





'''simple docstring'''


import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[Any]					,  __lowerCamelCase  :     Union[str, Any]					,  __lowerCamelCase  :     int     )   ->    int:
   a__     = AutoConfig.from_pretrained(_lowercase     )
   a__     = FlaxAutoModelForSeqaSeqLM.from_config(config=_lowercase     )
   a__     = checkpoints.load_tax_checkpoint(_lowercase     )

   a__     = "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

   if config.model_type == "t5":
      a__     = "SelfAttention"
   if config.model_type == "longt5" and config.encoder_attention_type == "local":
      a__     = "LocalSelfAttention"
   elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      a__     = "TransientGlobalSelfAttention"
   else:
      raise ValueError(
          "Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`"
          " attribute with a value from [\'local\', \'transient-global]."     )

   # Encoder
   for layer_index in range(config.num_layers     ):
      a__     = f'''layers_{str(_lowercase     )}'''

      # Self-Attention
      a__     = tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
      a__     = tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
      a__     = tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
      a__     = tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

      # Global input layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
         a__     = tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

      # Layer Normalization
      a__     = tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

      if split_mlp_wi:
         a__     = tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
         a__     = tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
         a__     = tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

      a__     = tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

      # Layer Normalization
      a__     = tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

      # Assigning
      a__     = flax_model.params["encoder"]["block"][str(_lowercase     )]["layer"]
      a__     = tax_attention_key
      a__     = tax_attention_out
      a__     = tax_attention_query
      a__     = tax_attention_value

      a__     = tax_attention_layer_norm

      # Global input layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
         a__     = tax_global_layer_norm

      if split_mlp_wi:
         a__     = tax_mlp_wi_a
         a__     = tax_mlp_wi_a
      else:
         a__     = tax_mlp_wi

      a__     = tax_mlp_wo
      a__     = tax_mlp_layer_norm

      a__     = flax_model_encoder_layer_block

   # Only for layer 0:
   a__     = tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
   a__     = tax_encoder_rel_embedding

   # Side/global relative position_bias + layer norm
   if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      a__     = tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
      a__     = tax_encoder_global_rel_embedding

   # Assigning
   a__     = tax_model["target"]["encoder"]["encoder_norm"]["scale"]
   a__     = tax_encoder_norm

   # Decoder
   for layer_index in range(config.num_layers     ):
      a__     = f'''layers_{str(_lowercase     )}'''

      # Self-Attention
      a__     = tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
      a__     = tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
      a__     = tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
      a__     = tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

      # Layer Normalization
      a__     = tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
          "scale"
      ]

      # Encoder-Decoder-Attention
      a__     = tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
      a__     = tax_enc_dec_attention_module["key"]["kernel"]
      a__     = tax_enc_dec_attention_module["out"]["kernel"]
      a__     = tax_enc_dec_attention_module["query"]["kernel"]
      a__     = tax_enc_dec_attention_module["value"]["kernel"]

      # Layer Normalization
      a__     = tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

      # MLP
      if split_mlp_wi:
         a__     = tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
         a__     = tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
         a__     = tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

      a__     = tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

      # Layer Normalization
      a__     = tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

      # Assigning
      a__     = flax_model.params["decoder"]["block"][str(_lowercase     )]["layer"]
      a__     = tax_attention_key
      a__     = tax_attention_out
      a__     = tax_attention_query
      a__     = tax_attention_value

      a__     = tax_pre_attention_layer_norm

      a__     = tax_enc_dec_attention_key
      a__     = tax_enc_dec_attention_out
      a__     = tax_enc_dec_attention_query
      a__     = tax_enc_dec_attention_value

      a__     = tax_cross_layer_norm

      if split_mlp_wi:
         a__     = tax_mlp_wi_a
         a__     = tax_mlp_wi_a
      else:
         a__     = tax_mlp_wi

      a__     = tax_mlp_wo

      a__     = txa_mlp_layer_norm

      a__     = flax_model_decoder_layer_block

   # Decoder Normalization
   a__     = tax_model["target"]["decoder"]["decoder_norm"]["scale"]
   a__     = txa_decoder_norm

   # Only for layer 0:
   a__     = tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
   a__     = tax_decoder_rel_embedding

   # Token Embeddings
   a__     = tax_model["target"]["token_embedder"]["embedding"]
   a__     = txa_token_embeddings

   # LM Head (only in v1.1 and LongT5 checkpoints)
   if "logits_dense" in tax_model["target"]["decoder"]:
      a__     = tax_model["target"]["decoder"]["logits_dense"]["kernel"]

   flax_model.save_pretrained(_lowercase     )
   print("T5X Model was sucessfully converted!"     )


if __name__ == "__main__":
  lowerCAmelCase_   :   Tuple       =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
  )
  parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
  parser.add_argument(
      "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
  )
  lowerCAmelCase_   :   Any       =					parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)










'''simple docstring'''


import os
import time

import numpy as np
import onnxruntime as ort


lowerCAmelCase_   :   Optional[int]       =					"1"
lowerCAmelCase_   :   List[str]       =					"0"
lowerCAmelCase_   :   Tuple       =					"1"

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

lowerCAmelCase_   :   Optional[int]       =					128
lowerCAmelCase_   :   Union[str, Any]       =					1
lowerCAmelCase_   :   List[str]       =					np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase_   :   Optional[Any]       =					np.ones((batch, sequence), dtype=np.intaa)
lowerCAmelCase_   :   str       =					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_   :   Tuple       =					time.time()
lowerCAmelCase_   :   int       =					2000
lowerCAmelCase_   :   Optional[Any]       =					{}
for iter in range(max_iters):
		lowerCAmelCase_   :   List[str]       =					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) * 1000 / max_iters))



