





'''simple docstring'''

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import YolosImageProcessor
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def __init__(							self       :		List[Any]	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		Optional[int]=7	,			lowerCAmelCase__       :		Optional[Any]=3	,			lowerCAmelCase__       :		List[str]=30	,			lowerCAmelCase__       :		Union[str, Any]=400	,			lowerCAmelCase__       :		int=True	,			lowerCAmelCase__       :		Optional[int]=None	,			lowerCAmelCase__       :		Optional[Any]=True	,			lowerCAmelCase__       :		Tuple=[0.5, 0.5, 0.5]	,			lowerCAmelCase__       :		Union[str, Any]=[0.5, 0.5, 0.5]	,			lowerCAmelCase__       :		Tuple=True	,			lowerCAmelCase__       :		Optional[int]=1 / 255	,			lowerCAmelCase__       :		Any=True	,			)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
            _UpperCamelCase       =	parent
            _UpperCamelCase       =	batch_size
            _UpperCamelCase       =	num_channels
            _UpperCamelCase       =	min_resolution
            _UpperCamelCase       =	max_resolution
            _UpperCamelCase       =	do_resize
            _UpperCamelCase       =	size
            _UpperCamelCase       =	do_normalize
            _UpperCamelCase       =	image_mean
            _UpperCamelCase       =	image_std
            _UpperCamelCase       =	do_rescale
            _UpperCamelCase       =	rescale_factor
            _UpperCamelCase       =	do_pad





       def snake_case__						(							self       :		List[Any]					)   ->					List[Any]:

            '''simple docstring'''




            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_rescale": self.do_rescale,
                "rescale_factor": self.rescale_factor,
                "do_pad": self.do_pad,
            }







       def snake_case__						(							self       :		Optional[Any]	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Tuple=False					)   ->					List[Any]:

            '''simple docstring'''




            if not batched:
                 _UpperCamelCase       =	image_inputs[0]
                 if isinstance(lowerCAmelCase__	,			Image.Image					):
                      _UpperCamelCase   ,    _UpperCamelCase       =	image.size
                 else:
                      _UpperCamelCase   ,    _UpperCamelCase       =	image.shape[1], image.shape[2]
                 if w < h:
                      _UpperCamelCase       =	int(self.size['''shortest_edge'''] * h / w					)
                      _UpperCamelCase       =	self.size['''shortest_edge''']
                 elif w > h:
                      _UpperCamelCase       =	self.size['''shortest_edge''']
                      _UpperCamelCase       =	int(self.size['''shortest_edge'''] * w / h					)
                 else:
                      _UpperCamelCase       =	self.size['''shortest_edge''']
                      _UpperCamelCase       =	self.size['''shortest_edge''']

            else:
                 _UpperCamelCase       =	[]
                 for image in image_inputs:
                      _UpperCamelCase   ,    _UpperCamelCase       =	self.get_expected_values([image]					)
                      expected_values.append((expected_height, expected_width)					)
                 _UpperCamelCase       =	max(lowerCAmelCase__	,			key=lambda lowerCAmelCase__					: item[0]					)[0]
                 _UpperCamelCase       =	max(lowerCAmelCase__	,			key=lambda lowerCAmelCase__					: item[1]					)[1]

            return expected_height, expected_width

@require_torch
@require_vision
class     __lowerCAmelCase					(   __magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Optional[int]						    =   YolosImageProcessor if is_vision_available() else None





       def snake_case__						(							self       :		str					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	YolosImageProcessingTester(self					)





       @property
       def snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()





       def snake_case__						(							self       :		Any					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processing_class(**self.image_processor_dict					)
            self.assertTrue(hasattr(lowerCAmelCase__	,			'''image_mean'''					)					)
            self.assertTrue(hasattr(lowerCAmelCase__	,			'''image_std'''					)					)
            self.assertTrue(hasattr(lowerCAmelCase__	,			'''do_normalize'''					)					)
            self.assertTrue(hasattr(lowerCAmelCase__	,			'''do_resize'''					)					)
            self.assertTrue(hasattr(lowerCAmelCase__	,			'''size'''					)					)





       def snake_case__						(							self       :		Dict					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processing_class.from_dict(self.image_processor_dict					)
            self.assertEqual(image_processor.size	,			{'''shortest_edge''': 18, '''longest_edge''': 1333}					)
            self.assertEqual(image_processor.do_pad	,			lowerCAmelCase__					)

