




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_ (						):
				__magic_name__						:						List[str]										="""https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
				__magic_name__						:						Dict										=Image.open(requests.get(lowerCamelCase						,  stream=lowerCamelCase ).raw ).convert("""RGB""" )

				return image




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						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_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Optional[int]										=dct.pop(lowerCamelCase )
				__magic_name__						:						int										=val




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				for i in range(config.vision_config.num_hidden_layers ):
								# read in original q and v biases
								__magic_name__						:						List[Any]										=state_dict.pop(F"visual_encoder.blocks.{i}.attn.q_bias" )
								__magic_name__						:						Tuple										=state_dict.pop(F"visual_encoder.blocks.{i}.attn.v_bias" )

								# next, set bias in the state dict
								__magic_name__						:						Tuple										=torch.cat((q_bias, torch.zeros_like(lowerCamelCase						,  requires_grad=lowerCamelCase ), v_bias) )
								__magic_name__						:						Dict										=qkv_bias




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						int										=364 if """coco""" in model_name else 224
				__magic_name__						:						Union[str, Any]										=InstructBlipVisionConfig(image_size=lowerCamelCase ).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:
								__magic_name__						:						List[Any]										=TaConfig.from_pretrained("""google/flan-t5-xl"""						,  dense_act_fn="""gelu"""						,  bos_token_id=1 ).to_dict()
				elif "t5-xxl" in model_name:
								__magic_name__						:						Dict										=TaConfig.from_pretrained("""google/flan-t5-xxl"""						,  dense_act_fn="""gelu"""						,  bos_token_id=1 ).to_dict()
				elif "vicuna-7b" in model_name:
								__magic_name__						:						Union[str, Any]										=LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf"""						,  vocab_size=32001 ).to_dict()
				elif "vicuna-13b" in model_name:
								__magic_name__						:						Union[str, Any]										=LlamaConfig.from_pretrained("""decapoda-research/llama-13b-hf"""						,  vocab_size=32001 ).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
				__magic_name__						:						Dict										=InstructBlipQFormerConfig(vocab_size=30523 ).to_dict()
				__magic_name__						:						Optional[Any]										=InstructBlipConfig(vision_config=lowerCamelCase						,  text_config=lowerCamelCase						,  qformer_config=lowerCamelCase )

				return config, image_size




@torch.no_grad()
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=None						,  lowerCamelCase=False ):
				__magic_name__						:						Dict										=AutoTokenizer.from_pretrained("""bert-base-uncased"""						,  truncation_side="""left""" )
				qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""} )

				if "t5" in model_name:
								__magic_name__						:						List[str]										=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>"})
								__magic_name__						:						Union[str, Any]										=LlamaTokenizerFast.from_pretrained(
								    """huggyllama/llama-7b"""						,  truncation_side="""left"""						,  bos_token="""</s>"""						,  unk_token="""</s>""" )
								tokenizer.add_special_tokens({"""pad_token""": """[PAD]"""} )

				__magic_name__					,			__magic_name__						:						Union[str, Any]										=get_blipa_config(lowerCamelCase )
				__magic_name__						:						List[Any]										=InstructBlipForConditionalGeneration(lowerCamelCase ).eval()

				__magic_name__						:						List[Any]										={
				    """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"""),
				}

				__magic_name__					,			__magic_name__						:						List[str]										=model_name_to_original[model_name]

				# load original model
				print("""Loading original model...""" )
				__magic_name__						:						Union[str, Any]										="""cuda:1""" if torch.cuda.is_available() else """cpu"""
				__magic_name__						:						str										="""cuda:2""" if torch.cuda.is_available() else """cpu"""
				__magic_name__					,			__magic_name__					,			__magic_name__						:						Dict										=load_model_and_preprocess(
				    name=lowerCamelCase						,  model_type=lowerCamelCase						,  is_eval=lowerCamelCase						,  device=lowerCamelCase )
				original_model.eval()
				print("""Done!""" )

				# update state dict keys
				__magic_name__						:						List[str]										=original_model.state_dict()
				__magic_name__						:						Dict										=create_rename_keys(lowerCamelCase )
				for src, dest in rename_keys:
								rename_key(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )

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

				# read in qv biases
				read_in_q_v_bias(lowerCamelCase						,  lowerCamelCase )

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

				__magic_name__						:						Tuple										=load_demo_image()
				__magic_name__						:						Any										="""What is unusual about this image?"""

