
from abc import ABC, abstractmethod
from typing import List, Optional




class   SCREAMING_SNAKE_CASE__	(      _UpperCamelCase   ):





	'''simple docstring'''



	def __init__(    self      :       List[Any]      ):


				'''simple docstring'''


				self.test()



	def 					A				(    self      :       int      ):


				'''simple docstring'''


				_snake_case            =     0
				_snake_case            =     False
				while not completed:
							if counter == 1:
										self.reset()
							_snake_case            =     self.advance()
							if not self.does_advance(a_      ):
										raise Exception(
										    'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'      )

							_snake_case            =     self.update(a_      )
							counter += 1

							if counter > 10_000:
										raise Exception('update() does not fulfill the constraint.'      )

				if self.remaining() != 0:
							raise Exception('Custom Constraint is not defined correctly.'      )



	@abstractmethod
	def 					A				(    self      :       Optional[int]      ):


				'''simple docstring'''


				raise NotImplementedError(
				    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )



	@abstractmethod
	def 					A				(    self      :       List[str]     ,    lowercase      :       int      ):


				'''simple docstring'''


				raise NotImplementedError(
				    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )



	@abstractmethod
	def 					A				(    self      :       List[str]     ,    lowercase      :       int      ):


				'''simple docstring'''


				raise NotImplementedError(
				    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )



	@abstractmethod
	def 					A				(    self      :       int      ):


				'''simple docstring'''


				raise NotImplementedError(
				    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )



	@abstractmethod
	def 					A				(    self      :       Optional[Any]      ):


				'''simple docstring'''


				raise NotImplementedError(
				    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )





	@abstractmethod
	def 					A				(    self      :       Optional[Any]     ,    lowercase      :       int=False      ):


				'''simple docstring'''


				raise NotImplementedError(
				    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )




class   SCREAMING_SNAKE_CASE__	(      _UpperCamelCase   ):





	'''simple docstring'''



	def __init__(    self      :       List[Any]     ,    lowercase      :       List[int]      ):


				'''simple docstring'''


				super(a_     ,    self      ).__init__()

				if not isinstance(a_     ,    a_      ) or len(a_      ) == 0:
							raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.'''      )
				if any((not isinstance(a_     ,    a_      ) or token_id < 0) for token_id in token_ids      ):
							raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.'''      )

				_snake_case            =     token_ids

				_snake_case            =     len(self.token_ids      )
				_snake_case            =     -1  # the index of the currently fulfilled step
				_snake_case            =     False



	def 					A				(    self      :       Optional[Any]      ):


				'''simple docstring'''


				if self.completed:
							return None
				return self.token_ids[self.fulfilled_idx + 1]



	def 					A				(    self      :       int     ,    lowercase      :       int      ):


				'''simple docstring'''


				if not isinstance(a_     ,    a_      ):
							raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(a_      )}'''      )

				if self.completed:
							return False

				return token_id == self.token_ids[self.fulfilled_idx + 1]



	def 					A				(    self      :       Any     ,    lowercase      :       int      ):


				'''simple docstring'''


				if not isinstance(a_     ,    a_      ):
							raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(a_      )}'''      )

				_snake_case            =     False
				_snake_case            =     False
				_snake_case            =     False

				if self.does_advance(a_      ):
							self.fulfilled_idx += 1
							_snake_case            =     True
							if self.fulfilled_idx == (self.seqlen - 1):
										_snake_case            =     True
							_snake_case            =     completed
				else:
							# failed to make progress.
							_snake_case            =     True
							self.reset()
				return stepped, completed, reset



	def 					A				(    self      :       Optional[int]      ):


				'''simple docstring'''


				_snake_case            =     False
				_snake_case            =     0



	def 					A				(    self      :       int      ):


				'''simple docstring'''


				return self.seqlen - (self.fulfilled_idx + 1)





	def 					A				(    self      :       List[str]     ,    lowercase      :       Optional[int]=False      ):


				'''simple docstring'''