            _UpperCamelCase       =	self.image_processing_class.from_dict(
                self.image_processor_dict	,			size=42	,			max_size=84	,			pad_and_return_pixel_mask=lowerCAmelCase__					)
            self.assertEqual(image_processor.size	,			{'''shortest_edge''': 42, '''longest_edge''': 84}					)
            self.assertEqual(image_processor.do_pad	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		str					)   ->					Optional[int]:

            '''simple docstring'''




            pass





       def snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            _UpperCamelCase       =	prepare_image_inputs(self.image_processor_tester	,			equal_resolution=lowerCAmelCase__					)
            for image in image_inputs:
                 self.assertIsInstance(lowerCAmelCase__	,			Image.Image					)

            # Test not batched input
            _UpperCamelCase       =	image_processing(image_inputs[0]	,			return_tensors='''pt'''					).pixel_values

            _UpperCamelCase   ,    _UpperCamelCase       =	self.image_processor_tester.get_expected_values(lowerCAmelCase__					)

            self.assertEqual(
                encoded_images.shape	,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,			)

            # Test batched
            _UpperCamelCase   ,    _UpperCamelCase       =	self.image_processor_tester.get_expected_values(lowerCAmelCase__	,			batched=lowerCAmelCase__					)

            _UpperCamelCase       =	image_processing(lowerCAmelCase__	,			return_tensors='''pt'''					).pixel_values
            self.assertEqual(
                encoded_images.shape	,			(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )	,			)





       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processing_class(**self.image_processor_dict					)
            # create random numpy tensors
            _UpperCamelCase       =	prepare_image_inputs(self.image_processor_tester	,			equal_resolution=lowerCAmelCase__	,			numpify=lowerCAmelCase__					)
            for image in image_inputs:
                 self.assertIsInstance(lowerCAmelCase__	,			np.ndarray					)

            # Test not batched input
            _UpperCamelCase       =	image_processing(image_inputs[0]	,			return_tensors='''pt'''					).pixel_values

            _UpperCamelCase   ,    _UpperCamelCase       =	self.image_processor_tester.get_expected_values(lowerCAmelCase__					)

            self.assertEqual(
                encoded_images.shape	,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,			)

            # Test batched
            _UpperCamelCase       =	image_processing(lowerCAmelCase__	,			return_tensors='''pt'''					).pixel_values

            _UpperCamelCase   ,    _UpperCamelCase       =	self.image_processor_tester.get_expected_values(lowerCAmelCase__	,			batched=lowerCAmelCase__					)

            self.assertEqual(
                encoded_images.shape	,			(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )	,			)





       def snake_case__						(							self       :		str					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processing_class(**self.image_processor_dict					)
            # create random PyTorch tensors
            _UpperCamelCase       =	prepare_image_inputs(self.image_processor_tester	,			equal_resolution=lowerCAmelCase__	,			torchify=lowerCAmelCase__					)
            for image in image_inputs:
                 self.assertIsInstance(lowerCAmelCase__	,			torch.Tensor					)

            # Test not batched input
            _UpperCamelCase       =	image_processing(image_inputs[0]	,			return_tensors='''pt'''					).pixel_values

            _UpperCamelCase   ,    _UpperCamelCase       =	self.image_processor_tester.get_expected_values(lowerCAmelCase__					)

            self.assertEqual(
                encoded_images.shape	,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,			)

            # Test batched
            _UpperCamelCase       =	image_processing(lowerCAmelCase__	,			return_tensors='''pt'''					).pixel_values

