



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput



@dataclass
class 				__snake_case						(     UpperCamelCase_						):
							_a			      = 42
							_a			      = 42





class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						):
							_a			      = 1




							@register_to_config
							def __init__(       self     :       Union[str, Any]				,				A_     :       List[Any] = 2_0_0_0				,				A_     :       Optional[Any] = 0.15				,				A_     :       Any = 0.01				,				A_     :       str = 1348.0				,				A_     :       Any = 1e-5				,				A_     :       Dict = 1				,				):
									lowerCAmelCase_			:     List[Any]									= sigma_max

									# setable values
									lowerCAmelCase_			:     Optional[int]									= None

									self.set_sigmas(lowerCamelCase_				,				lowerCamelCase_				,				lowerCamelCase_				,				lowerCamelCase_)




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Union[str, Any]				,				A_     :       Union[str, Any] = None):
									return sample




							def    UpperCAmelCase__			(       self     :       str				,				A_     :       Any				,				A_     :       int = None				,				A_     :       List[str] = None):
									lowerCAmelCase_			:     Any									= sampling_eps if sampling_eps is not None else self.config.sampling_eps

									lowerCAmelCase_			:     Optional[Any]									= torch.linspace(1				,				lowerCamelCase_				,				lowerCamelCase_				,				device=lowerCamelCase_)




							def    UpperCAmelCase__			(       self     :       Dict				,				A_     :       int				,				A_     :       Any = None				,				A_     :       List[Any] = None				,				A_     :       Optional[Any] = None):
									lowerCAmelCase_			:     int									= sigma_min if sigma_min is not None else self.config.sigma_min
									lowerCAmelCase_			:     List[str]									= sigma_max if sigma_max is not None else self.config.sigma_max
									lowerCAmelCase_			:     Any									= sampling_eps if sampling_eps is not None else self.config.sampling_eps
									if self.timesteps is None:
											self.set_timesteps(lowerCamelCase_				,				lowerCamelCase_)

									lowerCAmelCase_			:     Optional[Any]									= sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
									lowerCAmelCase_			:     Tuple									= torch.exp(torch.linspace(math.log(lowerCamelCase_)				,				math.log(lowerCamelCase_)				,				lowerCamelCase_))
									lowerCAmelCase_			:     int									= torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])




							def    UpperCAmelCase__			(       self     :       str				,				A_     :       int				,				A_     :       Dict):
									return torch.where(
									    timesteps == 0				,				torch.zeros_like(t.to(timesteps.device))				,				self.discrete_sigmas[timesteps - 1].to(timesteps.device)				,				)




							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Optional[Any]				,				A_     :       Optional[Any]				,				A_     :       Optional[Any]				,				A_     :       str = None				,				A_     :       Union[str, Any] = True				,				):

									if self.timesteps is None:
											raise ValueError(
											    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''')

									lowerCAmelCase_			:     List[Any]									= timestep * torch.ones(
									    sample.shape[0]				,				device=sample.device)  # torch.repeat_interleave(timestep, sample.shape[0])
									lowerCAmelCase_			:     Any									= (timestep * (len(self.timesteps) - 1)).long()

									# mps requires indices to be in the same device, so we use cpu as is the default with cuda
									lowerCAmelCase_			:     Union[str, Any]									= timesteps.to(self.discrete_sigmas.device)

									lowerCAmelCase_			:     int									= self.discrete_sigmas[timesteps].to(sample.device)
									lowerCAmelCase_			:     Optional[int]									= self.get_adjacent_sigma(lowerCamelCase_				,				lowerCamelCase_).to(sample.device)
									lowerCAmelCase_			:     List[Any]									= torch.zeros_like(lowerCamelCase_)
									lowerCAmelCase_			:     Optional[int]									= (sigma**2 - adjacent_sigma**2) ** 0.5

									# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
									# also equation 47 shows the analog from SDE models to ancestral sampling methods
									lowerCAmelCase_			:     int									= diffusion.flatten()
									while len(diffusion.shape) < len(sample.shape):
											lowerCAmelCase_			:     Optional[int]									= diffusion.unsqueeze(-1)
									lowerCAmelCase_			:     str									= drift - diffusion**2 * model_output

