




'''simple docstring'''







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline





def 		snake_case_							(_a			:							str					,     _a			:							Union[str, Any]					,     _a			:							Tuple					,     _a			:							List[Any]					,     _a			:							Tuple  ):
    # load base model
    UpperCAmelCase     =	StableDiffusionPipeline.from_pretrained(_a					,     torch_dtype=torch.floataa  )

    # load LoRA weight from .safetensors
    UpperCAmelCase     =	load_file(_a  )

    UpperCAmelCase     =	[]

    # directly update weight in diffusers model
    for key in state_dict:
        # it is suggested to print out the key, it usually will be something like below
        # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

        # as we have set the alpha beforehand, so just skip
        if ".alpha" in key or key in visited:
            continue

        if "text" in key:
            UpperCAmelCase     =	key.split('''.'''  )[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''  )[-1].split('''_'''  )
            UpperCAmelCase     =	pipeline.text_encoder
        else:
            UpperCAmelCase     =	key.split('''.'''  )[0].split(LORA_PREFIX_UNET + '''_'''  )[-1].split('''_'''  )
            UpperCAmelCase     =	pipeline.unet

        # find the target layer
        UpperCAmelCase     =	layer_infos.pop(0  )
        while len(_a  ) > -1:
            try:
                UpperCAmelCase     =	curr_layer.__getattr__(_a  )
                if len(_a  ) > 0:
                    UpperCAmelCase     =	layer_infos.pop(0  )
                elif len(_a  ) == 0:
                    break
            except Exception:
                if len(_a  ) > 0:
                    temp_name += "_" + layer_infos.pop(0  )
                else:
                    UpperCAmelCase     =	layer_infos.pop(0  )

        UpperCAmelCase     =	[]
        if "lora_down" in key:
            pair_keys.append(key.replace('''lora_down'''					,     '''lora_up'''  )  )
            pair_keys.append(_a  )
        else:
            pair_keys.append(_a  )
            pair_keys.append(key.replace('''lora_up'''					,     '''lora_down'''  )  )

        # update weight
        if len(state_dict[pair_keys[0]].shape  ) == 4:
            UpperCAmelCase     =	state_dict[pair_keys[0]].squeeze(3  ).squeeze(2  ).to(torch.floataa  )
            UpperCAmelCase     =	state_dict[pair_keys[1]].squeeze(3  ).squeeze(2  ).to(torch.floataa  )
            curr_layer.weight.data += alpha * torch.mm(_a					,     _a  ).unsqueeze(2  ).unsqueeze(3  )
        else:
            UpperCAmelCase     =	state_dict[pair_keys[0]].to(torch.floataa  )
            UpperCAmelCase     =	state_dict[pair_keys[1]].to(torch.floataa  )
            curr_layer.weight.data += alpha * torch.mm(_a					,     _a  )

        # update visited list
        for item in pair_keys:
            visited.append(_a  )

    return pipeline


if __name__ == "__main__":
   A									=argparse.ArgumentParser()

   parser.add_argument(
       '--base_model_path', default=None, type=str, required=True, help='Path to the base model in diffusers format.'
   )
   parser.add_argument(
       '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
   )
   parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
   parser.add_argument(
       '--lora_prefix_unet', default='lora_unet', type=str, help='The prefix of UNet weight in safetensors'
   )
   parser.add_argument(
       '--lora_prefix_text_encoder',
       default='lora_te',
       type=str,
       help='The prefix of text encoder weight in safetensors',
   )
   parser.add_argument('--alpha', default=0.75, type=float, help='The merging ratio in W = W0 + alpha * deltaW')
   parser.add_argument(
       '--to_safetensors', action='store_true', help='Whether to store pipeline in safetensors format or not.'
   )
   parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')

   A									=parser.parse_args()

   A									=args.base_model_path
   A									=args.checkpoint_path
   A									=args.dump_path
   A									=args.lora_prefix_unet
   A									=args.lora_prefix_text_encoder
   A									=args.alpha

   A									=convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

   A									=pipe.to(args.device)
   pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)










'''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									=logging.getLogger(__name__)

A									={'facebook/bart-base': BartForConditionalGeneration}
A									={'facebook/bart-base': BartTokenizer}





def 		snake_case_							():
    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 		snake_case_							(_a			:							Tuple					,     _a			:							str="cpu"  ):
    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 		snake_case_							(_a			:							Optional[int]					,     _a			:							List[str]					,     _a			:							str					,     _a			:							Optional[Any]					,     _a			:							str  ):
    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 		snake_case_							():
    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()