            _UpperCamelCase   ,    _UpperCamelCase       =	self.image_processor_tester.get_expected_values(lowerCAmelCase__	,			batched=lowerCAmelCase__					)

            self.assertEqual(
                encoded_images.shape	,			(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )	,			)





       def snake_case__						(							self       :		int					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processing_class(**self.image_processor_dict					)
            _UpperCamelCase       =	self.image_processing_class(do_resize=lowerCAmelCase__	,			do_normalize=lowerCAmelCase__	,			do_rescale=lowerCAmelCase__					)
            # create random PyTorch tensors
            _UpperCamelCase       =	prepare_image_inputs(self.image_processor_tester	,			equal_resolution=lowerCAmelCase__	,			torchify=lowerCAmelCase__					)
            for image in image_inputs:
                 self.assertIsInstance(lowerCAmelCase__	,			torch.Tensor					)

            # Test whether the method "pad" and calling the image processor return the same tensors
            _UpperCamelCase       =	image_processing_a.pad(lowerCAmelCase__	,			return_tensors='''pt'''					)
            _UpperCamelCase       =	image_processing_a(lowerCAmelCase__	,			return_tensors='''pt'''					)

            self.assertTrue(
                torch.allclose(encoded_images_with_method['''pixel_values''']	,			encoded_images['''pixel_values''']	,			atol=1e-4					)					)





       @slow
       def snake_case__						(							self       :		List[Any]					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
            with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''	,			'''r'''					) as f:
                 _UpperCamelCase       =	json.loads(f.read()					)

            _UpperCamelCase       =	{'''image_id''': 39769, '''annotations''': target}

            # encode them
            _UpperCamelCase       =	YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''					)
            _UpperCamelCase       =	image_processing(images=lowerCAmelCase__	,			annotations=lowerCAmelCase__	,			return_tensors='''pt'''					)

            # verify pixel values
            _UpperCamelCase       =	torch.Size([1, 3, 800, 1066]					)
            self.assertEqual(encoding['''pixel_values'''].shape	,			lowerCAmelCase__					)

            _UpperCamelCase       =	torch.tensor([0.2796, 0.3138, 0.3481]					)
            self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]	,			lowerCAmelCase__	,			atol=1e-4					)					)

            # verify area
            _UpperCamelCase       =	torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]					)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']	,			lowerCAmelCase__					)					)
            # verify boxes
            _UpperCamelCase       =	torch.Size([6, 4]					)
            self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape	,			lowerCAmelCase__					)
            _UpperCamelCase       =	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]					)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]	,			lowerCAmelCase__	,			atol=1e-3					)					)
            # verify image_id
            _UpperCamelCase       =	torch.tensor([39769]					)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']	,			lowerCAmelCase__					)					)
            # verify is_crowd
            _UpperCamelCase       =	torch.tensor([0, 0, 0, 0, 0, 0]					)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']	,			lowerCAmelCase__					)					)
            # verify class_labels
            _UpperCamelCase       =	torch.tensor([75, 75, 63, 65, 17, 17]					)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']	,			lowerCAmelCase__					)					)
            # verify orig_size
            _UpperCamelCase       =	torch.tensor([480, 640]					)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']	,			lowerCAmelCase__					)					)
            # verify size
            _UpperCamelCase       =	torch.tensor([800, 1066]					)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']	,			lowerCAmelCase__					)					)







       @slow
       def snake_case__						(							self       :		Dict					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
            with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''	,			'''r'''					) as f:
                 _UpperCamelCase       =	json.loads(f.read()					)

            _UpperCamelCase       =	{'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

            _UpperCamelCase       =	pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''					)

            # encode them
            _UpperCamelCase       =	YolosImageProcessor(format='''coco_panoptic'''					)
            _UpperCamelCase       =	image_processing(images=lowerCAmelCase__	,			annotations=lowerCAmelCase__	,			masks_path=lowerCAmelCase__	,			return_tensors='''pt'''					)

            # verify pixel values
            _UpperCamelCase       =	torch.Size([1, 3, 800, 1066]					)
            self.assertEqual(encoding['''pixel_values'''].shape	,			lowerCAmelCase__					)

            _UpperCamelCase       =	torch.tensor([0.2796, 0.3138, 0.3481]					)
            self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]	,			lowerCAmelCase__	,			atol=1e-4					)					)

