




import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	List[str]:

    """simple docstring"""


    return params[F"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_="attention"      )       ->	Optional[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						__UpperCamelCase  		=						np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :]      )
    __UpperCamelCase  		=						k_tmp.reshape(k_tmp.shape[0]					,     k_tmp.shape[1] * k_tmp.shape[2]      )
    __UpperCamelCase  		=						np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :]      )
    __UpperCamelCase  		=						o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]					,     o_tmp.shape[2]      )
    __UpperCamelCase  		=						np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :]      )
    __UpperCamelCase  		=						q_tmp.reshape(q_tmp.shape[0]					,     q_tmp.shape[1] * q_tmp.shape[2]      )
    __UpperCamelCase  		=						np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :]      )
    __UpperCamelCase  		=						v_tmp.reshape(v_tmp.shape[0]					,     v_tmp.shape[1] * v_tmp.shape[2]      )
    return k, o, q, v




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_=False      )       ->	Dict:

    """simple docstring"""


    if split_mlp_wi:
        __UpperCamelCase  		=						params[F"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
        __UpperCamelCase  		=						params[F"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
        __UpperCamelCase  		=						(wi_a, wi_a)
    else:
        __UpperCamelCase  		=						params[F"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

    __UpperCamelCase  		=						params[F"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
    return wi, wo




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


    return params[F"{prefix}/{prefix}/{layer_name}/scale"][:, i]




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     *, lowercase_					,     lowercase_					,     lowercase_ = False      )       ->	Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase  		=						traverse_util.flatten_dict(variables['''target''']      )
    __UpperCamelCase  		=						{'''/'''.join(lowercase_      ): v for k, v in old.items()}

    # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
    __UpperCamelCase  		=						'''encoder/encoder/mlp/wi_0/kernel''' in old
    print('''Split MLP:'''					,     lowercase_      )

    __UpperCamelCase  		=						collections.OrderedDict()

    # Shared embeddings.
    __UpperCamelCase  		=						old['''token_embedder/embedding''']

    # Encoder.
    for i in range(lowercase_      ):
        # Block i, layer 0 (Self Attention).
        __UpperCamelCase  		=						tax_layer_norm_lookup(lowercase_					,     lowercase_					,     '''encoder'''					,     '''pre_attention_layer_norm'''      )
        __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						tax_attention_lookup(lowercase_					,     lowercase_					,     '''encoder'''					,     '''attention'''      )
        __UpperCamelCase  		=						layer_norm
        __UpperCamelCase  		=						k.T
        __UpperCamelCase  		=						o.T
        __UpperCamelCase  		=						q.T
        __UpperCamelCase  		=						v.T

        # Block i, layer 1 (MLP).
        __UpperCamelCase  		=						tax_layer_norm_lookup(lowercase_					,     lowercase_					,     '''encoder'''					,     '''pre_mlp_layer_norm'''      )
        __UpperCamelCase		,      __UpperCamelCase  		=						tax_mlp_lookup(lowercase_					,     lowercase_					,     '''encoder'''					,     lowercase_      )
        __UpperCamelCase  		=						layer_norm
        if split_mlp_wi:
            __UpperCamelCase  		=						wi[0].T
            __UpperCamelCase  		=						wi[1].T
        else:
            __UpperCamelCase  		=						wi.T
        __UpperCamelCase  		=						wo.T
        if scalable_attention:
            # convert the rel_embedding of each layer
            __UpperCamelCase  		=						tax_relpos_bias_lookup(
                lowercase_					,     lowercase_					,     '''encoder'''      ).T

    __UpperCamelCase  		=						old['''encoder/encoder_norm/scale''']

    if not scalable_attention:
        __UpperCamelCase  		=						tax_relpos_bias_lookup(
            lowercase_					,     0					,     '''encoder'''      ).T
        __UpperCamelCase  		=						tax_relpos_bias_lookup(
            lowercase_					,     0					,     '''decoder'''      ).T

    if not is_encoder_only:
        # Decoder.
        for i in range(lowercase_      ):
            # Block i, layer 0 (Self Attention).
            __UpperCamelCase  		=						tax_layer_norm_lookup(lowercase_					,     lowercase_					,     '''decoder'''					,     '''pre_self_attention_layer_norm'''      )
            __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						tax_attention_lookup(lowercase_					,     lowercase_					,     '''decoder'''					,     '''self_attention'''      )
            __UpperCamelCase  		=						layer_norm
            __UpperCamelCase  		=						k.T
            __UpperCamelCase  		=						o.T
            __UpperCamelCase  		=						q.T
            __UpperCamelCase  		=						v.T

