
"""simple docstring"""



import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)


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





    """simple docstring"""

    _UpperCAmelCase          =   """https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
    _UpperCAmelCase          =   Image.open(requests.get(__SCREAMING_SNAKE_CASE					,      stream=__SCREAMING_SNAKE_CASE      ).raw      ).convert("""RGB"""      )

    _UpperCAmelCase          =   transforms.Compose(
        [
            transforms.Resize((image_size, image_size)					,      interpolation=InterpolationMode.BICUBIC      ),
            transforms.ToTensor(),
            transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)					,      (0.26_862_954, 0.26_130_258, 0.27_577_711)      ),
        ]      )
    _UpperCAmelCase          =   transform(__SCREAMING_SNAKE_CASE      ).unsqueeze(0      ).to(__SCREAMING_SNAKE_CASE      )
    return image


def  _UpperCamelCase	(						_A      )    ->  int:





    """simple docstring"""

    if "visual_encoder" in key:
        _UpperCAmelCase          =   re.sub("""visual_encoder*"""					,      """vision_model.encoder"""					,      __SCREAMING_SNAKE_CASE      )
    if "blocks" in key:
        _UpperCAmelCase          =   re.sub(R"""blocks"""					,      """layers"""					,      __SCREAMING_SNAKE_CASE      )
    if "attn" in key:
        _UpperCAmelCase          =   re.sub(R"""attn"""					,      """self_attn"""					,      __SCREAMING_SNAKE_CASE      )
    if "norm1" in key:
        _UpperCAmelCase          =   re.sub(R"""norm1"""					,      """layer_norm1"""					,      __SCREAMING_SNAKE_CASE      )
    if "norm2" in key:
        _UpperCAmelCase          =   re.sub(R"""norm2"""					,      """layer_norm2"""					,      __SCREAMING_SNAKE_CASE      )
    if "encoder.norm" in key:
        _UpperCAmelCase          =   re.sub(R"""encoder.norm"""					,      """post_layernorm"""					,      __SCREAMING_SNAKE_CASE      )
    if "encoder.patch_embed.proj" in key:
        _UpperCAmelCase          =   re.sub(R"""encoder.patch_embed.proj"""					,      """embeddings.patch_embedding"""					,      __SCREAMING_SNAKE_CASE      )

    if "encoder.pos_embed" in key:
        _UpperCAmelCase          =   re.sub(R"""encoder.pos_embed"""					,      """embeddings.position_embedding"""					,      __SCREAMING_SNAKE_CASE      )
    if "encoder.cls_token" in key:
        _UpperCAmelCase          =   re.sub(R"""encoder.cls_token"""					,      """embeddings.class_embedding"""					,      __SCREAMING_SNAKE_CASE      )

    if "self_attn" in key:
        _UpperCAmelCase          =   re.sub(R"""self_attn.proj"""					,      """self_attn.projection"""					,      __SCREAMING_SNAKE_CASE      )

    return key


@torch.no_grad()
def  _UpperCamelCase	(						_A					,      _A=None      )    ->  Optional[Any]:





    """simple docstring"""

    if config_path is not None:
        _UpperCAmelCase          =   BlipConfig.from_pretrained(__SCREAMING_SNAKE_CASE      )
    else:
        _UpperCAmelCase          =   BlipConfig(projection_dim=5_1_2					,      text_config={}					,      vision_config={}      )

    _UpperCAmelCase          =   BlipForConditionalGeneration(__SCREAMING_SNAKE_CASE      ).eval()

    _UpperCAmelCase          =   """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"""

    _UpperCAmelCase          =   blip_decoder(pretrained=__SCREAMING_SNAKE_CASE					,      image_size=3_8_4					,      vit="""base"""      )
    _UpperCAmelCase          =   pt_model.eval()

    _UpperCAmelCase          =   pt_model.state_dict()
    for key in modified_state_dict.copy():
        _UpperCAmelCase          =   modified_state_dict.pop(__SCREAMING_SNAKE_CASE      )
        _UpperCAmelCase          =   rename_key(__SCREAMING_SNAKE_CASE      )
        _UpperCAmelCase          =   value

    hf_model.load_state_dict(__SCREAMING_SNAKE_CASE      )

    _UpperCAmelCase          =   3_8_4
    _UpperCAmelCase          =   load_demo_image(image_size=__SCREAMING_SNAKE_CASE					,      device="""cpu"""      )
    _UpperCAmelCase          =   BertTokenizer.from_pretrained("""bert-base-uncased"""      )
    _UpperCAmelCase          =   tokenizer(["""a picture of"""]      ).input_ids

    _UpperCAmelCase          =   hf_model.generate(__SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE      )

    assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

    _UpperCAmelCase          =   hf_model.generate(__SCREAMING_SNAKE_CASE      )

    assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

    if pytorch_dump_folder_path is not None:
        hf_model.save_pretrained(__SCREAMING_SNAKE_CASE      )

    # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
    _UpperCAmelCase          =   (
        """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"""
    )

    _UpperCAmelCase          =   blip_vqa(pretrained=__SCREAMING_SNAKE_CASE					,      image_size=__SCREAMING_SNAKE_CASE					,      vit="""base"""      )
    vqa_model.eval()

    _UpperCAmelCase          =   vqa_model.state_dict()
    for key in modified_state_dict.copy():
        _UpperCAmelCase          =   modified_state_dict.pop(__SCREAMING_SNAKE_CASE      )
        _UpperCAmelCase          =   rename_key(__SCREAMING_SNAKE_CASE      )
        _UpperCAmelCase          =   value

    _UpperCAmelCase          =   BlipForQuestionAnswering(__SCREAMING_SNAKE_CASE      )

    hf_vqa_model.load_state_dict(__SCREAMING_SNAKE_CASE      )