				# create processor
				__magic_name__						:						int										=BlipImageProcessor(
				    size={"""height""": image_size, """width""": image_size}						,  image_mean=lowerCamelCase						,  image_std=lowerCamelCase )
				__magic_name__						:						Optional[int]										=InstructBlipProcessor(
				    image_processor=lowerCamelCase						,  tokenizer=lowerCamelCase						,  qformer_tokenizer=lowerCamelCase						,  )
				__magic_name__						:						Union[str, Any]										=processor(images=lowerCamelCase						,  text=lowerCamelCase						,  return_tensors="""pt""" ).to(lowerCamelCase )

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

				original_model.to(lowerCamelCase )
				hf_model.to(lowerCamelCase )
				with torch.no_grad():
								if "vicuna" in model_name:
												__magic_name__						:						Dict										=original_model({"""image""": original_pixel_values, """text_input""": [prompt]} ).logits
												__magic_name__						:						Tuple										=hf_model(**lowerCamelCase ).logits
								else:
												__magic_name__						:						int										=original_model(
												    {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]} ).logits
												__magic_name__						:						Tuple										=tokenizer("""\n"""						,  return_tensors="""pt""" ).input_ids.to(lowerCamelCase )
												__magic_name__						:						Union[str, Any]										=label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id						,  -100 )
												__magic_name__						:						Tuple										=hf_model(**lowerCamelCase						,  labels=lowerCamelCase ).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
				__magic_name__						:						Optional[int]										=1E-4 if """vicuna""" in model_name else 1E-5
				assert torch.allclose(original_logits.to(logits.device )						,  lowerCamelCase						,  atol=lowerCamelCase )
				print("""Looks ok!""" )

				print("""Generating with original model...""" )
				__magic_name__						:						Optional[int]										=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...""" )
				__magic_name__						:						List[Any]										=hf_model.generate(
				    **lowerCamelCase						,  do_sample=lowerCamelCase						,  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?
								__magic_name__						:						Optional[Any]										=2
				print("""Original generation:"""						,  lowerCamelCase )
				__magic_name__						:						int										=processor.batch_decode(lowerCamelCase						,  skip_special_tokens=lowerCamelCase )
				__magic_name__						:						Any										=[text.strip() for text in output_text]
				print("""HF generation:"""						,  lowerCamelCase )

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

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


if __name__ == "__main__":
							UpperCAmelCase_		:					Tuple          =  argparse.ArgumentParser()
							UpperCAmelCase_		:					Dict          =  [
							    "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",
							)

							UpperCAmelCase_		:					Union[str, Any]          =  parser.parse_args()

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










import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       KandinskyInpaintPipeline
						UpperCamelCase      =       ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
						UpperCamelCase      =       [
						    """prompt""",
						    """negative_prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						    """mask_image""",
						]
						UpperCamelCase      =       [
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						UpperCamelCase      =       False

						@property
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return self.time_input_dim

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										return self.time_input_dim * 4

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return 1_00

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Dict										=XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""			)
										return tokenizer

						@property
						def      A__    (		self :str			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						str										=MCLIPConfig(
										    numDims=self.cross_attention_dim     ,			transformerDimensions=self.text_embedder_hidden_size     ,			hidden_size=self.text_embedder_hidden_size     ,			intermediate_size=37     ,			num_attention_heads=4     ,			num_hidden_layers=5     ,			vocab_size=10_05     ,			)

										__magic_name__						:						Tuple										=MultilingualCLIP(__snake_case			)
										__magic_name__						:						Optional[int]										=text_encoder.eval()

										return text_encoder

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										torch.manual_seed(0			)

										__magic_name__						:						Optional[Any]										={
										    """in_channels""": 9,
										    # Out channels is double in channels because predicts mean and variance
										    """out_channels""": 8,
										    """addition_embed_type""": """text_image""",
										    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
										    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
										    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
										    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
										    """layers_per_block""": 1,
										    """encoder_hid_dim""": self.text_embedder_hidden_size,
										    """encoder_hid_dim_type""": """text_image_proj""",
										    """cross_attention_dim""": self.cross_attention_dim,
										    """attention_head_dim""": 4,
										    """resnet_time_scale_shift""": """scale_shift""",
										    """class_embed_type""": None,
										}

										__magic_name__						:						Union[str, Any]										=UNetaDConditionModel(**__snake_case			)
										return model

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return {
										    "block_out_channels": [32, 64],
										    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
										    "in_channels": 3,
										    "latent_channels": 4,
										    "layers_per_block": 1,
										    "norm_num_groups": 8,
										    "norm_type": "spatial",
										    "num_vq_embeddings": 12,
										    "out_channels": 3,
										    "up_block_types": [
										        "AttnUpDecoderBlock2D",
										        "UpDecoderBlock2D",
										    ],
										    "vq_embed_dim": 4,
										}