            # verify area
            _UpperCamelCase       =	torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]					)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']	,			lowerCAmelCase__					)					)
            # verify boxes
            _UpperCamelCase       =	torch.Size([6, 4]					)
            self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape	,			lowerCAmelCase__					)
            _UpperCamelCase       =	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]					)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]	,			lowerCAmelCase__	,			atol=1e-3					)					)
            # verify image_id
            _UpperCamelCase       =	torch.tensor([39769]					)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']	,			lowerCAmelCase__					)					)
            # verify is_crowd
            _UpperCamelCase       =	torch.tensor([0, 0, 0, 0, 0, 0]					)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']	,			lowerCAmelCase__					)					)
            # verify class_labels
            _UpperCamelCase       =	torch.tensor([17, 17, 63, 75, 75, 93]					)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']	,			lowerCAmelCase__					)					)
            # verify masks
            _UpperCamelCase       =	822873
            self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()	,			lowerCAmelCase__					)
            # verify orig_size
            _UpperCamelCase       =	torch.tensor([480, 640]					)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']	,			lowerCAmelCase__					)					)
            # verify size
            _UpperCamelCase       =	torch.tensor([800, 1066]					)
            self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']	,			lowerCAmelCase__					)					)










'''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__						:      List[str]	=    sys.version_info >= (3, 10)







def  a__    (    lowercase :					Dict=None, lowercase :					List[str]=None				)		->		List[Any]:





     """simple docstring"""

     return field(default_factory=lambda: default, metadata=lowercase				)
@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					int
       _snake_case     :					float
       _snake_case     :					str
       _snake_case     :					bool

@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					int						    =   4_2
       _snake_case     :					str						    =   field(default='toto'						,						metadata={'help': 'help message'}			)

@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					bool						    =   False
       _snake_case     :					bool						    =   True
       _snake_case     :					Optional[bool]						    =   None

class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					str						    =   'titi'
       _snake_case     :					Union[str, Any]						    =   'toto'

class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					str						    =   'titi'
       _snake_case     :					Union[str, Any]						    =   'toto'
       _snake_case     :					Any						    =   4_2

@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					BasicEnum						    =   "toto"





       def snake_case__						(							self       :		List[Any]					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	BasicEnum(self.foo					)

@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					MixedTypeEnum						    =   "toto"





       def snake_case__						(							self       :		Dict					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	MixedTypeEnum(self.foo					)

@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					Optional[int]						    =   None
       _snake_case     :					Optional[float]						    =   field(default=__magic_name__						,						metadata={'help': 'help message'}			)
       _snake_case     :					Optional[str]						    =   None
       _snake_case     :					Optional[List[str]]						    =   list_field(default=[]			)
       _snake_case     :					Optional[List[int]]						    =   list_field(default=[]			)

@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					List[int]						    =   list_field(default=[]			)
       _snake_case     :					List[int]						    =   list_field(default=[1, 2, 3]			)
       _snake_case     :					List[str]						    =   list_field(default=['Hallo', 'Bonjour', 'Hello']			)
       _snake_case     :					List[float]						    =   list_field(default=[0.1, 0.2, 0.3]			)

@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					List[int]						    =   field()
       _snake_case     :					str						    =   field()
       _snake_case     :					BasicEnum						    =   field()





       def snake_case__						(							self       :		str					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	BasicEnum(self.required_enum					)

@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					int
       _snake_case     :					"BasicEnum"						    =   field()
       _snake_case     :					"Optional[bool]"						    =   None
       _snake_case     :					"str"						    =   field(default='toto'						,						metadata={'help': 'help message'}			)
       _snake_case     :					"List[str]"						    =   list_field(default=['Hallo', 'Bonjour', 'Hello']			)


if is_python_no_less_than_3_10:
   @dataclass
   class     __lowerCAmelCase					:






          """simple docstring"""
          _snake_case     :					bool						    =   False
          _snake_case     :					bool						    =   True
          _snake_case     :					bool | None						    =   None

   @dataclass
   class     __lowerCAmelCase					:






          """simple docstring"""
          _snake_case     :					int | None						    =   None
          _snake_case     :					float | None						    =   field(default=__magic_name__						,						metadata={'help': 'help message'}			)
          _snake_case     :					str | None						    =   None
          _snake_case     :					list[str] | None						    =   list_field(default=[]			)
          _snake_case     :					list[int] | None						    =   list_field(default=[]			)




class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def snake_case__						(							self       :		Optional[Any]	,			lowerCAmelCase__       :		argparse.ArgumentParser	,			lowerCAmelCase__       :		argparse.ArgumentParser					)   ->					str:

            '''simple docstring'''




            self.assertEqual(len(a._actions					)	,			len(b._actions					)					)
            for x, y in zip(a._actions	,			b._actions					):
                 _UpperCamelCase       =	{k: v for k, v in vars(lowerCAmelCase__					).items() if k != '''container'''}
                 _UpperCamelCase       =	{k: v for k, v in vars(lowerCAmelCase__					).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'''	,			lowerCAmelCase__					) and yy.get('''choices'''	,			lowerCAmelCase__					):
                      for expected_choice in yy["choices"] + xx["choices"]:
                           self.assertEqual(xx['''type'''](lowerCAmelCase__					)	,			yy['''type'''](lowerCAmelCase__					)					)
                      del xx["type"], yy["type"]

                 self.assertEqual(lowerCAmelCase__	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Dict					)   ->					List[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	HfArgumentParser(lowerCAmelCase__					)

            _UpperCamelCase       =	argparse.ArgumentParser()
            expected.add_argument('''--foo'''	,			type=lowerCAmelCase__	,			required=lowerCAmelCase__					)
            expected.add_argument('''--bar'''	,			type=lowerCAmelCase__	,			required=lowerCAmelCase__					)
            expected.add_argument('''--baz'''	,			type=lowerCAmelCase__	,			required=lowerCAmelCase__					)
            expected.add_argument('''--flag'''	,			type=lowerCAmelCase__	,			default=lowerCAmelCase__	,			const=lowerCAmelCase__	,			nargs='''?'''					)
            self.argparsersEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	['''--foo''', '''1''', '''--baz''', '''quux''', '''--bar''', '''0.5''']
            ((_UpperCamelCase)   ,    )       =	parser.parse_args_into_dataclasses(lowerCAmelCase__	,			look_for_args_file=lowerCAmelCase__					)
            self.assertFalse(example.flag					)





       def snake_case__						(							self       :		int					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	HfArgumentParser(lowerCAmelCase__					)

            _UpperCamelCase       =	argparse.ArgumentParser()
            expected.add_argument('''--foo'''	,			default=42	,			type=lowerCAmelCase__					)
            expected.add_argument('''--baz'''	,			default='''toto'''	,			type=lowerCAmelCase__	,			help='''help message'''					)
            self.argparsersEqual(lowerCAmelCase__	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Tuple					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	argparse.ArgumentParser()
            expected.add_argument('''--foo'''	,			type=lowerCAmelCase__	,			default=lowerCAmelCase__	,			const=lowerCAmelCase__	,			nargs='''?'''					)
            expected.add_argument('''--baz'''	,			type=lowerCAmelCase__	,			default=lowerCAmelCase__	,			const=lowerCAmelCase__	,			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=lowerCAmelCase__	,			dest='''baz'''					)
            expected.add_argument('''--opt'''	,			type=lowerCAmelCase__	,			default=lowerCAmelCase__					)

            _UpperCamelCase       =	[WithDefaultBoolExample]
            if is_python_no_less_than_3_10:
                 dataclass_types.append(lowerCAmelCase__					)

            for dataclass_type in dataclass_types:
                 _UpperCamelCase       =	HfArgumentParser(lowerCAmelCase__					)
                 self.argparsersEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

                 _UpperCamelCase       =	parser.parse_args([]					)
                 self.assertEqual(lowerCAmelCase__	,			Namespace(foo=lowerCAmelCase__	,			baz=lowerCAmelCase__	,			opt=lowerCAmelCase__					)					)

