

"""simple docstring"""



import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


SCREAMING_SNAKE_CASE_       :						Any					=    logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

SCREAMING_SNAKE_CASE_       :						Tuple					=    {
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
SCREAMING_SNAKE_CASE_       :						List[Any]					=    {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
SCREAMING_SNAKE_CASE_       :						str					=    sorted(arg_to_scheduler.keys())
SCREAMING_SNAKE_CASE_       :						Optional[Any]					=    '{' + ', '.join(arg_to_scheduler_choices) + '}'



class        a    (							pl.LightningModule					):



    """simple docstring"""



    def __init__(  self:			List[Any]     ,    UpperCamelCase:			argparse.Namespace     ,    UpperCamelCase:			int=None     ,    UpperCamelCase:			Optional[int]="base"     ,    UpperCamelCase:			Optional[Any]=None     ,    UpperCamelCase:			int=None     ,    UpperCamelCase:			Tuple=None     ,    **UpperCamelCase:			Dict     ,    ):






         """simple docstring"""


         super().__init__()
         # TODO: move to self.save_hyperparameters()
         # self.save_hyperparameters()
         # can also expand arguments into trainer signature for easier reading

         self.save_hyperparameters(UpperCamelCase							)
         A__				      =	0
         A__				      =	Path(self.hparams.output_dir							)
         A__				      =	self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
              A__				      =	AutoConfig.from_pretrained(
                  self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path     ,    **({"""num_labels""": num_labels} if num_labels is not None else {})     ,    cache_dir=UpperCamelCase     ,    **UpperCamelCase     ,    )
         else:
              A__				      =	config

         A__				      =	("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
         for p in extra_model_params:
              if getattr(self.hparams     ,    UpperCamelCase     ,    UpperCamelCase							):
                   assert hasattr(self.config     ,    UpperCamelCase							), f"""model config doesn't have a `{p}` attribute"""
                   setattr(self.config     ,    UpperCamelCase     ,    getattr(self.hparams     ,    UpperCamelCase							)							)

         if tokenizer is None:
              A__				      =	AutoTokenizer.from_pretrained(
                  self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,    cache_dir=UpperCamelCase     ,    )
         else:
              A__				      =	tokenizer
         A__				      =	MODEL_MODES[mode]
         if model is None:
              A__				      =	self.model_type.from_pretrained(
                  self.hparams.model_name_or_path     ,    from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path							)     ,    config=self.config     ,    cache_dir=UpperCamelCase     ,    )
         else:
              A__				      =	model

    def 					UpperCamelCase						(  self:			int     ,    *UpperCamelCase:			List[Any]     ,    **UpperCamelCase:			Optional[int]							):






         """simple docstring"""


         A__				      =	self.model_type.from_pretrained(*UpperCamelCase     ,    **UpperCamelCase							)

    def 					UpperCamelCase						(  self:			Optional[Any]							):






         """simple docstring"""


         A__				      =	arg_to_scheduler[self.hparams.lr_scheduler]
         A__				      =	get_schedule_func(
             self.opt     ,    num_warmup_steps=self.hparams.warmup_steps     ,    num_training_steps=self.total_steps()							)
         A__				      =	{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
         return scheduler

    def 					UpperCamelCase						(  self:			List[Any]							):






         """simple docstring"""


         A__				      =	self.model
         A__				      =	["""bias""", """LayerNorm.weight"""]
         A__				      =	[
             {
                 """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay							)],
                 """weight_decay""": self.hparams.weight_decay,
             },
             {
                 """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay							)],
                 """weight_decay""": 0.0,
             },
         ]
         if self.hparams.adafactor:
              A__				      =	Adafactor(
                  UpperCamelCase     ,    lr=self.hparams.learning_rate     ,    scale_parameter=UpperCamelCase     ,    relative_step=UpperCamelCase							)

         else:
              A__				      =	AdamW(
                  UpperCamelCase     ,    lr=self.hparams.learning_rate     ,    eps=self.hparams.adam_epsilon							)
         A__				      =	optimizer

