
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


SCREAMING_SNAKE_CASE							:Optional[int]											=       datasets.utils.logging.get_logger(__name__)





@dataclass
class 							UpperCAmelCase   (						datasets.BuilderConfig						):




	'''simple docstring'''





	snake_case_      					=  None
	snake_case_      					=  None
	snake_case_      					=  None
	snake_case_      					=  True
	snake_case_      					=  None
	snake_case_      					=  None
	snake_case_      					=  None
	snake_case_      					=  10000
	snake_case_      					=  None






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






	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				dict							,A      :				Optional[datasets.Features] = None							,):
					__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(A							,A				):
									if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
													import sqlalchemy

													if isinstance(A							,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(A				)}: {sql}'''				)
									else:
													raise TypeError(
													    f'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(A				)}: {sql}'''				)
					__A         =				config_kwargs["con"]
					if not isinstance(A							,A				):
									__A         =				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        UpperCamelCase_						( self      :				str				):
					__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 							UpperCAmelCase   (						datasets.ArrowBasedBuilder						):




	'''simple docstring'''





	snake_case_      					=  SqlConfig






	def        UpperCamelCase_						( self      :				List[Any]				):
					return datasets.DatasetInfo(features=self.config.features				)






	def        UpperCamelCase_						( self      :				Any							,A      :				Optional[int]				):
					return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,gen_kwargs={}				)]






	def        UpperCamelCase_						( self      :				Any							,A      :				pa.Table				):
					if self.config.features is not None:
									__A         =				self.config.features.arrow_schema
									if all(not require_storage_cast(A				) for feature in self.config.features.values()				):
													# cheaper cast
													__A         =				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         =				table_cast(A							,A				)
					return pa_table







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


import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 		UpperCAmelCase    (			a_		)	->			Any:







				"""simple docstring"""



				if "model" in orig_key:
								__A         =				orig_key.replace("model."	,     ""		)
				if "norm1" in orig_key:
								__A         =				orig_key.replace("norm1"	,     "attention.output.LayerNorm"		)
				if "norm2" in orig_key:
								__A         =				orig_key.replace("norm2"	,     "output.LayerNorm"		)
				if "norm" in orig_key:
								__A         =				orig_key.replace("norm"	,     "LayerNorm"		)
				if "transformer" in orig_key:
								__A         =				orig_key.split("."		)[0].split("_"		)[-1]
								__A         =				orig_key.replace(F'''transformer_{layer_num}'''	,     F'''encoder.layer.{layer_num}'''		)
				if "mha.attn" in orig_key:
								__A         =				orig_key.replace("mha.attn"	,     "attention.self"		)
				if "mha" in orig_key:
								__A         =				orig_key.replace("mha"	,     "attention"		)
				if "W_q" in orig_key:
								__A         =				orig_key.replace("W_q"	,     "self.query"		)
				if "W_k" in orig_key:
								__A         =				orig_key.replace("W_k"	,     "self.key"		)
				if "W_v" in orig_key:
								__A         =				orig_key.replace("W_v"	,     "self.value"		)
				if "ff.0" in orig_key:
								__A         =				orig_key.replace("ff.0"	,     "intermediate.dense"		)
				if "ff.2" in orig_key:
								__A         =				orig_key.replace("ff.2"	,     "output.dense"		)
				if "ff" in orig_key:
								__A         =				orig_key.replace("ff"	,     "output.dense"		)
				if "mlm_class" in orig_key:
								__A         =				orig_key.replace("mlm.mlm_class"	,     "cls.predictions.decoder"		)
				if "mlm" in orig_key:
								__A         =				orig_key.replace("mlm"	,     "cls.predictions.transform"		)
				if "backbone.backbone.encoders" in orig_key:
								__A         =				orig_key.replace("backbone.backbone.encoders"	,     "encoder.layer"		)
				if "cls" not in orig_key:
								__A         =				"mra." + orig_key

				return orig_key


def 		UpperCAmelCase    (			a_	,     a_		)	->			List[str]:







				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__A         =				orig_state_dict.pop(a_		)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												__A         =				val

				__A         =				orig_state_dict["cls.predictions.decoder.bias"]
				__A         =				torch.arange(a_		).expand((1, -1)		) + 2

				return orig_state_dict


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Dict:







				"""simple docstring"""



				__A         =				torch.load(a_	,     map_location="cpu"		)["model_state_dict"]
				__A         =				MraConfig.from_json_file(a_		)
				__A         =				MraForMaskedLM(a_		)

				__A         =				convert_checkpoint_helper(config.max_position_embeddings	,     a_		)

				print(model.load_state_dict(a_		)		)
				model.eval()
				model.save_pretrained(a_		)

				print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[Any]											=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for Mra model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					SCREAMING_SNAKE_CASE							:Any											=       parser.parse_args()
					convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