									#  equation 6: sample noise for the diffusion term of
									lowerCAmelCase_			:     str									= randn_tensor(
									    sample.shape				,				layout=sample.layout				,				generator=lowerCamelCase_				,				device=sample.device				,				dtype=sample.dtype)
									lowerCAmelCase_			:     Optional[int]									= sample - drift  # subtract because `dt` is a small negative timestep
									# TODO is the variable diffusion the correct scaling term for the noise?
									lowerCAmelCase_			:     Optional[int]									= prev_sample_mean + diffusion * noise  # add impact of diffusion field g

									if not return_dict:
											return (prev_sample, prev_sample_mean)

									return SdeVeOutput(prev_sample=lowerCamelCase_				,				prev_sample_mean=lowerCamelCase_)




							def    UpperCAmelCase__			(       self     :       List[Any]				,				A_     :       Any				,				A_     :       List[str]				,				A_     :       Optional[int] = None				,				A_     :       int = True				,				):

									if self.timesteps is None:
											raise ValueError(
											    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''')

									# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
									# sample noise for correction
									lowerCAmelCase_			:     Tuple									= randn_tensor(sample.shape				,				layout=sample.layout				,				generator=lowerCamelCase_).to(sample.device)

									# compute step size from the model_output, the noise, and the snr
									lowerCAmelCase_			:     List[Any]									= torch.norm(model_output.reshape(model_output.shape[0]				,				-1)				,				dim=-1).mean()
									lowerCAmelCase_			:     Any									= torch.norm(noise.reshape(noise.shape[0]				,				-1)				,				dim=-1).mean()
									lowerCAmelCase_			:     Optional[int]									= (self.config.snr * noise_norm / grad_norm) ** 2 * 2
									lowerCAmelCase_			:     List[Any]									= step_size * torch.ones(sample.shape[0]).to(sample.device)
									# self.repeat_scalar(step_size, sample.shape[0])

									# compute corrected sample: model_output term and noise term
									lowerCAmelCase_			:     Optional[int]									= step_size.flatten()
									while len(step_size.shape) < len(sample.shape):
											lowerCAmelCase_			:     Optional[int]									= step_size.unsqueeze(-1)
									lowerCAmelCase_			:     Union[str, Any]									= sample + step_size * model_output
									lowerCAmelCase_			:     Any									= prev_sample_mean + ((step_size * 2) ** 0.5) * noise

									if not return_dict:
											return (prev_sample,)

									return SchedulerOutput(prev_sample=lowerCamelCase_)




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       str				,				A_     :       List[str]				,				A_     :       List[Any]				,				):
									lowerCAmelCase_			:     Optional[Any]									= timesteps.to(original_samples.device)
									lowerCAmelCase_			:     int									= self.discrete_sigmas.to(original_samples.device)[timesteps]
									lowerCAmelCase_			:     str									= (
									    noise * sigmas[:, None, None, None]
									    if noise is not None
									    else torch.randn_like(lowerCamelCase_) * sigmas[:, None, None, None]
									)
									lowerCAmelCase_			:     int									= noise + original_samples
									return noisy_samples







							def __len__(       self     :       Union[str, Any]):
									return self.config.num_train_timesteps










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     a(      )		->       Optional[int]:
					"""simple docstring"""





					a					     =   "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
					a					     =   Image.open(requests.get(A ,     stream=A       ).raw       ).convert("RGB"       )

					return image






def     a(      A       :    Tuple       )		->       List[str]:
					"""simple docstring"""





					a					     =   []
					# 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     a(      A       :    str ,     A       :    Tuple ,     A       :    Optional[int]       )		->       int:
					"""simple docstring"""





					a					     =   dct.pop(A       )
					a					     =   val






def     a(      A       :    Union[str, Any] ,     A       :    str       )		->       List[Any]:
					"""simple docstring"""