            # Block i, layer 1 (Cross Attention).
            __UpperCamelCase  		=						tax_layer_norm_lookup(lowercase_					,     lowercase_					,     '''decoder'''					,     '''pre_cross_attention_layer_norm'''      )
            __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						tax_attention_lookup(lowercase_					,     lowercase_					,     '''decoder'''					,     '''encoder_decoder_attention'''      )
            __UpperCamelCase  		=						layer_norm
            __UpperCamelCase  		=						k.T
            __UpperCamelCase  		=						o.T
            __UpperCamelCase  		=						q.T
            __UpperCamelCase  		=						v.T

            # Block i, layer 2 (MLP).
            __UpperCamelCase  		=						tax_layer_norm_lookup(lowercase_					,     lowercase_					,     '''decoder'''					,     '''pre_mlp_layer_norm'''      )
            __UpperCamelCase		,      __UpperCamelCase  		=						tax_mlp_lookup(lowercase_					,     lowercase_					,     '''decoder'''					,     lowercase_      )
            __UpperCamelCase  		=						layer_norm
            if split_mlp_wi:
                __UpperCamelCase  		=						wi[0].T
                __UpperCamelCase  		=						wi[1].T
            else:
                __UpperCamelCase  		=						wi.T
            __UpperCamelCase  		=						wo.T

            if scalable_attention:
                # convert the rel_embedding of each layer
                __UpperCamelCase  		=						tax_relpos_bias_lookup(lowercase_					,     lowercase_					,     '''decoder'''      ).T

        __UpperCamelCase  		=						old['''decoder/decoder_norm/scale''']

        # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
        if "decoder/logits_dense/kernel" in old:
            __UpperCamelCase  		=						old['''decoder/logits_dense/kernel'''].T

    return new




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						collections.OrderedDict([(k, torch.from_numpy(v.copy()      )) for (k, v) in converted_params.items()]      )

    # Add what is missing.
    if "encoder.embed_tokens.weight" not in state_dict:
        __UpperCamelCase  		=						state_dict['''shared.weight''']

    if not is_encoder_only:
        if "decoder.embed_tokens.weight" not in state_dict:
            __UpperCamelCase  		=						state_dict['''shared.weight''']

        if "lm_head.weight" not in state_dict:  # For old 1.0 models.
            print('''Using shared word embeddings as lm_head.'''      )
            __UpperCamelCase  		=						state_dict['''shared.weight''']

    return state_dict




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Dict:

    """simple docstring"""


    __UpperCamelCase  		=						checkpoints.load_tax_checkpoint(lowercase_      )
    __UpperCamelCase  		=						convert_tax_to_pytorch(
        lowercase_					,     num_layers=config.num_layers					,     is_encoder_only=lowercase_					,     scalable_attention=lowercase_      )
    __UpperCamelCase  		=						make_state_dict(lowercase_					,     lowercase_      )
    model.load_state_dict(lowercase_					,     strict=lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_ = False					,     lowercase_ = False					,     )       ->	List[str]:

    """simple docstring"""


    __UpperCamelCase  		=						MTaConfig.from_json_file(lowercase_      )
    print(F"Building PyTorch model from configuration: {config}"      )
    # Non-v1.1 checkpoints could also use T5Model, but this works for all.
    # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
    if is_encoder_only:
        __UpperCamelCase  		=						UMTaEncoderModel(lowercase_      )
    else:
        __UpperCamelCase  		=						UMTaForConditionalGeneration(lowercase_      )

    # Load weights from tf checkpoint
    load_tax_weights_in_ta(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )

    # Save pytorch-model
    print(F"Save PyTorch model to {pytorch_dump_path}"      )
    model.save_pretrained(lowercase_      )

    # Verify that we can load the checkpoint.
    model.from_pretrained(lowercase_      )
    print('''Done'''      )


if __name__ == "__main__":
 a_   		=     argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
 # Required parameters
 parser.add_argument(
     "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 parser.add_argument(
     "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
 )
 parser.add_argument(
     "--scalable_attention",
     action="store_true",
     help="Whether the model uses scaled attention (umt5 model)",
     default=False,
 )
 a_   		=     parser.parse_args()
 convert_tax_checkpoint_to_pytorch(
     args.tax_checkpoint_path,
     args.config_file,
     args.pytorch_dump_path,
     args.is_encoder_only,
     args.scalable_attention,
 )












import os
import time

import numpy as np
import onnxruntime as ort


a_   		=     "1"
a_   		=     "0"
a_   		=     "1"

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

a_   		=     128
a_   		=     1
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     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...")
a_   		=     time.time()
a_   		=     2000
a_   		=     {}
for iter in range(max_iters):
 a_   		=     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))