                 _UpperCamelCase       =	parser.parse_args(['''--foo''', '''--no_baz''']					)
                 self.assertEqual(lowerCAmelCase__	,			Namespace(foo=lowerCAmelCase__	,			baz=lowerCAmelCase__	,			opt=lowerCAmelCase__					)					)

                 _UpperCamelCase       =	parser.parse_args(['''--foo''', '''--baz''']					)
                 self.assertEqual(lowerCAmelCase__	,			Namespace(foo=lowerCAmelCase__	,			baz=lowerCAmelCase__	,			opt=lowerCAmelCase__					)					)

                 _UpperCamelCase       =	parser.parse_args(['''--foo''', '''True''', '''--baz''', '''True''', '''--opt''', '''True''']					)
                 self.assertEqual(lowerCAmelCase__	,			Namespace(foo=lowerCAmelCase__	,			baz=lowerCAmelCase__	,			opt=lowerCAmelCase__					)					)

                 _UpperCamelCase       =	parser.parse_args(['''--foo''', '''False''', '''--baz''', '''False''', '''--opt''', '''False''']					)
                 self.assertEqual(lowerCAmelCase__	,			Namespace(foo=lowerCAmelCase__	,			baz=lowerCAmelCase__	,			opt=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		Union[str, Any]					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	HfArgumentParser(lowerCAmelCase__					)

            _UpperCamelCase       =	argparse.ArgumentParser()
            expected.add_argument(
                '''--foo'''	,			default='''toto'''	,			choices=['''titi''', '''toto''', 42]	,			type=make_choice_type_function(['''titi''', '''toto''', 42]					)	,			)
            self.argparsersEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	parser.parse_args([]					)
            self.assertEqual(args.foo	,			'''toto'''					)
            _UpperCamelCase       =	parser.parse_args_into_dataclasses([]					)[0]
            self.assertEqual(enum_ex.foo	,			MixedTypeEnum.toto					)

            _UpperCamelCase       =	parser.parse_args(['''--foo''', '''titi''']					)
            self.assertEqual(args.foo	,			'''titi'''					)
            _UpperCamelCase       =	parser.parse_args_into_dataclasses(['''--foo''', '''titi''']					)[0]
            self.assertEqual(enum_ex.foo	,			MixedTypeEnum.titi					)

            _UpperCamelCase       =	parser.parse_args(['''--foo''', '''42''']					)
            self.assertEqual(args.foo	,			42					)
            _UpperCamelCase       =	parser.parse_args_into_dataclasses(['''--foo''', '''42''']					)[0]
            self.assertEqual(enum_ex.foo	,			MixedTypeEnum.fourtytwo					)





       def snake_case__						(							self       :		Any					)   ->					List[Any]:

            '''simple docstring'''




            @dataclass
            class     __lowerCAmelCase					:






                   """simple docstring"""
                   _snake_case     :					Literal["titi", "toto", 4_2]						    =   "toto"

            _UpperCamelCase       =	HfArgumentParser(lowerCAmelCase__					)

            _UpperCamelCase       =	argparse.ArgumentParser()
            expected.add_argument(
                '''--foo'''	,			default='''toto'''	,			choices=('''titi''', '''toto''', 42)	,			type=make_choice_type_function(['''titi''', '''toto''', 42]					)	,			)
            self.argparsersEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	parser.parse_args([]					)
            self.assertEqual(args.foo	,			'''toto'''					)

            _UpperCamelCase       =	parser.parse_args(['''--foo''', '''titi''']					)
            self.assertEqual(args.foo	,			'''titi'''					)

            _UpperCamelCase       =	parser.parse_args(['''--foo''', '''42''']					)
            self.assertEqual(args.foo	,			42					)





       def snake_case__						(							self       :		int					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	HfArgumentParser(lowerCAmelCase__					)

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

            self.argparsersEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

            _UpperCamelCase       =	parser.parse_args([]					)
            self.assertEqual(
                lowerCAmelCase__	,			Namespace(foo_int=[]	,			bar_int=[1, 2, 3]	,			foo_str=['''Hallo''', '''Bonjour''', '''Hello''']	,			foo_float=[0.1, 0.2, 0.3]					)	,			)

