






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				(      )					->				str:

						"""simple docstring"""



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

						return image







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[Any]						)					->				Dict:

						"""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				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   List[Any]						)					->				str:

						"""simple docstring"""



						lowerCAmelCase__        =  dct.pop(UpperCamelCase_						)
						lowerCAmelCase__        =  val







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   int						)					->				Optional[int]:

						"""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(UpperCamelCase_      ,    requires_grad=UpperCamelCase_						), v_bias)						)
												lowerCAmelCase__        =  qkv_bias







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple						)					->				Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  364 if 'coco' in model_name else 224
						lowerCAmelCase__        =  InstructBlipVisionConfig(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 "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=3_2001						).to_dict()
						elif "vicuna-13b" in model_name:
												lowerCAmelCase__        =  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
						lowerCAmelCase__        =  InstructBlipQFormerConfig(vocab_size=3_0523						).to_dict()
						lowerCAmelCase__        =  InstructBlipConfig(vision_config=UpperCamelCase_      ,    text_config=UpperCamelCase_      ,    qformer_config=UpperCamelCase_						)

						return config, image_size







@torch.no_grad()
def 		_UpperCamelCase				(      UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   List[Any]=None      ,    UpperCamelCase_    :   Dict=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(UpperCamelCase_						)
						lowerCAmelCase__        =  InstructBlipForConditionalGeneration(UpperCamelCase_						).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=UpperCamelCase_      ,    model_type=UpperCamelCase_      ,    is_eval=UpperCamelCase_      ,    device=UpperCamelCase_						)
						original_model.eval()
						print('Done!'						)

						# update state dict keys
						lowerCAmelCase__        =  original_model.state_dict()
						lowerCAmelCase__        =  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():
												lowerCAmelCase__        =  state_dict.pop(UpperCamelCase_						)
												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(UpperCamelCase_      ,    UpperCamelCase_						)

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

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

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

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

						original_model.to(UpperCamelCase_						)
						hf_model.to(UpperCamelCase_						)
						with torch.no_grad():
												if "vicuna" in model_name:
																		lowerCAmelCase__        =  original_model({'image': original_pixel_values, 'text_input': [prompt]}						).logits
																		lowerCAmelCase__        =  hf_model(**UpperCamelCase_						).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(UpperCamelCase_						)
																		lowerCAmelCase__        =  label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id      ,    -100						)
																		lowerCAmelCase__        =  hf_model(**UpperCamelCase_      ,    labels=UpperCamelCase_						).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						)      ,    UpperCamelCase_      ,    atol=UpperCamelCase_						)
						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(
						    **UpperCamelCase_      ,    do_sample=UpperCamelCase_      ,    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:'      ,    UpperCamelCase_						)
						lowerCAmelCase__        =  processor.batch_decode(UpperCamelCase_      ,    skip_special_tokens=UpperCamelCase_						)
						lowerCAmelCase__        =  [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"Salesforce/{model_name}"						)
												hf_model.push_to_hub(F"Salesforce/{model_name}"						)


if __name__ == "__main__":
					__snake_case      :      List[Any]             =       argparse.ArgumentParser()
					__snake_case      :      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""",
					)

					__snake_case      :      Dict             =       parser.parse_args()

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













import qiskit







def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   int						)					->				qiskit.result.counts.Counts:

						"""simple docstring"""



						lowerCAmelCase__        =  qiskit.Aer.get_backend('aer_simulator'						)

						# Create a Quantum Circuit acting on the q register
						lowerCAmelCase__        =  qiskit.QuantumCircuit(UpperCamelCase_      ,    UpperCamelCase_						)

						# Map the quantum measurement to the classical bits
						circuit.measure([0]      ,    [0]						)

						# Execute the circuit on the simulator
						lowerCAmelCase__        =  qiskit.execute(UpperCamelCase_      ,    UpperCamelCase_      ,    shots=1000						)

						# Return the histogram data of the results of the experiment.
						return job.result().get_counts(UpperCamelCase_						)


if __name__ == "__main__":
					print(f'Total count for various states are: {single_qubit_measure(1, 1)}')





