






"""simple docstring"""
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       =				"pt"
elif is_tf_available():
							_lowerCAmelCase       =				"tf"
else:
							_lowerCAmelCase       =				"jax"



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							unittest.TestCase    ):





	"""simple docstring"""




	_A							:					List[str] 			=					PerceiverTokenizer
	_A							:					Optional[int] 			=					False





	def  lowerCamelCase(self				):
		super().setUp()
		A_		:    Optional[Any]						     = PerceiverTokenizer()
		tokenizer.save_pretrained(self.tmpdirname				)





	@cached_property
	def  lowerCamelCase(self				):
		return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""				)





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		return self.tokenizer_class.from_pretrained(self.tmpdirname  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=False  ,  lowerCAmelCase_=20  ,  lowerCAmelCase_=5				):
		# XXX The default common tokenizer tests assume that every ID is decodable on its own.
		# This assumption is invalid for Perceiver because single bytes might not be
		# valid utf-8 (byte 128 for instance).
		# Here we're overriding the smallest possible method to provide
		# a clean sequence without making the same assumption.

		A_		:    Union[str, Any]						     = []
		for i in range(len(lowerCAmelCase_				)				):
			try:
				A_		:    Any						     = tokenizer.decode([i]  ,  clean_up_tokenization_spaces=lowerCAmelCase_				)
			except UnicodeDecodeError:
				pass
			toks.append((i, tok)				)

		A_		:    Tuple						     = list(filter(lambda lowerCAmelCase_				: re.match(r"""^[ a-zA-Z]+$"""  ,  t[1]				)  ,  lowerCAmelCase_				)				)
		A_		:    int						     = 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:
			A_		:    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:
				A_		:    List[Any]						     = toks + toks
        # toks_str = [t[1] for t in toks]
		A_		:    int						     = [t[0] for t in toks]

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





	def  lowerCamelCase(self				):
		A_		:    str						     = self.perceiver_tokenizer
		A_		:    str						     = """Unicode €."""
		A_		:    Optional[int]						     = tokenizer(lowerCAmelCase_				)
		A_		:    int						     = [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
		self.assertEqual(encoded["""input_ids"""]  ,  lowerCAmelCase_				)

		# decoding
		A_		:    Tuple						     = tokenizer.decode(lowerCAmelCase_				)
		self.assertEqual(lowerCAmelCase_  ,  """[CLS]Unicode €.[SEP]"""				)

		A_		:    List[str]						     = tokenizer("""e è é ê ë"""				)
		A_		:    List[Any]						     = [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
		self.assertEqual(encoded["""input_ids"""]  ,  lowerCAmelCase_				)
		# decoding
		A_		:    Union[str, Any]						     = tokenizer.decode(lowerCAmelCase_				)
		self.assertEqual(lowerCAmelCase_  ,  """[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  lowerCamelCase(self				):
		A_		:    List[str]						     = self.perceiver_tokenizer
		A_		:    str						     = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
		# fmt: off
		A_		:    Dict						     = [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
		A_		:    List[str]						     = tokenizer(lowerCAmelCase_  ,  padding=lowerCAmelCase_  ,  return_tensors=lowerCAmelCase_				)
		self.assertIsInstance(lowerCAmelCase_  ,  lowerCAmelCase_				)

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

		self.assertListEqual(lowerCAmelCase_  ,  lowerCAmelCase_				)

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





	def  lowerCamelCase(self				):
		A_		:    List[Any]						     = self.perceiver_tokenizer
		A_		:    Any						     = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
		A_		:    Optional[Any]						     = 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				):
		A_		:    Union[str, Any]						     = self.perceiver_tokenizer
		A_		:    Optional[Any]						     = [
		    """Summary of the text.""",
		    """Another summary.""",
		]
		A_		:    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				):
		# safety check on max_len default value so we are sure the test works
		A_		:    Optional[int]						     = 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
		A_		:    str						     = 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
				A_		:    Optional[Any]						     = tempfile.mkdtemp()

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

				A_		:    Any						     = tokenizer.__class__.from_pretrained(lowerCAmelCase_				)
				A_		:    List[Any]						     = after_tokenizer.encode(lowerCAmelCase_  ,  add_special_tokens=lowerCAmelCase_				)
				self.assertListEqual(lowerCAmelCase_  ,  lowerCAmelCase_				)

				shutil.rmtree(lowerCAmelCase_				)

		A_		:    List[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
				A_		:    Optional[int]						     = tempfile.mkdtemp()

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

				A_		:    List[str]						     = tokenizer.__class__.from_pretrained(lowerCAmelCase_				)
				A_		:    Dict						     = 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				)

				A_		:    Union[str, Any]						     = tokenizer.__class__.from_pretrained(lowerCAmelCase_  ,  model_max_length=43				)
				self.assertEqual(tokenizer.model_max_length  ,  43				)

				shutil.rmtree(lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    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_				)

				with open(os.path.join(lowerCAmelCase_  ,  """special_tokens_map.json"""				)  ,  encoding="""utf-8"""				) as json_file:
					A_		:    int						     = json.load(lowerCAmelCase_				)

				with open(os.path.join(lowerCAmelCase_  ,  """tokenizer_config.json"""				)  ,  encoding="""utf-8"""				) as json_file:
					A_		:    Tuple						     = json.load(lowerCAmelCase_				)

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

				A_		:    List[str]						     = added_tokens_extra_ids + [
				    """an_additional_special_token"""
				]
				A_		:    List[Any]						     = 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
				A_		:    List[str]						     = tokenizer_class.from_pretrained(
				    lowerCAmelCase_  ,  )
				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
				A_		:    Optional[Any]						     = added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""  ,  lstrip=lowerCAmelCase_				)]
				A_		:    Dict						     = 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				):
		A_		:    Dict						     = self.perceiver_tokenizer
		self.assertEqual(tokenizer.decode([178]				)  ,  """�"""				)





	def  lowerCamelCase(self				):
		pass





	def  lowerCamelCase(self				):
		pass





	def  lowerCamelCase(self				):
		pass





	def  lowerCamelCase(self				):
		pass





	def  lowerCamelCase(self				):
		# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
		# strings and special added tokens as tokens
		A_		:    str						     = self.get_tokenizers(fast=lowerCAmelCase_  ,  do_lower_case=lowerCAmelCase_				)
		for tokenizer in tokenizers:
			with self.subTest(f"""{tokenizer.__class__.__name__}"""				):
				A_		:    List[str]						     = ["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
				A_		:    int						     = tokenizer.convert_tokens_to_string(lowerCAmelCase_				)

				self.assertIsInstance(lowerCAmelCase_  ,  lowerCAmelCase_				)









"""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

