"""simple docstring"""


import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


__UpperCamelCase									=			logging.getLogger(__name__)




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      "token-classification"







       def __init__( self,							lowerCAmelCase__)      ->      Any:
            if type(lowerCAmelCase__) == dict:
                 snake_case_					       =					Namespace(**lowerCAmelCase__)
            snake_case_					       =					import_module('tasks')
            try:
                 snake_case_					       =					getattr(lowerCAmelCase__,							hparams.task_type)
                 snake_case_					       =					token_classification_task_clazz()
            except AttributeError:
                 raise ValueError(
                     f'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
                     f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}')
            snake_case_					       =					self.token_classification_task.get_labels(hparams.labels)
            snake_case_					       =					CrossEntropyLoss().ignore_index
            super().__init__(lowerCAmelCase__,							len(self.labels),							self.mode)







       def 		a_						( self,							**lowerCAmelCase__)      ->      Tuple:
            return self.model(**lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__)      ->      Optional[int]:
            snake_case_					       =					{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
            if self.config.model_type != "distilbert":
                 snake_case_					       =					(
                     batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
                 )  # XLM and RoBERTa don"t use token_type_ids

            snake_case_					       =					self(**lowerCAmelCase__)
            snake_case_					       =					outputs[0]
            # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
            return {"loss": loss}







       def 		a_						( self)      ->      str:
            snake_case_					       =					self.hparams
            for mode in ["train", "dev", "test"]:
                 snake_case_					       =					self._feature_file(lowerCAmelCase__)
                 if os.path.exists(lowerCAmelCase__) and not args.overwrite_cache:
                      logger.info('Loading features from cached file %s',							lowerCAmelCase__)
                      snake_case_					       =					torch.load(lowerCAmelCase__)
                 else:
                      logger.info('Creating features from dataset file at %s',							args.data_dir)
                      snake_case_					       =					self.token_classification_task.read_examples_from_file(args.data_dir,							lowerCAmelCase__)
                      snake_case_					       =					self.token_classification_task.convert_examples_to_features(
                          lowerCAmelCase__,							self.labels,							args.max_seq_length,							self.tokenizer,							cls_token_at_end=bool(self.config.model_type in ['xlnet']),							cls_token=self.tokenizer.cls_token,							cls_token_segment_id=2 if self.config.model_type in ['xlnet'] else 0,							sep_token=self.tokenizer.sep_token,							sep_token_extra=lowerCAmelCase__,							pad_on_left=bool(self.config.model_type in ['xlnet']),							pad_token=self.tokenizer.pad_token_id,							pad_token_segment_id=self.tokenizer.pad_token_type_id,							pad_token_label_id=self.pad_token_label_id,							)
                      logger.info('Saving features into cached file %s',							lowerCAmelCase__)
                      torch.save(lowerCAmelCase__,							lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = False)      ->      DataLoader:
            snake_case_					       =					self._feature_file(lowerCAmelCase__)
            logger.info('Loading features from cached file %s',							lowerCAmelCase__)
            snake_case_					       =					torch.load(lowerCAmelCase__)
            snake_case_					       =					torch.tensor([f.input_ids for f in features],							dtype=torch.long)
            snake_case_					       =					torch.tensor([f.attention_mask for f in features],							dtype=torch.long)
            if features[0].token_type_ids is not None:
                 snake_case_					       =					torch.tensor([f.token_type_ids for f in features],							dtype=torch.long)
            else:
                 snake_case_					       =					torch.tensor([0 for f in features],							dtype=torch.long)
                 # HACK(we will not use this anymore soon)
            snake_case_					       =					torch.tensor([f.label_ids for f in features],							dtype=torch.long)
            return DataLoader(
                TensorDataset(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__),							batch_size=lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__)      ->      str:
            """Compute validation""" ""
            snake_case_					       =					{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
            if self.config.model_type != "distilbert":
                 snake_case_					       =					(
                     batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
                 )  # XLM and RoBERTa don"t use token_type_ids
            snake_case_					       =					self(**lowerCAmelCase__)
            snake_case_       ,	snake_case_					       =					outputs[:2]
            snake_case_					       =					logits.detach().cpu().numpy()
            snake_case_					       =					inputs['labels'].detach().cpu().numpy()
            return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}