				_snake_case            =     PhrasalConstraint(self.token_ids      )

				if stateful:
							_snake_case            =     self.seqlen
							_snake_case            =     self.fulfilled_idx
							_snake_case            =     self.completed

				return new_constraint




class   SCREAMING_SNAKE_CASE__	:





	'''simple docstring'''



	def __init__(    self      :       Dict     ,    lowercase      :       List[List[int]]     ,    lowercase      :       List[str]=True      ):


				'''simple docstring'''


				_snake_case            =     max([len(a_      ) for one in nested_token_ids]      )

				_snake_case            =     {}
				for token_ids in nested_token_ids:
							_snake_case            =     root
							for tidx, token_id in enumerate(a_      ):
										if token_id not in level:
													_snake_case            =     {}

										_snake_case            =     level[token_id]

				if no_subsets and self.has_subsets(a_     ,    a_      ):
							raise ValueError(
							    'Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'
							    f''' {nested_token_ids}.'''      )

				_snake_case            =     root



	def 					A				(    self      :       Optional[Any]     ,    lowercase      :       str      ):


				'''simple docstring'''


				_snake_case            =     self.trie

				for current_token in current_seq:
							_snake_case            =     start[current_token]

				_snake_case            =     list(start.keys()      )

				return next_tokens



	def 					A				(    self      :       Dict     ,    lowercase      :       Any      ):


				'''simple docstring'''


				_snake_case            =     self.next_tokens(a_      )

				return len(a_      ) == 0



	def 					A				(    self      :       Union[str, Any]     ,    lowercase      :       Any      ):


				'''simple docstring'''


				_snake_case            =     list(root.values()      )
				if len(a_      ) == 0:
							return 1
				else:
							return sum([self.count_leaves(a_      ) for nn in next_nodes]      )





	def 					A				(    self      :       Tuple     ,    lowercase      :       str     ,    lowercase      :       Union[str, Any]      ):


				'''simple docstring'''


				_snake_case            =     self.count_leaves(a_      )
				return len(a_      ) != leaf_count




class   SCREAMING_SNAKE_CASE__	(      _UpperCamelCase   ):





	'''simple docstring'''



	def __init__(    self      :       List[str]     ,    lowercase      :       List[List[int]]      ):


				'''simple docstring'''


				super(a_     ,    self      ).__init__()

				if not isinstance(a_     ,    a_      ) or len(a_      ) == 0:
							raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.'''      )
				if any(not isinstance(a_     ,    a_      ) for token_ids in nested_token_ids      ):
							raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.'''      )
				if any(
				    any((not isinstance(a_     ,    a_      ) or token_id < 0) for token_id in token_ids      )
				    for token_ids in nested_token_ids      ):
							raise ValueError(
							    f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.'''      )

				_snake_case            =     DisjunctiveTrie(a_      )
				_snake_case            =     nested_token_ids

				_snake_case            =     self.trie.max_height
				_snake_case            =     []
				_snake_case            =     False



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


				'''simple docstring'''


				_snake_case            =     self.trie.next_tokens(self.current_seq      )

				if len(a_      ) == 0:
							return None
				else:
							return token_list



	def 					A				(    self      :       Dict     ,    lowercase      :       int      ):


				'''simple docstring'''


				if not isinstance(a_     ,    a_      ):
							raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(a_      )}'''      )

				_snake_case            =     self.trie.next_tokens(self.current_seq      )

				return token_id in next_tokens



	def 					A				(    self      :       List[str]     ,    lowercase      :       int      ):


				'''simple docstring'''


				if not isinstance(a_     ,    a_      ):
							raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(a_      )}'''      )

				_snake_case            =     False
				_snake_case            =     False
				_snake_case            =     False

				if self.does_advance(a_      ):
							self.current_seq.append(a_      )
							_snake_case            =     True
				else:
							_snake_case            =     True
							self.reset()

				_snake_case            =     self.trie.reached_leaf(self.current_seq      )
				_snake_case            =     completed

				return stepped, completed, reset



	def 					A				(    self      :       List[str]      ):


				'''simple docstring'''


				_snake_case            =     False
				_snake_case            =     []



	def 					A				(    self      :       int      ):


				'''simple docstring'''


				if self.completed:
							# since this can be completed without reaching max height
							return 0
				else:
							return self.seqlen - len(self.current_seq      )





	def 					A				(    self      :       List[str]     ,    lowercase      :       str=False      ):


				'''simple docstring'''


				_snake_case            =     DisjunctiveConstraint(self.token_ids      )

				if stateful:
							_snake_case            =     self.seqlen
							_snake_case            =     self.current_seq
							_snake_case            =     self.completed

				return new_constraint




class   SCREAMING_SNAKE_CASE__	:





	'''simple docstring'''



	def __init__(    self      :       Tuple     ,    lowercase      :       List[Constraint]      ):


				'''simple docstring'''


				_snake_case            =     constraints

				# max # of steps required to fulfill a given constraint
				_snake_case            =     max([c.seqlen for c in constraints]      )
				_snake_case            =     len(a_      )
				_snake_case            =     False

				self.init_state()



	def 					A				(    self      :       List[Any]      ):


				'''simple docstring'''


				_snake_case            =     []
				_snake_case            =     None
				_snake_case            =     [constraint.copy(stateful=a_      ) for constraint in self.constraints]



	def 					A				(    self      :       List[Any]      ):


				'''simple docstring'''


				_snake_case            =     0
				if self.inprogress_constraint:
							# extra points for having a constraint mid-fulfilled
							add += self.max_seqlen - self.inprogress_constraint.remaining()

				return (len(self.complete_constraints      ) * self.max_seqlen) + add



	def 					A				(    self      :       str      ):


				'''simple docstring'''


				_snake_case            =     []
				if self.inprogress_constraint is None:
							for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
										_snake_case            =     constraint.advance()
										if isinstance(a_     ,    a_      ):
													token_list.append(a_      )
										elif isinstance(a_     ,    a_      ):
													token_list.extend(a_      )
				else:
							_snake_case            =     self.inprogress_constraint.advance()
							if isinstance(a_     ,    a_      ):
										token_list.append(a_      )
							elif isinstance(a_     ,    a_      ):
										token_list.extend(a_      )

				if len(a_      ) == 0:
							return None
				else:
							return token_list



	def 					A				(    self      :       List[Any]     ,    lowercase      :       Optional[List[int]]      ):


				'''simple docstring'''


				self.init_state()

				if token_ids is not None:
							for token in token_ids:
										# completes or steps **one** constraint
										_snake_case            =     self.add(a_      )

										# the entire list of constraints are fulfilled
										if self.completed:
													break



	def 					A				(    self      :       Optional[int]     ,    lowercase      :       int      ):


				'''simple docstring'''


				if not isinstance(a_     ,    a_      ):
							raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.'''      )

				_snake_case            =     False, False

				if self.completed:
							_snake_case            =     True
							_snake_case            =     False
							return complete, stepped

				if self.inprogress_constraint is not None:
							# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
							# job, simply update the state

							_snake_case            =     self.inprogress_constraint.update(a_      )
							if reset:
										# 1. If the next token breaks the progress, then we must restart.
										#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

										#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
										#     constraint, not the full list of constraints.

										self.pending_constraints.append(self.inprogress_constraint.copy(stateful=a_      )      )
										_snake_case            =     None

							if complete:
										# 2. If the next token completes the constraint, move it to completed list, set
										#     inprogress to None. If there are no pending constraints either, then this full list of constraints
										#     is complete.

										self.complete_constraints.append(self.inprogress_constraint      )
										_snake_case            =     None

										if len(self.pending_constraints      ) == 0:
													# we're done!
													_snake_case            =     True

				else:
							# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
							# of constraints?

							for cidx, pending_constraint in enumerate(self.pending_constraints      ):
										if pending_constraint.does_advance(a_      ):
													_snake_case            =     pending_constraint.update(a_      )

													if not stepped:
																raise Exception(
																    '`constraint.update(token_id)` is not yielding incremental progress, '
																    'even though `constraint.does_advance(token_id)` is true.'      )

													if complete:
																self.complete_constraints.append(a_      )
																_snake_case            =     None

													if not complete and stepped:
																_snake_case            =     pending_constraint

													if complete or stepped:
																# If we made any progress at all, then it's at least not a "pending constraint".

