





"""simple docstring"""



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 		__lowercase					(       snake_case_  :  List[str]    ,snake_case_  :  Dict    ,snake_case_  :  int						)       ->Optional[int]:



    '''simple docstring'''







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







def 		__lowercase					(       snake_case_  :  Optional[Any]    ,snake_case_  :  Tuple    ,snake_case_  :  List[Any]    ,snake_case_  :  Dict="attention"						)       ->List[str]:



    '''simple docstring'''







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







def 		__lowercase					(       snake_case_  :  List[Any]    ,snake_case_  :  int    ,snake_case_  :  Dict    ,snake_case_  :  int=False						)       ->List[Any]:



    '''simple docstring'''







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

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







def 		__lowercase					(       snake_case_  :  Optional[int]    ,snake_case_  :  Optional[int]    ,snake_case_  :  Optional[int]    ,snake_case_  :  List[Any]						)       ->Optional[Any]:



    '''simple docstring'''







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







def 		__lowercase					(       snake_case_  :  dict    ,*, snake_case_  :  int    ,snake_case_  :  bool    ,snake_case_  :  bool = False						)       ->Dict:



    '''simple docstring'''







    __A   :						Optional[Any]									=					traverse_util.flatten_dict(variables['''target''']						)
    __A   :						List[Any]									=					{'''/'''.join(snake_case_						): 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[str]									=					'''encoder/encoder/mlp/wi_0/kernel''' in old
    print('''Split MLP:'''    ,snake_case_						)

    __A   :						Tuple									=					collections.OrderedDict()

    # Shared embeddings.
    __A   :						str									=					old['''token_embedder/embedding''']

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

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

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

    if not scalable_attention:
        __A   :						Any									=					tax_relpos_bias_lookup(
            snake_case_    ,0    ,'''encoder'''						).T
        __A   :						Union[str, Any]									=					tax_relpos_bias_lookup(
            snake_case_    ,0    ,'''decoder'''						).T

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

            # Block i, layer 1 (Cross Attention).
            __A   :						List[str]									=					tax_layer_norm_lookup(snake_case_    ,snake_case_    ,'''decoder'''    ,'''pre_cross_attention_layer_norm'''						)
            __A			,	__A			,	__A			,	__A   :						Dict									=					tax_attention_lookup(snake_case_    ,snake_case_    ,'''decoder'''    ,'''encoder_decoder_attention'''						)
            __A   :						List[str]									=					layer_norm
            __A   :						List[str]									=					k.T
            __A   :						List[Any]									=					o.T
            __A   :						Tuple									=					q.T
            __A   :						Optional[Any]									=					v.T

            # Block i, layer 2 (MLP).
            __A   :						int									=					tax_layer_norm_lookup(snake_case_    ,snake_case_    ,'''decoder'''    ,'''pre_mlp_layer_norm'''						)
            __A			,	__A   :						Dict									=					tax_mlp_lookup(snake_case_    ,snake_case_    ,'''decoder'''    ,snake_case_						)
            __A   :						Any									=					layer_norm
            if split_mlp_wi:
                __A   :						List[Any]									=					wi[0].T
                __A   :						List[Any]									=					wi[1].T
            else:
                __A   :						str									=					wi.T
            __A   :						List[Any]									=					wo.T

            if scalable_attention:
                # convert the rel_embedding of each layer
                __A   :						int									=					tax_relpos_bias_lookup(snake_case_    ,snake_case_    ,'''decoder'''						).T

        __A   :						Dict									=					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   :						Optional[int]									=					old['''decoder/logits_dense/kernel'''].T

    return new







def 		__lowercase					(       snake_case_  :  Optional[int]    ,snake_case_  :  bool						)       ->str:



    '''simple docstring'''







    __A   :						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   :						Union[str, Any]									=					state_dict['''shared.weight''']

    if not is_encoder_only:
        if "decoder.embed_tokens.weight" not in state_dict:
            __A   :						Tuple									=					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   :						Union[str, Any]									=					state_dict['''shared.weight''']

    return state_dict







def 		__lowercase					(       snake_case_  :  Any    ,snake_case_  :  Optional[int]    ,snake_case_  :  List[Any]    ,snake_case_  :  Optional[int]    ,snake_case_  :  str						)       ->Dict:



    '''simple docstring'''







    __A   :						str									=					checkpoints.load_tax_checkpoint(snake_case_						)
    __A   :						str									=					convert_tax_to_pytorch(
        snake_case_    ,num_layers=config.num_layers    ,is_encoder_only=snake_case_    ,scalable_attention=snake_case_						)
    __A   :						int									=					make_state_dict(snake_case_    ,snake_case_						)
    model.load_state_dict(snake_case_    ,strict=snake_case_						)







def 		__lowercase					(       snake_case_  :  int    ,snake_case_  :  Union[str, Any]    ,snake_case_  :  Union[str, Any]    ,snake_case_  :  bool = False    ,snake_case_  :  bool = False    ,)       ->Optional[Any]:



    '''simple docstring'''







    __A   :						Optional[int]									=					MTaConfig.from_json_file(snake_case_						)
    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   :						Any									=					UMTaEncoderModel(snake_case_						)
    else:
        __A   :						Optional[int]									=					UMTaForConditionalGeneration(snake_case_						)

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

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

    # Verify that we can load the checkpoint.
    model.from_pretrained(snake_case_						)
    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,
       )











"""simple docstring"""



from typing import TYPE_CHECKING

from ....utils import _LazyModule


a_			=      {"""tokenization_tapex""": ["""TapexTokenizer"""]}


if TYPE_CHECKING:
       from .tokenization_tapex import TapexTokenizer


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure)




