





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 A_     (		A__		,				A__		,				A__    )  ->		int:
       return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]



def A_     (		A__		,				A__		,				A__		,				A__="attention"    )  ->		Union[str, Any]:
       a__						:     Any			=    np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]    )
       a__						:     Any			=    k_tmp.reshape(k_tmp.shape[0]		,				k_tmp.shape[1] * k_tmp.shape[2]    )
       a__						:     int			=    np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]    )
       a__						:     Optional[int]			=    o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]		,				o_tmp.shape[2]    )
       a__						:     Any			=    np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]    )
       a__						:     Tuple			=    q_tmp.reshape(q_tmp.shape[0]		,				q_tmp.shape[1] * q_tmp.shape[2]    )
       a__						:     Union[str, Any]			=    np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]    )
       a__						:     Tuple			=    v_tmp.reshape(v_tmp.shape[0]		,				v_tmp.shape[1] * v_tmp.shape[2]    )
       return k, o, q, v



def A_     (		A__		,				A__		,				A__		,				A__=False    )  ->		Optional[Any]:
       if split_mlp_wi:
              a__						:     Union[str, Any]			=    params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
              a__						:     Dict			=    params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
              a__						:     List[str]			=    (wi_a, wi_a)
       else:
              a__						:     str			=    params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

       a__						:     List[Any]			=    params[F'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
       return wi, wo



def A_     (		A__		,				A__		,				A__		,				A__    )  ->		List[str]:
       return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]



def A_     (		A__		,				*, A__		,				A__		,				A__ = False    )  ->		List[str]:
       a__						:     Dict			=    traverse_util.flatten_dict(variables['target']    )
       a__						:     str			=    {'/'.join(A__    ): v for k, v in old.items()}

       # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
       a__						:     List[Any]			=    'encoder/encoder/mlp/wi_0/kernel' in old
       print('Split MLP:'		,				A__    )

       a__						:     Any			=    collections.OrderedDict()

       # Shared embeddings.
       a__						:     Union[str, Any]			=    old['token_embedder/embedding']

       # Encoder.
       for i in range(A__    ):
              # Block i, layer 0 (Self Attention).
              a__						:     List[str]			=    tax_layer_norm_lookup(A__		,				A__		,				'encoder'		,				'pre_attention_layer_norm'    )
              a__			,							a__			,							a__			,							a__						:     Tuple			=    tax_attention_lookup(A__		,				A__		,				'encoder'		,				'attention'    )
              a__						:     Optional[int]			=    layer_norm
              a__						:     List[Any]			=    k.T
              a__						:     Dict			=    o.T
              a__						:     Dict			=    q.T
              a__						:     List[str]			=    v.T

              # Block i, layer 1 (MLP).
              a__						:     Any			=    tax_layer_norm_lookup(A__		,				A__		,				'encoder'		,				'pre_mlp_layer_norm'    )
              a__			,							a__						:     Any			=    tax_mlp_lookup(A__		,				A__		,				'encoder'		,				A__    )
              a__						:     Optional[Any]			=    layer_norm
              if split_mlp_wi:
                     a__						:     List[Any]			=    wi[0].T
                     a__						:     Union[str, Any]			=    wi[1].T
              else:
                     a__						:     Optional[int]			=    wi.T
              a__						:     Any			=    wo.T
              if scalable_attention:
                     # convert the rel_embedding of each layer
                     a__						:     Union[str, Any]			=    tax_relpos_bias_lookup(
                         A__		,				A__		,				'encoder'    ).T

       a__						:     str			=    old['encoder/encoder_norm/scale']

       if not scalable_attention:
              a__						:     List[str]			=    tax_relpos_bias_lookup(
                  A__		,				0		,				'encoder'    ).T
              a__						:     str			=    tax_relpos_bias_lookup(
                  A__		,				0		,				'decoder'    ).T

       if not is_encoder_only:
              # Decoder.
              for i in range(A__    ):
                     # Block i, layer 0 (Self Attention).
                     a__						:     Any			=    tax_layer_norm_lookup(A__		,				A__		,				'decoder'		,				'pre_self_attention_layer_norm'    )
                     a__			,							a__			,							a__			,							a__						:     Tuple			=    tax_attention_lookup(A__		,				A__		,				'decoder'		,				'self_attention'    )
                     a__						:     Any			=    layer_norm
                     a__						:     Optional[int]			=    k.T
                     a__						:     List[Any]			=    o.T
                     a__						:     Any			=    q.T
                     a__						:     Optional[Any]			=    v.T

