


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 			_a (      ):


		"""simple docstring"""




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

		return image
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""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 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      dct.pop(SCREAMING_SNAKE_CASE     )
		lowercase__            =      val
def 			_a (      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
				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(SCREAMING_SNAKE_CASE						,				requires_grad=SCREAMING_SNAKE_CASE     ), v_bias)     )
				lowercase__            =      qkv_bias
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      3_64 if '''coco''' in model_name else 2_24
		lowercase__            =      InstructBlipVisionConfig(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 "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_20_01     ).to_dict()
		elif "vicuna-13b" in model_name:
				lowercase__            =      LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf'''						,				vocab_size=3_20_01     ).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_05_23     ).to_dict()
		lowercase__            =      InstructBlipConfig(vision_config=SCREAMING_SNAKE_CASE						,				text_config=SCREAMING_SNAKE_CASE						,				qformer_config=SCREAMING_SNAKE_CASE     )

		return config, image_size
@torch.no_grad()
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=None						,				SCREAMING_SNAKE_CASE=False     ):


		"""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__     ,		lowercase__            =      get_blipa_config(SCREAMING_SNAKE_CASE     )
		lowercase__            =      InstructBlipForConditionalGeneration(SCREAMING_SNAKE_CASE     ).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__     ,		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__     ,		lowercase__     ,		lowercase__            =      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
		lowercase__            =      original_model.state_dict()
		lowercase__            =      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():
				lowercase__            =      state_dict.pop(SCREAMING_SNAKE_CASE     )
				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(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )

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

		lowercase__            =      load_demo_image()
		lowercase__            =      '''What is unusual about this image?'''

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

		# make sure processor creates exact same pixel values
		lowercase__            =      vis_processors['''eval'''](SCREAMING_SNAKE_CASE     ).unsqueeze(0     ).to(SCREAMING_SNAKE_CASE     )
		lowercase__            =      inputs.pixel_values
		assert torch.allclose(original_pixel_values.to(pixel_values.device     )						,				SCREAMING_SNAKE_CASE     )

		original_model.to(SCREAMING_SNAKE_CASE     )
		hf_model.to(SCREAMING_SNAKE_CASE     )
		with torch.no_grad():
				if "vicuna" in model_name:
						lowercase__            =      original_model({'''image''': original_pixel_values, '''text_input''': [prompt]}     ).logits
						lowercase__            =      hf_model(**SCREAMING_SNAKE_CASE     ).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(SCREAMING_SNAKE_CASE     )
						lowercase__            =      label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id						,				-1_00     )
						lowercase__            =      hf_model(**SCREAMING_SNAKE_CASE						,				labels=SCREAMING_SNAKE_CASE     ).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     )						,				SCREAMING_SNAKE_CASE						,				atol=SCREAMING_SNAKE_CASE     )
		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(
		    **SCREAMING_SNAKE_CASE						,				do_sample=SCREAMING_SNAKE_CASE						,				num_beams=5						,				max_length=2_56						,				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:'''						,				SCREAMING_SNAKE_CASE     )
		lowercase__            =      processor.batch_decode(SCREAMING_SNAKE_CASE						,				skip_special_tokens=SCREAMING_SNAKE_CASE     )
		lowercase__            =      [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'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)






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase       =							{'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							['DPTFeatureExtractor']
			lowerCAmelCase       =							['DPTImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'DPTForDepthEstimation',
			    'DPTForSemanticSegmentation',
			    'DPTModel',
			    'DPTPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_dpt import DPTFeatureExtractor
						from .image_processing_dpt import DPTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_dpt import (
						    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DPTForDepthEstimation,
						    DPTForSemanticSegmentation,
						    DPTModel,
						    DPTPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase       =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