         A__				      =	self.get_lr_scheduler()

         return [optimizer], [scheduler]

    def 					UpperCamelCase						(  self:			Optional[Any]     ,    UpperCamelCase:			Any     ,    UpperCamelCase:			Optional[Any]							):






         """simple docstring"""


         return self.validation_step(UpperCamelCase     ,    UpperCamelCase							)

    def 					UpperCamelCase						(  self:			Optional[int]     ,    UpperCamelCase:			str							):






         """simple docstring"""


         return self.validation_end(UpperCamelCase							)

    def 					UpperCamelCase						(  self:			Optional[int]							):






         """simple docstring"""


         A__				      =	max(1     ,    self.hparams.gpus							)  # TODO: consider num_tpu_cores
         A__				      =	self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

    def 					UpperCamelCase						(  self:			Any     ,    UpperCamelCase:			Optional[int]							):






         """simple docstring"""


         if mode == "test":
              A__				      =	len(self.test_dataloader().dataset							)
         else:
              A__				      =	self.get_dataloader("""train"""     ,    self.hparams.train_batch_size     ,    shuffle=UpperCamelCase							)
              A__				      =	len(self.train_dataloader().dataset							)

    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			str     ,    UpperCamelCase:			int     ,    UpperCamelCase:			bool = False							):






         """simple docstring"""


         raise NotImplementedError("""You must implement this for your task"""							)

    def 					UpperCamelCase						(  self:			Union[str, Any]							):






         """simple docstring"""


         return self.train_loader

    def 					UpperCamelCase						(  self:			List[str]							):






         """simple docstring"""


         return self.get_dataloader("""dev"""     ,    self.hparams.eval_batch_size     ,    shuffle=UpperCamelCase							)

    def 					UpperCamelCase						(  self:			Tuple							):






         """simple docstring"""


         return self.get_dataloader("""test"""     ,    self.hparams.eval_batch_size     ,    shuffle=UpperCamelCase							)

    def 					UpperCamelCase						(  self:			List[str]     ,    UpperCamelCase:			str							):






         """simple docstring"""


         return os.path.join(
             self.hparams.data_dir     ,    """cached_{}_{}_{}""".format(
                 UpperCamelCase     ,    list(filter(UpperCamelCase     ,    self.hparams.model_name_or_path.split("""/"""							)							)							).pop()     ,    str(self.hparams.max_seq_length							)     ,    )     ,    )

    @pl.utilities.rank_zero_only
    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			Dict[str, Any]							):






         """simple docstring"""


         A__				      =	self.output_dir.joinpath("""best_tfmr"""							)
         A__				      =	self.step_count
         self.model.save_pretrained(UpperCamelCase							)
         self.tokenizer.save_pretrained(UpperCamelCase							)





    @staticmethod
    def 					UpperCamelCase						(  UpperCamelCase:			Dict     ,    UpperCamelCase:			int							):






         """simple docstring"""


