



'''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 						__UpperCamelCase				(							) ->			Optional[int]:
	_lowercase					:       Optional[Any]       =		'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
	_lowercase					:       Tuple       =		Image.open(requests.get(_lowercase,				stream=_lowercase					).raw					).convert('RGB'					)

	return image







def 						__UpperCamelCase				(							_lowercase					) ->			str:
	_lowercase					:       Optional[int]       =		[]
	# 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 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			str:
	_lowercase					:       int       =		dct.pop(_lowercase					)
	_lowercase					:       int       =		val







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			Any:
	for i in range(config.vision_config.num_hidden_layers					):
		# read in original q and v biases
		_lowercase					:       Optional[Any]       =		state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias'''					)
		_lowercase					:       Optional[int]       =		state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias'''					)

		# next, set bias in the state dict
		_lowercase					:       int       =		torch.cat((q_bias, torch.zeros_like(_lowercase,				requires_grad=_lowercase					), v_bias)					)
		_lowercase					:       Union[str, Any]       =		qkv_bias







def 						__UpperCamelCase				(							_lowercase					) ->			str:
	_lowercase					:       int       =		364 if 'coco' in model_name else 224
	_lowercase					:       int       =		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					:       Dict       =		TaConfig.from_pretrained('google/flan-t5-xl',				dense_act_fn='gelu',				bos_token_id=1					).to_dict()
	elif "t5-xxl" in model_name:
		_lowercase					:       Union[str, Any]       =		TaConfig.from_pretrained('google/flan-t5-xxl',				dense_act_fn='gelu',				bos_token_id=1					).to_dict()
	elif "vicuna-7b" in model_name:
		_lowercase					:       Dict       =		LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf',				vocab_size=3_2001					).to_dict()
	elif "vicuna-13b" in model_name:
		_lowercase					:       Dict       =		LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf',				vocab_size=3_2001					).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					:       Optional[int]       =		InstructBlipQFormerConfig(vocab_size=3_0523					).to_dict()
	_lowercase					:       Optional[int]       =		InstructBlipConfig(vision_config=_lowercase,				text_config=_lowercase,				qformer_config=_lowercase					)

	return config, image_size







@torch.no_grad()
def 						__UpperCamelCase				(							_lowercase,				_lowercase=None,				_lowercase=False					) ->			Optional[Any]:
	_lowercase					:       List[Any]       =		AutoTokenizer.from_pretrained('bert-base-uncased',				truncation_side='left'					)
	qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}					)

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

	_lowercase  ,    _lowercase					:       int       =		get_blipa_config(_lowercase					)
	_lowercase					:       List[Any]       =		InstructBlipForConditionalGeneration(_lowercase					).eval()

	_lowercase					:       Dict       =		{
	    '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					:       Dict       =		model_name_to_original[model_name]

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

	# update state dict keys
	_lowercase					:       Optional[int]       =		original_model.state_dict()
	_lowercase					:       Optional[int]       =		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					:       List[Any]       =		state_dict.pop(_lowercase					)
		if key.startswith('Qformer.bert'					):
			_lowercase					:       List[Any]       =		key.replace('Qformer.bert',				'qformer'					)
		if "attention.self" in key:
			_lowercase					:       Tuple       =		key.replace('self',				'attention'					)
		if "llm_proj" in key:
			_lowercase					:       int       =		key.replace('llm_proj',				'language_projection'					)
		if "t5_proj" in key:
			_lowercase					:       List[str]       =		key.replace('t5_proj',				'language_projection'					)
		if key.startswith('llm_model'					):
			_lowercase					:       List[Any]       =		key.replace('llm_model',				'language_model'					)
		if key.startswith('t5'					):
			_lowercase					:       Union[str, Any]       =		key.replace('t5',				'language'					)
		_lowercase					:       Tuple       =		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					:       Any       =		load_demo_image()
	_lowercase					:       Tuple       =		'What is unusual about this image?'

