




import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[Any]     ,							lowercase     :	str     ,							lowercase     :	str     ,							lowercase     :	Path     ,							lowercase     :	str = None     ,							lowercase     :	str = None     ,							lowercase     :	str = None     ,							):


      '''simple docstring'''



      if config_name_or_path is None:
            lowerCamelCase_							=   'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

      if generator_tokenizer_name_or_path is None:
            lowerCamelCase_							=   generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            lowerCamelCase_							=   question_encoder_name_or_path

      lowerCamelCase_							=   RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

      # Save model.
      lowerCamelCase_							=   RagConfig.from_pretrained(lowercase					)
      lowerCamelCase_							=   AutoConfig.from_pretrained(lowercase					)
      lowerCamelCase_							=   AutoConfig.from_pretrained(lowercase					)

      lowerCamelCase_							=   gen_config
      lowerCamelCase_							=   question_encoder_config

      lowerCamelCase_							=   model_class.from_pretrained_question_encoder_generator(
          lowercase     ,							lowercase     ,							config=lowercase					)
      rag_model.save_pretrained(lowercase					)

      # Sanity check.
      model_class.from_pretrained(lowercase					)

      # Save tokenizers.
      lowerCamelCase_							=   AutoTokenizer.from_pretrained(lowercase					)
      gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'					)
      lowerCamelCase_							=   AutoTokenizer.from_pretrained(lowercase					)
      question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'					)


if __name__ == "__main__":
 lowerCamelCase    :		Union[str, Any]			   =       argparse.ArgumentParser()
 parser.add_argument(
     "--model_type",
     choices=["rag_sequence", "rag_token"],
     required=True,
     type=str,
     help="RAG model type: rag_sequence, rag_token",
 )
 parser.add_argument("--dest", type=str, required=True, help="Path to the output checkpoint directory.")
 parser.add_argument("--generator_name_or_path", type=str, required=True, help="Generator model identifier")
 parser.add_argument(
     "--question_encoder_name_or_path", type=str, required=True, help="Question encoder model identifier"
 )

 parser.add_argument(
     "--generator_tokenizer_name_or_path",
     type=str,
     help="Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``",
 )
 parser.add_argument(
     "--question_encoder_tokenizer_name_or_path",
     type=str,
     help="Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``",
 )
 parser.add_argument(
     "--config_name_or_path",
     type=str,
     help=(
         "Identifier of the model config to use, if not provided, resolves to a base config for a given"
         " ``model_type``"
     ),
 )

 lowerCamelCase    :		List[Any]			   =       parser.parse_args()

 lowerCamelCase    :		str			   =       Path(args.dest)
 dest_dir.mkdir(exist_ok=True)

 consolidate(
     args.model_type,
     args.generator_name_or_path,
     args.question_encoder_name_or_path,
     dest_dir,
     args.config_name_or_path,
     args.generator_tokenizer_name_or_path,
     args.question_encoder_tokenizer_name_or_path,
 )









import os
import time

import numpy as np
import onnxruntime as ort


lowerCamelCase    :		int			   =       "1"
lowerCamelCase    :		int			   =       "0"
lowerCamelCase    :		Union[str, Any]			   =       "1"

lowerCamelCase    :		List[Any]			   =       ort.SessionOptions()
lowerCamelCase    :		Optional[Any]			   =       ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
lowerCamelCase    :		Union[str, Any]			   =       ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
lowerCamelCase    :		Tuple			   =       ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
lowerCamelCase    :		List[Any]			   =       ort.RunOptions()

lowerCamelCase    :		List[str]			   =       128
lowerCamelCase    :		List[Any]			   =       1
lowerCamelCase    :		Union[str, Any]			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Dict			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Optional[Any]			   =       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...")
lowerCamelCase    :		int			   =       time.time()
lowerCamelCase    :		Dict			   =       2_000
lowerCamelCase    :		Any			   =       {}
for iter in range(max_iters):
 lowerCamelCase    :		Union[str, Any]			   =       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_000 / max_iters))