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

										# next, set bias in the state dict
										a					     =   torch.cat((q_bias, torch.zeros_like(A ,     requires_grad=A       ), v_bias)       )
										a					     =   qkv_bias






def     a(      A       :    Union[str, Any] ,     A       :    Tuple       )		->       str:
					"""simple docstring"""





					a					     =   364 if "coco" in model_name else 224
					a					     =   BlipaVisionConfig(image_size=A       ).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:
										a					     =   OPTConfig.from_pretrained("facebook/opt-2.7b" ,     eos_token_id=A       ).to_dict()
					elif "opt-6.7b" in model_name:
										a					     =   OPTConfig.from_pretrained("facebook/opt-6.7b" ,     eos_token_id=A       ).to_dict()
					elif "t5-xl" in model_name:
										a					     =   TaConfig.from_pretrained("google/flan-t5-xl" ,     dense_act_fn="gelu" ,     bos_token_id=1       ).to_dict()
					elif "t5-xxl" in model_name:
										a					     =   TaConfig.from_pretrained("google/flan-t5-xxl" ,     dense_act_fn="gelu" ,     bos_token_id=1       ).to_dict()

					a					     =   BlipaConfig(vision_config=A ,     text_config=A       )

					return config, image_size






@torch.no_grad()
def     a(      A       :    Optional[int] ,     A       :    List[str]=None ,     A       :    Optional[int]=False       )		->       Any:
					"""simple docstring"""





					a					     =   (
					    AutoTokenizer.from_pretrained("facebook/opt-2.7b"       )
					    if "opt" in model_name
					    else AutoTokenizer.from_pretrained("google/flan-t5-xl"       )
					)
					a					     =   tokenizer("\n" ,     add_special_tokens=A       ).input_ids[0]
					a     ,				a					     =   get_blipa_config(A ,     eos_token_id=A       )

					a					     =   BlipaForConditionalGeneration(A       ).eval()

					a					     =   {
					    "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"),
					}

					a     ,				a					     =   model_name_to_original[model_name]

					# load original model
					print("Loading original model..."       )
					a					     =   "cuda" if torch.cuda.is_available() else "cpu"
					a     ,				a     ,				a					     =   load_model_and_preprocess(
					    name=A ,     model_type=A ,     is_eval=A ,     device=A       )
					original_model.eval()
					print("Done!"       )

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

					# some keys can be renamed efficiently
					for key, val in state_dict.copy().items():
										a					     =   state_dict.pop(A       )
										if key.startswith("Qformer.bert"       ):
															a					     =   key.replace("Qformer.bert" ,     "qformer"       )
										if "attention.self" in key:
															a					     =   key.replace("self" ,     "attention"       )
										if "opt_proj" in key:
															a					     =   key.replace("opt_proj" ,     "language_projection"       )
										if "t5_proj" in key:
															a					     =   key.replace("t5_proj" ,     "language_projection"       )
										if key.startswith("opt"       ):
															a					     =   key.replace("opt" ,     "language"       )
										if key.startswith("t5"       ):
															a					     =   key.replace("t5" ,     "language"       )
										a					     =   val

					# read in qv biases
					read_in_q_v_bias(A ,     A       )

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

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

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

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

					original_model.to(A       )
					hf_model.to(A       )
					with torch.no_grad():
										if "opt" in model_name:
															a					     =   original_model({"image": original_pixel_values, "text_input": [""]}       ).logits
															a					     =   hf_model(A ,     A       ).logits
										else:
															a					     =   original_model(
															    {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}       ).logits
															a					     =   input_ids.masked_fill(input_ids == tokenizer.pad_token_id ,     -100       )
															a					     =   hf_model(A ,     A ,     labels=A       ).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":
										a					     =   torch.tensor(
										    [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]] ,     device=A       )
										assert torch.allclose(logits[0, :3, :3] ,     A ,     atol=1e-4       )
					elif model_name == "blip2-flan-t5-xl-coco":
										a					     =   torch.tensor(
										    [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]] ,     device=A       )
					else:
										# cast to same type
										a					     =   logits.dtype
										assert torch.allclose(original_logits.to(A       ) ,     A ,     atol=1e-2       )
					print("Looks ok!"       )

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

					a					     =   original_model.generate({"image": original_pixel_values}       )
					a					     =   hf_model.generate(
					    A ,     A ,     do_sample=A ,     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:" ,     A       )
					a					     =   input_ids.shape[1]
					a					     =   processor.batch_decode(outputs[:, prompt_length:] ,     skip_special_tokens=A       )
					a					     =   [text.strip() for text in output_text]
					print("HF generation:" ,     A       )

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

					if push_to_hub:
										processor.push_to_hub(f'''nielsr/{model_name}'''       )
										hf_model.push_to_hub(f'''nielsr/{model_name}'''       )


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

				_lowercase: Any      			=		parser.parse_args()

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