






"""simple docstring"""






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

UpperCAmelCase_   : Tuple   			= 16
UpperCAmelCase_   : List[str]   			= 32



def 					_A			(__a    ,				__a = 16							) ->       Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[str]    						=		AutoTokenizer.from_pretrained('''bert-base-cased'''							)
    SCREAMING_SNAKE_CASE_      :  Dict    						=		load_dataset('''glue'''    ,				'''mrpc'''							)

    def tokenize_function(__a							):
        # max_length=None => use the model max length (it's actually the default)
        SCREAMING_SNAKE_CASE_      :  Any    						=		tokenizer(examples['''sentence1''']    ,				examples['''sentence2''']    ,				truncation=__a    ,				max_length=__a							)
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		datasets.map(
            __a    ,				batched=__a    ,				remove_columns=['''idx''', '''sentence1''', '''sentence2''']    ,				)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    SCREAMING_SNAKE_CASE_      :  str    						=		tokenized_datasets.rename_column('''label'''    ,				'''labels'''							)

    def collate_fn(__a							):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		1_28 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            SCREAMING_SNAKE_CASE_      :  Any    						=		16
        elif accelerator.mixed_precision != "no":
            SCREAMING_SNAKE_CASE_      :  int    						=		8
        else:
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		None

        return tokenizer.pad(
            __a    ,				padding='''longest'''    ,				max_length=__a    ,				pad_to_multiple_of=__a    ,				return_tensors='''pt'''    ,				)

    # Instantiate dataloaders.
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		DataLoader(
        tokenized_datasets['''train''']    ,				shuffle=__a    ,				collate_fn=__a    ,				batch_size=__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		DataLoader(
        tokenized_datasets['''validation''']    ,				shuffle=__a    ,				collate_fn=__a    ,				batch_size=__a							)

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  UpperCAmelCase_   : int   			= mocked_dataloaders  # noqa: F811



def 					_A			(__a    ,				__a							) ->       Optional[int]:
    """simple docstring"""






    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    ,				__a							) == "1":
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		2
    # Initialize Accelerator

    # New Code #
    # We pass in "all" to `log_with` to grab all available trackers in the environment
    # Note: If using a custom `Tracker` class, should be passed in here such as:
    # >>> log_with = ["all", MyCustomTrackerClassInstance()]
    if args.with_tracking:
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		Accelerator(
            cpu=args.cpu    ,				mixed_precision=args.mixed_precision    ,				log_with='''all'''    ,				project_dir=args.project_dir							)
    else:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		Accelerator(cpu=args.cpu    ,				mixed_precision=args.mixed_precision							)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    SCREAMING_SNAKE_CASE_      :  Any    						=		config['''lr''']
    SCREAMING_SNAKE_CASE_      :  int    						=		int(config['''num_epochs''']							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		int(config['''seed''']							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		int(config['''batch_size''']							)
    set_seed(__a							)

    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Any    						=		get_dataloaders(__a    ,				__a							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		evaluate.load('''glue'''    ,				'''mrpc'''							)

    # If the batch size is too big we use gradient accumulation
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		batch_size // MAX_GPU_BATCH_SIZE
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		MAX_GPU_BATCH_SIZE

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''    ,				return_dict=__a							)

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		model.to(accelerator.device							)

    # Instantiate optimizer
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		AdamW(params=model.parameters()    ,				lr=__a							)

    # Instantiate scheduler
    SCREAMING_SNAKE_CASE_      :  int    						=		get_linear_schedule_with_warmup(
        optimizer=__a    ,				num_warmup_steps=1_00    ,				num_training_steps=(len(__a							) * num_epochs) // gradient_accumulation_steps    ,				)

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		accelerator.prepare(
        __a    ,				__a    ,				__a    ,				__a    ,				__a							)

    # New Code #
    # We need to initialize the trackers we use. Overall configurations can also be stored
    if args.with_tracking:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		os.path.split(__a							)[-1].split('''.'''							)[0]
        accelerator.init_trackers(__a    ,				__a							)

