



'''simple docstring'''







# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import tensorflow as tf

class       lowerCamelCase_							(	TensorFormatter[Mapping, "tf.Tensor", Mapping]  ):






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase=None     ,						**__UpperCamelCase ):
       super().__init__(features=__UpperCamelCase )
       UpperCamelCase__      =			tf_tensor_kwargs
       import tensorflow as tf  # noqa: import tf at initialization


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       import tensorflow as tf

       if isinstance(__UpperCamelCase     ,						__UpperCamelCase ) and column:
           if all(
               isinstance(__UpperCamelCase     ,						tf.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
               return tf.stack(__UpperCamelCase )
           elif all(
               isinstance(__UpperCamelCase     ,						(tf.Tensor, tf.RaggedTensor) ) and x.ndim == 1 and x.dtype == column[0].dtype
               for x in column ):
               # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
               return tf.ragged.stack(__UpperCamelCase )

       return column


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       import tensorflow as tf

       if value is None:
           return value

       UpperCamelCase__      =			{}

       if isinstance(__UpperCamelCase     ,						(np.number, np.ndarray) ) and np.issubdtype(value.dtype     ,						np.integer ):
           UpperCamelCase__      =			{"dtype": tf.intaa}
       elif isinstance(__UpperCamelCase     ,						(np.number, np.ndarray) ) and np.issubdtype(value.dtype     ,						np.floating ):
           UpperCamelCase__      =			{"dtype": tf.floataa}
       elif config.PIL_AVAILABLE and "PIL" in sys.modules:
           import PIL.Image

           if isinstance(__UpperCamelCase     ,						PIL.Image.Image ):
               UpperCamelCase__      =			np.asarray(__UpperCamelCase )

       return tf.convert_to_tensor(__UpperCamelCase     ,						**{**default_dtype, **self.tf_tensor_kwargs} )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       import tensorflow as tf

       # support for torch, tf, jax etc.
       if config.TORCH_AVAILABLE and "torch" in sys.modules:
           import torch

           if isinstance(__UpperCamelCase     ,						torch.Tensor ):
               return self._tensorize(data_struct.detach().cpu().numpy()[()] )
       if hasattr(__UpperCamelCase     ,						"__array__" ) and not isinstance(__UpperCamelCase     ,						tf.Tensor ):
           UpperCamelCase__      =			data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(__UpperCamelCase     ,						np.ndarray ):
           if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
               return self._consolidate([self.recursive_tensorize(__UpperCamelCase ) for substruct in data_struct] )
       elif isinstance(__UpperCamelCase     ,						(list, tuple) ):
           return self._consolidate([self.recursive_tensorize(__UpperCamelCase ) for substruct in data_struct] )
       return self._tensorize(__UpperCamelCase )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       return map_nested(self._recursive_tensorize     ,						__UpperCamelCase     ,						map_list=__UpperCamelCase )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.numpy_arrow_extractor().extract_row(__UpperCamelCase )
       UpperCamelCase__      =			self.python_features_decoder.decode_row(__UpperCamelCase )
       return self.recursive_tensorize(__UpperCamelCase )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.numpy_arrow_extractor().extract_column(__UpperCamelCase )
       UpperCamelCase__      =			self.python_features_decoder.decode_column(__UpperCamelCase     ,						pa_table.column_names[0] )
       UpperCamelCase__      =			self.recursive_tensorize(__UpperCamelCase )
       UpperCamelCase__      =			self._consolidate(__UpperCamelCase )
       return column







   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.numpy_arrow_extractor().extract_batch(__UpperCamelCase )
       UpperCamelCase__      =			self.python_features_decoder.decode_batch(__UpperCamelCase )
       UpperCamelCase__      =			self.recursive_tensorize(__UpperCamelCase )
       for column_name in batch:
           UpperCamelCase__      =			self._consolidate(batch[column_name] )
       return batch









'''simple docstring'''







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			TaConfig.from_pretrained(lowerCAmelCase_	)
    UpperCamelCase__      =			FlaxTaForConditionalGeneration(config=lowerCAmelCase_	)
    UpperCamelCase__      =			checkpoints.load_tax_checkpoint(lowerCAmelCase_	)

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

    # Encoder
    for layer_index in range(config.num_layers	):
        UpperCamelCase__      =			F'layers_{str(lowerCAmelCase_	)}'

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

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

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

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

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

        # Assigning
        UpperCamelCase__      =			tax_attention_key
        UpperCamelCase__      =			tax_attention_out
        UpperCamelCase__      =			tax_attention_query
        UpperCamelCase__      =			tax_attention_value

        UpperCamelCase__      =			tax_attention_layer_norm

        if split_mlp_wi:
            UpperCamelCase__      =			tax_mlp_wi_a
            UpperCamelCase__      =			tax_mlp_wi_a
        else:
            UpperCamelCase__      =			tax_mlp_wi

        UpperCamelCase__      =			tax_mlp_wo
        UpperCamelCase__      =			tax_mlp_layer_norm

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

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

    # Decoder
    for layer_index in range(config.num_decoder_layers	):
        UpperCamelCase__      =			F'layers_{str(lowerCAmelCase_	)}'

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

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

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

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

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

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

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

        # Assigning
        UpperCamelCase__      =			tax_attention_key
        UpperCamelCase__      =			tax_attention_out
        UpperCamelCase__      =			tax_attention_query
        UpperCamelCase__      =			tax_attention_value

        UpperCamelCase__      =			tax_pre_attention_layer_norm

        UpperCamelCase__      =			tax_enc_dec_attention_key
        UpperCamelCase__      =			tax_enc_dec_attention_out
        UpperCamelCase__      =			tax_enc_dec_attention_query
        UpperCamelCase__      =			tax_enc_dec_attention_value

        UpperCamelCase__      =			tax_cross_layer_norm

        if split_mlp_wi:
            UpperCamelCase__      =			tax_mlp_wi_a
            UpperCamelCase__      =			tax_mlp_wi_a
        else:
            UpperCamelCase__      =			tax_mlp_wi

        UpperCamelCase__      =			tax_mlp_wo

        UpperCamelCase__      =			txa_mlp_layer_norm

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

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

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

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

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


if __name__ == "__main__":
      lowercase__     :  Any    					=			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
      )
      parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
      parser.add_argument(
          '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
      )
      lowercase__     :  int    					=			parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




