






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

			return image






def 					lowerCamelCase__					(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE							=			[]
			# 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 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase):
			SCREAMING_SNAKE_CASE							=			dct.pop(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			val






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

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






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

			SCREAMING_SNAKE_CASE							=			BlipaConfig(vision_config=_UpperCAmelCase				,							text_config=_UpperCAmelCase)

			return config, image_size






@torch.no_grad()
def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase=None				,							_UpperCAmelCase=False):
			SCREAMING_SNAKE_CASE							=			(
			    AutoTokenizer.from_pretrained('facebook/opt-2.7b')
			    if 'opt' in model_name
			    else AutoTokenizer.from_pretrained('google/flan-t5-xl')
			)
			SCREAMING_SNAKE_CASE							=			tokenizer('\n'				,							add_special_tokens=_UpperCAmelCase).input_ids[0]
			SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			get_blipa_config(_UpperCAmelCase				,							eos_token_id=_UpperCAmelCase)

			SCREAMING_SNAKE_CASE							=			BlipaForConditionalGeneration(_UpperCAmelCase).eval()

			SCREAMING_SNAKE_CASE							=			{
			    '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'),
			}

			SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			model_name_to_original[model_name]

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

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

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

			# read in qv biases
			read_in_q_v_bias(_UpperCAmelCase				,							_UpperCAmelCase)

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

			SCREAMING_SNAKE_CASE							=			load_demo_image()
			SCREAMING_SNAKE_CASE							=			vis_processors['eval'](_UpperCAmelCase).unsqueeze(0).to(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			tokenizer(['\n']				,							return_tensors='pt').input_ids.to(_UpperCAmelCase)

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

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

			original_model.to(_UpperCAmelCase)
			hf_model.to(_UpperCAmelCase)
			with torch.no_grad():
						if "opt" in model_name:
									SCREAMING_SNAKE_CASE							=			original_model({'image': original_pixel_values, 'text_input': ['']}).logits
									SCREAMING_SNAKE_CASE							=			hf_model(_UpperCAmelCase				,							_UpperCAmelCase).logits
						else:
									SCREAMING_SNAKE_CASE							=			original_model(
									    {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}).logits
									SCREAMING_SNAKE_CASE							=			input_ids.masked_fill(input_ids == tokenizer.pad_token_id				,							-100)
									SCREAMING_SNAKE_CASE							=			hf_model(_UpperCAmelCase				,							_UpperCAmelCase				,							labels=_UpperCAmelCase).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":
						SCREAMING_SNAKE_CASE							=			torch.tensor(
						    [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]				,							device=_UpperCAmelCase)
						assert torch.allclose(logits[0, :3, :3]				,							_UpperCAmelCase				,							atol=1e-4)
			elif model_name == "blip2-flan-t5-xl-coco":
						SCREAMING_SNAKE_CASE							=			torch.tensor(
						    [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]				,							device=_UpperCAmelCase)
			else:
						# cast to same type
						SCREAMING_SNAKE_CASE							=			logits.dtype
						assert torch.allclose(original_logits.to(_UpperCAmelCase)				,							_UpperCAmelCase				,							atol=1e-2)
			print('Looks ok!')

			print('Generating a caption...')
			SCREAMING_SNAKE_CASE							=			''
			SCREAMING_SNAKE_CASE							=			tokenizer(_UpperCAmelCase				,							return_tensors='pt').input_ids.to(_UpperCAmelCase)

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

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

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


if __name__ == "__main__":
				a_		:    str   							=     argparse.ArgumentParser()
				a_		:    Optional[int]   							=     [
				    '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',
				)

				a_		:    int   							=     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


lowercase__							:	Union[str, Any]											=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Tuple											=		["DPTFeatureExtractor"]
		lowercase__							:	Optional[Any]											=		["DPTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Dict											=		[
		    "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

		lowercase__							:	List[Any]											=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