    # Now we train the model
    for epoch in range(__a							):
        model.train()
        # New Code #
        # For our tracking example, we will log the total loss of each epoch
        if args.with_tracking:
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		0
        for step, batch in enumerate(__a							):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device							)
            SCREAMING_SNAKE_CASE_      :  List[str]    						=		model(**__a							)
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		outputs.loss
            # New Code #
            if args.with_tracking:
                total_loss += loss.detach().float()
            SCREAMING_SNAKE_CASE_      :  List[Any]    						=		loss / gradient_accumulation_steps
            accelerator.backward(__a							)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        for step, batch in enumerate(__a							):
            # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
            batch.to(accelerator.device							)
            with torch.no_grad():
                SCREAMING_SNAKE_CASE_      :  str    						=		model(**__a							)
            SCREAMING_SNAKE_CASE_      :  str    						=		outputs.logits.argmax(dim=-1							)
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[Any]    						=		accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
            metric.add_batch(
                predictions=__a    ,				references=__a    ,				)

        SCREAMING_SNAKE_CASE_      :  Any    						=		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'epoch {epoch}:'    ,				__a							)

        # New Code #
        # To actually log, we call `Accelerator.log`
        # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
        if args.with_tracking:
            accelerator.log(
                {
                    '''accuracy''': eval_metric['''accuracy'''],
                    '''f1''': eval_metric['''f1'''],
                    '''train_loss''': total_loss.item() / len(__a							),
                    '''epoch''': epoch,
                }    ,				step=__a    ,				)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
    if args.with_tracking:
        accelerator.end_training()



def 					_A			() ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Tuple    						=		argparse.ArgumentParser(description='''Simple example of training script.'''							)
    parser.add_argument(
        '''--mixed_precision'''    ,				type=__a    ,				default=__a    ,				choices=['''no''', '''fp16''', '''bf16''', '''fp8''']    ,				help='''Whether to use mixed precision. Choose'''
        '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
        '''and an Nvidia Ampere GPU.'''    ,				)
    parser.add_argument('''--cpu'''    ,				action='''store_true'''    ,				help='''If passed, will train on the CPU.'''							)
    parser.add_argument(
        '''--with_tracking'''    ,				action='''store_true'''    ,				help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''    ,				)
    parser.add_argument(
        '''--project_dir'''    ,				type=__a    ,				default='''logs'''    ,				help='''Location on where to store experiment tracking logs` and relevent project information'''    ,				)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		parser.parse_args()
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(__a    ,				__a							)


if __name__ == "__main__":
  main()



'''simple docstring'''




import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional

import yaml

from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool


# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
_lowercase						: int						=						sys.version_info >= (3, 10)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Tuple=None      ,      UpperCAmelCase__       :					Union[str, Any]=None )			->      Tuple:
							return field(default_factory=lambda: default      ,      metadata=UpperCAmelCase__ )





@dataclass
class     __magic_name__						:
			UpperCamelCase__				=       42
			UpperCamelCase__				=       42
			UpperCamelCase__				=       42
			UpperCamelCase__				=       42





@dataclass
class     __magic_name__						:
			UpperCamelCase__				=       42
			UpperCamelCase__				=       field(default='''toto''',     metadata={'''help''': '''help message'''})





@dataclass
class     __magic_name__						:
			UpperCamelCase__				=       False
			UpperCamelCase__				=       True
			UpperCamelCase__				=       None





class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       '''titi'''
			UpperCamelCase__				=       '''toto'''





class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       '''titi'''
			UpperCamelCase__				=       '''toto'''
			UpperCamelCase__				=       42





@dataclass
class     __magic_name__						:
			UpperCamelCase__				=       "toto"







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Optional[int]										=       BasicEnum(self.foo    )





@dataclass
class     __magic_name__						:
			UpperCamelCase__				=       "toto"







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Optional[int]										=       MixedTypeEnum(self.foo    )





