



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_		(					)	->     int:



				"""simple docstring"""





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

				return image







def     UpperCAmelCase_		(					snake_case__					)	->     int:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				# 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_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Any:



				"""simple docstring"""





				lowerCAmelCase__		  =  dct.pop(snake_case__					)
				lowerCAmelCase__		  =  val







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     List[Any]:



				"""simple docstring"""





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

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







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  364 if 'coco' in model_name else 224
				lowerCAmelCase__		  =  InstructBlipVisionConfig(image_size=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:
								lowerCAmelCase__		  =  TaConfig.from_pretrained('google/flan-t5-xl'    ,			dense_act_fn='gelu'    ,			bos_token_id=1					).to_dict()
				elif "t5-xxl" in model_name:
								lowerCAmelCase__		  =  TaConfig.from_pretrained('google/flan-t5-xxl'    ,			dense_act_fn='gelu'    ,			bos_token_id=1					).to_dict()
				elif "vicuna-7b" in model_name:
								lowerCAmelCase__		  =  LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'    ,			vocab_size=32001					).to_dict()
				elif "vicuna-13b" in model_name:
								lowerCAmelCase__		  =  LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'    ,			vocab_size=32001					).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
				lowerCAmelCase__		  =  InstructBlipQFormerConfig(vocab_size=30523					).to_dict()
				lowerCAmelCase__		  =  InstructBlipConfig(vision_config=snake_case__    ,			text_config=snake_case__    ,			qformer_config=snake_case__					)

				return config, image_size







@torch.no_grad()
def     UpperCAmelCase_		(					snake_case__    ,			snake_case__=None    ,			snake_case__=False					)	->     Tuple:



				"""simple docstring"""





				lowerCAmelCase__		  =  AutoTokenizer.from_pretrained('bert-base-uncased'    ,			truncation_side='left'					)
				qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}					)

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

				lowerCAmelCase__  ,   lowerCAmelCase__		  =  get_blipa_config(snake_case__					)
				lowerCAmelCase__		  =  InstructBlipForConditionalGeneration(snake_case__					).eval()

				lowerCAmelCase__		  =  {
				    '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'),
				}

				lowerCAmelCase__  ,   lowerCAmelCase__		  =  model_name_to_original[model_name]

				# load original model
				print('Loading original model...'					)
				lowerCAmelCase__		  =  'cuda:1' if torch.cuda.is_available() else 'cpu'
				lowerCAmelCase__		  =  'cuda:2' if torch.cuda.is_available() else 'cpu'
				lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  load_model_and_preprocess(
				    name=snake_case__    ,			model_type=snake_case__    ,			is_eval=snake_case__    ,			device=snake_case__					)
				original_model.eval()
				print('Done!'					)

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

				# some keys can be renamed efficiently
				for key, val in state_dict.copy().items():
								lowerCAmelCase__		  =  state_dict.pop(snake_case__					)
								if key.startswith('Qformer.bert'					):
												lowerCAmelCase__		  =  key.replace('Qformer.bert'    ,			'qformer'					)
								if "attention.self" in key:
												lowerCAmelCase__		  =  key.replace('self'    ,			'attention'					)
								if "llm_proj" in key:
												lowerCAmelCase__		  =  key.replace('llm_proj'    ,			'language_projection'					)
								if "t5_proj" in key:
												lowerCAmelCase__		  =  key.replace('t5_proj'    ,			'language_projection'					)
								if key.startswith('llm_model'					):
												lowerCAmelCase__		  =  key.replace('llm_model'    ,			'language_model'					)
								if key.startswith('t5'					):
												lowerCAmelCase__		  =  key.replace('t5'    ,			'language'					)
								lowerCAmelCase__		  =  val

