
'''simple docstring'''

import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
		import torch

UpperCAmelCase__   : Tuple					=		logging.get_logger(__name__)







def   A     (      )					->       Optional[int]:







			'''simple docstring'''



			return importlib.util.find_spec("deepspeed"      ) is not None


if is_accelerate_available() and is_deepspeed_available():
		from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
		# Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
		# Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
		from builtins import object as DeepSpeedConfig






class    A     (	SCREAMING_SNAKE_CASE__    ):





	def __init__(					self			:       Optional[Any]      ,							__magic_name__			:       str							):


				"""simple docstring"""

				set_hf_deepspeed_config(self							)
				dep_version_check("accelerate"							)
				dep_version_check("deepspeed"							)
				super().__init__(__magic_name__							)






class    A     (	SCREAMING_SNAKE_CASE__    ):





	def __init__(					self			:       Optional[Any]      ,							__magic_name__			:       Optional[int]							):


				"""simple docstring"""

				super().__init__(__magic_name__							)
				lowerCAmelCase__								=							None
				lowerCAmelCase__								=							[]





	def 							__SCREAMING_SNAKE_CASE  (					self			:       str							):


				"""simple docstring"""

				if self._dtype is None:
							raise ValueError("trainer_config_process() wasn't called yet to tell dtype"							)
				return self._dtype





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]      ,							__magic_name__			:       List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.get_value(__magic_name__							)
				if val is None:
							return False
				else:
							return val == "auto"





	def 							__SCREAMING_SNAKE_CASE  (					self			:       str      ,							__magic_name__			:       List[str]      ,							__magic_name__			:       Union[str, Any]      ,							__magic_name__			:       Dict=None      ,							__magic_name__			:       Dict=True							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__								=							self.find_config_node(__magic_name__							)
				if config is None:
							return

				if config.get(__magic_name__							) == "auto":
							lowerCAmelCase__								=							hf_val
							return

				if not must_match:
							return

				lowerCAmelCase__								=							config.get(__magic_name__							)
				if ds_val is not None and ds_val != hf_val:
							self.mismatches.append(f"""- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}"""							)

	snake_case__      :Union[str, Any]        =     partialmethod(SCREAMING_SNAKE_CASE__  ,      must_match=SCREAMING_SNAKE_CASE__    )





	def 							__SCREAMING_SNAKE_CASE  (					self			:       int      ,							__magic_name__			:       Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__								=							args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
				self.fill_match(
				    "train_micro_batch_size_per_gpu"      ,							args.per_device_train_batch_size      ,							"per_device_train_batch_size"							)
				self.fill_match("gradient_accumulation_steps"      ,							args.gradient_accumulation_steps      ,							"gradient_accumulation_steps"							)
				self.fill_match("train_batch_size"      ,							__magic_name__      ,							"train_batch_size (calculated)"							)
				self.fill_match("gradient_clipping"      ,							args.max_grad_norm      ,							"max_grad_norm"							)

				self.fill_match("optimizer.params.lr"      ,							args.learning_rate      ,							"learning_rate"							)
				self.fill_match("optimizer.params.betas"      ,							[args.adam_betaa, args.adam_betaa]      ,							"adam_beta1+adam_beta2"							)
				self.fill_match("optimizer.params.eps"      ,							args.adam_epsilon      ,							"adam_epsilon"							)
				self.fill_match("optimizer.params.weight_decay"      ,							args.weight_decay      ,							"weight_decay"							)

				self.fill_only("scheduler.params.warmup_min_lr"      ,							0							)  # not a trainer arg
				self.fill_match("scheduler.params.warmup_max_lr"      ,							args.learning_rate      ,							"learning_rate"							)
				# total_num_steps - will get set in trainer_config_finalize

				# fp16
				if args.fpaa or args.fpaa_full_eval:
							lowerCAmelCase__								=							"apex" if args.fpaa_backend == "apex" else "amp"
				else:
							lowerCAmelCase__								=							None

				if args.save_on_each_node:
							# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
							lowerCAmelCase__								=							self.config.get("checkpoint"      ,							{}							)
							lowerCAmelCase__								=							args.save_on_each_node

				# amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
				# any here unless the user did the work
				self.fill_match(
				    "fp16.enabled"      ,							((args.fpaa or args.fpaa_full_eval) and fpaa_backend == "amp")      ,							"fp16|fp16_full_eval+fp16_backend(amp)"      ,							)

				# apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
				# ZeRO features
				self.fill_match("amp.enabled"      ,							fpaa_backend == "apex"      ,							"fp16+fp16_backend(apex)"							)
				self.fill_match("amp.opt_level"      ,							args.fpaa_opt_level      ,							"fp16_opt_level"							)

				self.fill_match("bf16.enabled"      ,							(args.bfaa or args.bfaa_full_eval)      ,							"bf16|bf16_full_eval"							)

				# deepspeed's default mode is fp16 unless there is a config that says differently
				if self.is_true("bf16.enabled"							):
							lowerCAmelCase__								=							torch.bfloataa
				elif self.is_false("fp16.enabled"							):
							lowerCAmelCase__								=							torch.floataa
				else:
							lowerCAmelCase__								=							torch.floataa







	def 							__SCREAMING_SNAKE_CASE  (					self			:       int      ,							__magic_name__			:       Optional[Any]      ,							__magic_name__			:       Any      ,							__magic_name__			:       List[str]							):


				"""simple docstring"""

				lowerCAmelCase__								=							[
				    "zero_optimization.reduce_bucket_size",
				    "zero_optimization.stage3_prefetch_bucket_size",
				    "zero_optimization.stage3_param_persistence_threshold",
				]
				lowerCAmelCase__								=							[x for x in hidden_size_based_keys if self.is_auto(__magic_name__							)]

				if len(__magic_name__							) > 0:
							if hasattr(model.config      ,							"hidden_size"							):
										lowerCAmelCase__								=							model.config.hidden_size
							elif hasattr(model.config      ,							"hidden_sizes"							):
										# if there are many hidden sizes pick the largest one
										lowerCAmelCase__								=							max(model.config.hidden_sizes							)
							else:
										raise ValueError(
										    "The model's config file has neither `hidden_size` nor `hidden_sizes` entry, "
										    "therefore it's not possible to automatically fill out the following `auto` entries "
										    f"""in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing """
										    "`auto` values for these keys with an integer value of your choice."							)

							self.fill_only("zero_optimization.reduce_bucket_size"      ,							hidden_size * hidden_size							)
							if self.is_zeroa():
										# automatically assign the optimal config values based on model config
										self.fill_only("zero_optimization.stage3_prefetch_bucket_size"      ,							0.9 * hidden_size * hidden_size							)
										self.fill_only("zero_optimization.stage3_param_persistence_threshold"      ,							10 * hidden_size							)

        # scheduler
				self.fill_match("scheduler.params.total_num_steps"      ,							__magic_name__      ,							"num_training_steps (calculated)"							)
				self.fill_match("scheduler.params.warmup_num_steps"      ,							args.get_warmup_steps(__magic_name__							)      ,							"warmup_steps"							)

				if len(self.mismatches							) > 0:
							lowerCAmelCase__								=							"\n".join(self.mismatches							)
							raise ValueError(
							    "Please correct the following DeepSpeed config values that mismatch TrainingArguments"
							    f""" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'."""							)

UpperCAmelCase__   : int					=		None







def   A     (      UpperCamelCase_					:     List[str]      )					->       Tuple:







			'''simple docstring'''



			global _hf_deepspeed_config_weak_ref
			# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
			lowerCAmelCase__								=							weakref.ref(UpperCamelCase_      )







def   A     (      )					->       Dict:







			'''simple docstring'''



			global _hf_deepspeed_config_weak_ref
			lowerCAmelCase__								=							None







def   A     (      )					->       List[str]:







			'''simple docstring'''



			if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
						return _hf_deepspeed_config_weak_ref().is_zeroa()
			else:
						return False







def   A     (      )					->       List[str]:







			'''simple docstring'''



			if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
						return _hf_deepspeed_config_weak_ref().config
			else:
						return None







def   A     (      UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     Any						,	UpperCamelCase_					:     Optional[Any]						,	UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Tuple      )					->       Optional[Any]:







			'''simple docstring'''



			from accelerate.utils import DummyOptim, DummyScheduler

			lowerCAmelCase__								=							hf_deepspeed_config.config

			# Optimizer + Scheduler
			# Currently supported combos:
			# 1. DS scheduler + DS optimizer: Yes
			# 2. HF scheduler + HF optimizer: Yes
			# 3. DS scheduler + HF optimizer: Yes
			# 4. HF scheduler + DS optimizer: No
			#
			# Unless Offload is enabled in which case it's:
			# 1. DS scheduler + DS optimizer: Yes
			# 2. HF scheduler + HF optimizer: Mostly*
			# 3. DS scheduler + HF optimizer: Mostly*
			# 4. HF scheduler + DS optimizer: No
			#
			# Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

			lowerCAmelCase__								=							None
			if "optimizer" in config:
						if args.adafactor:
									raise ValueError(
									    "--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. "
									    "Only one optimizer can be configured."      )
						lowerCAmelCase__								=							DummyOptim(params=UpperCamelCase_      )
			else:
						if hf_deepspeed_config.is_offload():
									logger.info(
									    "Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the"
									    " custom optimizer has both CPU and GPU implementation (except LAMB)"      )

						# ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
						# But trainer uses AdamW by default.
						lowerCAmelCase__								=							trainer.create_optimizer()
						# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
						lowerCAmelCase__								=							True

			lowerCAmelCase__								=							None
			if "scheduler" in config:
						lowerCAmelCase__								=							DummyScheduler(UpperCamelCase_      )
			else:
						if isinstance(UpperCamelCase_						,	UpperCamelCase_      ):
									raise ValueError(
									    "Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. "
									    "Please configure a scheduler in the DeepSpeed config."      )
						lowerCAmelCase__								=							trainer.create_scheduler(num_training_steps=UpperCamelCase_						,	optimizer=UpperCamelCase_      )

			return optimizer, lr_scheduler







def   A     (      UpperCamelCase_					:     int						,	UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     Union[str, Any]=False      )					->       int:







			'''simple docstring'''



			from deepspeed.utils import logger as ds_logger

			lowerCAmelCase__								=							trainer.model
			lowerCAmelCase__								=							trainer.args

			lowerCAmelCase__								=							trainer.accelerator.state.deepspeed_plugin.hf_ds_config

			# resume config update - some bits like `model` and `num_training_steps` only become available during train
			hf_deepspeed_config.trainer_config_finalize(UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_      )

			# set the Deepspeed log level consistent with the Trainer
			ds_logger.setLevel(args.get_process_log_level()      )

			if inference:
						# only Z3 makes sense for the inference
						if not hf_deepspeed_config.is_zeroa():
									raise ValueError("ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config"      )

						# in case the training config is re-used for inference
						hf_deepspeed_config.del_config_sub_tree("optimizer"      )
						hf_deepspeed_config.del_config_sub_tree("lr_scheduler"      )
						lowerCAmelCase__	,lowerCAmelCase__								=							None, None
						lowerCAmelCase__								=							None
			else:
						lowerCAmelCase__								=							None  # important for when deepspeed_init is used as re-init
						lowerCAmelCase__								=							list(filter(lambda UpperCamelCase_      : p.requires_grad						,	model.parameters()      )      )
						lowerCAmelCase__	,lowerCAmelCase__								=							deepspeed_optim_sched(
						    UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_						,	UpperCamelCase_      )

			# keep for quick debug:
			# from pprint import pprint; pprint(config)

			return optimizer, lr_scheduler







def   A     (      UpperCamelCase_					:     Any						,	UpperCamelCase_					:     Dict      )					->       Optional[Any]:







			'''simple docstring'''



			import glob

			lowerCAmelCase__								=							sorted(glob.glob(F"""{checkpoint_path}/global_step*"""      )      )

			if len(UpperCamelCase_      ) > 0:
						logger.info(F"""Attempting to resume from {checkpoint_path}"""      )
						# this magically updates self.optimizer and self.lr_scheduler
						lowerCAmelCase__	,lowerCAmelCase__								=							deepspeed_engine.load_checkpoint(
						    UpperCamelCase_						,	load_optimizer_states=UpperCamelCase_						,	load_lr_scheduler_states=UpperCamelCase_      )
						if load_path is None:
									raise ValueError(F"""[deepspeed] failed to resume from checkpoint {checkpoint_path}"""      )
			else:
						raise ValueError(F"""Can't find a valid checkpoint at {checkpoint_path}"""      )







'''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   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Any      )					->       Optional[Any]:







			'''simple docstring'''



			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   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Optional[Any]						,	UpperCamelCase_					:     Optional[Any]      )					->       Union[str, Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						lowerCAmelCase__								=							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   A     (      UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Tuple      )					->       Union[str, Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							features.copy() if features else default_expected_features
			lowerCAmelCase__								=							(
			    Features({feature: Value(UpperCamelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
			)
			lowerCAmelCase__								=							CsvDatasetReader(UpperCamelCase_						,	features=UpperCamelCase_						,	cache_dir=UpperCamelCase_      ).read()
			_check_csv_dataset(UpperCamelCase_						,	UpperCamelCase_      )







@pytest.mark.parametrize("split"						,	[None, NamedSplit("train"      ), "train", "test"]      )
def   A     (      UpperCamelCase_					:     str						,	UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     str      )					->       Any:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							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   A     (      UpperCamelCase_					:     Any						,	UpperCamelCase_					:     Tuple						,	UpperCamelCase_					:     List[str]      )					->       Optional[int]:







			'''simple docstring'''



			if issubclass(UpperCamelCase_						,	UpperCamelCase_      ):
						lowerCAmelCase__								=							csv_path
			elif issubclass(UpperCamelCase_						,	UpperCamelCase_      ):
						lowerCAmelCase__								=							[csv_path]
			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							CsvDatasetReader(UpperCamelCase_						,	cache_dir=UpperCamelCase_      ).read()
			_check_csv_dataset(UpperCamelCase_						,	UpperCamelCase_      )







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     List[str]=("train",)      )					->       Optional[int]:







			'''simple docstring'''



			assert isinstance(UpperCamelCase_						,	UpperCamelCase_      )
			for split in splits:
						lowerCAmelCase__								=							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   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Optional[int]      )					->       Optional[Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						lowerCAmelCase__								=							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   A     (      UpperCamelCase_					:     str						,	UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     Dict      )					->       Optional[int]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							features.copy() if features else default_expected_features
			lowerCAmelCase__								=							(
			    Features({feature: Value(UpperCamelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
			)
			lowerCAmelCase__								=							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   A     (      UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     List[Any]      )					->       Optional[int]:







			'''simple docstring'''



			if split:
						lowerCAmelCase__								=							{split: csv_path}
			else:
						lowerCAmelCase__								=							{"train": csv_path, "test": csv_path}
			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							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   A     (      UpperCamelCase_					:     str      )					->       List[str]:







			'''simple docstring'''



			with open(UpperCamelCase_						,	encoding="utf-8"      ) as csvfile:
						yield from csv.reader(UpperCamelCase_      )







def   A     (      UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     int      )					->       int:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							os.path.join(UpperCamelCase_						,	"tmp.csv"      )
			lowerCAmelCase__								=							CsvDatasetReader({"train": csv_path}						,	cache_dir=UpperCamelCase_      ).read()
			CsvDatasetWriter(dataset["train"]						,	UpperCamelCase_						,	num_proc=1      ).write()

			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )
			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )

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







def   A     (      UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     Dict      )					->       List[str]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							os.path.join(UpperCamelCase_						,	"tmp.csv"      )
			lowerCAmelCase__								=							CsvDatasetReader({"train": csv_path}						,	cache_dir=UpperCamelCase_      ).read()
			CsvDatasetWriter(dataset["train"]						,	UpperCamelCase_						,	num_proc=2      ).write()

			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )
			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )

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







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Tuple      )					->       Any:







			'''simple docstring'''



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