@dataclass
class     __magic_name__						:
			UpperCamelCase__				=       None
			UpperCamelCase__				=       field(default=_UpperCAmelCase,     metadata={'''help''': '''help message'''})
			UpperCamelCase__				=       None
			UpperCamelCase__				=       list_field(default=[])
			UpperCamelCase__				=       list_field(default=[])





@dataclass
class     __magic_name__						:
			UpperCamelCase__				=       list_field(default=[])
			UpperCamelCase__				=       list_field(default=[1, 2, 3])
			UpperCamelCase__				=       list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''])
			UpperCamelCase__				=       list_field(default=[0.1, 0.2, 0.3])





@dataclass
class     __magic_name__						:
			UpperCamelCase__				=       field()
			UpperCamelCase__				=       field()
			UpperCamelCase__				=       field()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_					:			List[Any]										=       BasicEnum(self.required_enum    )





@dataclass
class     __magic_name__						:
			UpperCamelCase__				=       42
			UpperCamelCase__				=       field()
			UpperCamelCase__				=       None
			UpperCamelCase__				=       field(default='''toto''',     metadata={'''help''': '''help message'''})
			UpperCamelCase__				=       list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''])


if is_python_no_less_than_3_10:





						@dataclass
						class     __magic_name__						:
									UpperCamelCase__				=       False
									UpperCamelCase__				=       True
									UpperCamelCase__				=       None





						@dataclass
						class     __magic_name__						:
									UpperCamelCase__				=       None
									UpperCamelCase__				=       field(default=_UpperCAmelCase,     metadata={'''help''': '''help message'''})
									UpperCamelCase__				=       None
									UpperCamelCase__				=       list_field(default=[])
									UpperCamelCase__				=       list_field(default=[])





class     __magic_name__						(    unittest.TestCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict				,			lowercase_				:  argparse.ArgumentParser				,			lowercase_				:  argparse.ArgumentParser    ):
										self.assertEqual(len(a._actions    )				,			len(b._actions    )    )
										for x, y in zip(a._actions				,			b._actions    ):
																	lowercase_					:			List[str]										=       {k: v for k, v in vars(lowercase_    ).items() if k != """container"""}
																	lowercase_					:			Any										=       {k: v for k, v in vars(lowercase_    ).items() if k != """container"""}

																	# Choices with mixed type have custom function as "type"
																	# So we need to compare results directly for equality
																	if xx.get("""choices"""				,			lowercase_    ) and yy.get("""choices"""				,			lowercase_    ):
																								for expected_choice in yy["choices"] + xx["choices"]:
																															self.assertEqual(xx["""type"""](lowercase_    )				,			yy["""type"""](lowercase_    )    )
																								del xx["type"], yy["type"]

																	self.assertEqual(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			List[str]										=       HfArgumentParser(lowercase_    )

										lowercase_					:			str										=       argparse.ArgumentParser()
										expected.add_argument("""--foo"""				,			type=lowercase_				,			required=lowercase_    )
										expected.add_argument("""--bar"""				,			type=lowercase_				,			required=lowercase_    )
										expected.add_argument("""--baz"""				,			type=lowercase_				,			required=lowercase_    )
										expected.add_argument("""--flag"""				,			type=lowercase_				,			default=lowercase_				,			const=lowercase_				,			nargs="""?"""    )
										self.argparsersEqual(lowercase_				,			lowercase_    )

										lowercase_					:			List[str]										=       ["""--foo""", """1""", """--baz""", """quux""", """--bar""", """0.5"""]
										((lowercase_)						,						)					:			Optional[Any]										=       parser.parse_args_into_dataclasses(lowercase_				,			look_for_args_file=lowercase_    )
										self.assertFalse(example.flag    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			Any										=       HfArgumentParser(lowercase_    )

										lowercase_					:			str										=       argparse.ArgumentParser()
										expected.add_argument("""--foo"""				,			default=42				,			type=lowercase_    )
										expected.add_argument("""--baz"""				,			default="""toto"""				,			type=lowercase_				,			help="""help message"""    )
										self.argparsersEqual(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Optional[int]										=       argparse.ArgumentParser()
										expected.add_argument("""--foo"""				,			type=lowercase_				,			default=lowercase_				,			const=lowercase_				,			nargs="""?"""    )
										expected.add_argument("""--baz"""				,			type=lowercase_				,			default=lowercase_				,			const=lowercase_				,			nargs="""?"""    )
										# A boolean no_* argument always has to come after its "default: True" regular counter-part
										# and its default must be set to False
										expected.add_argument("""--no_baz"""				,			action="""store_false"""				,			default=lowercase_				,			dest="""baz"""    )
										expected.add_argument("""--opt"""				,			type=lowercase_				,			default=lowercase_    )

										lowercase_					:			List[Any]										=       [WithDefaultBoolExample]
										if is_python_no_less_than_3_10:
																	dataclass_types.append(lowercase_    )

										for dataclass_type in dataclass_types:
																	lowercase_					:			List[str]										=       HfArgumentParser(lowercase_    )
																	self.argparsersEqual(lowercase_				,			lowercase_    )

																	lowercase_					:			str										=       parser.parse_args([]    )
																	self.assertEqual(lowercase_				,			Namespace(foo=lowercase_				,			baz=lowercase_				,			opt=lowercase_    )    )

																	lowercase_					:			Optional[Any]										=       parser.parse_args(["""--foo""", """--no_baz"""]    )
																	self.assertEqual(lowercase_				,			Namespace(foo=lowercase_				,			baz=lowercase_				,			opt=lowercase_    )    )

																	lowercase_					:			Optional[Any]										=       parser.parse_args(["""--foo""", """--baz"""]    )
																	self.assertEqual(lowercase_				,			Namespace(foo=lowercase_				,			baz=lowercase_				,			opt=lowercase_    )    )

																	lowercase_					:			Dict										=       parser.parse_args(["""--foo""", """True""", """--baz""", """True""", """--opt""", """True"""]    )
																	self.assertEqual(lowercase_				,			Namespace(foo=lowercase_				,			baz=lowercase_				,			opt=lowercase_    )    )

																	lowercase_					:			int										=       parser.parse_args(["""--foo""", """False""", """--baz""", """False""", """--opt""", """False"""]    )
																	self.assertEqual(lowercase_				,			Namespace(foo=lowercase_				,			baz=lowercase_				,			opt=lowercase_    )    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										lowercase_					:			str										=       HfArgumentParser(lowercase_    )

										lowercase_					:			Tuple										=       argparse.ArgumentParser()
										expected.add_argument(
										    """--foo"""				,			default="""toto"""				,			choices=["""titi""", """toto""", 42]				,			type=make_choice_type_function(["""titi""", """toto""", 42]    )				,			)
										self.argparsersEqual(lowercase_				,			lowercase_    )

										lowercase_					:			Optional[int]										=       parser.parse_args([]    )
										self.assertEqual(args.foo				,			"""toto"""    )
										lowercase_					:			Union[str, Any]										=       parser.parse_args_into_dataclasses([]    )[0]
										self.assertEqual(enum_ex.foo				,			MixedTypeEnum.toto    )

										lowercase_					:			int										=       parser.parse_args(["""--foo""", """titi"""]    )
										self.assertEqual(args.foo				,			"""titi"""    )
										lowercase_					:			int										=       parser.parse_args_into_dataclasses(["""--foo""", """titi"""]    )[0]
										self.assertEqual(enum_ex.foo				,			MixedTypeEnum.titi    )

										lowercase_					:			List[str]										=       parser.parse_args(["""--foo""", """42"""]    )
										self.assertEqual(args.foo				,			42    )
										lowercase_					:			List[str]										=       parser.parse_args_into_dataclasses(["""--foo""", """42"""]    )[0]
										self.assertEqual(enum_ex.foo				,			MixedTypeEnum.fourtytwo    )







