




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 		lowercase__   (		):





					'''simple docstring'''






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

					return image







def 		lowercase__   (		__A:	List[Any]				):





					'''simple docstring'''






					__magic_name__							:	Any    							=							[]
					# 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 		lowercase__   (		__A:	str							,__A:	Union[str, Any]							,__A:	List[str]				):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							dct.pop(__A				)
					__magic_name__							:	Union[str, Any]    							=							val







def 		lowercase__   (		__A:	str							,__A:	List[str]				):





					'''simple docstring'''






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

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







def 		lowercase__   (		__A:	Optional[int]				):





					'''simple docstring'''






					__magic_name__							:	str    							=							3_6_4 if '''coco''' in model_name else 2_2_4
					__magic_name__							:	Optional[int]    							=							InstructBlipVisionConfig(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 "t5-xl" in model_name:
										__magic_name__							:	Tuple    							=							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__							:	Optional[int]    							=							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__							:	Tuple    							=							LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf'''							,vocab_size=3_2_0_0_1				).to_dict()
					elif "vicuna-13b" in model_name:
										__magic_name__							:	Optional[int]    							=							LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf'''							,vocab_size=3_2_0_0_1				).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__							:	Optional[int]    							=							InstructBlipQFormerConfig(vocab_size=3_0_5_2_3				).to_dict()
					__magic_name__							:	int    							=							InstructBlipConfig(vision_config=__A							,text_config=__A							,qformer_config=__A				)

					return config, image_size







@torch.no_grad()
def 		lowercase__   (		__A:	List[str]							,__A:	List[Any]=None							,__A:	Tuple=False				):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							AutoTokenizer.from_pretrained('''bert-base-uncased'''							,truncation_side='''left'''				)
					qformer_tokenizer.add_special_tokens({'''bos_token''': '''[DEC]'''}				)

					if "t5" in model_name:
										__magic_name__							:	Optional[Any]    							=							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__							:	List[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__							:	Dict    							=							get_blipa_config(__A				)
					__magic_name__							:	Optional[Any]    							=							InstructBlipForConditionalGeneration(__A				).eval()

					__magic_name__							:	Optional[int]    							=							{
					    '''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__							:	Tuple    							=							model_name_to_original[model_name]

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

					# update state dict keys
					__magic_name__							:	List[str]    							=							original_model.state_dict()
					__magic_name__							:	int    							=							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():
										__magic_name__							:	Any    							=							state_dict.pop(__A				)
										if key.startswith('''Qformer.bert'''				):
															__magic_name__							:	Any    							=							key.replace('''Qformer.bert'''							,'''qformer'''				)
										if "attention.self" in key:
															__magic_name__							:	Tuple    							=							key.replace('''self'''							,'''attention'''				)
										if "llm_proj" in key:
															__magic_name__							:	Dict    							=							key.replace('''llm_proj'''							,'''language_projection'''				)
										if "t5_proj" in key:
															__magic_name__							:	List[Any]    							=							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__							:	List[Any]    							=							key.replace('''t5'''							,'''language'''				)
										__magic_name__							:	List[Any]    							=							val

					# read in qv biases
					read_in_q_v_bias(__A							,__A				)

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

					__magic_name__							:	int    							=							load_demo_image()
					__magic_name__							:	Optional[int]    							=							'''What is unusual about this image?'''

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

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

					original_model.to(__A				)
					hf_model.to(__A				)
					with torch.no_grad():
										if "vicuna" in model_name:
															__magic_name__							:	Union[str, Any]    							=							original_model({'''image''': original_pixel_values, '''text_input''': [prompt]}				).logits
															__magic_name__							:	Tuple    							=							hf_model(**__A				).logits
										else:
															__magic_name__							:	Optional[Any]    							=							original_model(
															    {'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']}				).logits
															__magic_name__							:	Optional[Any]    							=							tokenizer('''\n'''							,return_tensors='''pt'''				).input_ids.to(__A				)
															__magic_name__							:	Union[str, Any]    							=							label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id							,-1_0_0				)
															__magic_name__							:	str    							=							hf_model(**__A							,labels=__A				).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__							:	List[Any]    							=							1e-4 if '''vicuna''' in model_name else 1e-5
					assert torch.allclose(original_logits.to(logits.device				)							,__A							,atol=__A				)
					print('''Looks ok!'''				)

					print('''Generating with original model...'''				)
					__magic_name__							:	str    							=							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[str]    							=							hf_model.generate(
					    **__A							,do_sample=__A							,num_beams=5							,max_length=2_5_6							,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__							:	Union[str, Any]    							=							2
					print('''Original generation:'''							,__A				)
					__magic_name__							:	Optional[Any]    							=							processor.batch_decode(__A							,skip_special_tokens=__A				)
					__magic_name__							:	Optional[int]    							=							[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'''Salesforce/{model_name}'''				)
										hf_model.push_to_hub(F'''Salesforce/{model_name}'''				)


if __name__ == "__main__":
	__lowerCamelCase   :						Union[str, Any]				=			argparse.ArgumentParser()
	__lowerCamelCase   :						str				=			[
	    '''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   :						List[Any]				=			parser.parse_args()

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









# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    '''stable diffusion controlnet''',
    '''0.22.0''',
    '''Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.''',
    standard_warn=False,
    stacklevel=3,
)