            _UpperCamelCase       =	parser.parse_args('''--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'''.split()					)
            self.assertEqual(lowerCAmelCase__	,			Namespace(foo_int=[1]	,			bar_int=[2, 3]	,			foo_str=['''a''', '''b''', '''c''']	,			foo_float=[0.1, 0.7]					)					)





       def snake_case__						(							self       :		List[str]					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	argparse.ArgumentParser()
            expected.add_argument('''--foo'''	,			default=lowerCAmelCase__	,			type=lowerCAmelCase__					)
            expected.add_argument('''--bar'''	,			default=lowerCAmelCase__	,			type=lowerCAmelCase__	,			help='''help message'''					)
            expected.add_argument('''--baz'''	,			default=lowerCAmelCase__	,			type=lowerCAmelCase__					)
            expected.add_argument('''--ces'''	,			nargs='''+'''	,			default=[]	,			type=lowerCAmelCase__					)
            expected.add_argument('''--des'''	,			nargs='''+'''	,			default=[]	,			type=lowerCAmelCase__					)

            _UpperCamelCase       =	[OptionalExample]
            if is_python_no_less_than_3_10:
                 dataclass_types.append(lowerCAmelCase__					)

            for dataclass_type in dataclass_types:
                 _UpperCamelCase       =	HfArgumentParser(lowerCAmelCase__					)

                 self.argparsersEqual(lowerCAmelCase__	,			lowerCAmelCase__					)

                 _UpperCamelCase       =	parser.parse_args([]					)
                 self.assertEqual(lowerCAmelCase__	,			Namespace(foo=lowerCAmelCase__	,			bar=lowerCAmelCase__	,			baz=lowerCAmelCase__	,			ces=[]	,			des=[]					)					)

                 _UpperCamelCase       =	parser.parse_args('''--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'''.split()					)
                 self.assertEqual(lowerCAmelCase__	,			Namespace(foo=12	,			bar=3.14	,			baz='''42'''	,			ces=['''a''', '''b''', '''c''']	,			des=[1, 2, 3]					)					)





       def snake_case__						(							self       :		Any					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	HfArgumentParser(lowerCAmelCase__					)

            _UpperCamelCase       =	argparse.ArgumentParser()
            expected.add_argument('''--required_list'''	,			nargs='''+'''	,			type=lowerCAmelCase__	,			required=lowerCAmelCase__					)
            expected.add_argument('''--required_str'''	,			type=lowerCAmelCase__	,			required=lowerCAmelCase__					)
            expected.add_argument(
                '''--required_enum'''	,			type=make_choice_type_function(['''titi''', '''toto''']					)	,			choices=['''titi''', '''toto''']	,			required=lowerCAmelCase__	,			)
            self.argparsersEqual(lowerCAmelCase__	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Any					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	HfArgumentParser(lowerCAmelCase__					)

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





       def snake_case__						(							self       :		int					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	HfArgumentParser(lowerCAmelCase__					)

            _UpperCamelCase       =	{
                '''foo''': 12,
                '''bar''': 3.14,
                '''baz''': '''42''',
                '''flag''': True,
            }

            _UpperCamelCase       =	parser.parse_dict(lowerCAmelCase__					)[0]
            _UpperCamelCase       =	BasicExample(**lowerCAmelCase__					)
            self.assertEqual(lowerCAmelCase__	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Any					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	HfArgumentParser(lowerCAmelCase__					)

            _UpperCamelCase       =	{
                '''foo''': 12,
                '''bar''': 3.14,
                '''baz''': '''42''',
                '''flag''': True,
                '''extra''': 42,
            }

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





       def snake_case__						(							self       :		str					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	HfArgumentParser(lowerCAmelCase__					)

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

            _UpperCamelCase       =	BasicExample(**lowerCAmelCase__					)
            self.assertEqual(lowerCAmelCase__	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	HfArgumentParser(lowerCAmelCase__					)

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







       def snake_case__						(							self       :		str					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	HfArgumentParser(lowerCAmelCase__					)
            self.assertIsNotNone(lowerCAmelCase__					)



