
"""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__   :					Dict							=   logging.getLogger(__name__)

A__   :					Any							=   {'facebook/bart-base': BartForConditionalGeneration}
A__   :					Optional[int]							=   {'facebook/bart-base': BartTokenizer}




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     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.'''						)

  _lowercase:			Tuple       =     parser.parse_args()

  return args




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase="cpu"						):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     model_dict[model_name].from_pretrained(_UpperCamelCase						).to(_UpperCamelCase						)
  _lowercase:			Union[str, Any]       =     tokenizer_dict[model_name].from_pretrained(_UpperCamelCase						)

  if model_name in ["facebook/bart-base"]:
    _lowercase:			List[str]       =     0
    _lowercase:			Any       =     None
    _lowercase:			List[str]       =     0

  return huggingface_model, tokenizer




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  model.eval()

  _lowercase:			Optional[int]       =     None
  _lowercase:			int       =     torch.jit.script(BARTBeamSearchGenerator(_UpperCamelCase						)						)

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

    _lowercase:			int       =     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						)						)

    _lowercase:			Optional[int]       =     remove_dup_initializers(os.path.abspath(_UpperCamelCase						)						)

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

    _lowercase:			Tuple       =     onnxruntime.InferenceSession(_UpperCamelCase						)
    _lowercase:			Union[str, Any]       =     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  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			Tuple       =     parse_args()
  _lowercase:			Union[str, Any]       =     5
  _lowercase:			List[Any]       =     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()

  _lowercase:			int       =     torch.device(args.device						)

  _lowercase:			Optional[Any]       =     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:
    _lowercase:			List[Any]       =     args.max_length

  if args.num_beams:
    _lowercase:			int       =     args.num_beams

  if args.output_file_path:
    _lowercase:			Dict       =     args.output_file_path
  else:
    _lowercase:			Tuple       =     '''BART.onnx'''

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


if __name__ == "__main__":
  main()


"""simple docstring"""




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A__   :					List[Any]							=   logging.get_logger()




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase = True						):






  """simple docstring"""



  print(f'''Converting {name}...'''						)

  with torch.no_grad():
    if hidden_sizes == 128:
      if name[-1] == "S":
        _lowercase:			int       =     timm.create_model('''levit_128s''' ,				pretrained=_UpperCamelCase						)
      else:
        _lowercase:			int       =     timm.create_model('''levit_128''' ,				pretrained=_UpperCamelCase						)
    if hidden_sizes == 192:
      _lowercase:			str       =     timm.create_model('''levit_192''' ,				pretrained=_UpperCamelCase						)
    if hidden_sizes == 256:
      _lowercase:			int       =     timm.create_model('''levit_256''' ,				pretrained=_UpperCamelCase						)
    if hidden_sizes == 384:
      _lowercase:			Dict       =     timm.create_model('''levit_384''' ,				pretrained=_UpperCamelCase						)

    from_model.eval()
    _lowercase:			Any       =     LevitForImageClassificationWithTeacher(_UpperCamelCase						).eval()
    _lowercase:			Union[str, Any]       =     OrderedDict()

    _lowercase:			Optional[Any]       =     from_model.state_dict()
    _lowercase:			List[Any]       =     list(from_model.state_dict().keys()						)
    _lowercase:			int       =     list(our_model.state_dict().keys()						)
    print(len(_UpperCamelCase						) ,				len(_UpperCamelCase						)						)
    for i in range(len(_UpperCamelCase						)						):
      _lowercase:			Any       =     weights[og_keys[i]]
    our_model.load_state_dict(_UpperCamelCase						)

    _lowercase:			int       =     torch.randn((2, 3, 224, 224)						)
    _lowercase:			List[str]       =     from_model(_UpperCamelCase						)
    _lowercase:			Optional[int]       =     our_model(_UpperCamelCase						).logits

  assert torch.allclose(_UpperCamelCase ,				_UpperCamelCase						), "The model logits don't match the original one."

  _lowercase:			Union[str, Any]       =     name
  print(_UpperCamelCase						)

  if push_to_hub:
    our_model.save_pretrained(save_directory / checkpoint_name						)
    _lowercase:			Dict       =     LevitImageProcessor()
    image_processor.save_pretrained(save_directory / checkpoint_name						)

    print(f'''Pushed {checkpoint_name}'''						)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase = None ,				_UpperCamelCase = True						):






  """simple docstring"""



  _lowercase:			int       =     '''imagenet-1k-id2label.json'''
  _lowercase:			int       =     1_000
  _lowercase:			Tuple       =     (1, num_labels)

  _lowercase:			Dict       =     '''huggingface/label-files'''
  _lowercase:			Optional[int]       =     num_labels
  _lowercase:			List[str]       =     json.load(open(hf_hub_download(_UpperCamelCase ,				_UpperCamelCase ,				repo_type='''dataset'''						) ,				'''r'''						)						)
  _lowercase:			List[str]       =     {int(_UpperCamelCase						): v for k, v in idalabel.items()}

  _lowercase:			int       =     idalabel
  _lowercase:			Tuple       =     {v: k for k, v in idalabel.items()}

  _lowercase:			Tuple       =     partial(_UpperCamelCase ,				num_labels=_UpperCamelCase ,				idalabel=_UpperCamelCase ,				labelaid=_UpperCamelCase						)

  _lowercase:			Union[str, Any]       =     {
      '''levit-128S''': 128,
      '''levit-128''': 128,
      '''levit-192''': 192,
      '''levit-256''': 256,
      '''levit-384''': 384,
  }

  _lowercase:			int       =     {
      '''levit-128S''': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384] ,				num_attention_heads=[4, 6, 8] ,				depths=[2, 3, 4] ,				key_dim=[16, 16, 16] ,				drop_path_rate=0 ,				),
      '''levit-128''': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384] ,				num_attention_heads=[4, 8, 12] ,				depths=[4, 4, 4] ,				key_dim=[16, 16, 16] ,				drop_path_rate=0 ,				),
      '''levit-192''': ImageNetPreTrainedConfig(
          hidden_sizes=[192, 288, 384] ,				num_attention_heads=[3, 5, 6] ,				depths=[4, 4, 4] ,				key_dim=[32, 32, 32] ,				drop_path_rate=0 ,				),
      '''levit-256''': ImageNetPreTrainedConfig(
          hidden_sizes=[256, 384, 512] ,				num_attention_heads=[4, 6, 8] ,				depths=[4, 4, 4] ,				key_dim=[32, 32, 32] ,				drop_path_rate=0 ,				),
      '''levit-384''': ImageNetPreTrainedConfig(
          hidden_sizes=[384, 512, 768] ,				num_attention_heads=[6, 9, 12] ,				depths=[4, 4, 4] ,				key_dim=[32, 32, 32] ,				drop_path_rate=0.1 ,				),
  }

  if model_name:
    convert_weight_and_push(
        names_to_hidden_sizes[model_name] ,				_UpperCamelCase ,				names_to_config[model_name] ,				_UpperCamelCase ,				_UpperCamelCase						)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name] ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						)
  return config, expected_shape


if __name__ == "__main__":
  A__   :					Any							=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default=None,
      type=str,
      help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default='levit-dump-folder/',
      type=Path,
      required=False,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
  parser.add_argument(
      '--no-push_to_hub',
      dest='push_to_hub',
      action='store_false',
      help='Do not push model and image processor to the hub',
  )

  A__   :					Optional[Any]							=   parser.parse_args()
  A__   :					Path							=   args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
