




'''simple docstring'''



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






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self  ):







             '''simple docstring'''







             self.test()







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						0
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						False
             while not completed:
                   if counter == 1:
                         self.reset()
                   SCREAMING_SNAKE_CASE   :      str    					=						self.advance()
                   if not self.does_advance(A  ):
                         raise Exception(
                             'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'  )

                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       @abstractmethod
       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







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







       @abstractmethod
       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







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







       @abstractmethod
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       @abstractmethod
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       @abstractmethod
       def        UpperCamelCase_							(      self,							A=False  ):







             '''simple docstring'''







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






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''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}."  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						token_ids

             SCREAMING_SNAKE_CASE   :      Tuple    					=						len(self.token_ids  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						-1  # the index of the currently fulfilled step
             SCREAMING_SNAKE_CASE   :      Any    					=						False







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A  ):







             '''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        UpperCamelCase_							(      self,							A  ):







             '''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  )}"  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						False
             SCREAMING_SNAKE_CASE   :      str    					=						False
             SCREAMING_SNAKE_CASE   :      int    					=						False

             if self.does_advance(A  ):
                   self.fulfilled_idx += 1
                   SCREAMING_SNAKE_CASE   :      int    					=						True
                   if self.fulfilled_idx == (self.seqlen - 1):
                         SCREAMING_SNAKE_CASE   :      Optional[int]    					=						True
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						completed
             else:
                   # failed to make progress.
                   SCREAMING_SNAKE_CASE   :      int    					=						True
                   self.reset()
             return stepped, completed, reset







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						False
             SCREAMING_SNAKE_CASE   :      Dict    					=						0







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A=False  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						PhrasalConstraint(self.token_ids  )

             if stateful:
                   SCREAMING_SNAKE_CASE   :      str    					=						self.seqlen
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.fulfilled_idx
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.completed

             return new_constraint






class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A,							A=True  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						max([len(A  ) for one in nested_token_ids]  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{}
             for token_ids in nested_token_ids:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						root
                   for tidx, token_id in enumerate(A  ):
                         if token_id not in level:
                               SCREAMING_SNAKE_CASE   :      Dict    					=						{}

                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						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}."  )

             SCREAMING_SNAKE_CASE   :      Any    					=						root







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.trie

             for current_token in current_seq:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						start[current_token]

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						list(start.keys()  )

             return next_tokens







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.next_tokens(A  )

             return len(A  ) == 0







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						list(root.values()  )
             if len(A  ) == 0:
                   return 1
             else:
                   return sum([self.count_leaves(A  ) for nn in next_nodes]  )







       def        UpperCamelCase_							(      self,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.count_leaves(A  )
             return len(A  ) != leaf_count






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''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}."  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						DisjunctiveTrie(A  )
             SCREAMING_SNAKE_CASE   :      int    					=						nested_token_ids

             SCREAMING_SNAKE_CASE   :      int    					=						self.trie.max_height
             SCREAMING_SNAKE_CASE   :      str    					=						[]
             SCREAMING_SNAKE_CASE   :      List[str]    					=						False







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.trie.next_tokens(self.current_seq  )

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







       def        UpperCamelCase_							(      self,							A  ):







             '''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  )}"  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.trie.next_tokens(self.current_seq  )

             return token_id in next_tokens







       def        UpperCamelCase_							(      self,							A  ):







             '''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  )}"  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						False
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						False
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						False

             if self.does_advance(A  ):
                   self.current_seq.append(A  )
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						True
             else:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						True
                   self.reset()

             SCREAMING_SNAKE_CASE   :      int    					=						self.trie.reached_leaf(self.current_seq  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						completed

             return stepped, completed, reset







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						False
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[]







       def        UpperCamelCase_							(      self  ):







             '''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        UpperCamelCase_							(      self,							A=False  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						DisjunctiveConstraint(self.token_ids  )

             if stateful:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						self.seqlen
                   SCREAMING_SNAKE_CASE   :      Dict    					=						self.current_seq
                   SCREAMING_SNAKE_CASE   :      str    					=						self.completed

             return new_constraint






class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						constraints

             # max # of steps required to fulfill a given constraint
             SCREAMING_SNAKE_CASE   :      List[str]    					=						max([c.seqlen for c in constraints]  )
             SCREAMING_SNAKE_CASE   :      str    					=						len(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						False

             self.init_state()







