
import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def  lowerCAmelCase__			(  )				->		Dict:
			lowerCAmelCase__      :      Union[str, Any]         =		'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
			lowerCAmelCase__      :      Union[str, Any]         =		Image.open(requests.get(SCREAMING_SNAKE_CASE_       ,							stream=SCREAMING_SNAKE_CASE_     ).raw     ).convert('RGB'     )

			return image
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Optional[Any]:
			lowerCAmelCase__      :      int         =		[]
			# fmt: off

			# vision encoder
			rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')     )
			rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')     )
			rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')     )
			rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')     )
			rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')     )
			rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')     )

			for i in range(config.vision_config.num_hidden_layers     ):
						rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''')     )
						rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''')     )
						rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''')     )
						rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''')     )
						rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')     )
						rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)     )
						rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')     )
						rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')     )
						rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')     )
						rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')     )
						rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')     )

			# QFormer
			rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight')     )
			rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')     )

			# fmt: on
			return rename_keys
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Optional[int]:
			lowerCAmelCase__      :      str         =		dct.pop(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Optional[int]         =		val
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Optional[Any]:
			for i in range(config.vision_config.num_hidden_layers     ):
						# read in original q and v biases
						lowerCAmelCase__      :      int         =		state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''     )
						lowerCAmelCase__      :      List[str]         =		state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''     )

						# next, set bias in the state dict
						lowerCAmelCase__      :      Optional[Any]         =		torch.cat((q_bias, torch.zeros_like(SCREAMING_SNAKE_CASE_       ,							requires_grad=SCREAMING_SNAKE_CASE_     ), v_bias)     )
						lowerCAmelCase__      :      List[str]         =		qkv_bias
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		str:
			lowerCAmelCase__      :      Tuple         =		364 if 'coco' in model_name else 224
			lowerCAmelCase__      :      Tuple         =		InstructBlipVisionConfig(image_size=SCREAMING_SNAKE_CASE_     ).to_dict()

			# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
			# seems like flan-T5 models don't have bos_token_id properly set?
			if "t5-xl" in model_name:
						lowerCAmelCase__      :      Dict         =		TaConfig.from_pretrained('google/flan-t5-xl'       ,							dense_act_fn='gelu'       ,							bos_token_id=1     ).to_dict()
			elif "t5-xxl" in model_name:
						lowerCAmelCase__      :      str         =		TaConfig.from_pretrained('google/flan-t5-xxl'       ,							dense_act_fn='gelu'       ,							bos_token_id=1     ).to_dict()
			elif "vicuna-7b" in model_name:
						lowerCAmelCase__      :      List[str]         =		LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'       ,							vocab_size=32_001     ).to_dict()
			elif "vicuna-13b" in model_name:
						lowerCAmelCase__      :      List[Any]         =		LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'       ,							vocab_size=32_001     ).to_dict()
			else:
						raise ValueError('Model name not supported'     )

			# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
			lowerCAmelCase__      :      Any         =		InstructBlipQFormerConfig(vocab_size=30_523     ).to_dict()
			lowerCAmelCase__      :      int         =		InstructBlipConfig(vision_config=SCREAMING_SNAKE_CASE_       ,							text_config=SCREAMING_SNAKE_CASE_       ,							qformer_config=SCREAMING_SNAKE_CASE_     )

			return config, image_size
@torch.no_grad()
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=None       ,							SCREAMING_SNAKE_CASE_=False     )				->		Dict:
			lowerCAmelCase__      :      Optional[int]         =		AutoTokenizer.from_pretrained('bert-base-uncased'       ,							truncation_side='left'     )
			qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}     )

			if "t5" in model_name:
						lowerCAmelCase__      :      Tuple         =		TaTokenizerFast.from_pretrained('google/flan-t5-xl'       ,							truncation_side='left'     )
			elif "vicuna" in model_name:
						# the following was used in the original implementation:
						# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
						# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
						# tokenizer.add_special_tokens({"bos_token": "</s>"})
						# tokenizer.add_special_tokens({"eos_token": "</s>"})
						# tokenizer.add_special_tokens({"unk_token": "</s>"})
						lowerCAmelCase__      :      Tuple         =		LlamaTokenizerFast.from_pretrained(
						    'huggyllama/llama-7b'       ,							truncation_side='left'       ,							bos_token='</s>'       ,							unk_token='</s>'     )
						tokenizer.add_special_tokens({'pad_token': '[PAD]'}     )

