


from ...configuration_utils import PretrainedConfig







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    List[Any]          =			'''bert-generation'''
					def __init__(      self   :Any			,__snake_case   :Optional[Any]=5_03_58			,__snake_case   :Optional[Any]=10_24			,__snake_case   :str=24			,__snake_case   :Tuple=16			,__snake_case   :Union[str, Any]=40_96			,__snake_case   :Dict="gelu"			,__snake_case   :List[str]=0.1			,__snake_case   :List[str]=0.1			,__snake_case   :int=5_12			,__snake_case   :Union[str, Any]=0.02			,__snake_case   :Tuple=1E-12			,__snake_case   :Dict=0			,__snake_case   :List[Any]=2			,__snake_case   :List[Any]=1			,__snake_case   :Any="absolute"			,__snake_case   :Optional[int]=True			,**__snake_case   :Union[str, Any]			,)					->    Union[str, Any]:
												super().__init__(pad_token_id=__snake_case			,bos_token_id=__snake_case			,eos_token_id=__snake_case			,**__snake_case      )

												a__             =     vocab_size
												a__             =     hidden_size
												a__             =     num_hidden_layers
												a__             =     num_attention_heads
												a__             =     hidden_act
												a__             =     intermediate_size
												a__             =     hidden_dropout_prob
												a__             =     attention_probs_dropout_prob
												a__             =     max_position_embeddings
												a__             =     initializer_range
												a__             =     layer_norm_eps
												a__             =     position_embedding_type
												a__             =     use_cache








import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


snake_case							:       int     			=      False







@skip_mps
class        snake_case_   (lowerCamelCase_	,						lowerCamelCase_	,						lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Union[str, Any]          =			StableDiffusionAttendAndExcitePipeline
					UpperCAmelCase__					:    List[str]          =			False
					UpperCAmelCase__					:    int          =			TEXT_TO_IMAGE_PARAMS
					UpperCAmelCase__					:    List[Any]          =			TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}      )
					UpperCAmelCase__					:    List[str]          =			TEXT_TO_IMAGE_IMAGE_PARAMS
					UpperCAmelCase__					:    str          =			TEXT_TO_IMAGE_IMAGE_PARAMS
					@classmethod
					def  lowerCamelCase__(      cls   :int      )					->    int:
												super().setUpClass()
												torch.use_deterministic_algorithms(__snake_case      )
					@classmethod
					def  lowerCamelCase__(      cls   :Optional[Any]      )					->    int:
												super().tearDownClass()
												torch.use_deterministic_algorithms(__snake_case      )
					def  lowerCamelCase__(      self   :Dict      )					->    Optional[Any]:
												torch.manual_seed(0      )
												a__             =     UNetaDConditionModel(
												    block_out_channels=(32, 64)			,layers_per_block=1			,sample_size=32			,in_channels=4			,out_channels=4			,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,cross_attention_dim=32			,attention_head_dim=(2, 4)			,use_linear_projection=__snake_case			,)
												a__             =     DDIMScheduler(
												    beta_start=0.0_00_85			,beta_end=0.0_12			,beta_schedule='scaled_linear'			,clip_sample=__snake_case			,set_alpha_to_one=__snake_case			,)
												torch.manual_seed(0      )
												a__             =     AutoencoderKL(
												    block_out_channels=[32, 64]			,in_channels=3			,out_channels=3			,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']			,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']			,latent_channels=4			,sample_size=1_28			,)
												torch.manual_seed(0      )
												a__             =     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='gelu'			,projection_dim=5_12			,)
												a__             =     CLIPTextModel(__snake_case      )
												a__             =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

												a__             =     {
												    'unet': unet,
												    'scheduler': scheduler,
												    'vae': vae,
												    'text_encoder': text_encoder,
												    'tokenizer': tokenizer,
												    'safety_checker': None,
												    'feature_extractor': None,
												}

												return components
					def  lowerCamelCase__(      self   :Any			,__snake_case   :Union[str, Any]			,__snake_case   :Tuple=0      )					->    Any:
												if str(__snake_case      ).startswith('mps'      ):
																			a__             =     torch.manual_seed(__snake_case      )
												else:
																			a__             =     torch.Generator(device=__snake_case      ).manual_seed(__snake_case      )
												a__             =     a__             =     {
												    'prompt': 'a cat and a frog',
												    'token_indices': [2, 5],
												    'generator': generator,
												    'num_inference_steps': 1,
												    'guidance_scale': 6.0,
												    'output_type': 'numpy',
												    'max_iter_to_alter': 2,
												    'thresholds': {0: 0.7},
												}
												return inputs
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Any:
												a__             =     'cpu'

												a__             =     self.get_dummy_components()
												a__             =     self.pipeline_class(**__snake_case      )
												pipe.to(__snake_case      )
												pipe.set_progress_bar_config(disable=__snake_case      )

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

												self.assertEqual(image.shape			,(1, 64, 64, 3)      )
												a__             =     np.array(
												    [0.63_90_53_64, 0.62_89_73_07, 0.48_59_90_17, 0.5_13_36_24, 0.5_55_00_48, 0.45_76_95_16, 0.50_32_69_73, 0.5_02_31_39, 0.45_38_44_96]      )
												a__             =     np.abs(image_slice.flatten() - expected_slice      ).max()
												self.assertLessEqual(__snake_case			,1E-3      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    List[str]:
												super().test_cpu_offload_forward_pass(expected_max_diff=5E-4      )
					def  lowerCamelCase__(      self   :Dict      )					->    List[str]:
												# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
												self._test_inference_batch_consistent(batch_sizes=[1, 2]      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Optional[int]:
												self._test_inference_batch_single_identical(batch_size=2			,expected_max_diff=7E-4      )
					def  lowerCamelCase__(      self   :Dict      )					->    Dict:
												super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )
					def  lowerCamelCase__(      self   :Dict      )					->    Union[str, Any]:
												super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Dict:
												super().test_save_load_local(expected_max_difference=5E-4      )



					def  lowerCamelCase__(      self   :List[Any]      )					->    List[Any]:
												super().test_save_load_optional_components(expected_max_difference=4E-4      )







@require_torch_gpu
@slow
class        snake_case_   (unittest.TestCase      ):
					@classmethod
					def  lowerCamelCase__(      cls   :Optional[Any]      )					->    Tuple:
												super().setUpClass()
												torch.use_deterministic_algorithms(__snake_case      )
					@classmethod
					def  lowerCamelCase__(      cls   :Optional[int]      )					->    Tuple:
												super().tearDownClass()
												torch.use_deterministic_algorithms(__snake_case      )
					def  lowerCamelCase__(      self   :Tuple      )					->    Union[str, Any]:
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()



					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Tuple:
												a__             =     torch.manual_seed(51      )

												a__             =     StableDiffusionAttendAndExcitePipeline.from_pretrained(
												    'CompVis/stable-diffusion-v1-4'			,safety_checker=__snake_case			,torch_dtype=torch.floataa      )
												pipe.to('cuda'      )

												a__             =     'a painting of an elephant with glasses'
												a__             =     [5, 7]

												a__             =     pipe(
												    prompt=__snake_case			,token_indices=__snake_case			,guidance_scale=7.5			,generator=__snake_case			,num_inference_steps=5			,max_iter_to_alter=5			,output_type='numpy'			,).images[0]

												a__             =     load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'      )
												assert np.abs((expected_image - image).max()      ) < 5E-1