         parser.add_argument(
             """--model_name_or_path"""     ,    default=UpperCamelCase     ,    type=UpperCamelCase     ,    required=UpperCamelCase     ,    help="""Path to pretrained model or model identifier from huggingface.co/models"""     ,    )
         parser.add_argument(
             """--config_name"""     ,    default=""""""     ,    type=UpperCamelCase     ,    help="""Pretrained config name or path if not the same as model_name"""							)
         parser.add_argument(
             """--tokenizer_name"""     ,    default=UpperCamelCase     ,    type=UpperCamelCase     ,    help="""Pretrained tokenizer name or path if not the same as model_name"""     ,    )
         parser.add_argument(
             """--cache_dir"""     ,    default=""""""     ,    type=UpperCamelCase     ,    help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""     ,    )
         parser.add_argument(
             """--encoder_layerdrop"""     ,    type=UpperCamelCase     ,    help="""Encoder layer dropout probability (Optional). Goes into model.config"""     ,    )
         parser.add_argument(
             """--decoder_layerdrop"""     ,    type=UpperCamelCase     ,    help="""Decoder layer dropout probability (Optional). Goes into model.config"""     ,    )
         parser.add_argument(
             """--dropout"""     ,    type=UpperCamelCase     ,    help="""Dropout probability (Optional). Goes into model.config"""     ,    )
         parser.add_argument(
             """--attention_dropout"""     ,    type=UpperCamelCase     ,    help="""Attention dropout probability (Optional). Goes into model.config"""     ,    )
         parser.add_argument("""--learning_rate"""     ,    default=5e-5     ,    type=UpperCamelCase     ,    help="""The initial learning rate for Adam."""							)
         parser.add_argument(
             """--lr_scheduler"""     ,    default="""linear"""     ,    choices=UpperCamelCase     ,    metavar=UpperCamelCase     ,    type=UpperCamelCase     ,    help="""Learning rate scheduler"""     ,    )
         parser.add_argument("""--weight_decay"""     ,    default=0.0     ,    type=UpperCamelCase     ,    help="""Weight decay if we apply some."""							)
         parser.add_argument("""--adam_epsilon"""     ,    default=1e-8     ,    type=UpperCamelCase     ,    help="""Epsilon for Adam optimizer."""							)
         parser.add_argument("""--warmup_steps"""     ,    default=0     ,    type=UpperCamelCase     ,    help="""Linear warmup over warmup_steps."""							)
         parser.add_argument("""--num_workers"""     ,    default=4     ,    type=UpperCamelCase     ,    help="""kwarg passed to DataLoader"""							)
         parser.add_argument("""--num_train_epochs"""     ,    dest="""max_epochs"""     ,    default=3     ,    type=UpperCamelCase							)
         parser.add_argument("""--train_batch_size"""     ,    default=32     ,    type=UpperCamelCase							)
         parser.add_argument("""--eval_batch_size"""     ,    default=32     ,    type=UpperCamelCase							)
         parser.add_argument("""--adafactor"""     ,    action="""store_true"""							)



class        a    (							pl.Callback					):



    """simple docstring"""



    def 					UpperCamelCase						(  self:			Optional[Any]     ,    UpperCamelCase:			str     ,    UpperCamelCase:			Any							):






         """simple docstring"""


         A__				      =	trainer.lr_schedulers[0]["""scheduler"""]
         A__				      =	{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
         pl_module.logger.log_metrics(UpperCamelCase							)

    def 					UpperCamelCase						(  self:			Union[str, Any]     ,    UpperCamelCase:			pl.Trainer     ,    UpperCamelCase:			pl.LightningModule							):






         """simple docstring"""


         rank_zero_info("""***** Validation results *****"""							)
         A__				      =	trainer.callback_metrics
         # Log results
         for key in sorted(UpperCamelCase							):
              if key not in ["log", "progress_bar"]:
                   rank_zero_info("""{} = {}\n""".format(UpperCamelCase     ,    str(metrics[key]							)							)							)





    def 					UpperCamelCase						(  self:			str     ,    UpperCamelCase:			pl.Trainer     ,    UpperCamelCase:			pl.LightningModule							):






         """simple docstring"""


         rank_zero_info("""***** Test results *****"""							)
         A__				      =	trainer.callback_metrics
         # Log and save results to file
         A__				      =	os.path.join(pl_module.hparams.output_dir     ,    """test_results.txt"""							)
         with open(UpperCamelCase     ,    """w"""							) as writer:
              for key in sorted(UpperCamelCase							):
                   if key not in ["log", "progress_bar"]:
                        rank_zero_info("""{} = {}\n""".format(UpperCamelCase     ,    str(metrics[key]							)							)							)
                        writer.write("""{} = {}\n""".format(UpperCamelCase     ,    str(metrics[key]							)							)							)





def 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       Any      ):
     #  To allow all pl args uncomment the following line
     #  parser = pl.Trainer.add_argparse_args(parser)
     parser.add_argument(
         """--output_dir"""       , default=UpperCAmelCase_       , type=UpperCAmelCase_       , required=UpperCAmelCase_       , help="""The output directory where the model predictions and checkpoints will be written."""       , )
     parser.add_argument(
         """--fp16"""       , action="""store_true"""       , help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""       , )