			lowerCAmelCase__      ,				lowerCAmelCase__      :      Dict         =		get_blipa_config(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Optional[int]         =		InstructBlipForConditionalGeneration(SCREAMING_SNAKE_CASE_     ).eval()

			lowerCAmelCase__      :      List[str]         =		{
			    'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
			    'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
			    'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
			    'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
			}

			lowerCAmelCase__      ,				lowerCAmelCase__      :      int         =		model_name_to_original[model_name]

			# load original model
			print('Loading original model...'     )
			lowerCAmelCase__      :      Optional[int]         =		'cuda:1' if torch.cuda.is_available() else 'cpu'
			lowerCAmelCase__      :      str         =		'cuda:2' if torch.cuda.is_available() else 'cpu'
			lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[Any]         =		load_model_and_preprocess(
			    name=SCREAMING_SNAKE_CASE_       ,							model_type=SCREAMING_SNAKE_CASE_       ,							is_eval=SCREAMING_SNAKE_CASE_       ,							device=SCREAMING_SNAKE_CASE_     )
			original_model.eval()
			print('Done!'     )

			# update state dict keys
			lowerCAmelCase__      :      Dict         =		original_model.state_dict()
			lowerCAmelCase__      :      int         =		create_rename_keys(SCREAMING_SNAKE_CASE_     )
			for src, dest in rename_keys:
						rename_key(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )

			# some keys can be renamed efficiently
			for key, val in state_dict.copy().items():
						lowerCAmelCase__      :      int         =		state_dict.pop(SCREAMING_SNAKE_CASE_     )
						if key.startswith('Qformer.bert'     ):
									lowerCAmelCase__      :      int         =		key.replace('Qformer.bert'       ,							'qformer'     )
						if "attention.self" in key:
									lowerCAmelCase__      :      Tuple         =		key.replace('self'       ,							'attention'     )
						if "llm_proj" in key:
									lowerCAmelCase__      :      List[Any]         =		key.replace('llm_proj'       ,							'language_projection'     )
						if "t5_proj" in key:
									lowerCAmelCase__      :      int         =		key.replace('t5_proj'       ,							'language_projection'     )
						if key.startswith('llm_model'     ):
									lowerCAmelCase__      :      str         =		key.replace('llm_model'       ,							'language_model'     )
						if key.startswith('t5'     ):
									lowerCAmelCase__      :      Optional[Any]         =		key.replace('t5'       ,							'language'     )
						lowerCAmelCase__      :      Tuple         =		val

			# read in qv biases
			read_in_q_v_bias(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )

			# note: weights get loaded in torch.float32 by default
			hf_model.load_state_dict(SCREAMING_SNAKE_CASE_       ,							strict=SCREAMING_SNAKE_CASE_     )

			lowerCAmelCase__      :      str         =		load_demo_image()
			lowerCAmelCase__      :      List[Any]         =		'What is unusual about this image?'

			# create processor
			lowerCAmelCase__      :      Union[str, Any]         =		BlipImageProcessor(
			    size={'height': image_size, 'width': image_size}       ,							image_mean=SCREAMING_SNAKE_CASE_       ,							image_std=SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      List[Any]         =		InstructBlipProcessor(
			    image_processor=SCREAMING_SNAKE_CASE_       ,							tokenizer=SCREAMING_SNAKE_CASE_       ,							qformer_tokenizer=SCREAMING_SNAKE_CASE_       ,							)
			lowerCAmelCase__      :      Any         =		processor(images=SCREAMING_SNAKE_CASE_       ,							text=SCREAMING_SNAKE_CASE_       ,							return_tensors='pt'     ).to(SCREAMING_SNAKE_CASE_     )

