





"""simple docstring"""






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__ (					)    ->			str:



			'''simple docstring'''
			a__  :	Union[str, Any]		 =			"https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
			a__  :	List[str]		 =			Image.open(requests.get(lowerCAmelCase__							,			stream=lowerCAmelCase__			).raw			).convert("RGB"			)

			return image






def      lowercase__ (					lowerCAmelCase__   :			List[Any]			)    ->			int:



			'''simple docstring'''
			a__  :	Dict		 =			[]
			# 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__ (					lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			Optional[Any]							,			lowerCAmelCase__   :			str			)    ->			int:



			'''simple docstring'''
			a__  :	int		 =			dct.pop(lowerCAmelCase__			)
			a__  :	Tuple		 =			val






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



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

						# next, set bias in the state dict
						a__  :	Optional[Any]		 =			torch.cat((q_bias, torch.zeros_like(lowerCAmelCase__							,			requires_grad=lowerCAmelCase__			), v_bias)			)
						a__  :	List[str]		 =			qkv_bias






def      lowercase__ (					lowerCAmelCase__   :			Any			)    ->			int:



			'''simple docstring'''
			a__  :	Tuple		 =			3_6_4 if "coco" in model_name else 2_2_4
			a__  :	Union[str, Any]		 =			InstructBlipVisionConfig(image_size=lowerCAmelCase__			).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:
						a__  :	int		 =			TaConfig.from_pretrained("google/flan-t5-xl"							,			dense_act_fn="gelu"							,			bos_token_id=1			).to_dict()
			elif "t5-xxl" in model_name:
						a__  :	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:
						a__  :	str		 =			LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf"							,			vocab_size=3_2_0_0_1			).to_dict()
			elif "vicuna-13b" in model_name:
						a__  :	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
			a__  :	Union[str, Any]		 =			InstructBlipQFormerConfig(vocab_size=3_0_5_2_3			).to_dict()
			a__  :	int		 =			InstructBlipConfig(vision_config=lowerCAmelCase__							,			text_config=lowerCAmelCase__							,			qformer_config=lowerCAmelCase__			)

			return config, image_size






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



			'''simple docstring'''
			a__  :	List[Any]		 =			AutoTokenizer.from_pretrained("bert-base-uncased"							,			truncation_side="left"			)
			qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"}			)

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

			a__ ,	a__  :	str		 =			get_blipa_config(lowerCAmelCase__			)
			a__  :	Optional[int]		 =			InstructBlipForConditionalGeneration(lowerCAmelCase__			).eval()

			a__  :	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"),
			}

			a__ ,	a__  :	Optional[int]		 =			model_name_to_original[model_name]

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

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

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

			# read in qv biases
			read_in_q_v_bias(lowerCAmelCase__							,			lowerCAmelCase__			)

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

			a__  :	str		 =			load_demo_image()
			a__  :	Tuple		 =			"What is unusual about this image?"

			# create processor
			a__  :	List[Any]		 =			BlipImageProcessor(
			    size={"height": image_size, "width": image_size}							,			image_mean=lowerCAmelCase__							,			image_std=lowerCAmelCase__			)
			a__  :	Optional[int]		 =			InstructBlipProcessor(
			    image_processor=lowerCAmelCase__							,			tokenizer=lowerCAmelCase__							,			qformer_tokenizer=lowerCAmelCase__							,			)
			a__  :	str		 =			processor(images=lowerCAmelCase__							,			text=lowerCAmelCase__							,			return_tensors="pt"			).to(lowerCAmelCase__			)

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

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

			print("Generating with original model..."			)
			a__  :	Optional[int]		 =			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..."			)
			a__  :	List[str]		 =			hf_model.generate(
			    **lowerCAmelCase__							,			do_sample=lowerCAmelCase__							,			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?
						a__  :	Any		 =			2
			print("Original generation:"							,			lowerCAmelCase__			)
			a__  :	Tuple		 =			processor.batch_decode(lowerCAmelCase__							,			skip_special_tokens=lowerCAmelCase__			)
			a__  :	Any		 =			[text.strip() for text in output_text]
			print("HF generation:"							,			lowerCAmelCase__			)

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

			if push_to_hub:
						processor.push_to_hub(F"Salesforce/{model_name}"			)
						hf_model.push_to_hub(F"Salesforce/{model_name}"			)


if __name__ == "__main__":
		__UpperCAmelCase						=			argparse.ArgumentParser()
		__UpperCAmelCase						=			[
		    '''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''',
		)

		__UpperCAmelCase						=			parser.parse_args()

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






"""simple docstring"""






import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class 					__UpperCAmelCase							(						unittest.TestCase						,  _UpperCamelCase  ):
				def       UpperCAmelCase      (		self    :			Dict    )	->	List[Any]:



							'''simple docstring'''



							a__  :	Any		 =			load_tool("text-classification"    )
							self.tool.setup()
							a__  :	List[Any]		 =			load_tool("text-classification"	, remote=a_    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	str		 =			self.tool("That's quite cool"	, ["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	List[str]		 =			self.remote_tool("That's quite cool"	, ["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	Any:



							'''simple docstring'''



							a__  :	List[str]		 =			self.tool(text="That's quite cool"	, labels=["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )






				def       UpperCAmelCase      (		self    :			List[Any]    )	->	List[Any]:



							'''simple docstring'''



							a__  :	Tuple		 =			self.remote_tool(text="That's quite cool"	, labels=["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )