



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 		a__      (		):
   __lowerCamelCase    				=     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.'''				)

   __lowerCamelCase    				=     parser.parse_args()

   return args





def 		a__      (		_UpperCamelCase      :			Optional[Any]			,_UpperCamelCase      :			str="cpu"				):
   __lowerCamelCase    				=     model_dict[model_name].from_pretrained(_UpperCamelCase				).to(_UpperCamelCase				)
   __lowerCamelCase    				=     tokenizer_dict[model_name].from_pretrained(_UpperCamelCase				)

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

   return huggingface_model, tokenizer





def 		a__      (		_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			str			,_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			str				):
   model.eval()

   __lowerCamelCase    				=     None
   __lowerCamelCase    				=     torch.jit.script(BARTBeamSearchGenerator(_UpperCamelCase				)				)

   with torch.no_grad():
      __lowerCamelCase    				=     '''My friends are cool but they eat too many carbs.'''
      __lowerCamelCase    				=     tokenizer([ARTICLE_TO_SUMMARIZE]			,max_length=10_24			,return_tensors='''pt'''				).to(model.device				)

      __lowerCamelCase    				=     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				)				)

      __lowerCamelCase    				=     remove_dup_initializers(os.path.abspath(_UpperCamelCase				)				)

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

      __lowerCamelCase    				=     onnxruntime.InferenceSession(_UpperCamelCase				)
      __lowerCamelCase    				=     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__      (		):
   __lowerCamelCase    				=     parse_args()
   __lowerCamelCase    				=     5
   __lowerCamelCase    				=     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()

   __lowerCamelCase    				=     torch.device(args.device				)

   __lowerCamelCase				,__lowerCamelCase    				=     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:
      __lowerCamelCase    				=     args.max_length

   if args.num_beams:
      __lowerCamelCase    				=     args.num_beams

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

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


if __name__ == "__main__":
  main()











import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


a_								=       {
    """iou_prediction_head.layers.0""": """iou_prediction_head.proj_in""",
    """iou_prediction_head.layers.1""": """iou_prediction_head.layers.0""",
    """iou_prediction_head.layers.2""": """iou_prediction_head.proj_out""",
    """mask_decoder.output_upscaling.0""": """mask_decoder.upscale_conv1""",
    """mask_decoder.output_upscaling.1""": """mask_decoder.upscale_layer_norm""",
    """mask_decoder.output_upscaling.3""": """mask_decoder.upscale_conv2""",
    """mask_downscaling.0""": """mask_embed.conv1""",
    """mask_downscaling.1""": """mask_embed.layer_norm1""",
    """mask_downscaling.3""": """mask_embed.conv2""",
    """mask_downscaling.4""": """mask_embed.layer_norm2""",
    """mask_downscaling.6""": """mask_embed.conv3""",
    """point_embeddings""": """point_embed""",
    """pe_layer.positional_encoding_gaussian_matrix""": """shared_embedding.positional_embedding""",
    """image_encoder""": """vision_encoder""",
    """neck.0""": """neck.conv1""",
    """neck.1""": """neck.layer_norm1""",
    """neck.2""": """neck.conv2""",
    """neck.3""": """neck.layer_norm2""",
    """patch_embed.proj""": """patch_embed.projection""",
    """.norm""": """.layer_norm""",
    """blocks""": """layers""",
}





def 		a__      (		_UpperCamelCase      :			Optional[Any]				):
   __lowerCamelCase    				=     {}
   state_dict.pop('''pixel_mean'''			,_UpperCamelCase				)
   state_dict.pop('''pixel_std'''			,_UpperCamelCase				)

   __lowerCamelCase    				=     R'''.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'''

   for key, value in state_dict.items():
      for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
         if key_to_modify in key:
            __lowerCamelCase    				=     key.replace(_UpperCamelCase			,_UpperCamelCase				)

      if re.match(_UpperCamelCase			,_UpperCamelCase				):
         __lowerCamelCase    				=     int(re.match(_UpperCamelCase			,_UpperCamelCase				).group(2				)				)
         if layer_nb == 0:
            __lowerCamelCase    				=     key.replace('''layers.0'''			,'''proj_in'''				)
         elif layer_nb == 1:
            __lowerCamelCase    				=     key.replace('''layers.1'''			,'''layers.0'''				)
         elif layer_nb == 2:
            __lowerCamelCase    				=     key.replace('''layers.2'''			,'''proj_out'''				)

      __lowerCamelCase    				=     value

   __lowerCamelCase    				=     model_state_dict[
       '''prompt_encoder.shared_embedding.positional_embedding'''
   ]

   return model_state_dict





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Any			,_UpperCamelCase      :			Optional[int]="ybelkada/segment-anything"				):
   __lowerCamelCase    				=     hf_hub_download(_UpperCamelCase			,F"""checkpoints/{model_name}.pth"""				)

   if "sam_vit_b" in model_name:
      __lowerCamelCase    				=     SamConfig()
   elif "sam_vit_l" in model_name:
      __lowerCamelCase    				=     SamVisionConfig(
          hidden_size=10_24			,num_hidden_layers=24			,num_attention_heads=16			,global_attn_indexes=[5, 11, 17, 23]			,)

      __lowerCamelCase    				=     SamConfig(
          vision_config=_UpperCamelCase			,)
   elif "sam_vit_h" in model_name:
      __lowerCamelCase    				=     SamVisionConfig(
          hidden_size=12_80			,num_hidden_layers=32			,num_attention_heads=16			,global_attn_indexes=[7, 15, 23, 31]			,)

      __lowerCamelCase    				=     SamConfig(
          vision_config=_UpperCamelCase			,)

   __lowerCamelCase    				=     torch.load(_UpperCamelCase			,map_location='''cpu'''				)
   __lowerCamelCase    				=     replace_keys(_UpperCamelCase				)

   __lowerCamelCase    				=     SamImageProcessor()

   __lowerCamelCase    				=     SamProcessor(image_processor=_UpperCamelCase				)
   __lowerCamelCase    				=     SamModel(_UpperCamelCase				)

   hf_model.load_state_dict(_UpperCamelCase				)
   __lowerCamelCase    				=     hf_model.to('''cuda'''				)

   __lowerCamelCase    				=     '''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
   __lowerCamelCase    				=     Image.open(requests.get(_UpperCamelCase			,stream=_UpperCamelCase				).raw				).convert('''RGB'''				)

   __lowerCamelCase    				=     [[[4_00, 6_50]]]
   __lowerCamelCase    				=     [[1]]

   __lowerCamelCase    				=     processor(images=np.array(_UpperCamelCase				)			,return_tensors='''pt'''				).to('''cuda'''				)

   with torch.no_grad():
      __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
   __lowerCamelCase    				=     output.iou_scores.squeeze()

   if model_name == "sam_vit_h_4b8939":
      assert scores[-1].item() == 0.579_890_251_159_668

      __lowerCamelCase    				=     processor(
          images=np.array(_UpperCamelCase				)			,input_points=_UpperCamelCase			,input_labels=_UpperCamelCase			,return_tensors='''pt'''				).to('''cuda'''				)

      with torch.no_grad():
         __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
      __lowerCamelCase    				=     output.iou_scores.squeeze()

      assert scores[-1].item() == 0.9_712_603_092_193_604

      __lowerCamelCase    				=     ((75, 2_75, 17_25, 8_50),)

      __lowerCamelCase    				=     processor(images=np.array(_UpperCamelCase				)			,input_boxes=_UpperCamelCase			,return_tensors='''pt'''				).to('''cuda'''				)

      with torch.no_grad():
         __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
      __lowerCamelCase    				=     output.iou_scores.squeeze()

      assert scores[-1].item() == 0.8_686_015_605_926_514

      # Test with 2 points and 1 image.
      __lowerCamelCase    				=     [[[4_00, 6_50], [8_00, 6_50]]]
      __lowerCamelCase    				=     [[1, 1]]

      __lowerCamelCase    				=     processor(
          images=np.array(_UpperCamelCase				)			,input_points=_UpperCamelCase			,input_labels=_UpperCamelCase			,return_tensors='''pt'''				).to('''cuda'''				)

      with torch.no_grad():
         __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
      __lowerCamelCase    				=     output.iou_scores.squeeze()

      assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
  a_								=       argparse.ArgumentParser()
  a_								=       ["""sam_vit_b_01ec64""", """sam_vit_h_4b8939""", """sam_vit_l_0b3195"""]
  parser.add_argument(
      """--model_name""",
      default="""sam_vit_h_4b8939""",
      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""",
  )
  parser.add_argument(
      """--model_hub_id""",
      default="""ybelkada/segment-anything""",
      choices=choices,
      type=str,
      help="""Path to hf config.json of model to convert""",
  )

  a_								=       parser.parse_args()

  convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)