						@property
						def      A__    (		self :Tuple			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						Dict										=VQModel(**self.dummy_movq_kwargs			)
										return model

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=self.dummy_text_encoder
										__magic_name__						:						Optional[Any]										=self.dummy_tokenizer
										__magic_name__						:						Optional[Any]										=self.dummy_unet
										__magic_name__						:						Tuple										=self.dummy_movq

										__magic_name__						:						List[str]										=DDIMScheduler(
										    num_train_timesteps=10_00     ,			beta_schedule="""linear"""     ,			beta_start=0.00085     ,			beta_end=0.012     ,			clip_sample=__snake_case     ,			set_alpha_to_one=__snake_case     ,			steps_offset=1     ,			prediction_type="""epsilon"""     ,			thresholding=__snake_case     ,			)

										__magic_name__						:						str										={
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """movq""": movq,
										}

										return components

						def      A__    (		self :str     ,			__snake_case :Optional[Any]     ,			__snake_case :int=0			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						Dict										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(seed + 1			)			).to(__snake_case			)
										# create init_image
										__magic_name__						:						str										=floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						int										=image.cpu().permute(0     ,			2     ,			3     ,			1			)[0]
										__magic_name__						:						str										=Image.fromarray(np.uinta(__snake_case			)			).convert("""RGB"""			).resize((2_56, 2_56)			)
										# create mask
										__magic_name__						:						Dict										=np.ones((64, 64)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										if str(__snake_case			).startswith("""mps"""			):
														__magic_name__						:						Dict										=torch.manual_seed(__snake_case			)
										else:
														__magic_name__						:						Tuple										=torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
										__magic_name__						:						List[Any]										={
										    """prompt""": """horse""",
										    """image""": init_image,
										    """mask_image""": mask,
										    """image_embeds""": image_embeds,
										    """negative_image_embeds""": negative_image_embeds,
										    """generator""": generator,
										    """height""": 64,
										    """width""": 64,
										    """num_inference_steps""": 2,
										    """guidance_scale""": 4.0,
										    """output_type""": """np""",
										}
										return inputs

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										="""cpu"""

										__magic_name__						:						List[Any]										=self.get_dummy_components()

										__magic_name__						:						Union[str, Any]										=self.pipeline_class(**__snake_case			)
										__magic_name__						:						Tuple										=pipe.to(__snake_case			)

										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Tuple										=pipe(**self.get_dummy_inputs(__snake_case			)			)
										__magic_name__						:						List[Any]										=output.images

										__magic_name__						:						Any										=pipe(
										    **self.get_dummy_inputs(__snake_case			)     ,			return_dict=__snake_case     ,			)[0]

										__magic_name__						:						int										=image[0, -3:, -3:, -1]
										__magic_name__						:						str										=image_from_tuple[0, -3:, -3:, -1]

										print(f"image.shape {image.shape}"			)

										assert image.shape == (1, 64, 64, 3)

										__magic_name__						:						Optional[Any]										=np.array(
										    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]			)

										assert (
										    np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
										), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
										assert (
										    np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2
										), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"


						def      A__    (		self :Dict			):
										'''simple docstring'''

										super().test_inference_batch_single_identical(expected_max_diff=3E-3			)




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

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

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


						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""			)

										__magic_name__						:						int										=load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""			)
										__magic_name__						:						List[Any]										=np.ones((7_68, 7_68)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										__magic_name__						:						int										="""a hat"""

										__magic_name__						:						int										=KandinskyPriorPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-prior"""     ,			torch_dtype=torch.floataa			)
										pipe_prior.to(__snake_case			)

										__magic_name__						:						Dict										=KandinskyInpaintPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-inpaint"""     ,			torch_dtype=torch.floataa			)
										__magic_name__						:						int										=pipeline.to(__snake_case			)
										pipeline.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Union[str, Any]										=torch.Generator(device="""cpu"""			).manual_seed(0			)
										__magic_name__					,			__magic_name__						:						Dict										=pipe_prior(
										    __snake_case     ,			generator=__snake_case     ,			num_inference_steps=5     ,			negative_prompt=""""""     ,			).to_tuple()

										__magic_name__						:						Optional[Any]										=pipeline(
										    __snake_case     ,			image=__snake_case     ,			mask_image=__snake_case     ,			image_embeds=__snake_case     ,			negative_image_embeds=__snake_case     ,			generator=__snake_case     ,			num_inference_steps=1_00     ,			height=7_68     ,			width=7_68     ,			output_type="""np"""     ,			)

										__magic_name__						:						Optional[int]										=output.images[0]

										assert image.shape == (7_68, 7_68, 3)

										assert_mean_pixel_difference(__snake_case     ,			__snake_case			)




