


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(      )		->      List[str]:

		'''simple docstring'''







		__lowercase=     """https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
		__lowercase=     Image.open(requests.get(lowercase_  ,					stream=lowercase_      ).raw      ).convert('RGB'      )

		return image







def 			_lowerCamelCase(      lowercase__      )		->      List[str]:

		'''simple docstring'''







		__lowercase=     []
		# 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(      lowercase__  ,					lowercase__  ,					lowercase__      )		->      int:

		'''simple docstring'''







		__lowercase=     dct.pop(lowercase_      )
		__lowercase=     val







def 			_lowerCamelCase(      lowercase__  ,					lowercase__      )		->      Union[str, Any]:

		'''simple docstring'''







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

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







def 			_lowerCamelCase(      lowercase__      )		->      List[Any]:

		'''simple docstring'''







		__lowercase=     3_6_4 if """coco""" in model_name else 2_2_4
		__lowercase=     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:
				__lowercase=     TaConfig.from_pretrained('google/flan-t5-xl'  ,					dense_act_fn='gelu'  ,					bos_token_id=1      ).to_dict()
		elif "t5-xxl" in model_name:
				__lowercase=     TaConfig.from_pretrained('google/flan-t5-xxl'  ,					dense_act_fn='gelu'  ,					bos_token_id=1      ).to_dict()
		elif "vicuna-7b" in model_name:
				__lowercase=     LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'  ,					vocab_size=3_2_0_0_1      ).to_dict()
		elif "vicuna-13b" in model_name:
				__lowercase=     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
		__lowercase=     InstructBlipQFormerConfig(vocab_size=3_0_5_2_3      ).to_dict()
		__lowercase=     InstructBlipConfig(vision_config=lowercase_  ,					text_config=lowercase_  ,					qformer_config=lowercase_      )

		return config, image_size







@torch.no_grad()
def 			_lowerCamelCase(      lowercase__  ,					lowercase__=None  ,					lowercase__=False      )		->      Optional[int]:

		'''simple docstring'''







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

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

		__lowercase=     get_blipa_config(lowercase_      )
		__lowercase=     InstructBlipForConditionalGeneration(lowercase_      ).eval()

		__lowercase=     {
		    """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"""),
		}

		__lowercase=     model_name_to_original[model_name]

		# load original model
		print('Loading original model...'      )
		__lowercase=     """cuda:1""" if torch.cuda.is_available() else """cpu"""
		__lowercase=     """cuda:2""" if torch.cuda.is_available() else """cpu"""
		__lowercase=     load_model_and_preprocess(
		    name=lowercase_  ,					model_type=lowercase_  ,					is_eval=lowercase_  ,					device=lowercase_      )
		original_model.eval()
		print('Done!'      )

		# update state dict keys
		__lowercase=     original_model.state_dict()
		__lowercase=     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():
				__lowercase=     state_dict.pop(lowercase_      )
				if key.startswith('Qformer.bert'      ):
						__lowercase=     key.replace('Qformer.bert'  ,					'qformer'      )
				if "attention.self" in key:
						__lowercase=     key.replace('self'  ,					'attention'      )
				if "llm_proj" in key:
						__lowercase=     key.replace('llm_proj'  ,					'language_projection'      )
				if "t5_proj" in key:
						__lowercase=     key.replace('t5_proj'  ,					'language_projection'      )
				if key.startswith('llm_model'      ):
						__lowercase=     key.replace('llm_model'  ,					'language_model'      )
				if key.startswith('t5'      ):
						__lowercase=     key.replace('t5'  ,					'language'      )
				__lowercase=     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_      )

		__lowercase=     load_demo_image()
		__lowercase=     """What is unusual about this image?"""

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

		# make sure processor creates exact same pixel values
		__lowercase=     vis_processors["""eval"""](lowercase_      ).unsqueeze(0      ).to(lowercase_      )
		__lowercase=     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:
						__lowercase=     original_model({'image': original_pixel_values, 'text_input': [prompt]}      ).logits
						__lowercase=     hf_model(**lowercase_      ).logits
				else:
						__lowercase=     original_model(
						    {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}      ).logits
						__lowercase=     tokenizer('\n'  ,					return_tensors='pt'      ).input_ids.to(lowercase_      )
						__lowercase=     label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id  ,					-1_0_0      )
						__lowercase=     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
		__lowercase=     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...'      )
		__lowercase=     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...'      )
		__lowercase=     hf_model.generate(
		    **lowercase_  ,					do_sample=lowercase_  ,					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?
				__lowercase=     2
		print('Original generation:'  ,					lowercase_      )
		__lowercase=     processor.batch_decode(lowercase_  ,					skip_special_tokens=lowercase_      )
		__lowercase=     [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)









def 		UpperCamelCase   (lowercase_:					str   )	-> Optional[Any]:
	A__				:       Optional[int]	  =						[]
	A__				:       Optional[int]	  =						set({"""(""", """[""", """{"""}   )
	A__				:       str	  =						set({""")""", """]""", """}"""}   )
	A__				:       Optional[Any]	  =						{"""{""": """}""", """[""": """]""", """(""": """)"""}

	for i in range(len(lowercase_   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(lowercase_   ) == 0 or (len(lowercase_   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(lowercase_   ) == 0

def 		UpperCamelCase   ()	-> List[str]:
	A__				:       Optional[Any]	  =						input("""Enter sequence of brackets: """   )
	if is_balanced(lowercase_   ):
		print(lowercase_       , """is balanced"""   )
	else:
		print(lowercase_       , """is not balanced"""   )


if __name__ == "__main__":
	main()



