



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(_UpperCAmelCase							,     stream=_UpperCAmelCase					).raw					).convert('RGB'					)

			return image





def 							A			(  _UpperCAmelCase       :     Optional[int]					)       ->				Union[str, 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			(  _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Optional[Any]					)       ->				Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase	 =     dct.pop(_UpperCAmelCase					)
			_UpperCAmelCase	 =     val





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Tuple					)       ->				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(_UpperCAmelCase							,     requires_grad=_UpperCAmelCase					), v_bias)					)
						_UpperCAmelCase	 =     qkv_bias





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     str					)       ->				int:







			'''simple docstring'''
			_UpperCAmelCase	 =     364 if 'coco' in model_name else 224
			_UpperCAmelCase	 =     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:
						_UpperCAmelCase	 =     OPTConfig.from_pretrained('facebook/opt-2.7b'							,     eos_token_id=_UpperCAmelCase					).to_dict()
			elif "opt-6.7b" in model_name:
						_UpperCAmelCase	 =     OPTConfig.from_pretrained('facebook/opt-6.7b'							,     eos_token_id=_UpperCAmelCase					).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=_UpperCAmelCase							,     text_config=_UpperCAmelCase					)

			return config, image_size





@torch.no_grad()
def 							A			(  _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     List[str]=None							,     _UpperCAmelCase       :     int=False					)       ->				Optional[int]:







			'''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=_UpperCAmelCase					).input_ids[0]
			_UpperCAmelCase ,  _UpperCAmelCase	 =     get_blipa_config(_UpperCAmelCase							,     eos_token_id=_UpperCAmelCase					)

			_UpperCAmelCase	 =     BlipaForConditionalGeneration(_UpperCAmelCase					).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=_UpperCAmelCase							,     model_type=_UpperCAmelCase							,     is_eval=_UpperCAmelCase							,     device=_UpperCAmelCase					)
			original_model.eval()
			print('Done!'					)

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

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

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

			# create processor
			_UpperCAmelCase	 =     BlipImageProcessor(
			    size={'height': image_size, 'width': image_size}							,     image_mean=_UpperCAmelCase							,     image_std=_UpperCAmelCase					)
			_UpperCAmelCase	 =     BlipaProcessor(image_processor=_UpperCAmelCase							,     tokenizer=_UpperCAmelCase					)
			_UpperCAmelCase	 =     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:
									_UpperCAmelCase	 =     original_model({'image': original_pixel_values, 'text_input': ['']}					).logits
									_UpperCAmelCase	 =     hf_model(_UpperCAmelCase							,     _UpperCAmelCase					).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(_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":
						_UpperCAmelCase	 =     torch.tensor(
						    [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]							,     device=_UpperCAmelCase					)
						assert torch.allclose(logits[0, :3, :3]							,     _UpperCAmelCase							,     atol=1E-4					)
			elif model_name == "blip2-flan-t5-xl-coco":
						_UpperCAmelCase	 =     torch.tensor(
						    [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]							,     device=_UpperCAmelCase					)
			else:
						# cast to same type
						_UpperCAmelCase	 =     logits.dtype
						assert torch.allclose(original_logits.to(_UpperCAmelCase					)							,     _UpperCAmelCase							,     atol=1E-2					)
			print('Looks ok!'					)

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

			_UpperCAmelCase	 =     original_model.generate({'image': original_pixel_values}					)
			_UpperCAmelCase	 =     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					)
			_UpperCAmelCase	 =     input_ids.shape[1]
			_UpperCAmelCase	 =     processor.batch_decode(outputs[:, prompt_length:]							,     skip_special_tokens=_UpperCAmelCase					)
			_UpperCAmelCase	 =     [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__":
							UpperCAmelCase__     = argparse.ArgumentParser()
							UpperCAmelCase__     = [
							    "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__     = parser.parse_args()

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





import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format="%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
    level=os.environ.get("LOGLEVEL", "INFO").upper(),
    stream=sys.stdout,
)

UpperCAmelCase__     = logging.getLogger(__name__)

UpperCAmelCase__     = {"facebook/bart-base": BartForConditionalGeneration}
UpperCAmelCase__     = {"facebook/bart-base": BartTokenizer}





def 							A			(  )       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     argparse.ArgumentParser(description='Export Bart model + Beam Search to ONNX graph.'					)
			parser.add_argument(
			    '--validation_file'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help='A csv or a json file containing the validation data.'					)
			parser.add_argument(
			    '--max_length'							,     type=_UpperCAmelCase							,     default=5							,     help='The maximum total input sequence length after tokenization.'							,     )
			parser.add_argument(
			    '--num_beams'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help=(
			        'Number of beams to use for evaluation. This argument will be '
			        'passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'
			    )							,     )
			parser.add_argument(
			    '--model_name_or_path'							,     type=_UpperCAmelCase							,     help='Path to pretrained model or model identifier from huggingface.co/models.'							,     required=_UpperCAmelCase							,     )
			parser.add_argument(
			    '--config_name'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help='Pretrained config name or path if not the same as model_name'							,     )
			parser.add_argument(
			    '--device'							,     type=_UpperCAmelCase							,     default='cpu'							,     help='Device where the model will be run'							,     )
			parser.add_argument('--output_file_path'							,     type=_UpperCAmelCase							,     default=_UpperCAmelCase							,     help='Where to store the final ONNX file.'					)

