
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase :			List[Any]              =	logging.get_logger(__name__)

_UpperCAmelCase :			Optional[Any]              =	{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}


class     lowercase (		UpperCamelCase__       ):
		__lowercase	:	Tuple     					=    "lilt"



		def __init__(			self    ,				A_=30_522    ,				A_=768    ,				A_=12    ,				A_=12    ,				A_=3_072    ,				A_="gelu"    ,				A_=0.1    ,				A_=0.1    ,				A_=512    ,				A_=2    ,				A_=0.02    ,				A_=1e-12    ,				A_=0    ,				A_="absolute"    ,				A_=None    ,				A_=4    ,				A_=1_024    ,				**A_    ,				)  -> List[str]:
				"""simple docstring"""
				super().__init__(pad_token_id=__lowerCamelCase    ,				**__lowerCamelCase	)

				UpperCamelCase          =					vocab_size
				UpperCamelCase          =					hidden_size
				UpperCamelCase          =					num_hidden_layers
				UpperCamelCase          =					num_attention_heads
				UpperCamelCase          =					hidden_act
				UpperCamelCase          =					intermediate_size
				UpperCamelCase          =					hidden_dropout_prob
				UpperCamelCase          =					attention_probs_dropout_prob
				UpperCamelCase          =					max_position_embeddings
				UpperCamelCase          =					type_vocab_size
				UpperCamelCase          =					initializer_range
				UpperCamelCase          =					layer_norm_eps
				UpperCamelCase          =					position_embedding_type
				UpperCamelCase          =					classifier_dropout
				UpperCamelCase          =					channel_shrink_ratio
				UpperCamelCase          =					max_ad_position_embeddings


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 				A	( )       ->  int:
		'''simple docstring'''

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

		return image


def 				A	( lowercase			)       ->  Any:
		'''simple docstring'''

		UpperCamelCase          =					[]
		# 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 				A	( lowercase   ,	lowercase   ,	lowercase			)       ->  Dict:
		'''simple docstring'''

		UpperCamelCase          =					dct.pop(lowercase			)
		UpperCamelCase          =					val


def 				A	( lowercase   ,	lowercase			)       ->  List[str]:
		'''simple docstring'''

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

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


def 				A	( lowercase   ,	lowercase			)       ->  int:
		'''simple docstring'''

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

		UpperCamelCase          =					BlipaConfig(vision_config=lowercase   ,	text_config=lowercase			)

		return config, image_size


@torch.no_grad()
def 				A	( lowercase   ,	lowercase=None   ,	lowercase=False			)       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					(
		    AutoTokenizer.from_pretrained('facebook/opt-2.7b'			)
		    if 'opt' in model_name
		    else AutoTokenizer.from_pretrained('google/flan-t5-xl'			)
		)
		UpperCamelCase          =					tokenizer('\n'   ,	add_special_tokens=lowercase			).input_ids[0]
		UpperCamelCase     ,  UpperCamelCase          =					get_blipa_config(lowercase   ,	eos_token_id=lowercase			)

		UpperCamelCase          =					BlipaForConditionalGeneration(lowercase			).eval()

		UpperCamelCase          =					{
		    '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'),
		}

		UpperCamelCase     ,  UpperCamelCase          =					model_name_to_original[model_name]

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

		# update state dict keys
		UpperCamelCase          =					original_model.state_dict()
		UpperCamelCase          =					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():
				UpperCamelCase          =					state_dict.pop(lowercase			)
				if key.startswith('Qformer.bert'			):
						UpperCamelCase          =					key.replace('Qformer.bert'   ,	'qformer'			)
				if "attention.self" in key:
						UpperCamelCase          =					key.replace('self'   ,	'attention'			)
				if "opt_proj" in key:
						UpperCamelCase          =					key.replace('opt_proj'   ,	'language_projection'			)
				if "t5_proj" in key:
						UpperCamelCase          =					key.replace('t5_proj'   ,	'language_projection'			)
				if key.startswith('opt'			):
						UpperCamelCase          =					key.replace('opt'   ,	'language'			)
				if key.startswith('t5'			):
						UpperCamelCase          =					key.replace('t5'   ,	'language'			)
				UpperCamelCase          =					val

		# read in qv biases
		read_in_q_v_bias(lowercase   ,	lowercase			)

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

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

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

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

		original_model.to(lowercase			)
		hf_model.to(lowercase			)
		with torch.no_grad():
				if "opt" in model_name:
						UpperCamelCase          =					original_model({'image': original_pixel_values, 'text_input': ['']}			).logits
						UpperCamelCase          =					hf_model(lowercase   ,	lowercase			).logits
				else:
						UpperCamelCase          =					original_model(
						    {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}			).logits
						UpperCamelCase          =					input_ids.masked_fill(input_ids == tokenizer.pad_token_id   ,	-100			)
						UpperCamelCase          =					hf_model(lowercase   ,	lowercase   ,	labels=lowercase			).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":
				UpperCamelCase          =					torch.tensor(
				    [[-4_1.5_8_5_0, -4.4_4_4_0, -8.9_9_2_2], [-4_7.4_3_2_2, -5.9_1_4_3, -1.7_3_4_0]]   ,	device=lowercase			)
				assert torch.allclose(logits[0, :3, :3]   ,	lowercase   ,	atol=1e-4			)
		elif model_name == "blip2-flan-t5-xl-coco":
				UpperCamelCase          =					torch.tensor(
				    [[-5_7.0_1_0_9, -9.8_9_6_7, -1_2.6_2_8_0], [-6_8.6_5_7_8, -1_2.7_1_9_1, -1_0.5_0_6_5]]   ,	device=lowercase			)
		else:
				# cast to same type
				UpperCamelCase          =					logits.dtype
				assert torch.allclose(original_logits.to(lowercase			)   ,	lowercase   ,	atol=1e-2			)
		print('Looks ok!'			)

		print('Generating a caption...'			)
		UpperCamelCase          =					''
		UpperCamelCase          =					tokenizer(lowercase   ,	return_tensors='pt'			).input_ids.to(lowercase			)

		UpperCamelCase          =					original_model.generate({'image': original_pixel_values}			)
		UpperCamelCase          =					hf_model.generate(
		    lowercase   ,	lowercase   ,	do_sample=lowercase   ,	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:'   ,	lowercase			)
		UpperCamelCase          =					input_ids.shape[1]
		UpperCamelCase          =					processor.batch_decode(outputs[:, prompt_length:]   ,	skip_special_tokens=lowercase			)
		UpperCamelCase          =					[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'''nielsr/{model_name}'''			)
				hf_model.push_to_hub(f'''nielsr/{model_name}'''			)


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

					_UpperCAmelCase :			List[str]              =	parser.parse_args()

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