




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
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					"""https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"""
			SCREAMING_SNAKE_CASE           =					Image.open(requests.get(SCREAMING_SNAKE_CASE					,	stream=SCREAMING_SNAKE_CASE   ).raw   ).convert("""RGB"""   )

			return image
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					[]
			# 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.layernorm.weight""")   )
			rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.layernorm.bias""")   )

			# fmt: on
			return rename_keys
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					dct.pop(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					val
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			for i in range(config.vision_config.num_hidden_layers   ):
						# read in original q and v biases
						SCREAMING_SNAKE_CASE           =					state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias"""   )
						SCREAMING_SNAKE_CASE           =					state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias"""   )

						# next, set bias in the state dict
						SCREAMING_SNAKE_CASE           =					torch.cat((q_bias, torch.zeros_like(SCREAMING_SNAKE_CASE					,	requires_grad=SCREAMING_SNAKE_CASE   ), v_bias)   )
						SCREAMING_SNAKE_CASE           =					qkv_bias
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					3_64 if """coco""" in model_name else 2_24
			SCREAMING_SNAKE_CASE           =					BlipaVisionConfig(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 "opt-2.7b" in model_name:
						SCREAMING_SNAKE_CASE           =					OPTConfig.from_pretrained("""facebook/opt-2.7b"""					,	eos_token_id=SCREAMING_SNAKE_CASE   ).to_dict()
			elif "opt-6.7b" in model_name:
						SCREAMING_SNAKE_CASE           =					OPTConfig.from_pretrained("""facebook/opt-6.7b"""					,	eos_token_id=SCREAMING_SNAKE_CASE   ).to_dict()
			elif "t5-xl" in model_name:
						SCREAMING_SNAKE_CASE           =					TaConfig.from_pretrained("""google/flan-t5-xl"""					,	dense_act_fn="""gelu"""					,	bos_token_id=1   ).to_dict()
			elif "t5-xxl" in model_name:
						SCREAMING_SNAKE_CASE           =					TaConfig.from_pretrained("""google/flan-t5-xxl"""					,	dense_act_fn="""gelu"""					,	bos_token_id=1   ).to_dict()

			SCREAMING_SNAKE_CASE           =					BlipaConfig(vision_config=SCREAMING_SNAKE_CASE					,	text_config=SCREAMING_SNAKE_CASE   )

			return config, image_size
@torch.no_grad()
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=None					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					(
			    AutoTokenizer.from_pretrained("""facebook/opt-2.7b"""   )
			    if """opt""" in model_name
			    else AutoTokenizer.from_pretrained("""google/flan-t5-xl"""   )
			)
			SCREAMING_SNAKE_CASE           =					tokenizer("""\n"""					,	add_special_tokens=SCREAMING_SNAKE_CASE   ).input_ids[0]
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					get_blipa_config(SCREAMING_SNAKE_CASE					,	eos_token_id=SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					BlipaForConditionalGeneration(SCREAMING_SNAKE_CASE   ).eval()

			SCREAMING_SNAKE_CASE           =					{
			    """blip2-opt-2.7b""": ("""blip2_opt""", """pretrain_opt2.7b"""),
			    """blip2-opt-6.7b""": ("""blip2_opt""", """pretrain_opt6.7b"""),
			    """blip2-opt-2.7b-coco""": ("""blip2_opt""", """caption_coco_opt2.7b"""),
			    """blip2-opt-6.7b-coco""": ("""blip2_opt""", """caption_coco_opt6.7b"""),
			    """blip2-flan-t5-xl""": ("""blip2_t5""", """pretrain_flant5xl"""),
			    """blip2-flan-t5-xl-coco""": ("""blip2_t5""", """caption_coco_flant5xl"""),
			    """blip2-flan-t5-xxl""": ("""blip2_t5""", """pretrain_flant5xxl"""),
			}

			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					model_name_to_original[model_name]