       def 		a_						( self,							lowerCAmelCase__)      ->      Optional[Any]:
            snake_case_					       =					torch.stack([x['val_loss'] for x in outputs]).mean()
            snake_case_					       =					np.concatenate([x['pred'] for x in outputs],							axis=0)
            snake_case_					       =					np.argmax(lowerCAmelCase__,							axis=2)
            snake_case_					       =					np.concatenate([x['target'] for x in outputs],							axis=0)

            snake_case_					       =					dict(enumerate(self.labels))
            snake_case_					       =					[[] for _ in range(out_label_ids.shape[0])]
            snake_case_					       =					[[] for _ in range(out_label_ids.shape[0])]

            for i in range(out_label_ids.shape[0]):
                 for j in range(out_label_ids.shape[1]):
                      if out_label_ids[i, j] != self.pad_token_label_id:
                           out_label_list[i].append(label_map[out_label_ids[i][j]])
                           preds_list[i].append(label_map[preds[i][j]])

            snake_case_					       =					{
                'val_loss': val_loss_mean,
                'accuracy_score': accuracy_score(lowerCAmelCase__,							lowerCAmelCase__),
                'precision': precision_score(lowerCAmelCase__,							lowerCAmelCase__),
                'recall': recall_score(lowerCAmelCase__,							lowerCAmelCase__),
                'f1': fa_score(lowerCAmelCase__,							lowerCAmelCase__),
            }

            snake_case_					       =					dict(results.items())
            snake_case_					       =					results
            return ret, preds_list, out_label_list







       def 		a_						( self,							lowerCAmelCase__)      ->      List[Any]:
            # when stable
            snake_case_       ,	snake_case_       ,	snake_case_					       =					self._eval_end(lowerCAmelCase__)
            snake_case_					       =					ret['log']
            return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







       def 		a_						( self,							lowerCAmelCase__)      ->      Union[str, Any]:
            # updating to test_epoch_end instead of deprecated test_end
            snake_case_       ,	snake_case_       ,	snake_case_					       =					self._eval_end(lowerCAmelCase__)

            # Converting to the dict required by pl
            # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
            # pytorch_lightning/trainer/logging.py#L139
            snake_case_					       =					ret['log']
            # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
            return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







       @staticmethod
       def 		a_						( lowerCAmelCase__,							lowerCAmelCase__)      ->      Any:
            # Add NER specific options
            BaseTransformer.add_model_specific_args(lowerCAmelCase__,							lowerCAmelCase__)
            parser.add_argument(
                '--task_type',							default='NER',							type=lowerCAmelCase__,							help='Task type to fine tune in training (e.g. NER, POS, etc)')
            parser.add_argument(
                '--max_seq_length',							default=128,							type=lowerCAmelCase__,							help=(
                    'The maximum total input sequence length after tokenization. Sequences longer '
                    'than this will be truncated, sequences shorter will be padded.'
                ),							)

            parser.add_argument(
                '--labels',							default='',							type=lowerCAmelCase__,							help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.',							)
            parser.add_argument(
                '--gpus',							default=0,							type=lowerCAmelCase__,							help='The number of GPUs allocated for this, it is by default 0 meaning none',							)

            parser.add_argument(
                '--overwrite_cache',							action='store_true',							help='Overwrite the cached training and evaluation sets')

            return parser


if __name__ == "__main__":
    __UpperCamelCase									=			argparse.ArgumentParser()
    add_generic_args(parser, os.getcwd())
    __UpperCamelCase									=			NERTransformer.add_model_specific_args(parser, os.getcwd())
    __UpperCamelCase									=			parser.parse_args()
    __UpperCamelCase									=			NERTransformer(args)
    __UpperCamelCase									=			generic_train(model, args)

    if args.do_predict:
        # See https://github.com/huggingface/transformers/issues/3159
        # pl use this default format to create a checkpoint:
        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
        # /pytorch_lightning/callbacks/model_checkpoint.py#L322
        __UpperCamelCase									=			sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
        __UpperCamelCase									=			model.load_from_checkpoint(checkpoints[-1])
        trainer.test(model)


'''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_    )