																_snake_case            =     (
																    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
																)

																if len(self.pending_constraints      ) == 0 and self.inprogress_constraint is None:
																			# If there's no longer any pending after this and no inprogress either, then we must be
																			# complete.

																			_snake_case            =     True

																break  # prevent accidentally stepping through multiple constraints with just one token.

				return complete, stepped





	def 					A				(    self      :       Optional[int]     ,    lowercase      :       Optional[Any]=True      ):


				'''simple docstring'''


				_snake_case            =     ConstraintListState(self.constraints      )  # we actually never though self.constraints objects
				# throughout this process. So it's at initialization state.

				if stateful:
							_snake_case            =     [
							    constraint.copy(stateful=a_      ) for constraint in self.complete_constraints
							]
							if self.inprogress_constraint is not None:
										_snake_case            =     self.inprogress_constraint.copy(stateful=a_      )
							_snake_case            =     [constraint.copy() for constraint in self.pending_constraints]

				return new_state






"""simple docstring"""






import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort



class 					__UpperCAmelCase							(						_UpperCamelCase						,  unittest.TestCase  ):
				__lowerCamelCase				:	List[Any]				     =				"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
				def       UpperCAmelCase      (		self    :			str	, a_    :			Optional[Any]=0    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			np.random.RandomState(a_    )
							a__  :	List[Any]		 =			{
							    "prompt": "A painting of a squirrel eating a burger",
							    "generator": generator,
							    "num_inference_steps": 2,
							    "guidance_scale": 7.5,
							    "output_type": "numpy",
							}
							return inputs
				def       UpperCAmelCase      (		self    :			Dict    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	Tuple		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Optional[int]		 =			self.get_dummy_inputs()
							a__  :	Union[str, Any]		 =			pipe(**a_    ).images
							a__  :	Tuple		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	List[str]		 =			np.array([0.6_5072, 0.5_8492, 0.4_8219, 0.5_5521, 0.5_3180, 0.5_5939, 0.5_0697, 0.3_9800, 0.4_6455]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Dict		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	List[str]		 =			PNDMScheduler.from_config(pipe.scheduler.config	, skip_prk_steps=a_    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Optional[int]		 =			self.get_dummy_inputs()
							a__  :	List[Any]		 =			pipe(**a_    ).images
							a__  :	Union[str, Any]		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	List[str]		 =			np.array([0.6_5863, 0.5_9425, 0.4_9326, 0.5_6313, 0.5_3875, 0.5_6627, 0.5_1065, 0.3_9777, 0.4_6330]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Tuple:



							'''simple docstring'''



							a__  :	Dict		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	Tuple		 =			LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	List[Any]		 =			self.get_dummy_inputs()
							a__  :	Optional[Any]		 =			pipe(**a_    ).images
							a__  :	int		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	Dict		 =			np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Dict:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	Dict		 =			EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Optional[int]		 =			self.get_dummy_inputs()
							a__  :	int		 =			pipe(**a_    ).images
							a__  :	Tuple		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	str		 =			np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	str:



							'''simple docstring'''



							a__  :	str		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	Dict		 =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Any		 =			self.get_dummy_inputs()
							a__  :	List[str]		 =			pipe(**a_    ).images
							a__  :	Union[str, Any]		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	Any		 =			np.array([0.5_3817, 0.6_0812, 0.4_7384, 0.4_9530, 0.5_1894, 0.4_9814, 0.4_7984, 0.3_8958, 0.4_4271]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Any:



							'''simple docstring'''



							a__  :	str		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							a__  :	str		 =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Tuple		 =			self.get_dummy_inputs()
							a__  :	List[str]		 =			pipe(**a_    ).images
							a__  :	Any		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_28, 1_28, 3)
							a__  :	Any		 =			np.array([0.5_3895, 0.6_0808, 0.4_7933, 0.4_9608, 0.5_1886, 0.4_9950, 0.4_8053, 0.3_8957, 0.4_4200]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
				def       UpperCAmelCase      (		self    :			str    )	->	Tuple:



							'''simple docstring'''



							a__  :	Dict		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Any		 =			self.get_dummy_inputs()
							a__  :	Any		 =			3 * [inputs["prompt"]]

							# forward
							a__  :	Union[str, Any]		 =			pipe(**a_    )
							a__  :	int		 =			output.images[0, -3:, -3:, -1]

							a__  :	Union[str, Any]		 =			self.get_dummy_inputs()
							a__  :	List[Any]		 =			3 * [inputs.pop("prompt"    )]

							a__  :	Optional[Any]		 =			pipe.tokenizer(
							    a_	, padding="max_length"	, max_length=pipe.tokenizer.model_max_length	, truncation=a_	, return_tensors="np"	, )
							a__  :	List[str]		 =			text_inputs["input_ids"]

							a__  :	int		 =			pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]

							a__  :	List[Any]		 =			prompt_embeds

							# forward
							a__  :	List[Any]		 =			pipe(**a_    )
							a__  :	List[str]		 =			output.images[0, -3:, -3:, -1]

							assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1E-4






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



							'''simple docstring'''



							a__  :	Tuple		 =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	, provider="CPUExecutionProvider"    )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	Tuple		 =			self.get_dummy_inputs()
							a__  :	Dict		 =			3 * ["this is a negative prompt"]
							a__  :	Optional[Any]		 =			negative_prompt
							a__  :	Any		 =			3 * [inputs["prompt"]]

							# forward
							a__  :	str		 =			pipe(**a_    )
							a__  :	List[str]		 =			output.images[0, -3:, -3:, -1]

							a__  :	Union[str, Any]		 =			self.get_dummy_inputs()
							a__  :	Union[str, Any]		 =			3 * [inputs.pop("prompt"    )]

							a__  :	List[Any]		 =			[]
							for p in [prompt, negative_prompt]:
										a__  :	int		 =			pipe.tokenizer(
										    a_	, padding="max_length"	, max_length=pipe.tokenizer.model_max_length	, truncation=a_	, return_tensors="np"	, )
										a__  :	Any		 =			text_inputs["input_ids"]

										embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]    )

							a__ ,	a__  :	Union[str, Any]		 =			embeds

							# forward
							a__  :	Dict		 =			pipe(**a_    )
							a__  :	Any		 =			output.images[0, -3:, -3:, -1]

							assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1E-4



@nightly
@require_onnxruntime
@require_torch_gpu
class 					__UpperCAmelCase							(						unittest.TestCase  ):
				@property
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Optional[int]:



							'''simple docstring'''



							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)
				@property
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Tuple:



							'''simple docstring'''



							a__  :	List[str]		 =			ort.SessionOptions()
							a__  :	List[str]		 =			False
							return options
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	List[Any]:



							'''simple docstring'''



							a__  :	Dict		 =			OnnxStableDiffusionPipeline.from_pretrained(
							    "CompVis/stable-diffusion-v1-4"	, revision="onnx"	, safety_checker=a_	, feature_extractor=a_	, provider=self.gpu_provider	, sess_options=self.gpu_options	, )
							sd_pipe.set_progress_bar_config(disable=a_    )

							a__  :	Optional[int]		 =			"A painting of a squirrel eating a burger"
							np.random.seed(0    )
							a__  :	Dict		 =			sd_pipe([prompt]	, guidance_scale=6.0	, num_inference_steps=10	, output_type="np"    )
							a__  :	Any		 =			output.images

							a__  :	Optional[int]		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							a__  :	str		 =			np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Any		 =			DDIMScheduler.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, subfolder="scheduler"	, revision="onnx"    )
							a__  :	str		 =			OnnxStableDiffusionPipeline.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, revision="onnx"	, scheduler=a_	, safety_checker=a_	, feature_extractor=a_	, provider=self.gpu_provider	, sess_options=self.gpu_options	, )
							sd_pipe.set_progress_bar_config(disable=a_    )

							a__  :	str		 =			"open neural network exchange"
							a__  :	Tuple		 =			np.random.RandomState(0    )
							a__  :	Dict		 =			sd_pipe([prompt]	, guidance_scale=7.5	, num_inference_steps=10	, generator=a_	, output_type="np"    )
							a__  :	Dict		 =			output.images
							a__  :	Union[str, Any]		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							a__  :	Dict		 =			np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Dict:



							'''simple docstring'''



							a__  :	List[Any]		 =			LMSDiscreteScheduler.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, subfolder="scheduler"	, revision="onnx"    )
							a__  :	List[Any]		 =			OnnxStableDiffusionPipeline.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, revision="onnx"	, scheduler=a_	, safety_checker=a_	, feature_extractor=a_	, provider=self.gpu_provider	, sess_options=self.gpu_options	, )
							sd_pipe.set_progress_bar_config(disable=a_    )

							a__  :	Any		 =			"open neural network exchange"
							a__  :	Optional[Any]		 =			np.random.RandomState(0    )
							a__  :	Optional[int]		 =			sd_pipe([prompt]	, guidance_scale=7.5	, num_inference_steps=10	, generator=a_	, output_type="np"    )
							a__  :	int		 =			output.images
							a__  :	Tuple		 =			image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_12, 5_12, 3)
							a__  :	Dict		 =			np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3
				def       UpperCAmelCase      (		self    :			int    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	List[str]		 =			0

							def test_callback_fn(a_    :			int	, a_    :			int	, a_    :			np.ndarray    ) -> None:
										a__  :	Optional[int]		 =			True
										nonlocal number_of_steps
										number_of_steps += 1
										if step == 0:
													assert latents.shape == (1, 4, 64, 64)
													a__  :	Any		 =			latents[0, -3:, -3:, -1]
													a__  :	Union[str, Any]		 =			np.array(
													    [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]    )

													assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1E-3
										elif step == 5:
													assert latents.shape == (1, 4, 64, 64)
													a__  :	Union[str, Any]		 =			latents[0, -3:, -3:, -1]
													a__  :	Optional[int]		 =			np.array(
													    [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]    )

													assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1E-3

							a__  :	Tuple		 =			False

							a__  :	Optional[int]		 =			OnnxStableDiffusionPipeline.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, revision="onnx"	, safety_checker=a_	, feature_extractor=a_	, provider=self.gpu_provider	, sess_options=self.gpu_options	, )
							pipe.set_progress_bar_config(disable=a_    )

							a__  :	List[Any]		 =			"Andromeda galaxy in a bottle"

							a__  :	str		 =			np.random.RandomState(0    )
							pipe(
							    prompt=a_	, num_inference_steps=5	, guidance_scale=7.5	, generator=a_	, callback=a_	, callback_steps=1	, )
							assert test_callback_fn.has_been_called
							assert number_of_steps == 6






				def       UpperCAmelCase      (		self    :			Tuple    )	->	List[str]:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			OnnxStableDiffusionPipeline.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"	, revision="onnx"	, safety_checker=a_	, feature_extractor=a_	, provider=self.gpu_provider	, sess_options=self.gpu_options	, )
							assert isinstance(a_	, a_    )
							assert pipe.safety_checker is None

							a__  :	Tuple		 =			pipe("example prompt"	, num_inference_steps=2    ).images[0]
							assert image is not None

							# check that there's no error when saving a pipeline with one of the models being None
							with tempfile.TemporaryDirectory() as tmpdirname:
										pipe.save_pretrained(a_    )
										a__  :	Optional[int]		 =			OnnxStableDiffusionPipeline.from_pretrained(a_    )

							# sanity check that the pipeline still works
							assert pipe.safety_checker is None
							a__  :	Dict		 =			pipe("example prompt"	, num_inference_steps=2    ).images[0]
							assert image is not None