





import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase="attention"				)    ->  List[Any]:
    lowerCamelCase__					:   Dict      				=	params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
    lowerCamelCase__					:   Optional[Any]      				=	params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
    lowerCamelCase__					:   str      				=	params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
    lowerCamelCase__					:   str      				=	params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
    return k, o, q, v


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase=False				)    ->  Union[str, Any]:
    if split_mlp_wi:
        lowerCamelCase__					:   Optional[Any]      				=	params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
        lowerCamelCase__					:   Dict      				=	params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
        lowerCamelCase__					:   Optional[int]      				=	(wi_a, wi_a)
    else:
        lowerCamelCase__					:   Dict      				=	params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

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


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  str:
    return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			*, _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Any:
    lowerCamelCase__					:   Any      				=	traverse_util.flatten_dict(variables['target']				)
    lowerCamelCase__					:   Tuple      				=	{'/'.join(_UpperCAmelCase				): v for k, v in old.items()}

    # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
    lowerCamelCase__					:   str      				=	'encoder/layers_0/mlp/wi_0/kernel' in old
    print('Split MLP:'		,			_UpperCAmelCase				)

    lowerCamelCase__					:   Union[str, Any]      				=	collections.OrderedDict()

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

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

        # Block i, layer 1 (MLP).
        lowerCamelCase__					:   str      				=	tax_layer_norm_lookup(_UpperCAmelCase		,			_UpperCAmelCase		,			'encoder'		,			'pre_mlp_layer_norm'				)
        lowerCamelCase__				,							lowerCamelCase__					:   List[Any]      				=	tax_mlp_lookup(_UpperCAmelCase		,			_UpperCAmelCase		,			'encoder'		,			_UpperCAmelCase				)
        lowerCamelCase__					:   Optional[Any]      				=	layer_norm
        if split_mlp_wi:
            lowerCamelCase__					:   Tuple      				=	wi[0].T
            lowerCamelCase__					:   Dict      				=	wi[1].T
        else:
            lowerCamelCase__					:   Tuple      				=	wi.T
        lowerCamelCase__					:   Dict      				=	wo.T

    lowerCamelCase__					:   str      				=	old[
        'encoder/relpos_bias/rel_embedding'
    ].T
    lowerCamelCase__					:   str      				=	old['encoder/encoder_norm/scale']

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

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

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

        lowerCamelCase__					:   Tuple      				=	old['decoder/decoder_norm/scale']
        lowerCamelCase__					:   Optional[int]      				=	old[
            'decoder/relpos_bias/rel_embedding'
        ].T

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

    return new


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  List[Any]:
    lowerCamelCase__					:   int      				=	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:
        lowerCamelCase__					:   List[str]      				=	state_dict['shared.weight']

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

    return state_dict


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[Any]:
    lowerCamelCase__					:   Optional[int]      				=	checkpoints.load_tax_checkpoint(_UpperCAmelCase				)
    lowerCamelCase__					:   List[Any]      				=	convert_tax_to_pytorch(_UpperCAmelCase		,			num_layers=config.num_layers		,			is_encoder_only=_UpperCAmelCase				)
    lowerCamelCase__					:   Any      				=	make_state_dict(_UpperCAmelCase		,			_UpperCAmelCase				)
    model.load_state_dict(_UpperCAmelCase		,			strict=_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase = False				)    ->  Union[str, Any]:
    lowerCamelCase__					:   Dict      				=	TaConfig.from_json_file(_UpperCAmelCase				)
    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:
        lowerCamelCase__					:   str      				=	TaEncoderModel(_UpperCAmelCase				)
    else:
        lowerCamelCase__					:   Tuple      				=	TaForConditionalGeneration(_UpperCAmelCase				)

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

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

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


if __name__ == "__main__":
    _UpperCAmelCase       : int			     =       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
    )
    _UpperCAmelCase       : Union[str, Any]			     =       parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
    )












from ..utils import DummyObject, requires_backends

class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   Tuple    ,  *UpperCAmelCase						:   str    ,  **UpperCAmelCase						:   Optional[int]			)    ->			List[str]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   Union[str, Any]    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   List[str]			)    ->			int:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   Any    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   Tuple			)    ->			Union[str, Any]:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   List[Any]    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Dict			)    ->			Any:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   str    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Union[str, Any]			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   str    ,  **UpperCAmelCase						:   Dict			)    ->			str:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   Dict    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   Dict			)    ->			Optional[int]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   Optional[Any]    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Any			)    ->			Any:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   List[str]    ,  *UpperCAmelCase						:   Any    ,  **UpperCAmelCase						:   Dict			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   Union[str, Any]    ,  *UpperCAmelCase						:   Optional[int]    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Any:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   int			)    ->			Dict:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   Dict    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Optional[int]:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   List[str]    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   List[str]			)    ->			Optional[int]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   Optional[Any]    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			str:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   Optional[int]    ,  **UpperCAmelCase						:   Any			)    ->			Any:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   List[str]    ,  *UpperCAmelCase						:   Tuple    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Optional[Any]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   Union[str, Any]    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   Tuple    ,  *UpperCAmelCase						:   List[str]    ,  **UpperCAmelCase						:   List[str]			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)





