






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   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_  )   ->  List[Any]:


	"""simple docstring"""



	a		=			FNetConfig.from_json_file(snake_case_  )
	print(f"""Building PyTorch model from configuration: {config}"""  )
	a		=			FNetForPreTraining(snake_case_  )

	a		=			restore_checkpoint(snake_case_,  snake_case_  )
	a		=			checkpoint_dict['''target''']

	# Embeddings
	# Position IDs
	a		=			fnet_pretraining_model.state_dict()

	a		=			state_dict['''fnet.embeddings.position_ids''']
	a		=			{'''fnet.embeddings.position_ids''': position_ids}
	# Embedding Layers
	a		=			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']  )
	a		=			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0]  )
	a		=			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding''']  )
	a		=			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel''']  ).T
	a		=			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias''']  )
	a		=			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale''']  )
	a		=			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''bias''']  )

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

		a		=			torch.tensor(
		    pretrained_model_params['''encoder'''][f"""feed_forward_{layer}"""]['''intermediate''']['''kernel''']  ).T
		a		=			torch.tensor(
		    pretrained_model_params['''encoder'''][f"""feed_forward_{layer}"""]['''intermediate''']['''bias''']  )

		a		=			torch.tensor(
		    pretrained_model_params['''encoder'''][f"""feed_forward_{layer}"""]['''output''']['''kernel''']  ).T
		a		=			torch.tensor(
		    pretrained_model_params['''encoder'''][f"""feed_forward_{layer}"""]['''output''']['''bias''']  )

		a		=			torch.tensor(
		    pretrained_model_params['''encoder'''][f"""encoder_{layer}"""]['''output_layer_norm''']['''scale''']  )
		a		=			torch.tensor(
		    pretrained_model_params['''encoder'''][f"""encoder_{layer}"""]['''output_layer_norm''']['''bias''']  )

	# Pooler Layers
	a		=			torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']  ).T
	a		=			torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']  )

	# Masked LM Layers
	a		=			torch.tensor(
	    pretrained_model_params['''predictions_dense''']['''kernel''']  ).T
	a		=			torch.tensor(
	    pretrained_model_params['''predictions_dense''']['''bias''']  )
	a		=			torch.tensor(
	    pretrained_model_params['''predictions_layer_norm''']['''scale''']  )
	a		=			torch.tensor(
	    pretrained_model_params['''predictions_layer_norm''']['''bias''']  )
	a		=			torch.tensor(
	    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']  )
	a		=			torch.tensor(
	    pretrained_model_params['''predictions_output''']['''output_bias''']  )
	a		=			torch.tensor(pretrained_model_params['''predictions_output''']['''output_bias''']  )

	# Seq Relationship Layers
	a		=			torch.tensor(
	    pretrained_model_params['''classification''']['''output_kernel''']  )
	a		=			torch.tensor(
	    pretrained_model_params['''classification''']['''output_bias''']  )

	# Load State Dict
	fnet_pretraining_model.load_state_dict(snake_case_  )

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


if __name__ == "__main__":
				UpperCamelCase__						:      Any								=					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.""")
				UpperCamelCase__						:      Tuple								=					parser.parse_args()
				convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)













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__						:      Tuple								=					datasets.utils.logging.get_logger(__name__)







@dataclass
class 				lowerCamelCase_     (	datasets.BuilderConfig     ):
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			True
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			None
							SCREAMING_SNAKE_CASE_						     =			1_00_00
							SCREAMING_SNAKE_CASE_						     =			None
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[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  SCREAMING_SNAKE_CASE_ (						self		:					Any							,__lowerCamelCase		:					dict							,__lowerCamelCase		:					Optional[datasets.Features] = None							,):





								'''simple docstring'''



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

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

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




							@property
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								a		=			{
								    '''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 				lowerCamelCase_     (	datasets.ArrowBasedBuilder     ):
							SCREAMING_SNAKE_CASE_						     =			SqlConfig
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]					):





								'''simple docstring'''



								return datasets.DatasetInfo(features=self.config.features					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]							,__lowerCamelCase		:					int					):





								'''simple docstring'''



								return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,gen_kwargs={}					)]
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,__lowerCamelCase		:					pa.Table					):





								'''simple docstring'''



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




							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''simple docstring'''



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