			# load original model
			print("""Loading original model..."""   )
			SCREAMING_SNAKE_CASE           =					"""cuda""" if torch.cuda.is_available() else """cpu"""
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					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
			SCREAMING_SNAKE_CASE           =					original_model.state_dict()
			SCREAMING_SNAKE_CASE           =					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():
						SCREAMING_SNAKE_CASE           =					state_dict.pop(SCREAMING_SNAKE_CASE   )
						if key.startswith("""Qformer.bert"""   ):
									SCREAMING_SNAKE_CASE           =					key.replace("""Qformer.bert"""					,	"""qformer"""   )
						if "attention.self" in key:
									SCREAMING_SNAKE_CASE           =					key.replace("""self"""					,	"""attention"""   )
						if "opt_proj" in key:
									SCREAMING_SNAKE_CASE           =					key.replace("""opt_proj"""					,	"""language_projection"""   )
						if "t5_proj" in key:
									SCREAMING_SNAKE_CASE           =					key.replace("""t5_proj"""					,	"""language_projection"""   )
						if key.startswith("""opt"""   ):
									SCREAMING_SNAKE_CASE           =					key.replace("""opt"""					,	"""language"""   )
						if key.startswith("""t5"""   ):
									SCREAMING_SNAKE_CASE           =					key.replace("""t5"""					,	"""language"""   )
						SCREAMING_SNAKE_CASE           =					val

			# read in qv biases
			read_in_q_v_bias(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					hf_model.load_state_dict(SCREAMING_SNAKE_CASE					,	strict=SCREAMING_SNAKE_CASE   )
			assert len(SCREAMING_SNAKE_CASE   ) == 0
			assert unexpected_keys == ["qformer.embeddings.position_ids"]

			SCREAMING_SNAKE_CASE           =					load_demo_image()
			SCREAMING_SNAKE_CASE           =					vis_processors["""eval"""](SCREAMING_SNAKE_CASE   ).unsqueeze(0   ).to(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					tokenizer(["""\n"""]					,	return_tensors="""pt"""   ).input_ids.to(SCREAMING_SNAKE_CASE   )

			# create processor
			SCREAMING_SNAKE_CASE           =					BlipImageProcessor(
			    size={"""height""": image_size, """width""": image_size}					,	image_mean=SCREAMING_SNAKE_CASE					,	image_std=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					BlipaProcessor(image_processor=SCREAMING_SNAKE_CASE					,	tokenizer=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					processor(images=SCREAMING_SNAKE_CASE					,	return_tensors="""pt"""   ).pixel_values.to(SCREAMING_SNAKE_CASE   )

			# make sure processor creates exact same pixel values
			assert torch.allclose(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

			original_model.to(SCREAMING_SNAKE_CASE   )
			hf_model.to(SCREAMING_SNAKE_CASE   )
			with torch.no_grad():
						if "opt" in model_name:
									SCREAMING_SNAKE_CASE           =					original_model({"""image""": original_pixel_values, """text_input""": [""""""]}   ).logits
									SCREAMING_SNAKE_CASE           =					hf_model(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ).logits
						else:
									SCREAMING_SNAKE_CASE           =					original_model(
									    {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]}   ).logits
									SCREAMING_SNAKE_CASE           =					input_ids.masked_fill(input_ids == tokenizer.pad_token_id					,	-1_00   )
									SCREAMING_SNAKE_CASE           =					hf_model(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	labels=SCREAMING_SNAKE_CASE   ).logits

			assert original_logits.shape == logits.shape
			print("""First values of original logits:"""					,	original_logits[0, :3, :3]   )
			print("""First values of HF logits:"""					,	logits[0, :3, :3]   )

			# assert values
			if model_name == "blip2-flan-t5-xl":
						SCREAMING_SNAKE_CASE           =					torch.tensor(
						    [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]					,	device=SCREAMING_SNAKE_CASE   )
						assert torch.allclose(logits[0, :3, :3]					,	SCREAMING_SNAKE_CASE					,	atol=1E-4   )
			elif model_name == "blip2-flan-t5-xl-coco":
						SCREAMING_SNAKE_CASE           =					torch.tensor(
						    [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]					,	device=SCREAMING_SNAKE_CASE   )
			else:
						# cast to same type
						SCREAMING_SNAKE_CASE           =					logits.dtype
						assert torch.allclose(original_logits.to(SCREAMING_SNAKE_CASE   )					,	SCREAMING_SNAKE_CASE					,	atol=1E-2   )
			print("""Looks ok!"""   )