     parser.add_argument(
         """--fp16_opt_level"""       , type=UpperCAmelCase_       , default="""O2"""       , help=(
             """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
             """See details at https://nvidia.github.io/apex/amp.html"""
         )       , )
     parser.add_argument("""--n_tpu_cores"""       , dest="""tpu_cores"""       , type=UpperCAmelCase_      )
     parser.add_argument("""--max_grad_norm"""       , dest="""gradient_clip_val"""       , default=1.0       , type=UpperCAmelCase_       , help="""Max gradient norm"""      )
     parser.add_argument("""--do_train"""       , action="""store_true"""       , help="""Whether to run training."""      )
     parser.add_argument("""--do_predict"""       , action="""store_true"""       , help="""Whether to run predictions on the test set."""      )
     parser.add_argument(
         """--gradient_accumulation_steps"""       , dest="""accumulate_grad_batches"""       , type=UpperCAmelCase_       , default=1       , help="""Number of updates steps to accumulate before performing a backward/update pass."""       , )
     parser.add_argument("""--seed"""       , type=UpperCAmelCase_       , default=42       , help="""random seed for initialization"""      )
     parser.add_argument(
         """--data_dir"""       , default=UpperCAmelCase_       , type=UpperCAmelCase_       , required=UpperCAmelCase_       , help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""       , )





def 			_snake_case  (     UpperCAmelCase_	:       BaseTransformer       , UpperCAmelCase_	:       argparse.Namespace       , UpperCAmelCase_	:       Optional[Any]=None       , UpperCAmelCase_	:       str=True       , UpperCAmelCase_	:       Dict=[]       , UpperCAmelCase_	:       Dict=None       , UpperCAmelCase_	:       Dict=None       , **UpperCAmelCase_	:       List[str]       , ):
     pl.seed_everything(args.seed      )

     # init model
     A__				      =	Path(model.hparams.output_dir      )
     odir.mkdir(exist_ok=UpperCAmelCase_      )

     # add custom checkpoints
     if checkpoint_callback is None:
          A__				      =	pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir       , prefix="""checkpoint"""       , monitor="""val_loss"""       , mode="""min"""       , save_top_k=1      )
     if early_stopping_callback:
          extra_callbacks.append(UpperCAmelCase_      )
     if logging_callback is None:
          A__				      =	LoggingCallback()

     A__				      =	{}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          A__				      =	16
          A__				      =	args.fpaa_opt_level

     if args.gpus > 1:
          A__				      =	"""ddp"""

     A__				      =	args.accumulate_grad_batches
     A__				      =	extra_train_kwargs.get("""accelerator"""       , UpperCAmelCase_      )
     A__				      =	extra_train_kwargs.get("""profiler"""       , UpperCAmelCase_      )

     A__				      =	pl.Trainer.from_argparse_args(
         UpperCAmelCase_       , weights_summary=UpperCAmelCase_       , callbacks=[logging_callback] + extra_callbacks       , logger=UpperCAmelCase_       , checkpoint_callback=UpperCAmelCase_       , **UpperCAmelCase_       , )

     if args.do_train:
          trainer.fit(UpperCAmelCase_      )

     return trainer









"""simple docstring"""



import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Any      ):
     assert isinstance(UpperCAmelCase_       , UpperCAmelCase_      )
     assert dataset.num_rows == 4
     assert dataset.num_columns == 3
     assert dataset.column_names == ["col_1", "col_2", "col_3"]
     for feature, expected_dtype in expected_features.items():
          assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""       , [False, True]      )
def 			_snake_case  (     UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Optional[Any]      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          A__				      =	CsvDatasetReader(UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , keep_in_memory=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )





@pytest.mark.parametrize(
    """features"""       , [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]       , )
def 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       List[str]       , UpperCAmelCase_	:       Dict      ):
     A__				      =	tmp_path / """cache"""
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	features.copy() if features else default_expected_features
     A__				      =	(
         Features({feature: Value(UpperCAmelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
     )
     A__				      =	CsvDatasetReader(UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )





@pytest.mark.parametrize("""split"""       , [None, NamedSplit("""train"""      ), """train""", """test"""]      )
def 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       int      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	CsvDatasetReader(UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , split=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )
     assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""       , [str, list]      )
def 			_snake_case  (     UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Dict      ):
     if issubclass(UpperCAmelCase_       , UpperCAmelCase_      ):
          A__				      =	csv_path
     elif issubclass(UpperCAmelCase_       , UpperCAmelCase_      ):
          A__				      =	[csv_path]
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	CsvDatasetReader(UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_dataset(UpperCAmelCase_       , UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]       , UpperCAmelCase_	:       Dict       , UpperCAmelCase_	:       int=("train",)      ):
     assert isinstance(UpperCAmelCase_       , UpperCAmelCase_      )
     for split in splits:
          A__				      =	dataset_dict[split]
          assert dataset.num_rows == 4
          assert dataset.num_columns == 3
          assert dataset.column_names == ["col_1", "col_2", "col_3"]
          for feature, expected_dtype in expected_features.items():
               assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""       , [False, True]      )
def 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       List[str]      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
          A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_       , keep_in_memory=UpperCAmelCase_      ).read()
     _check_csv_datasetdict(UpperCAmelCase_       , UpperCAmelCase_      )





@pytest.mark.parametrize(
    """features"""       , [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]       , )
def 			_snake_case  (     UpperCAmelCase_	:       Dict       , UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Optional[Any]      ):
     A__				      =	tmp_path / """cache"""
     # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	features.copy() if features else default_expected_features
     A__				      =	(
         Features({feature: Value(UpperCAmelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
     )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_datasetdict(UpperCAmelCase_       , UpperCAmelCase_      )





@pytest.mark.parametrize("""split"""       , [None, NamedSplit("""train"""      ), """train""", """test"""]      )
def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       List[str]       , UpperCAmelCase_	:       Optional[int]      ):
     if split:
          A__				      =	{split: csv_path}
     else:
          A__				      =	{"""train""": csv_path, """test""": csv_path}
     A__				      =	tmp_path / """cache"""
     A__				      =	{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
     A__				      =	CsvDatasetReader(UpperCAmelCase_       , cache_dir=UpperCAmelCase_      ).read()
     _check_csv_datasetdict(UpperCAmelCase_       , UpperCAmelCase_       , splits=list(path.keys()      )      )
     assert all(dataset[split].split == split for split in path.keys()      )





def 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]      ):
     with open(UpperCAmelCase_       , encoding="""utf-8"""      ) as csvfile:
          yield from csv.reader(UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       str       , UpperCAmelCase_	:       int      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	os.path.join(UpperCAmelCase_       , """tmp.csv"""      )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_      ).read()
     CsvDatasetWriter(dataset["""train"""]       , UpperCAmelCase_       , num_proc=1      ).write()

     A__				      =	iter_csv_file(UpperCAmelCase_      )
     A__				      =	iter_csv_file(UpperCAmelCase_      )

     for rowa, rowa in zip(UpperCAmelCase_       , UpperCAmelCase_      ):
          assert rowa == rowa





def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       List[Any]      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	os.path.join(UpperCAmelCase_       , """tmp.csv"""      )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_      ).read()
     CsvDatasetWriter(dataset["""train"""]       , UpperCAmelCase_       , num_proc=2      ).write()

     A__				      =	iter_csv_file(UpperCAmelCase_      )
     A__				      =	iter_csv_file(UpperCAmelCase_      )

     for rowa, rowa in zip(UpperCAmelCase_       , UpperCAmelCase_      ):
          assert rowa == rowa





def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Any      ):
     A__				      =	tmp_path / """cache"""
     A__				      =	os.path.join(UpperCAmelCase_       , """tmp.csv"""      )
     A__				      =	CsvDatasetReader({"""train""": csv_path}       , cache_dir=UpperCAmelCase_      ).read()
     with pytest.raises(UpperCAmelCase_      ):
          CsvDatasetWriter(dataset["""train"""]       , UpperCAmelCase_       , num_proc=0      )