			# make sure processor creates exact same pixel values
			lowerCAmelCase__      :      Dict         =		vis_processors['eval'](SCREAMING_SNAKE_CASE_     ).unsqueeze(0     ).to(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Optional[int]         =		inputs.pixel_values
			assert torch.allclose(original_pixel_values.to(pixel_values.device     )       ,							SCREAMING_SNAKE_CASE_     )

			original_model.to(SCREAMING_SNAKE_CASE_     )
			hf_model.to(SCREAMING_SNAKE_CASE_     )
			with torch.no_grad():
						if "vicuna" in model_name:
									lowerCAmelCase__      :      Dict         =		original_model({'image': original_pixel_values, 'text_input': [prompt]}     ).logits
									lowerCAmelCase__      :      str         =		hf_model(**SCREAMING_SNAKE_CASE_     ).logits
						else:
									lowerCAmelCase__      :      List[Any]         =		original_model(
									    {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}     ).logits
									lowerCAmelCase__      :      List[Any]         =		tokenizer('\n'       ,							return_tensors='pt'     ).input_ids.to(SCREAMING_SNAKE_CASE_     )
									lowerCAmelCase__      :      Tuple         =		label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id       ,							-100     )
									lowerCAmelCase__      :      int         =		hf_model(**SCREAMING_SNAKE_CASE_       ,							labels=SCREAMING_SNAKE_CASE_     ).logits

			print('First values of original logits:'       ,							original_logits[0, :3, :3]     )
			print('First values of HF logits:'       ,							logits[0, :3, :3]     )

			# assert values
			assert original_logits.shape == logits.shape
			lowerCAmelCase__      :      Any         =		1e-4 if 'vicuna' in model_name else 1e-5
			assert torch.allclose(original_logits.to(logits.device     )       ,							SCREAMING_SNAKE_CASE_       ,							atol=SCREAMING_SNAKE_CASE_     )
			print('Looks ok!'     )

			print('Generating with original model...'     )
			lowerCAmelCase__      :      Dict         =		original_model.generate({'image': original_pixel_values, 'prompt': prompt}       ,							num_beams=5     )

			# important: we need to cast the weights of the HF model to the appropriate type
			print('Generating with HF model...'     )
			lowerCAmelCase__      :      str         =		hf_model.generate(
			    **SCREAMING_SNAKE_CASE_       ,							do_sample=SCREAMING_SNAKE_CASE_       ,							num_beams=5       ,							max_length=256       ,							min_length=1       ,							top_p=0.9       ,							repetition_penalty=1.5       ,							length_penalty=1.0       ,							temperature=1       ,							)
			if "vicuna" in model_name:
						# convert output id 0 to 2 (eos_token_id)
						# TODO add this in the generate method?
						lowerCAmelCase__      :      int         =		2
			print('Original generation:'       ,							SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      str         =		processor.batch_decode(SCREAMING_SNAKE_CASE_       ,							skip_special_tokens=SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Any         =		[text.strip() for text in output_text]
			print('HF generation:'       ,							SCREAMING_SNAKE_CASE_     )

			if pytorch_dump_folder_path is not None:
						processor.save_pretrained(SCREAMING_SNAKE_CASE_     )
						hf_model.save_pretrained(SCREAMING_SNAKE_CASE_     )

			if push_to_hub:
						processor.push_to_hub(F'''Salesforce/{model_name}'''     )
						hf_model.push_to_hub(F'''Salesforce/{model_name}'''     )


if __name__ == "__main__":
					lowerCamelCase__          =		argparse.ArgumentParser()
					lowerCamelCase__          =		[
					    """instructblip-vicuna-7b""",
					    """instructblip-vicuna-13b""",
					    """instructblip-flan-t5-xl""",
					    """instructblip-flan-t5-xxl""",
					]
					parser.add_argument(
					    """--model_name""",
					    default="""instructblip-flan-t5-xl""",
					    choices=choices,
					    type=str,
					    help="""Path to hf config.json of model to convert""",
					)
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument(
					    """--push_to_hub""",
					    action="""store_true""",
					    help="""Whether to push the model and processor to the hub after converting""",
					)

					lowerCamelCase__          =		parser.parse_args()

					convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

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


lowerCamelCase__          =		False







@skip_mps
class      A__							(     __magic_name__						,   __magic_name__						,   __magic_name__						,   unittest.TestCase	):
				lowercase			    =  StableDiffusionAttendAndExcitePipeline
				lowercase			    =  False
				lowercase			    =  TEXT_TO_IMAGE_PARAMS
				lowercase			    =  TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'}	)
				lowercase			    =  TEXT_TO_IMAGE_IMAGE_PARAMS
				lowercase			    =  TEXT_TO_IMAGE_IMAGE_PARAMS