				# read in qv biases
				read_in_q_v_bias(snake_case__    ,			snake_case__					)

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

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

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

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

				original_model.to(snake_case__					)
				hf_model.to(snake_case__					)
				with torch.no_grad():
								if "vicuna" in model_name:
												lowerCAmelCase__		  =  original_model({'image': original_pixel_values, 'text_input': [prompt]}					).logits
												lowerCAmelCase__		  =  hf_model(**snake_case__					).logits
								else:
												lowerCAmelCase__		  =  original_model(
												    {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}					).logits
												lowerCAmelCase__		  =  tokenizer('\n'    ,			return_tensors='pt'					).input_ids.to(snake_case__					)
												lowerCAmelCase__		  =  label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id    ,			-100					)
												lowerCAmelCase__		  =  hf_model(**snake_case__    ,			labels=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
				lowerCAmelCase__		  =  1E-4 if 'vicuna' in model_name else 1E-5
				assert torch.allclose(original_logits.to(logits.device					)    ,			snake_case__    ,			atol=snake_case__					)
				print('Looks ok!'					)

				print('Generating with original model...'					)
				lowerCAmelCase__		  =  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...'					)
				lowerCAmelCase__		  =  hf_model.generate(
				    **snake_case__    ,			do_sample=snake_case__    ,			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?
								lowerCAmelCase__		  =  2
				print('Original generation:'    ,			snake_case__					)
				lowerCAmelCase__		  =  processor.batch_decode(snake_case__    ,			skip_special_tokens=snake_case__					)
				lowerCAmelCase__		  =  [text.strip() for text in output_text]
				print('HF generation:'    ,			snake_case__					)

				if pytorch_dump_folder_path is not None:
								processor.save_pretrained(snake_case__					)
								hf_model.save_pretrained(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	:   Dict    =   argparse.ArgumentParser()
	_lowerCAmelCase	:   Any    =   [
	    "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	:   Optional[Any]    =   parser.parse_args()

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





import numpy as np







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__ = 1E-12    ,			snake_case__ = 100    ,			)	->     tuple[float, np.ndarray]:



				"""simple docstring"""





				assert np.shape(snake_case__					)[0] == np.shape(snake_case__					)[1]
				# Ensure proper dimensionality.
				assert np.shape(snake_case__					)[0] == np.shape(snake_case__					)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(snake_case__					) == np.iscomplexobj(snake_case__					)
				lowerCAmelCase__		  =  np.iscomplexobj(snake_case__					)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(snake_case__    ,			input_matrix.conj().T					)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				lowerCAmelCase__		  =  False
				lowerCAmelCase__		  =  0
				lowerCAmelCase__		  =  0
				lowerCAmelCase__		  =  1E12

				while not convergence:
								# Multiple matrix by the vector.
								lowerCAmelCase__		  =  np.dot(snake_case__    ,			snake_case__					)
								# Normalize the resulting output vector.
								lowerCAmelCase__		  =  w / np.linalg.norm(snake_case__					)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								lowerCAmelCase__		  =  vector.conj().T if is_complex else vector.T
								lowerCAmelCase__		  =  np.dot(snake_case__    ,			np.dot(snake_case__    ,			snake_case__					)					)

								# Check convergence.
								lowerCAmelCase__		  =  np.abs(lambda_ - lambda_previous					) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												lowerCAmelCase__		  =  True

								lowerCAmelCase__		  =  lambda_

				if is_complex:
								lowerCAmelCase__		  =  np.real(lambda_					)

				return lambda_, vector







def     UpperCAmelCase_		(					)	->     None:



				"""simple docstring"""





				lowerCAmelCase__		  =  np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]					)
				lowerCAmelCase__		  =  np.array([41, 4, 20]					)
				lowerCAmelCase__		  =  real_input_matrix.astype(np.complexaaa					)
				lowerCAmelCase__		  =  np.triu(1j * complex_input_matrix    ,			1					)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				lowerCAmelCase__		  =  np.array([41, 4, 20]					).astype(np.complexaaa					)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												lowerCAmelCase__		  =  real_input_matrix
												lowerCAmelCase__		  =  real_vector
								elif problem_type == "complex":
												lowerCAmelCase__		  =  complex_input_matrix
												lowerCAmelCase__		  =  complex_vector

								# Our implementation.
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  power_iteration(snake_case__    ,			snake_case__					)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  np.linalg.eigh(snake_case__					)
								# Last eigenvalue is the maximum one.
								lowerCAmelCase__		  =  eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								lowerCAmelCase__		  =  eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max					) <= 1E-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(snake_case__					) - np.abs(snake_case__					)					) <= 1E-6


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	test_power_iteration()