			print("""Generating a caption..."""   )
			SCREAMING_SNAKE_CASE           =					""""""
			SCREAMING_SNAKE_CASE           =					tokenizer(SCREAMING_SNAKE_CASE					,	return_tensors="""pt"""   ).input_ids.to(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					original_model.generate({"""image""": original_pixel_values}   )
			SCREAMING_SNAKE_CASE           =					hf_model.generate(
			    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	do_sample=SCREAMING_SNAKE_CASE					,	num_beams=5					,	max_length=30					,	min_length=1					,	top_p=0.9					,	repetition_penalty=1.0					,	length_penalty=1.0					,	temperature=1					,	)
			print("""Original generation:"""					,	SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					input_ids.shape[1]
			SCREAMING_SNAKE_CASE           =					processor.batch_decode(outputs[:, prompt_length:]					,	skip_special_tokens=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					[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"""nielsr/{model_name}"""   )
						hf_model.push_to_hub(f"""nielsr/{model_name}"""   )


if __name__ == "__main__":
		__A						:							str							=  argparse.ArgumentParser()
		__A						:							Optional[Any]							=  [
		    """blip2-opt-2.7b""",
		    """blip2-opt-6.7b""",
		    """blip2-opt-2.7b-coco""",
		    """blip2-opt-6.7b-coco""",
		    """blip2-flan-t5-xl""",
		    """blip2-flan-t5-xl-coco""",
		    """blip2-flan-t5-xxl""",
		]
		parser.add_argument(
		    """--model_name""",
		    default="""blip2-opt-2.7b""",
		    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""",
		)

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

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












from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     42
							a__						  =     42





							def __init__(					self       :	Any       ,     a       :	UNetaDModel       ,     a       :	ScoreSdeVeScheduler			)			->					str:
										super().__init__()
										self.register_modules(unet=a       ,     scheduler=a			)







							@torch.no_grad()
							def __call__(					self       :	List[Any]       ,     a       :	int = 1       ,     a       :	int = 2_000       ,     a       :	Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,     a       :	Optional[str] = "pil"       ,     a       :	bool = True       ,     **a       :	Optional[int]       ,     )			->					Union[ImagePipelineOutput, Tuple]:
										SCREAMING_SNAKE_CASE           =					self.unet.config.sample_size
										SCREAMING_SNAKE_CASE           =					(batch_size, 3, img_size, img_size)

										SCREAMING_SNAKE_CASE           =					self.unet

										SCREAMING_SNAKE_CASE           =					randn_tensor(a       ,     generator=a			) * self.scheduler.init_noise_sigma
										SCREAMING_SNAKE_CASE           =					sample.to(self.device			)

										self.scheduler.set_timesteps(a			)
										self.scheduler.set_sigmas(a			)

										for i, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
													SCREAMING_SNAKE_CASE           =					self.scheduler.sigmas[i] * torch.ones(shape[0]       ,     device=self.device			)

													# correction step
													for _ in range(self.scheduler.config.correct_steps			):
																SCREAMING_SNAKE_CASE           =					self.unet(a       ,     a			).sample
																SCREAMING_SNAKE_CASE           =					self.scheduler.step_correct(a       ,     a       ,     generator=a			).prev_sample

													# prediction step
													SCREAMING_SNAKE_CASE           =					model(a       ,     a			).sample
													SCREAMING_SNAKE_CASE           =					self.scheduler.step_pred(a       ,     a       ,     a       ,     generator=a			)

													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					output.prev_sample, output.prev_sample_mean

										SCREAMING_SNAKE_CASE           =					sample_mean.clamp(0       ,     1			)
										SCREAMING_SNAKE_CASE           =					sample.cpu().permute(0       ,     2       ,     3       ,     1			).numpy()
										if output_type == "pil":
													SCREAMING_SNAKE_CASE           =					self.numpy_to_pil(a			)

										if not return_dict:
													return (sample,)

										return ImagePipelineOutput(images=a			)