    _UpperCAmelCase          =   ["""How many dogs are in this image?"""]
    _UpperCAmelCase          =   tokenizer(__SCREAMING_SNAKE_CASE					,      return_tensors="""pt"""      ).input_ids

    _UpperCAmelCase          =   hf_vqa_model.generate(__SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE      )
    print(tokenizer.decode(answer[0]      )      )

    assert tokenizer.decode(answer[0]      ) == "[UNK] 1 [SEP]"
    if pytorch_dump_folder_path is not None:
        hf_vqa_model.save_pretrained(pytorch_dump_folder_path + """_vqa"""      )

    _UpperCAmelCase          =   """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

    _UpperCAmelCase          =   blip_itm(pretrained=__SCREAMING_SNAKE_CASE					,      image_size=__SCREAMING_SNAKE_CASE					,      vit="""base"""      )
    itm_model.eval()

    _UpperCAmelCase          =   itm_model.state_dict()
    for key in modified_state_dict.copy():
        _UpperCAmelCase          =   modified_state_dict.pop(__SCREAMING_SNAKE_CASE      )
        _UpperCAmelCase          =   rename_key(__SCREAMING_SNAKE_CASE      )
        _UpperCAmelCase          =   value

    _UpperCAmelCase          =   BlipForImageTextRetrieval(__SCREAMING_SNAKE_CASE      )

    _UpperCAmelCase          =   ["""A picture of a woman with a dog sitting in a beach"""]
    _UpperCAmelCase          =   tokenizer(
        __SCREAMING_SNAKE_CASE					,      return_tensors="""pt"""					,      padding="""max_length"""					,      truncation=__SCREAMING_SNAKE_CASE					,      max_length=3_5					,      ).input_ids

    hf_itm_model.load_state_dict(__SCREAMING_SNAKE_CASE      )
    hf_itm_model.eval()

    _UpperCAmelCase          =   hf_itm_model(__SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE					,      use_itm_head=__SCREAMING_SNAKE_CASE      )
    _UpperCAmelCase          =   hf_itm_model(__SCREAMING_SNAKE_CASE					,      __SCREAMING_SNAKE_CASE					,      use_itm_head=__SCREAMING_SNAKE_CASE      )

    assert out[0].item() == 0.2_110_687_494_277_954
    assert torch.nn.functional.softmax(out_itm[0]					,      dim=1      )[:, 1].item() == 0.45_698_845_386_505_127

    if pytorch_dump_folder_path is not None:
        hf_itm_model.save_pretrained(pytorch_dump_folder_path + """_itm"""      )


if __name__ == "__main__":
       a					:	Dict							=		argparse.ArgumentParser()
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
       a					:	Any							=		parser.parse_args()

       convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

"""simple docstring"""



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,
)

a					:	List[str]							=		logging.getLogger(__name__)

a					:	int							=		{'''facebook/bart-base''': BartForConditionalGeneration}
a					:	Dict							=		{'''facebook/bart-base''': BartTokenizer}


def  _UpperCamelCase	(						)    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   argparse.ArgumentParser(description="""Export Bart model + Beam Search to ONNX graph."""      )
    parser.add_argument(
        """--validation_file"""					,      type=_A					,      default=_A					,      help="""A csv or a json file containing the validation data."""      )
    parser.add_argument(
        """--max_length"""					,      type=_A					,      default=5					,      help="""The maximum total input sequence length after tokenization."""					,      )
    parser.add_argument(
        """--num_beams"""					,      type=_A					,      default=_A					,      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=_A					,      help="""Path to pretrained model or model identifier from huggingface.co/models."""					,      required=_A					,      )
    parser.add_argument(
        """--config_name"""					,      type=_A					,      default=_A					,      help="""Pretrained config name or path if not the same as model_name"""					,      )
    parser.add_argument(
        """--device"""					,      type=_A					,      default="""cpu"""					,      help="""Device where the model will be run"""					,      )
    parser.add_argument("""--output_file_path"""					,      type=_A					,      default=_A					,      help="""Where to store the final ONNX file."""      )

    _UpperCAmelCase          =   parser.parse_args()

    return args


def  _UpperCamelCase	(						_A					,      _A="cpu"      )    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   model_dict[model_name].from_pretrained(_A      ).to(_A      )
    _UpperCAmelCase          =   tokenizer_dict[model_name].from_pretrained(_A      )

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

    return huggingface_model, tokenizer


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  Optional[int]:





    """simple docstring"""

    model.eval()

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

    with torch.no_grad():
        _UpperCAmelCase          =   """My friends are cool but they eat too many carbs."""
        _UpperCAmelCase          =   tokenizer([ARTICLE_TO_SUMMARIZE]					,      max_length=1_0_2_4					,      return_tensors="""pt"""      ).to(model.device      )

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

        torch.onnx.export(
            _A					,      (
                inputs["""input_ids"""],
                inputs["""attention_mask"""],
                num_beams,
                max_length,
                model.config.decoder_start_token_id,
            )					,      _A					,      opset_version=1_4					,      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=_A					,      )

        logger.info("""Model exported to {}""".format(_A      )      )

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

        logger.info("""Deduplicated and optimized model written to {}""".format(_A      )      )

        _UpperCAmelCase          =   onnxruntime.InferenceSession(_A      )
        _UpperCAmelCase          =   ort_sess.run(
            _A					,      {
                """input_ids""": inputs["""input_ids"""].cpu().numpy(),
                """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
                """num_beams""": np.array(_A      ),
                """max_length""": np.array(_A      ),
                """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  _UpperCamelCase	(						)    ->  Union[str, Any]:





    """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					,      _A      )

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

    model.to(_A      )

    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(_A					,      _A					,      _A					,      _A					,      _A      )


if __name__ == "__main__":
 main()