelCase      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      {
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**_UpperCAmelCase      )
								return config



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Union[str, Any]=0		,	**_UpperCAmelCase      :		str      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      dict(self.forward_default_kwargs      )
								_lowerCAmelCase    :    str										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      self.dummy_sample
								_lowerCAmelCase    :    List[str]										=      0.1 * sample
								_lowerCAmelCase    :    Tuple										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Any										=      self.get_scheduler_config(**_UpperCAmelCase      )
												_lowerCAmelCase    :    Optional[Any]										=      scheduler_class(**_UpperCAmelCase      )
												scheduler.set_timesteps(_UpperCAmelCase      )
												# copy over dummy past residuals
												_lowerCAmelCase    :    Tuple										=      dummy_past_residuals[:]

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

												_lowerCAmelCase    :    int										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    List[str]										=      new_scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Tuple										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[int]										=      new_scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



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





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		Any=0		,	**_UpperCAmelCase      :		Optional[int]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      dict(self.forward_default_kwargs      )
								_lowerCAmelCase    :    Any										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      self.dummy_sample
								_lowerCAmelCase    :    Dict										=      0.1 * sample
								_lowerCAmelCase    :    Dict										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Any										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Optional[Any]										=      scheduler_class(**_UpperCAmelCase      )
												scheduler.set_timesteps(_UpperCAmelCase      )

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

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_UpperCAmelCase      )
																_lowerCAmelCase    :    List[Any]										=      scheduler_class.from_pretrained(_UpperCAmelCase      )
																# copy over dummy past residuals
																new_scheduler.set_timesteps(_UpperCAmelCase      )

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

												_lowerCAmelCase    :    Tuple										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    str										=      new_scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Any										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Any										=      new_scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	**_UpperCAmelCase      :		Dict      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    int										=      self.get_scheduler_config(**_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      scheduler_class(**_UpperCAmelCase      )

								_lowerCAmelCase    :    List[Any]										=      10
								_lowerCAmelCase    :    Any										=      self.dummy_model()
								_lowerCAmelCase    :    Tuple										=      self.dummy_sample_deter
								scheduler.set_timesteps(_UpperCAmelCase      )

								for i, t in enumerate(scheduler.prk_timesteps      ):
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )
												_lowerCAmelCase    :    Tuple										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample

								for i, t in enumerate(scheduler.plms_timesteps      ):
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )
												_lowerCAmelCase    :    Dict										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample

								return sample



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      dict(self.forward_default_kwargs      )

								_lowerCAmelCase    :    int										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Tuple										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Union[str, Any]										=      scheduler_class(**_UpperCAmelCase      )

												_lowerCAmelCase    :    str										=      self.dummy_sample
												_lowerCAmelCase    :    str										=      0.1 * sample

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

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

												_lowerCAmelCase    :    Optional[int]										=      scheduler.step_prk(_UpperCAmelCase		,	0		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[int]										=      scheduler.step_prk(_UpperCAmelCase		,	1		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Tuple										=      scheduler.step_plms(_UpperCAmelCase		,	0		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[Any]										=      scheduler.step_plms(_UpperCAmelCase		,	1		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



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





								'''simple docstring'''




								for timesteps in [100, 1000]:
												self.check_over_configs(num_train_timesteps=_UpperCAmelCase      )



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





								'''simple docstring'''




								for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=_UpperCAmelCase      )

								_lowerCAmelCase    :    Dict										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    Any										=      self.get_scheduler_config(steps_offset=1      )
								_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )
								scheduler.set_timesteps(10      )
								assert torch.equal(
								    scheduler.timesteps		,	torch.LongTensor(
								        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]      )		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    List[Any]:





								'''simple docstring'''




								for beta_start, beta_end in zip([0.0001, 0.001]		,	[0.002, 0.02]      ):
												self.check_over_configs(beta_start=_UpperCAmelCase		,	beta_end=_UpperCAmelCase      )



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





								'''simple docstring'''




								for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=_UpperCAmelCase      )



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





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[str]:





								'''simple docstring'''




								for t in [1, 5, 10]:
												self.check_over_forward(time_step=_UpperCAmelCase      )



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





								'''simple docstring'''




								for t, num_inference_steps in zip([1, 5, 10]		,	[10, 50, 100]      ):
												self.check_over_forward(num_inference_steps=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      27

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Optional[Any]										=      self.dummy_sample
												_lowerCAmelCase    :    Any										=      0.1 * sample

												_lowerCAmelCase    :    List[str]										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )

												scheduler.set_timesteps(_UpperCAmelCase      )

												# before power of 3 fix, would error on first step, so we only need to do two
												for i, t in enumerate(scheduler.prk_timesteps[:2]      ):
																_lowerCAmelCase    :    List[Any]										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample



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





								'''simple docstring'''




								with self.assertRaises(_UpperCAmelCase      ):
												_lowerCAmelCase    :    Tuple										=      self.scheduler_classes[0]
												_lowerCAmelCase    :    List[str]										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )

												scheduler.step_plms(self.dummy_sample		,	1		,	self.dummy_sample      ).prev_sample



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.full_loop()
								_lowerCAmelCase    :    str										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    List[Any]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 198.1318      ) < 1E-2
								assert abs(result_mean.item() - 0.2580      ) < 1E-3



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





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.full_loop(prediction_type="""v_prediction"""      )
								_lowerCAmelCase    :    Optional[int]										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Optional[int]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 67.3986      ) < 1E-2
								assert abs(result_mean.item() - 0.0878      ) < 1E-3



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





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      self.full_loop(set_alpha_to_one=_UpperCAmelCase		,	beta_start=0.01      )
								_lowerCAmelCase    :    Any										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Optional[Any]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 230.0399      ) < 1E-2
								assert abs(result_mean.item() - 0.2995      ) < 1E-3



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





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.full_loop(set_alpha_to_one=_UpperCAmelCase		,	beta_start=0.01      )
								_lowerCAmelCase    :    List[Any]										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Tuple										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 186.9482      ) < 1E-2
								assert abs(result_mean.item() - 0.2434      ) < 1E-3

