




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)			-> List[Any]:
							lowercase__  :       Union[str, Any]   					=							FNetConfig.from_json_file(A_				)
							print(F"""Building PyTorch model from configuration: {config}"""				)
							lowercase__  :       Union[str, Any]   					=							FNetForPreTraining(A_				)

							lowercase__  :       str   					=							restore_checkpoint(A_				,A_				)
							lowercase__  :       Dict   					=							checkpoint_dict['target']

							# Embeddings
							# Position IDs
							lowercase__  :       Optional[int]   					=							fnet_pretraining_model.state_dict()

							lowercase__  :       Optional[int]   					=							state_dict['fnet.embeddings.position_ids']
							lowercase__  :       List[str]   					=							{'fnet.embeddings.position_ids': position_ids}
							# Embedding Layers
							lowercase__  :       List[Any]   					=							torch.tensor(
							    pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]				)
							lowercase__  :       List[str]   					=							torch.tensor(
							    pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]				)
							lowercase__  :       Optional[Any]   					=							torch.tensor(
							    pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]				)
							lowercase__  :       Any   					=							torch.tensor(
							    pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]				).T
							lowercase__  :       Any   					=							torch.tensor(
							    pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]				)
							lowercase__  :       Union[str, Any]   					=							torch.tensor(
							    pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]				)
							lowercase__  :       Any   					=							torch.tensor(
							    pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]				)

							# Encoder Layers
							for layer in range(config.num_hidden_layers				):
														lowercase__  :       Dict   					=							torch.tensor(
														    pretrained_model_params["encoder"][F"""encoder_{layer}"""]["mixing_layer_norm"]["scale"]				)
														lowercase__  :       Dict   					=							torch.tensor(
														    pretrained_model_params["encoder"][F"""encoder_{layer}"""]["mixing_layer_norm"]["bias"]				)

														lowercase__  :       int   					=							torch.tensor(
														    pretrained_model_params["encoder"][F"""feed_forward_{layer}"""]["intermediate"]["kernel"]				).T
														lowercase__  :       int   					=							torch.tensor(
														    pretrained_model_params["encoder"][F"""feed_forward_{layer}"""]["intermediate"]["bias"]				)

														lowercase__  :       str   					=							torch.tensor(
														    pretrained_model_params["encoder"][F"""feed_forward_{layer}"""]["output"]["kernel"]				).T
														lowercase__  :       str   					=							torch.tensor(
														    pretrained_model_params["encoder"][F"""feed_forward_{layer}"""]["output"]["bias"]				)

														lowercase__  :       Optional[Any]   					=							torch.tensor(
														    pretrained_model_params["encoder"][F"""encoder_{layer}"""]["output_layer_norm"]["scale"]				)
														lowercase__  :       Tuple   					=							torch.tensor(
														    pretrained_model_params["encoder"][F"""encoder_{layer}"""]["output_layer_norm"]["bias"]				)

							# Pooler Layers
							lowercase__  :       Union[str, Any]   					=							torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]				).T
							lowercase__  :       Dict   					=							torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]				)

							# Masked LM Layers
							lowercase__  :       Union[str, Any]   					=							torch.tensor(
							    pretrained_model_params["predictions_dense"]["kernel"]				).T
							lowercase__  :       Dict   					=							torch.tensor(
							    pretrained_model_params["predictions_dense"]["bias"]				)
							lowercase__  :       int   					=							torch.tensor(
							    pretrained_model_params["predictions_layer_norm"]["scale"]				)
							lowercase__  :       Union[str, Any]   					=							torch.tensor(
							    pretrained_model_params["predictions_layer_norm"]["bias"]				)
							lowercase__  :       Dict   					=							torch.tensor(
							    pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]				)
							lowercase__  :       List[str]   					=							torch.tensor(
							    pretrained_model_params["predictions_output"]["output_bias"]				)
							lowercase__  :       Tuple   					=							torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]				)

							# Seq Relationship Layers
							lowercase__  :       Any   					=							torch.tensor(
							    pretrained_model_params["classification"]["output_kernel"]				)
							lowercase__  :       Optional[Any]   					=							torch.tensor(
							    pretrained_model_params["classification"]["output_bias"]				)

							# Load State Dict
							fnet_pretraining_model.load_state_dict(A_				)

							# Save PreTrained
							print(F"""Saving pretrained model to {save_path}"""				)
							fnet_pretraining_model.save_pretrained(A_				)


if __name__ == "__main__":
						__a  :		Optional[int]			=    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--fnet_config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        '''The config json file corresponding to the pre-trained FNet model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
						__a  :		int			=    parser.parse_args()
						convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)







"""simple docstring"""


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 						snake_case  (  datasets.BuilderConfig       ):
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     True
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     1_0000
    __magic_name__         =     None





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''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 				lowerCamelCase__      (							self	:					int				,		A	:					dict				,		A	:					Optional[datasets.Features] = None				,		):


      '''simple docstring'''




      a     :			str				=   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
      a     :			List[str]				=   config_kwargs['sql']
      if not isinstance(A				,		A			):
        if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
          import sqlalchemy

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

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





    @property
    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			int				=   {
          '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 						snake_case  (  datasets.ArrowBasedBuilder       ):
    __magic_name__         =     SqlConfig





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Dict				,		A	:					Optional[int]			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Union[str, Any]				,		A	:					pa.Table			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




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