	# create processor
	_lowercase					:       Union[str, Any]       =		BlipImageProcessor(
	    size={'height': image_size, 'width': image_size},				image_mean=_lowercase,				image_std=_lowercase					)
	_lowercase					:       Union[str, Any]       =		InstructBlipProcessor(
	    image_processor=_lowercase,				tokenizer=_lowercase,				qformer_tokenizer=_lowercase,				)
	_lowercase					:       Tuple       =		processor(images=_lowercase,				text=_lowercase,				return_tensors='pt'					).to(_lowercase					)

	# make sure processor creates exact same pixel values
	_lowercase					:       int       =		vis_processors['eval'](_lowercase					).unsqueeze(0					).to(_lowercase					)
	_lowercase					:       Dict       =		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					:       Optional[int]       =		original_model({'image': original_pixel_values, 'text_input': [prompt]}					).logits
			_lowercase					:       List[Any]       =		hf_model(**_lowercase					).logits
		else:
			_lowercase					:       str       =		original_model(
			    {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}					).logits
			_lowercase					:       str       =		tokenizer('\n',				return_tensors='pt'					).input_ids.to(_lowercase					)
			_lowercase					:       int       =		label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id,				-100					)
			_lowercase					:       Any       =		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					:       Optional[int]       =		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					:       str       =		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					:       Tuple       =		hf_model.generate(
	    **_lowercase,				do_sample=_lowercase,				num_beams=5,				max_length=256,				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					:       int       =		2
	print('Original generation:',				_lowercase					)
	_lowercase					:       Any       =		processor.batch_decode(_lowercase,				skip_special_tokens=_lowercase					)
	_lowercase					:       str       =		[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__":
				_A				:    List[str]     						=argparse.ArgumentParser()
				_A				:    Optional[int]     						=[
				    '''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''',
				)

				_A				:    Any     						=parser.parse_args()

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





'''simple docstring'''

import os
from collections.abc import Iterator







def 						__UpperCamelCase				(							_lowercase = "."					) ->			Iterator[str]:
	for dir_path, dir_names, filenames in os.walk(_lowercase					):
		_lowercase					:       Optional[int]       =		[d for d in dir_names if d != 'scripts' and d[0] not in '._']
		for filename in filenames:
			if filename == "__init__.py":
				continue
			if os.path.splitext(_lowercase					)[1] in (".py", ".ipynb"):
				yield os.path.join(_lowercase,				_lowercase					).lstrip('./'					)







def 						__UpperCamelCase				(							_lowercase					) ->			List[str]:
	return f'''{i * "  "}*''' if i else "\n##"







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			str:
	_lowercase					:       Optional[Any]       =		old_path.split(os.sep					)
	for i, new_part in enumerate(new_path.split(os.sep					)					):
		if (i + 1 > len(_lowercase					) or old_parts[i] != new_part) and new_part:
			print(f'''{md_prefix(_lowercase					)} {new_part.replace("_",				" "					).title()}'''					)
	return new_path







def 						__UpperCamelCase				(							_lowercase = "."					) ->			None:
	_lowercase					:       Dict       =		''
	for filepath in sorted(good_file_paths(_lowercase					)					):
		_lowercase  ,    _lowercase					:       Optional[Any]       =		os.path.split(_lowercase					)
		if filepath != old_path:
			_lowercase					:       Dict       =		print_path(_lowercase,				_lowercase					)
		_lowercase					:       Optional[int]       =		(filepath.count(os.sep					) + 1) if filepath else 0
		_lowercase					:       Dict       =		f'''{filepath}/{filename}'''.replace(' ',				'%20'					)
		_lowercase					:       Optional[int]       =		os.path.splitext(filename.replace('_',				' '					).title()					)[0]
		print(f'''{md_prefix(_lowercase					)} [{filename}]({url})'''					)


if __name__ == "__main__":
		print_directory_md('''.''')