                     # Block i, layer 1 (Cross Attention).
                     a__						:     Any			=    tax_layer_norm_lookup(A__		,				A__		,				'decoder'		,				'pre_cross_attention_layer_norm'    )
                     a__			,							a__			,							a__			,							a__						:     List[Any]			=    tax_attention_lookup(A__		,				A__		,				'decoder'		,				'encoder_decoder_attention'    )
                     a__						:     Any			=    layer_norm
                     a__						:     Tuple			=    k.T
                     a__						:     Union[str, Any]			=    o.T
                     a__						:     int			=    q.T
                     a__						:     str			=    v.T

                     # Block i, layer 2 (MLP).
                     a__						:     Union[str, Any]			=    tax_layer_norm_lookup(A__		,				A__		,				'decoder'		,				'pre_mlp_layer_norm'    )
                     a__			,							a__						:     Optional[Any]			=    tax_mlp_lookup(A__		,				A__		,				'decoder'		,				A__    )
                     a__						:     Any			=    layer_norm
                     if split_mlp_wi:
                            a__						:     Any			=    wi[0].T
                            a__						:     Union[str, Any]			=    wi[1].T
                     else:
                            a__						:     str			=    wi.T
                     a__						:     Optional[int]			=    wo.T

                     if scalable_attention:
                            # convert the rel_embedding of each layer
                            a__						:     Tuple			=    tax_relpos_bias_lookup(A__		,				A__		,				'decoder'    ).T

              a__						:     Optional[int]			=    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:
                     a__						:     Tuple			=    old['decoder/logits_dense/kernel'].T

       return new



def A_     (		A__		,				A__    )  ->		List[Any]:
       a__						:     List[Any]			=    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:
              a__						:     Optional[int]			=    state_dict['shared.weight']

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     a__						:     int			=    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.'    )
                     a__						:     List[str]			=    state_dict['shared.weight']

       return state_dict



def A_     (		A__		,				A__		,				A__		,				A__		,				A__    )  ->		Union[str, Any]:
       a__						:     Union[str, Any]			=    checkpoints.load_tax_checkpoint(A__    )
       a__						:     List[str]			=    convert_tax_to_pytorch(
           A__		,				num_layers=config.num_layers		,				is_encoder_only=A__		,				scalable_attention=A__    )
       a__						:     Tuple			=    make_state_dict(A__		,				A__    )
       model.load_state_dict(A__		,				strict=A__    )



def A_     (		A__		,				A__		,				A__		,				A__ = False		,				A__ = False		,				)  ->		int:
       a__						:     List[str]			=    MTaConfig.from_json_file(A__    )
       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:
              a__						:     Optional[Any]			=    UMTaEncoderModel(A__    )
       else:
              a__						:     int			=    UMTaForConditionalGeneration(A__    )

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

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

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


if __name__ == "__main__":
    lowercase				:	str      						=  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,
    )
    lowercase				:	str      						=  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 re

from filelock import FileLock


try:
    import nltk

    lowercase				:	List[str]      						=  True
except (ImportError, ModuleNotFoundError):
    lowercase				:	Tuple      						=  False

if NLTK_AVAILABLE:
    with FileLock(""".lock""") as lock:
        nltk.download("""punkt""", quiet=True)



def A_     (		A__    )  ->		str:
       re.sub('<n>'		,				''		,				A__    )  # remove pegasus newline char
       assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
       return "\n".join(nltk.sent_tokenize(A__    )    )