			_UpperCAmelCase	 =     parser.parse_args()

			return args





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     List[Any]="cpu"					)       ->				Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase	 =     model_dict[model_name].from_pretrained(_UpperCAmelCase					).to(_UpperCAmelCase					)
			_UpperCAmelCase	 =     tokenizer_dict[model_name].from_pretrained(_UpperCAmelCase					)

			if model_name in ["facebook/bart-base"]:
						_UpperCAmelCase	 =     0
						_UpperCAmelCase	 =     None
						_UpperCAmelCase	 =     0

			return huggingface_model, tokenizer





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     Tuple					)       ->				List[Any]:







			'''simple docstring'''
			model.eval()

			_UpperCAmelCase	 =     None
			_UpperCAmelCase	 =     torch.jit.script(BARTBeamSearchGenerator(_UpperCAmelCase					)					)

			with torch.no_grad():
						_UpperCAmelCase	 =     'My friends are cool but they eat too many carbs.'
						_UpperCAmelCase	 =     tokenizer([ARTICLE_TO_SUMMARIZE]							,     max_length=1_024							,     return_tensors='pt'					).to(model.device					)

						_UpperCAmelCase	 =     model.generate(
						    inputs['input_ids']							,     attention_mask=inputs['attention_mask']							,     num_beams=_UpperCAmelCase							,     max_length=_UpperCAmelCase							,     early_stopping=_UpperCAmelCase							,     decoder_start_token_id=model.config.decoder_start_token_id							,     )

						torch.onnx.export(
						    _UpperCAmelCase							,     (
						        inputs['input_ids'],
						        inputs['attention_mask'],
						        num_beams,
						        max_length,
						        model.config.decoder_start_token_id,
						    )							,     _UpperCAmelCase							,     opset_version=14							,     input_names=['input_ids', 'attention_mask', 'num_beams', 'max_length', 'decoder_start_token_id']							,     output_names=['output_ids']							,     dynamic_axes={
						        'input_ids': {0: 'batch', 1: 'seq'},
						        'output_ids': {0: 'batch', 1: 'seq_out'},
						    }							,     example_outputs=_UpperCAmelCase							,     )

						logger.info('Model exported to {}'.format(_UpperCAmelCase					)					)

						_UpperCAmelCase	 =     remove_dup_initializers(os.path.abspath(_UpperCAmelCase					)					)

						logger.info('Deduplicated and optimized model written to {}'.format(_UpperCAmelCase					)					)

						_UpperCAmelCase	 =     onnxruntime.InferenceSession(_UpperCAmelCase					)
						_UpperCAmelCase	 =     ort_sess.run(
						    _UpperCAmelCase							,     {
						        'input_ids': inputs['input_ids'].cpu().numpy(),
						        'attention_mask': inputs['attention_mask'].cpu().numpy(),
						        'num_beams': np.array(_UpperCAmelCase					),
						        'max_length': np.array(_UpperCAmelCase					),
						        'decoder_start_token_id': np.array(model.config.decoder_start_token_id					),
						    }							,     )

						np.testing.assert_allclose(summary_ids.cpu().numpy()							,     ort_out[0]							,     rtol=1E-3							,     atol=1E-3					)

						logger.info('Model outputs from torch and ONNX Runtime are similar.'					)
						logger.info('Success.'					)





def 							A			(  )       ->				Dict:







			'''simple docstring'''
			_UpperCAmelCase	 =     parse_args()
			_UpperCAmelCase	 =     5
			_UpperCAmelCase	 =     4

			# Make one log on every process with the configuration for debugging.
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,     datefmt='%m/%d/%Y %H:%M:%S'							,     level=logging.INFO							,     )

			logger.setLevel(logging.INFO					)
			transformers.utils.logging.set_verbosity_error()

			_UpperCAmelCase	 =     torch.device(args.device					)

			_UpperCAmelCase ,  _UpperCAmelCase	 =     load_model_tokenizer(args.model_name_or_path							,     _UpperCAmelCase					)

			if model.config.decoder_start_token_id is None:
						raise ValueError('Make sure that `config.decoder_start_token_id` is correctly defined'					)

			model.to(_UpperCAmelCase					)

			if args.max_length:
						_UpperCAmelCase	 =     args.max_length

			if args.num_beams:
						_UpperCAmelCase	 =     args.num_beams

			if args.output_file_path:
						_UpperCAmelCase	 =     args.output_file_path
			else:
						_UpperCAmelCase	 =     'BART.onnx'

			logger.info('Exporting model to ONNX'					)
			export_and_validate_model(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)


if __name__ == "__main__":
							main()