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]
             SCREAMING_SNAKE_CASE   :      Any    					=						None
             SCREAMING_SNAKE_CASE   :      str    					=						[constraint.copy(stateful=A  ) for constraint in self.constraints]







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						[]
             if self.inprogress_constraint is None:
                   for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						constraint.advance()
                         if isinstance(A,							A  ):
                               token_list.append(A  )
                         elif isinstance(A,							A  ):
                               token_list.extend(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						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        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             self.init_state()

             if token_ids is not None:
                   for token in token_ids:
                         # completes or steps **one** constraint
                         SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.add(A  )

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







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if not isinstance(A,							A  ):
                   raise ValueError(F"`token_id` should be an `int`, but is `{token_id}`."  )

             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						False, False

             if self.completed:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						True
                   SCREAMING_SNAKE_CASE   :      Dict    					=						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

                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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  )  )
                         SCREAMING_SNAKE_CASE   :      Dict    					=						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  )
                         SCREAMING_SNAKE_CASE   :      Any    					=						None

                         if len(self.pending_constraints  ) == 0:
                               # we're done!
                               SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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  ):
                               SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						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  )
                                     SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						None

                               if not complete and stepped:
                                     SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pending_constraint

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

                                     SCREAMING_SNAKE_CASE   :      Any    					=						(
                                         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.

                                           SCREAMING_SNAKE_CASE   :      Dict    					=						True

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

             return complete, stepped







       def        UpperCamelCase_							(      self,							A=True  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						ConstraintListState(self.constraints  )  # we actually never though self.constraints objects
             # throughout this process. So it's at initialization state.

             if stateful:
                   SCREAMING_SNAKE_CASE   :      int    					=						[
                       constraint.copy(stateful=A  ) for constraint in self.complete_constraints
                   ]
                   if self.inprogress_constraint is not None:
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						self.inprogress_constraint.copy(stateful=A  )
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[constraint.copy() for constraint in self.pending_constraints]

             return new_state






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				List[Any]:
  snake_case__						= [tensor.shape for tensor in tensor_list]
  return all(shape == shapes[0] for shape in shapes[1:]				)
class 		__magic_name__						(snake_case_   ,snake_case_   ,snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Dict	   =       StableDiffusionLatentUpscalePipeline
     __lowercase				:							List[str]	   =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         'height',
         'width',
         'cross_attention_kwargs',
         'negative_prompt_embeds',
         'prompt_embeds',
     }
     __lowercase				:							List[Any]	   =       PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
     __lowercase				:							Any	   =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     __lowercase				:							int	   =       frozenset(
         []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     __lowercase				:							List[Any]	   =       frozenset([]				)

     __lowercase				:							Any	   =       True

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= 1
       snake_case__						= 4
       snake_case__						= (16, 16)

       snake_case__						= floats_tensor((batch_size, num_channels) + sizes     ,		rng=random.Random(0   )   ).to(_a   )
       return image

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       torch.manual_seed(0   )
       snake_case__						= UNetaDConditionModel(
           act_fn='''gelu'''     ,		attention_head_dim=8     ,		norm_num_groups=_a     ,		block_out_channels=[32, 32, 64, 64]     ,		time_cond_proj_dim=1_60     ,		conv_in_kernel=1     ,		conv_out_kernel=1     ,		cross_attention_dim=32     ,		down_block_types=(
               '''KDownBlock2D''',
               '''KCrossAttnDownBlock2D''',
               '''KCrossAttnDownBlock2D''',
               '''KCrossAttnDownBlock2D''',
           )     ,		in_channels=8     ,		mid_block_type=_a     ,		only_cross_attention=_a     ,		out_channels=5     ,		resnet_time_scale_shift='''scale_shift'''     ,		time_embedding_type='''fourier'''     ,		timestep_post_act='''gelu'''     ,		up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')     ,		)
       snake_case__						= AutoencoderKL(
           block_out_channels=[32, 32, 64, 64]     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=[
               '''DownEncoderBlock2D''',
               '''DownEncoderBlock2D''',
               '''DownEncoderBlock2D''',
               '''DownEncoderBlock2D''',
           ]     ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,		latent_channels=4     ,		)
       snake_case__						= EulerDiscreteScheduler(prediction_type='''sample'''   )
       snake_case__						= CLIPTextConfig(
           bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=32     ,		intermediate_size=37     ,		layer_norm_eps=1e-05     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=10_00     ,		hidden_act='''quick_gelu'''     ,		projection_dim=5_12     ,		)
       snake_case__						= CLIPTextModel(_a   )
       snake_case__						= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       snake_case__						= {
           '''unet''': model.eval(),
           '''vae''': vae.eval(),
           '''scheduler''': scheduler,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
       }

