
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__			(  )				->		Optional[int]:
			lowerCAmelCase__      :      Optional[int]         =		"""https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
			lowerCAmelCase__      :      Optional[Any]         =		Image.open(requests.get(_lowercase       ,							stream=_lowercase     ).raw     ).convert('RGB'     )

			return image
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Optional[Any]:
			lowerCAmelCase__      :      str         =		[]
			# 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(_lowercase     )
			lowerCAmelCase__      :      Dict         =		val
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		List[Any]:


			for i in range(config.vision_config.num_hidden_layers     ):
						# read in original q and v biases
						lowerCAmelCase__      :      Dict         =		state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''     )
						lowerCAmelCase__      :      Tuple         =		state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''     )

						# next, set bias in the state dict
						lowerCAmelCase__      :      Union[str, Any]         =		torch.cat((q_bias, torch.zeros_like(_lowercase       ,							requires_grad=_lowercase     ), v_bias)     )
						lowerCAmelCase__      :      int         =		qkv_bias
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Union[str, Any]:
			lowerCAmelCase__      :      List[str]         =		364 if """coco""" in model_name else 224
			lowerCAmelCase__      :      Tuple         =		InstructBlipVisionConfig(image_size=_lowercase     ).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__      :      Optional[Any]         =		TaConfig.from_pretrained('google/flan-t5-xl'       ,							dense_act_fn='gelu'       ,							bos_token_id=1     ).to_dict()
			elif "t5-xxl" in model_name:
						lowerCAmelCase__      :      List[Any]         =		TaConfig.from_pretrained('google/flan-t5-xxl'       ,							dense_act_fn='gelu'       ,							bos_token_id=1     ).to_dict()
			elif "vicuna-7b" in model_name:
						lowerCAmelCase__      :      Optional[int]         =		LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'       ,							vocab_size=32_001     ).to_dict()
			elif "vicuna-13b" in model_name:
						lowerCAmelCase__      :      Optional[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__      :      Optional[int]         =		InstructBlipQFormerConfig(vocab_size=30_523     ).to_dict()
			lowerCAmelCase__      :      List[str]         =		InstructBlipConfig(vision_config=_lowercase       ,							text_config=_lowercase       ,							qformer_config=_lowercase     )

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

			if "t5" in model_name:
						lowerCAmelCase__      :      Optional[int]         =		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__      :      str         =		LlamaTokenizerFast.from_pretrained(
						    'huggyllama/llama-7b'       ,							truncation_side='left'       ,							bos_token='</s>'       ,							unk_token='</s>'     )
						tokenizer.add_special_tokens({'pad_token': '[PAD]'}     )

			lowerCAmelCase__      :      Union[str, Any]         =		get_blipa_config(_lowercase     )
			lowerCAmelCase__      :      Union[str, Any]         =		InstructBlipForConditionalGeneration(_lowercase     ).eval()

			lowerCAmelCase__      :      Union[str, 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"""),
			}

			lowerCAmelCase__      :      List[Any]         =		model_name_to_original[model_name]

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

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

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

			# read in qv biases
			read_in_q_v_bias(_lowercase       ,							_lowercase     )

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

			lowerCAmelCase__      :      Optional[Any]         =		load_demo_image()
			lowerCAmelCase__      :      Tuple         =		"""What is unusual about this image?"""

			# create processor
			lowerCAmelCase__      :      Tuple         =		BlipImageProcessor(
			    size={'height': image_size, 'width': image_size}       ,							image_mean=_lowercase       ,							image_std=_lowercase     )
			lowerCAmelCase__      :      Tuple         =		InstructBlipProcessor(
			    image_processor=_lowercase       ,							tokenizer=_lowercase       ,							qformer_tokenizer=_lowercase       ,							)
			lowerCAmelCase__      :      Tuple         =		processor(images=_lowercase       ,							text=_lowercase       ,							return_tensors='pt'     ).to(_lowercase     )

			# make sure processor creates exact same pixel values
			lowerCAmelCase__      :      Optional[Any]         =		vis_processors["""eval"""](_lowercase     ).unsqueeze(0     ).to(_lowercase     )
			lowerCAmelCase__      :      List[str]         =		inputs.pixel_values
			assert torch.allclose(original_pixel_values.to(pixel_values.device     )       ,							_lowercase     )

			original_model.to(_lowercase     )
			hf_model.to(_lowercase     )
			with torch.no_grad():
						if "vicuna" in model_name:
									lowerCAmelCase__      :      List[Any]         =		original_model({'image': original_pixel_values, 'text_input': [prompt]}     ).logits
									lowerCAmelCase__      :      Union[str, Any]         =		hf_model(**_lowercase     ).logits
						else:
									lowerCAmelCase__      :      Dict         =		original_model(
									    {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}     ).logits
									lowerCAmelCase__      :      Dict         =		tokenizer('\n'       ,							return_tensors='pt'     ).input_ids.to(_lowercase     )
									lowerCAmelCase__      :      List[str]         =		label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id       ,							-100     )
									lowerCAmelCase__      :      List[Any]         =		hf_model(**_lowercase       ,							labels=_lowercase     ).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     )       ,							_lowercase       ,							atol=_lowercase     )
			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(
			    **_lowercase       ,							do_sample=_lowercase       ,							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__      :      Optional[int]         =		2
			print('Original generation:'       ,							_lowercase     )
			lowerCAmelCase__      :      str         =		processor.batch_decode(_lowercase       ,							skip_special_tokens=_lowercase     )
			lowerCAmelCase__      :      Any         =		[text.strip() for text in output_text]
			print('HF generation:'       ,							_lowercase     )

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

			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)




"""simple docstring"""
import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


__a      =				logging.get_logger(__name__)

enable_full_determinism()
class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[Any]						=     UNetaDModel
					_A		:			Union[str, Any]						=     """sample"""






					@property
					def    lowerCAmelCase_				(       self:							str						)   ->	Tuple:
											snake_case_ :List[str]						=			4
											snake_case_ :Tuple						=			3
											snake_case_ :Optional[Any]						=			(32, 32)

											snake_case_ :str						=			floats_tensor((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :Union[str, Any]						=			torch.tensor([10]						).to(snake_case						)

											return {"sample": noise, "timestep": time_step}






					@property
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Dict:
											return (3, 32, 32)






					@property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[int]:
											return (3, 32, 32)






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Dict:
											snake_case_ :Any						=			{
											    """block_out_channels""": (32, 64),
											    """down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
											    """up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
											    """attention_head_dim""": 3,
											    """out_channels""": 3,
											    """in_channels""": 3,
											    """layers_per_block""": 2,
											    """sample_size""": 32,
											}
											snake_case_ :Tuple						=			self.dummy_input
											return init_dict, inputs_dict

class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[str]						=     UNetaDModel
					_A		:			Union[str, Any]						=     """sample"""






					@property
					def    lowerCAmelCase_				(       self:							str						)   ->	str:
											snake_case_ :List[str]						=			4
											snake_case_ :Optional[int]						=			4
											snake_case_ :int						=			(32, 32)

											snake_case_ :Any						=			floats_tensor((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :List[Any]						=			torch.tensor([10]						).to(snake_case						)

											return {"sample": noise, "timestep": time_step}






					@property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[int]:
											return (4, 32, 32)






					@property
					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	int:
											return (4, 32, 32)






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	List[Any]:
											snake_case_ :Dict						=			{
											    """sample_size""": 32,
											    """in_channels""": 4,
											    """out_channels""": 4,
											    """layers_per_block""": 2,
											    """block_out_channels""": (32, 64),
											    """attention_head_dim""": 32,
											    """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
											    """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
											}
											snake_case_ :List[str]						=			self.dummy_input
											return init_dict, inputs_dict






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[Any]:
											snake_case_,       snake_case_ :List[str]						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						)

											self.assertIsNotNone(snake_case						)
											self.assertEqual(len(loading_info["""missing_keys"""]						)						,     0						)

											model.to(snake_case						)
											snake_case_ :List[str]						=			model(**self.dummy_input						).sample

											assert image is not None, "Make sure output is not None"






					@unittest.skipIf(torch_device != """cuda"""						,     """This test is supposed to run on GPU"""						)
					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Dict:
											snake_case_,       snake_case_ :Union[str, Any]						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						)
											model.to(snake_case						)
											snake_case_ :Union[str, Any]						=			model(**self.dummy_input						).sample

											assert image is not None, "Make sure output is not None"






					@unittest.skipIf(torch_device != """cuda"""						,     """This test is supposed to run on GPU"""						)
					def    lowerCAmelCase_				(       self:							str						)   ->	Any:
											# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
											snake_case_,       snake_case_ :List[str]						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						)
											model_accelerate.to(snake_case						)
											model_accelerate.eval()

											snake_case_ :List[Any]						=			torch.randn(
											    1						,     model_accelerate.config.in_channels						,     model_accelerate.config.sample_size						,     model_accelerate.config.sample_size						,     generator=torch.manual_seed(0						)						,     )
											snake_case_ :int						=			noise.to(snake_case						)
											snake_case_ :str						=			torch.tensor([10] * noise.shape[0]						).to(snake_case						)

											snake_case_ :Optional[int]						=			model_accelerate(snake_case						,     snake_case						)["""sample"""]

											# two models don't need to stay in the device at the same time
											del model_accelerate
											torch.cuda.empty_cache()
											gc.collect()

											snake_case_,       snake_case_ :str						=			UNetaDModel.from_pretrained(
											    """fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						,     low_cpu_mem_usage=snake_case						)
											model_normal_load.to(snake_case						)
											model_normal_load.eval()
											snake_case_ :int						=			model_normal_load(snake_case						,     snake_case						)["""sample"""]

											assert torch_all_close(snake_case						,     snake_case						,     rtol=1E-3						)