				@classmethod
				def 							_lowerCamelCase		(						cls					:      Tuple   ):






							'''simple docstring'''







							super().setUpClass()
							torch.use_deterministic_algorithms(a   )






				@classmethod
				def 							_lowerCamelCase		(						cls					:      Any   ):






							'''simple docstring'''







							super().tearDownClass()
							torch.use_deterministic_algorithms(a   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							torch.manual_seed(0   )
							lowerCAmelCase__      :      Any         =		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=a    ,     )
							lowerCAmelCase__      :      Any         =		DDIMScheduler(
							    beta_start=0.0_0_0_8_5    ,     beta_end=0.0_1_2    ,     beta_schedule='scaled_linear'    ,     clip_sample=a    ,     set_alpha_to_one=a    ,     )
							torch.manual_seed(0   )
							lowerCAmelCase__      :      Union[str, Any]         =		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=128    ,     )
							torch.manual_seed(0   )
							lowerCAmelCase__      :      Tuple         =		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=1_000    ,     hidden_act='gelu'    ,     projection_dim=512    ,     )
							lowerCAmelCase__      :      str         =		CLIPTextModel(a   )
							lowerCAmelCase__      :      Optional[Any]         =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

							lowerCAmelCase__      :      Union[str, Any]         =		{
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}

							return components






				def 							_lowerCamelCase		(						self					:      Union[str, Any]    ,     a					:      Tuple    ,     a					:      Union[str, Any]=0   ):






							'''simple docstring'''







							if str(a   ).startswith('mps'   ):
										lowerCAmelCase__      :      List[str]         =		torch.manual_seed(a   )
							else:
										lowerCAmelCase__      :      Any         =		torch.Generator(device=a   ).manual_seed(a   )
							lowerCAmelCase__      :      Optional[int]         =		{
							    '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]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		'cpu'

							lowerCAmelCase__      :      Optional[int]         =		self.get_dummy_components()
							lowerCAmelCase__      :      Dict         =		self.pipeline_class(**a   )
							pipe.to(a   )
							pipe.set_progress_bar_config(disable=a   )

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

							self.assertEqual(image.shape    ,     (1, 64, 64, 3)   )
							lowerCAmelCase__      :      Dict         =		np.array(
							    [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]   )
							lowerCAmelCase__      :      Optional[int]         =		np.abs(image_slice.flatten() - expected_slice   ).max()
							self.assertLessEqual(a    ,     1E-3   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							super().test_cpu_offload_forward_pass(expected_max_diff=5E-4   )






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							self._test_inference_batch_consistent(batch_sizes=[1, 2]   )






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							self._test_inference_batch_single_identical(batch_size=2    ,     expected_max_diff=7E-4   )






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3   )






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4   )






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							super().test_save_load_local(expected_max_difference=5E-4   )






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							super().test_save_load_optional_components(expected_max_difference=4E-4   )







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






				@classmethod
				def 							_lowerCamelCase		(						cls					:      List[str]   ):






							'''simple docstring'''







							super().setUpClass()
							torch.use_deterministic_algorithms(a   )






				@classmethod
				def 							_lowerCamelCase		(						cls					:      List[str]   ):






							'''simple docstring'''







							super().tearDownClass()
							torch.use_deterministic_algorithms(a   )






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		torch.manual_seed(51   )

							lowerCAmelCase__      :      Any         =		StableDiffusionAttendAndExcitePipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'    ,     safety_checker=a    ,     torch_dtype=torch.floataa   )
							pipe.to('cuda'   )

							lowerCAmelCase__      :      Optional[int]         =		'a painting of an elephant with glasses'
							lowerCAmelCase__      :      Any         =		[5, 7]

							lowerCAmelCase__      :      Optional[Any]         =		pipe(
							    prompt=a    ,     token_indices=a    ,     guidance_scale=7.5    ,     generator=a    ,     num_inference_steps=5    ,     max_iter_to_alter=5    ,     output_type='numpy'    ,     ).images[0]

							lowerCAmelCase__      :      str         =		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