       return components

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:Optional[Any]     ,		_a:List[str]=0   ):
       if str(_a   ).startswith('''mps'''   ):
         snake_case__						= torch.manual_seed(_a   )
       else:
         snake_case__						= torch.Generator(device=_a   ).manual_seed(_a   )
       snake_case__						= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': self.dummy_image.cpu(),
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= '''cpu'''

       snake_case__						= self.get_dummy_components()
       snake_case__						= self.pipeline_class(**_a   )
       pipe.to(_a   )
       pipe.set_progress_bar_config(disable=_a   )

       snake_case__						= self.get_dummy_inputs(_a   )
       snake_case__						= pipe(**_a   ).images
       snake_case__						= image[0, -3:, -3:, -1]

       self.assertEqual(image.shape     ,		(1, 2_56, 2_56, 3)   )
       snake_case__						= np.array(
           [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]   )
       snake_case__						= np.abs(image_slice.flatten() - expected_slice   ).max()
       self.assertLessEqual(_a     ,		1e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       super().test_attention_slicing_forward_pass(expected_max_diff=7e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       super().test_cpu_offload_forward_pass(expected_max_diff=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       super().test_inference_batch_single_identical(expected_max_diff=7e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       super().test_save_load_local(expected_max_difference=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       super().test_save_load_optional_components(expected_max_difference=3e-3   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= [
           '''DDIMScheduler''',
           '''DDPMScheduler''',
           '''PNDMScheduler''',
           '''HeunDiscreteScheduler''',
           '''EulerAncestralDiscreteScheduler''',
           '''KDPM2DiscreteScheduler''',
           '''KDPM2AncestralDiscreteScheduler''',
           '''DPMSolverSDEScheduler''',
       ]
       snake_case__						= self.get_dummy_components()
       snake_case__						= self.pipeline_class(**_a   )

       # make sure that PNDM does not need warm-up
       pipe.scheduler.register_to_config(skip_prk_steps=_a   )

       pipe.to(_a   )
       pipe.set_progress_bar_config(disable=_a   )
       snake_case__						= self.get_dummy_inputs(_a   )
       snake_case__						= 2

       snake_case__						= []
       for scheduler_enum in KarrasDiffusionSchedulers:
         if scheduler_enum.name in skip_schedulers:
           # no sigma schedulers are not supported
           # no schedulers
           continue

         snake_case__						= getattr(_a     ,		scheduler_enum.name   )
         snake_case__						= scheduler_cls.from_config(pipe.scheduler.config   )
         snake_case__						= pipe(**_a   )[0]
         outputs.append(_a   )

       assert check_same_shape(_a   )




@require_torch_gpu
@slow
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= torch.manual_seed(33   )

       snake_case__						= StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''     ,		torch_dtype=torch.floataa   )
       pipe.to('''cuda'''   )

       snake_case__						= StableDiffusionLatentUpscalePipeline.from_pretrained(
           '''stabilityai/sd-x2-latent-upscaler'''     ,		torch_dtype=torch.floataa   )
       upscaler.to('''cuda'''   )

       snake_case__						= '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

       snake_case__						= pipe(_a     ,		generator=_a     ,		output_type='''latent'''   ).images

       snake_case__						= upscaler(
           prompt=_a     ,		image=_a     ,		num_inference_steps=20     ,		guidance_scale=0     ,		generator=_a     ,		output_type='''np'''     ,		).images[0]

       snake_case__						= load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''   )
       assert np.abs((expected_image - image).mean()   ) < 5e-2







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= torch.manual_seed(33   )

       snake_case__						= StableDiffusionLatentUpscalePipeline.from_pretrained(
           '''stabilityai/sd-x2-latent-upscaler'''     ,		torch_dtype=torch.floataa   )
       upscaler.to('''cuda'''   )

       snake_case__						= '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

       snake_case__						= load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''   )

       snake_case__						= upscaler(
           prompt=_a     ,		image=_a     ,		num_inference_steps=20     ,		guidance_scale=0     ,		generator=_a     ,		output_type='''np'''     ,		).images[0]

       snake_case__						= load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''   )
       assert np.abs((expected_image - image).max()   ) < 5e-2

