import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig




def       lowerCAmelCase_     (		lowercase:							Any  ,					lowercase:							List[str]  ,					lowercase:							Any  ,					lowercase:							Union[str, Any]		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     List[Any]							= s.rsplit(lowercase  ,					lowercase		)
 return new.join(lowercase		)




def       lowerCAmelCase_     (		lowercase:							Any		)    ->				List[Any]:







 '''simple docstring'''
 return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items()		)




def       lowerCAmelCase_     (		lowercase:							Dict		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase:     Dict							= {}

 _UpperCamelCase:     Tuple							= ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
 for key, value in state_dict.items():
  for group_key in group_keys:
   if group_key in key:
    _UpperCamelCase:     Any							= key.replace(F"""{group_key}."""  ,					F"""{group_key}.group."""		)

  if "res_path" in key:
   _UpperCamelCase:     int							= key.replace('''res_path.'''  ,					'''res_path.path.'''		)

  if key.endswith('''.w'''		):
   _UpperCamelCase:     List[str]							= rreplace(lowercase  ,					'''.w'''  ,					'''.weight'''  ,					1		)
  if key.endswith('''.b'''		):
   _UpperCamelCase:     List[str]							= rreplace(lowercase  ,					'''.b'''  ,					'''.bias'''  ,					1		)

  _UpperCamelCase:     Optional[Any]							= value.float()

 return upgrade




@torch.no_grad()
def       lowerCAmelCase_     (		lowercase:							Union[str, Any]  ,					lowercase:							Dict  ,					lowercase:							List[str]=None  ,					lowercase:							List[str]=True		)    ->				Any:







 '''simple docstring'''
 from dall_e import Encoder

 _UpperCamelCase:     Any							= Encoder()
 if os.path.exists(lowercase		):
  _UpperCamelCase:     Union[str, Any]							= torch.load(lowercase		)
 else:
  _UpperCamelCase:     Union[str, Any]							= torch.hub.load_state_dict_from_url(lowercase		)

 if isinstance(lowercase  ,					lowercase		):
  _UpperCamelCase:     str							= ckpt.state_dict()
 encoder.load_state_dict(lowercase		)

 if config_path is not None:
  _UpperCamelCase:     List[Any]							= FlavaImageCodebookConfig.from_pretrained(lowercase		)
 else:
  _UpperCamelCase:     int							= FlavaImageCodebookConfig()

 _UpperCamelCase:     Dict							= FlavaImageCodebook(lowercase		).eval()
 _UpperCamelCase:     str							= encoder.state_dict()

 _UpperCamelCase:     Union[str, Any]							= upgrade_state_dict(lowercase		)
 hf_model.load_state_dict(lowercase		)
 _UpperCamelCase:     Union[str, Any]							= hf_model.state_dict()
 _UpperCamelCase:     Optional[Any]							= count_parameters(lowercase		)
 _UpperCamelCase:     str							= count_parameters(lowercase		)

 assert torch.allclose(lowercase  ,					lowercase  ,					atol=1E-3		)

 if save_checkpoint:
  hf_model.save_pretrained(lowercase		)
 else:
  return hf_state_dict


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

  convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
import os
import time

import numpy as np
import onnxruntime as ort


UpperCAmelCase_			    =							'''1'''
UpperCAmelCase_			    =							'''0'''
UpperCAmelCase_			    =							'''1'''

UpperCAmelCase_			    =							ort.SessionOptions()
UpperCAmelCase_			    =							ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
UpperCAmelCase_			    =							['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
UpperCAmelCase_			    =							ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
UpperCAmelCase_			    =							ort.RunOptions()

UpperCAmelCase_			    =							1_2_8
UpperCAmelCase_			    =							1
UpperCAmelCase_			    =							np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase_			    =							np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase_			    =							np.ones((batch, sequence), dtype=np.intaa)

print('''Warm up phase...''')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('''Start inference...''')
UpperCAmelCase_			    =							time.time()
UpperCAmelCase_			    =							2_0_0_0
UpperCAmelCase_			    =							{}
for iter in range(max_iters):
  UpperCAmelCase_			    =							sess.run(
      None,
      {
          sess.get_inputs()[0].name: input_ids,
          sess.get_inputs()[1].name: attention_mask,
          sess.get_inputs()[2].name: token_type_ids,
      },
      run_options=run_opt,
  )
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1_0_0_0 / max_iters))