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





										@dataclass
										class     __magic_name__						:
													UpperCamelCase__				=       "toto"




										lowercase_					:			Optional[int]										=       HfArgumentParser(lowercase_    )

										lowercase_					:			List[str]										=       argparse.ArgumentParser()
										expected.add_argument(
										    """--foo"""				,			default="""toto"""				,			choices=("""titi""", """toto""", 42)				,			type=make_choice_type_function(["""titi""", """toto""", 42]    )				,			)
										self.argparsersEqual(lowercase_				,			lowercase_    )

										lowercase_					:			List[str]										=       parser.parse_args([]    )
										self.assertEqual(args.foo				,			"""toto"""    )

										lowercase_					:			Optional[int]										=       parser.parse_args(["""--foo""", """titi"""]    )
										self.assertEqual(args.foo				,			"""titi"""    )

										lowercase_					:			List[Any]										=       parser.parse_args(["""--foo""", """42"""]    )
										self.assertEqual(args.foo				,			42    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										lowercase_					:			int										=       HfArgumentParser(lowercase_    )

										lowercase_					:			Dict										=       argparse.ArgumentParser()
										expected.add_argument("""--foo_int"""				,			nargs="""+"""				,			default=[]				,			type=lowercase_    )
										expected.add_argument("""--bar_int"""				,			nargs="""+"""				,			default=[1, 2, 3]				,			type=lowercase_    )
										expected.add_argument("""--foo_str"""				,			nargs="""+"""				,			default=["""Hallo""", """Bonjour""", """Hello"""]				,			type=lowercase_    )
										expected.add_argument("""--foo_float"""				,			nargs="""+"""				,			default=[0.1, 0.2, 0.3]				,			type=lowercase_    )

										self.argparsersEqual(lowercase_				,			lowercase_    )

										lowercase_					:			int										=       parser.parse_args([]    )
										self.assertEqual(
										    lowercase_				,			Namespace(foo_int=[]				,			bar_int=[1, 2, 3]				,			foo_str=["""Hallo""", """Bonjour""", """Hello"""]				,			foo_float=[0.1, 0.2, 0.3]    )				,			)

										lowercase_					:			Optional[int]										=       parser.parse_args("""--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7""".split()    )
										self.assertEqual(lowercase_				,			Namespace(foo_int=[1]				,			bar_int=[2, 3]				,			foo_str=["""a""", """b""", """c"""]				,			foo_float=[0.1, 0.7]    )    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			Tuple										=       argparse.ArgumentParser()
										expected.add_argument("""--foo"""				,			default=lowercase_				,			type=lowercase_    )
										expected.add_argument("""--bar"""				,			default=lowercase_				,			type=lowercase_				,			help="""help message"""    )
										expected.add_argument("""--baz"""				,			default=lowercase_				,			type=lowercase_    )
										expected.add_argument("""--ces"""				,			nargs="""+"""				,			default=[]				,			type=lowercase_    )
										expected.add_argument("""--des"""				,			nargs="""+"""				,			default=[]				,			type=lowercase_    )

										lowercase_					:			Optional[int]										=       [OptionalExample]
										if is_python_no_less_than_3_10:
																	dataclass_types.append(lowercase_    )

										for dataclass_type in dataclass_types:
																	lowercase_					:			Tuple										=       HfArgumentParser(lowercase_    )

																	self.argparsersEqual(lowercase_				,			lowercase_    )

																	lowercase_					:			Union[str, Any]										=       parser.parse_args([]    )
																	self.assertEqual(lowercase_				,			Namespace(foo=lowercase_				,			bar=lowercase_				,			baz=lowercase_				,			ces=[]				,			des=[]    )    )

																	lowercase_					:			List[Any]										=       parser.parse_args("""--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3""".split()    )
																	self.assertEqual(lowercase_				,			Namespace(foo=12				,			bar=3.14				,			baz="""42"""				,			ces=["""a""", """b""", """c"""]				,			des=[1, 2, 3]    )    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										lowercase_					:			Dict										=       HfArgumentParser(lowercase_    )

