

import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
  import sqlitea

  import sqlalchemy


UpperCAmelCase__			:  Optional[Any]					     =     datasets.utils.logging.get_logger(__name__)







@dataclass
class 							a__							( datasets.BuilderConfig							):







  """simple docstring"""




  UpperCAmelCase__   :			Optional[int]      							=None
  UpperCAmelCase__   :			List[Any]      							=None
  UpperCAmelCase__   :			str      							=None
  UpperCAmelCase__   :			Any      							=True
  UpperCAmelCase__   :			Union[str, Any]      							=None
  UpperCAmelCase__   :			Dict      							=None
  UpperCAmelCase__   :			Any      							=None
  UpperCAmelCase__   :			Optional[Any]      							=1_0_0_0_0
  UpperCAmelCase__   :			Union[str, Any]      							=None


  def 			_lowercase   (      self     :			Dict		)       ->List[Any]:
        """simple docstring"""







        if self.sql is None:
              raise ValueError("""sql must be specified"""		)
        if self.con is None:
              raise ValueError("""con must be specified"""		)


  def 			_lowercase   (      self     :			Union[str, Any]					,					UpperCAmelCase__     :			Optional[Any]					,					UpperCAmelCase__     :			Any = None					,					)       ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Any	  = config_kwargs.copy()
        # We need to stringify the Selectable object to make its hash deterministic

        # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
        SCREAMING_SNAKE_CASE			:   Any	  = config_kwargs["""sql"""]
        if not isinstance(_UpperCAmelCase					,					_UpperCAmelCase		):
              if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
                    import sqlalchemy

                    if isinstance(_UpperCAmelCase					,					sqlalchemy.sql.Selectable		):
                          SCREAMING_SNAKE_CASE			:   Optional[Any]	  = sqlalchemy.create_engine(config_kwargs["""con"""].split("""://"""		)[0] + """://"""		)
                          SCREAMING_SNAKE_CASE			:   Optional[Any]	  = str(sql.compile(dialect=engine.dialect		)		)
                          SCREAMING_SNAKE_CASE			:   Dict	  = sql_str
                    else:
                          raise TypeError(
                              f"Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(_UpperCAmelCase		)}: {sql}"		)
              else:
                    raise TypeError(
                        f"Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(_UpperCAmelCase		)}: {sql}"		)
        SCREAMING_SNAKE_CASE			:   List[Any]	  = config_kwargs["""con"""]
        if not isinstance(_UpperCAmelCase					,					_UpperCAmelCase		):
              SCREAMING_SNAKE_CASE			:   Any	  = id(_UpperCAmelCase		)
              logger.info(
                  f"SQL connection \'con\' of type {type(_UpperCAmelCase		)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead."		)

        return super().create_config_id(_UpperCAmelCase					,					custom_features=_UpperCAmelCase		)




  @property
  def 			_lowercase   (      self     :			Tuple		)       ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = {
            """index_col""": self.index_col,
            """columns""": self.columns,
            """params""": self.params,
            """coerce_float""": self.coerce_float,
            """parse_dates""": self.parse_dates,
        }
        return pd_read_sql_kwargs







class 							a__							( datasets.ArrowBasedBuilder							):







  """simple docstring"""




  UpperCAmelCase__   :			Tuple      							=SqlConfig


  def 			_lowercase   (      self     :			int		)       ->Union[str, Any]:
        """simple docstring"""







        return datasets.DatasetInfo(features=self.config.features		)


  def 			_lowercase   (      self     :			Tuple					,					UpperCAmelCase__     :			str		)       ->Union[str, Any]:
        """simple docstring"""







        return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,					gen_kwargs={}		)]


  def 			_lowercase   (      self     :			Optional[Any]					,					UpperCAmelCase__     :			Tuple		)       ->Optional[Any]:
        """simple docstring"""







        if self.config.features is not None:
              SCREAMING_SNAKE_CASE			:   Tuple	  = self.config.features.arrow_schema
              if all(not require_storage_cast(_UpperCAmelCase		) for feature in self.config.features.values()		):
                    # cheaper cast
                    SCREAMING_SNAKE_CASE			:   str	  = pa.Table.from_arrays([pa_table[field.name] for field in schema]					,					schema=_UpperCAmelCase		)
              else:
                    # more expensive cast; allows str <-> int/float or str to Audio for example
                    SCREAMING_SNAKE_CASE			:   Any	  = table_cast(_UpperCAmelCase					,					_UpperCAmelCase		)
        return pa_table




  def 			_lowercase   (      self     :			Dict		)       ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Any	  = self.config.chunksize
        SCREAMING_SNAKE_CASE			:   str	  = pd.read_sql(
            self.config.sql					,					self.config.con					,					chunksize=_UpperCAmelCase					,					**self.config.pd_read_sql_kwargs		)
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = [sql_reader] if chunksize is None else sql_reader
        for chunk_idx, df in enumerate(_UpperCAmelCase		):
              SCREAMING_SNAKE_CASE			:   str	  = pa.Table.from_pandas(_UpperCAmelCase		)
              yield chunk_idx, self._cast_table(_UpperCAmelCase		)





"""simple docstring"""


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				TaConfig.from_pretrained(A   )
     lowercase__								=				FlaxTaForConditionalGeneration(config=A   )
     lowercase__								=				checkpoints.load_tax_checkpoint(A   )

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

     # Encoder
     for layer_index in range(config.num_layers   ):
          lowercase__								=				f'''layers_{str(A   )}'''

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

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

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

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

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

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_attention_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo
          lowercase__								=				tax_mlp_layer_norm

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

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

     # Decoder
     for layer_index in range(config.num_decoder_layers   ):
          lowercase__								=				f'''layers_{str(A   )}'''

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

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

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

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

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

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

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

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_pre_attention_layer_norm

          lowercase__								=				tax_enc_dec_attention_key
          lowercase__								=				tax_enc_dec_attention_out
          lowercase__								=				tax_enc_dec_attention_query
          lowercase__								=				tax_enc_dec_attention_value

          lowercase__								=				tax_cross_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo

          lowercase__								=				txa_mlp_layer_norm

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

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

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

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

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


if __name__ == "__main__":
 lowerCAmelCase_:       List[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."
 )
 lowerCAmelCase_:       Dict			     =       parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




