"""simple docstring"""


import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
    import sqlitea

    import sqlalchemy




class  UpperCamelCase					(				lowerCAmelCase__   ):







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = False,							**lowerCAmelCase__,							)      ->      Dict:
            super().__init__(features=lowerCAmelCase__,							cache_dir=lowerCAmelCase__,							keep_in_memory=lowerCAmelCase__,							**lowerCAmelCase__)
            snake_case_					       =					Sql(
                cache_dir=lowerCAmelCase__,							features=lowerCAmelCase__,							sql=lowerCAmelCase__,							con=lowerCAmelCase__,							**lowerCAmelCase__,							)







       def 		a_						( self)      ->      int:
            snake_case_					       =					None
            snake_case_					       =					None
            snake_case_					       =					None
            snake_case_					       =					None

            self.builder.download_and_prepare(
                download_config=lowerCAmelCase__,							download_mode=lowerCAmelCase__,							verification_mode=lowerCAmelCase__,							base_path=lowerCAmelCase__,							)

            # Build dataset for splits
            snake_case_					       =					self.builder.as_dataset(
                split='train',							verification_mode=lowerCAmelCase__,							in_memory=self.keep_in_memory)
            return dataset




class  UpperCamelCase					:







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							**lowerCAmelCase__,							)      ->      Optional[Any]:
            if num_proc is not None and num_proc <= 0:
                 raise ValueError(f'num_proc {num_proc} must be an integer > 0.')

            snake_case_					       =					dataset
            snake_case_					       =					name
            snake_case_					       =					con
            snake_case_					       =					batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
            snake_case_					       =					num_proc
            snake_case_					       =					to_sql_kwargs







       def 		a_						( self)      ->      int:
            snake_case_					       =					self.to_sql_kwargs.pop('sql',							lowerCAmelCase__)
            snake_case_					       =					self.to_sql_kwargs.pop('con',							lowerCAmelCase__)
            snake_case_					       =					self.to_sql_kwargs.pop('index',							lowerCAmelCase__)

            snake_case_					       =					self._write(index=lowerCAmelCase__,							**self.to_sql_kwargs)
            return written







       def 		a_						( self,							lowerCAmelCase__)      ->      Any:
            snake_case_       ,	snake_case_       ,	snake_case_					       =					args
            snake_case_					       =					{**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
            snake_case_					       =					query_table(
                table=self.dataset.data,							key=slice(lowerCAmelCase__,							offset + self.batch_size),							indices=self.dataset._indices,							)
            snake_case_					       =					batch.to_pandas()
            snake_case_					       =					df.to_sql(self.name,							self.con,							index=lowerCAmelCase__,							**lowerCAmelCase__)
            return num_rows or len(lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							**lowerCAmelCase__)      ->      int:
            snake_case_					       =					0

            if self.num_proc is None or self.num_proc == 1:
                 for offset in logging.tqdm(
                     range(0,							len(self.dataset),							self.batch_size),							unit='ba',							disable=not logging.is_progress_bar_enabled(),							desc='Creating SQL from Arrow format',							):
                      written += self._batch_sql((offset, index, to_sql_kwargs))
            else:
                 snake_case_       ,	snake_case_					       =					len(self.dataset), self.batch_size
                 with multiprocessing.Pool(self.num_proc) as pool:
                      for num_rows in logging.tqdm(
                          pool.imap(
                              self._batch_sql,							[(offset, index, to_sql_kwargs) for offset in range(0,							lowerCAmelCase__,							lowerCAmelCase__)],							),							total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size,							unit='ba',							disable=not logging.is_progress_bar_enabled(),							desc='Creating SQL from Arrow format',							):
                           written += num_rows

            return written



import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase=2				,				_UpperCamelCase=3				,				_UpperCamelCase=64				,				_UpperCamelCase=None     )				->      Optional[Any]:
							lowerCAmelCase_						=	np.random.default_rng(_UpperCamelCase     )
							lowerCAmelCase_						=	length
							lowerCAmelCase_						=	rng.normal(size=(length,)     ).astype(np.floataa     )
							lowerCAmelCase_						=	a * self.x + b + rng.normal(scale=0.1				,				size=(length,)     ).astype(np.floataa     )




				def __len__(					self     )				->      int:
							return self.length




				def __getitem__(					self				,				_UpperCamelCase     )				->      Dict:
							return {"x": self.x[i], "y": self.y[i]}





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      List[Any]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      Any:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a[0] + self.b[0]





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      Optional[int]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      int:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a + self.b






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  int = 16				):




			"""simple docstring"""


			from datasets import load_dataset
			from transformers import AutoTokenizer

			lowerCAmelCase_						=	AutoTokenizer.from_pretrained("bert-base-cased"				)
			lowerCAmelCase_						=	{"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
			lowerCAmelCase_						=	load_dataset("csv"    ,							data_files=__lowerCAmelCase				)
			lowerCAmelCase_						=	datasets["train"].unique("label"				)

			lowerCAmelCase_						=	{v: i for i, v in enumerate(__lowerCAmelCase				)}

			def tokenize_function(__lowerCAmelCase  :  List[Any]				):
						# max_length=None => use the model max length (it's actually the default)
						lowerCAmelCase_						=	tokenizer(
						    examples["sentence1"]    ,							examples["sentence2"]    ,							truncation=__lowerCAmelCase    ,							max_length=__lowerCAmelCase    ,							padding="max_length"				)
						if "label" in examples:
									lowerCAmelCase_						=	[label_to_id[l] for l in examples["label"]]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			lowerCAmelCase_						=	datasets.map(
			    __lowerCAmelCase    ,							batched=__lowerCAmelCase    ,							remove_columns=["sentence1", "sentence2", "label"]    ,							)

			def collate_fn(__lowerCAmelCase  :  Optional[int]				):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(__lowerCAmelCase    ,							padding="max_length"    ,							max_length=128    ,							return_tensors="pt"				)
						return tokenizer.pad(__lowerCAmelCase    ,							padding="longest"    ,							return_tensors="pt"				)

			# Instantiate dataloaders.
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["train"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=2				)
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["validation"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=1				)

			return train_dataloader, eval_dataloader