										lowercase_					:			int										=       argparse.ArgumentParser()
										expected.add_argument("""--required_list"""				,			nargs="""+"""				,			type=lowercase_				,			required=lowercase_    )
										expected.add_argument("""--required_str"""				,			type=lowercase_				,			required=lowercase_    )
										expected.add_argument(
										    """--required_enum"""				,			type=make_choice_type_function(["""titi""", """toto"""]    )				,			choices=["""titi""", """toto"""]				,			required=lowercase_				,			)
										self.argparsersEqual(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			Dict										=       HfArgumentParser(lowercase_    )

										lowercase_					:			List[str]										=       argparse.ArgumentParser()
										expected.add_argument("""--foo"""				,			type=lowercase_				,			required=lowercase_    )
										expected.add_argument(
										    """--required_enum"""				,			type=make_choice_type_function(["""titi""", """toto"""]    )				,			choices=["""titi""", """toto"""]				,			required=lowercase_				,			)
										expected.add_argument("""--opt"""				,			type=lowercase_				,			default=lowercase_    )
										expected.add_argument("""--baz"""				,			default="""toto"""				,			type=lowercase_				,			help="""help message"""    )
										expected.add_argument("""--foo_str"""				,			nargs="""+"""				,			default=["""Hallo""", """Bonjour""", """Hello"""]				,			type=lowercase_    )
										self.argparsersEqual(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			str										=       HfArgumentParser(lowercase_    )

										lowercase_					:			Optional[int]										=       {
										    """foo""": 12,
										    """bar""": 3.14,
										    """baz""": """42""",
										    """flag""": True,
										}

										lowercase_					:			Optional[Any]										=       parser.parse_dict(lowercase_    )[0]
										lowercase_					:			Dict										=       BasicExample(**lowercase_    )
										self.assertEqual(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			Dict										=       HfArgumentParser(lowercase_    )

										lowercase_					:			Optional[int]										=       {
										    """foo""": 12,
										    """bar""": 3.14,
										    """baz""": """42""",
										    """flag""": True,
										    """extra""": 42,
										}

										self.assertRaises(lowercase_				,			parser.parse_dict				,			lowercase_				,			allow_extra_keys=lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_					:			List[Any]										=       HfArgumentParser(lowercase_    )

										lowercase_					:			int										=       {
										    """foo""": 12,
										    """bar""": 3.14,
										    """baz""": """42""",
										    """flag""": True,
										}
										with tempfile.TemporaryDirectory() as tmp_dir:
																	lowercase_					:			Optional[int]										=       os.path.join(lowercase_				,			"""temp_json"""    )
																	os.mkdir(lowercase_    )
																	with open(temp_local_path + """.json"""				,			"""w+"""    ) as f:
																								json.dump(lowercase_				,			lowercase_    )
																	lowercase_					:			str										=       parser.parse_yaml_file(Path(temp_local_path + """.json"""    )    )[0]

										lowercase_					:			Tuple										=       BasicExample(**lowercase_    )
										self.assertEqual(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			str										=       HfArgumentParser(lowercase_    )

										lowercase_					:			List[Any]										=       {
										    """foo""": 12,
										    """bar""": 3.14,
										    """baz""": """42""",
										    """flag""": True,
										}
										with tempfile.TemporaryDirectory() as tmp_dir:
																	lowercase_					:			Tuple										=       os.path.join(lowercase_				,			"""temp_yaml"""    )
																	os.mkdir(lowercase_    )
																	with open(temp_local_path + """.yaml"""				,			"""w+"""    ) as f:
																								yaml.dump(lowercase_				,			lowercase_    )
																	lowercase_					:			List[str]										=       parser.parse_yaml_file(Path(temp_local_path + """.yaml"""    )    )[0]
										lowercase_					:			Any										=       BasicExample(**lowercase_    )
										self.assertEqual(lowercase_				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										lowercase_					:			str										=       HfArgumentParser(lowercase_    )
										self.assertIsNotNone(lowercase_    )