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Any:
											snake_case_ :Tuple						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						)
											model.eval()
											model.to(snake_case						)

											snake_case_ :Optional[int]						=			torch.randn(
											    1						,     model.config.in_channels						,     model.config.sample_size						,     model.config.sample_size						,     generator=torch.manual_seed(0						)						,     )
											snake_case_ :int						=			noise.to(snake_case						)
											snake_case_ :List[Any]						=			torch.tensor([10] * noise.shape[0]						).to(snake_case						)

											with torch.no_grad():
																	snake_case_ :Union[str, Any]						=			model(snake_case						,     snake_case						).sample

											snake_case_ :Optional[int]						=			output[0, -1, -3:, -3:].flatten().cpu()
											# fmt: off
											snake_case_ :Dict						=			torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0]						)
											# fmt: on

											self.assertTrue(torch_all_close(snake_case						,     snake_case						,     rtol=1E-3						)						)

class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[Any]						=     UNetaDModel
					_A		:			List[Any]						=     """sample"""






					@property
					def    lowerCAmelCase_				(       self:							Union[str, Any]						,     snake_case:							int=(32, 32)						)   ->	Tuple:
											snake_case_ :Union[str, Any]						=			4
											snake_case_ :Any						=			3

											snake_case_ :int						=			floats_tensor((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :Any						=			torch.tensor(batch_size * [10]						).to(dtype=torch.intaa						,     device=snake_case						)

											return {"sample": noise, "timestep": time_step}






					@property
					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	Any:
											return (3, 32, 32)






					@property
					def    lowerCAmelCase_				(       self:							int						)   ->	Tuple:
											return (3, 32, 32)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Tuple:
											snake_case_ :List[Any]						=			{
											    """block_out_channels""": [32, 64, 64, 64],
											    """in_channels""": 3,
											    """layers_per_block""": 1,
											    """out_channels""": 3,
											    """time_embedding_type""": """fourier""",
											    """norm_eps""": 1E-6,
											    """mid_block_scale_factor""": math.sqrt(2.0						),
											    """norm_num_groups""": None,
											    """down_block_types""": [
											        """SkipDownBlock2D""",
											        """AttnSkipDownBlock2D""",
											        """SkipDownBlock2D""",
											        """SkipDownBlock2D""",
											    ],
											    """up_block_types""": [
											        """SkipUpBlock2D""",
											        """SkipUpBlock2D""",
											        """AttnSkipUpBlock2D""",
											        """SkipUpBlock2D""",
											    ],
											}
											snake_case_ :int						=			self.dummy_input
											return init_dict, inputs_dict






					@slow
					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	List[Any]:
											snake_case_,       snake_case_ :List[Any]						=			UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""						,     output_loading_info=snake_case						)
											self.assertIsNotNone(snake_case						)
											self.assertEqual(len(loading_info["""missing_keys"""]						)						,     0						)

											model.to(snake_case						)
											snake_case_ :Any						=			self.dummy_input
											snake_case_ :int						=			floats_tensor((4, 3) + (256, 256)						).to(snake_case						)
											snake_case_ :int						=			noise
											snake_case_ :int						=			model(**snake_case						)

											assert image is not None, "Make sure output is not None"






					@slow
					def    lowerCAmelCase_				(       self:							str						)   ->	Dict:
											snake_case_ :Dict						=			UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""						)
											model.to(snake_case						)

											snake_case_ :List[str]						=			4
											snake_case_ :Optional[int]						=			3
											snake_case_ :List[str]						=			(256, 256)

											snake_case_ :Tuple						=			torch.ones((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :str						=			torch.tensor(batch_size * [1E-4]						).to(snake_case						)

											with torch.no_grad():
																	snake_case_ :Dict						=			model(snake_case						,     snake_case						).sample

											snake_case_ :int						=			output[0, -3:, -3:, -1].flatten().cpu()
											# fmt: off
											snake_case_ :Optional[Any]						=			torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8]						)
											# fmt: on

											self.assertTrue(torch_all_close(snake_case						,     snake_case						,     rtol=1E-2						)						)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	List[Any]:
											snake_case_ :Optional[Any]						=			UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""						)
											model.to(snake_case						)

											snake_case_ :Optional[int]						=			4
											snake_case_ :Optional[Any]						=			3
											snake_case_ :Optional[Any]						=			(32, 32)

											snake_case_ :Dict						=			torch.ones((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :Any						=			torch.tensor(batch_size * [1E-4]						).to(snake_case						)

											with torch.no_grad():
																	snake_case_ :str						=			model(snake_case						,     snake_case						).sample

											snake_case_ :int						=			output[0, -3:, -3:, -1].flatten().cpu()
											# fmt: off
											snake_case_ :int						=			torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6]						)
											# fmt: on

											self.assertTrue(torch_all_close(snake_case						,     snake_case						,     rtol=1E-2						)						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	Optional[Any]:
											# not required for this model
											pass
