

"""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 			__a					(						unittest.TestCase       ):





       """simple docstring"""


       def __init__(   self			,					snake_case			,					snake_case=7			,					snake_case=3			,					snake_case=30			,					snake_case=400			,					snake_case=True			,					snake_case=None			,					snake_case=True			,					snake_case=[0.5, 0.5, 0.5]			,					snake_case=[0.5, 0.5, 0.5]			,					snake_case=True			,					snake_case=1 / 255			,					snake_case=True			,					):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
        lowerCAmelCase__   :				List[str] 			=				parent
        lowerCAmelCase__   :				Optional[Any] 			=				batch_size
        lowerCAmelCase__   :				Union[str, Any] 			=				num_channels
        lowerCAmelCase__   :				Optional[int] 			=				min_resolution
        lowerCAmelCase__   :				str 			=				max_resolution
        lowerCAmelCase__   :				Dict 			=				do_resize
        lowerCAmelCase__   :				Union[str, Any] 			=				size
        lowerCAmelCase__   :				Dict 			=				do_normalize
        lowerCAmelCase__   :				Dict 			=				image_mean
        lowerCAmelCase__   :				Dict 			=				image_std
        lowerCAmelCase__   :				Dict 			=				do_rescale
        lowerCAmelCase__   :				str 			=				rescale_factor
        lowerCAmelCase__   :				Tuple 			=				do_pad






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case=False   ):





        """simple docstring"""

        if not batched:
         lowerCAmelCase__   :				List[Any] 			=				image_inputs[0]
         if isinstance(snake_case			,					Image.Image   ):
          lowerCAmelCase__			,						lowerCAmelCase__   :				List[str] 			=				image.size
         else:
          lowerCAmelCase__			,						lowerCAmelCase__   :				Optional[int] 			=				image.shape[1], image.shape[2]
         if w < h:
          lowerCAmelCase__   :				Any 			=				int(self.size["shortest_edge"] * h / w   )
          lowerCAmelCase__   :				List[Any] 			=				self.size["shortest_edge"]
         elif w > h:
          lowerCAmelCase__   :				Optional[int] 			=				self.size["shortest_edge"]
          lowerCAmelCase__   :				Any 			=				int(self.size["shortest_edge"] * w / h   )
         else:
          lowerCAmelCase__   :				Tuple 			=				self.size["shortest_edge"]
          lowerCAmelCase__   :				Dict 			=				self.size["shortest_edge"]

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

        return expected_height, expected_width






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





       """simple docstring"""


       __UpperCamelCase					:      Any    =						YolosImageProcessor if is_vision_available() else None






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				YolosImageProcessingTester(self   )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self.image_processor_tester.prepare_image_processor_dict()






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(snake_case			,					"image_mean"   )   )
        self.assertTrue(hasattr(snake_case			,					"image_std"   )   )
        self.assertTrue(hasattr(snake_case			,					"do_normalize"   )   )
        self.assertTrue(hasattr(snake_case			,					"do_resize"   )   )
        self.assertTrue(hasattr(snake_case			,					"size"   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				self.image_processing_class.from_dict(self.image_processor_dict   )
        self.assertEqual(image_processor.size			,					{"shortest_edge": 18, "longest_edge": 1_333}   )
        self.assertEqual(image_processor.do_pad			,					snake_case   )

        lowerCAmelCase__   :				List[Any] 			=				self.image_processing_class.from_dict(
            self.image_processor_dict			,					size=42			,					max_size=84			,					pad_and_return_pixel_mask=snake_case   )
        self.assertEqual(image_processor.size			,					{"shortest_edge": 42, "longest_edge": 84}   )
        self.assertEqual(image_processor.do_pad			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        pass






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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

        # Test not batched input
        lowerCAmelCase__   :				Optional[int] 			=				image_processing(image_inputs[0]			,					return_tensors="pt"   ).pixel_values

        lowerCAmelCase__			,						lowerCAmelCase__   :				Any 			=				self.image_processor_tester.get_expected_values(snake_case   )

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

        # Test batched
        lowerCAmelCase__			,						lowerCAmelCase__   :				Optional[int] 			=				self.image_processor_tester.get_expected_values(snake_case			,					batched=snake_case   )

        lowerCAmelCase__   :				Dict 			=				image_processing(snake_case			,					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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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

        # Test not batched input
        lowerCAmelCase__   :				str 			=				image_processing(image_inputs[0]			,					return_tensors="pt"   ).pixel_values

        lowerCAmelCase__			,						lowerCAmelCase__   :				Dict 			=				self.image_processor_tester.get_expected_values(snake_case   )

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

        # Test batched
        lowerCAmelCase__   :				List[Any] 			=				image_processing(snake_case			,					return_tensors="pt"   ).pixel_values

        lowerCAmelCase__			,						lowerCAmelCase__   :				str 			=				self.image_processor_tester.get_expected_values(snake_case			,					batched=snake_case   )

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        lowerCAmelCase__   :				str 			=				prepare_image_inputs(self.image_processor_tester			,					equal_resolution=snake_case			,					torchify=snake_case   )
        for image in image_inputs:
         self.assertIsInstance(snake_case			,					torch.Tensor   )

        # Test not batched input
        lowerCAmelCase__   :				List[str] 			=				image_processing(image_inputs[0]			,					return_tensors="pt"   ).pixel_values

        lowerCAmelCase__			,						lowerCAmelCase__   :				List[Any] 			=				self.image_processor_tester.get_expected_values(snake_case   )

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

        # Test batched
        lowerCAmelCase__   :				Optional[int] 			=				image_processing(snake_case			,					return_tensors="pt"   ).pixel_values

        lowerCAmelCase__			,						lowerCAmelCase__   :				str 			=				self.image_processor_tester.get_expected_values(snake_case			,					batched=snake_case   )

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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

        # Test whether the method "pad" and calling the image processor return the same tensors
        lowerCAmelCase__   :				Any 			=				image_processing_a.pad(snake_case			,					return_tensors="pt"   )
        lowerCAmelCase__   :				Union[str, Any] 			=				image_processing_a(snake_case			,					return_tensors="pt"   )

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






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"			,					"r"   ) as f:
         lowerCAmelCase__   :				Optional[int] 			=				json.loads(f.read()   )

        lowerCAmelCase__   :				Union[str, Any] 			=				{"image_id": 39_769, "annotations": target}

        # encode them
        lowerCAmelCase__   :				Optional[int] 			=				YolosImageProcessor.from_pretrained("hustvl/yolos-small"   )
        lowerCAmelCase__   :				List[str] 			=				image_processing(images=snake_case			,					annotations=snake_case			,					return_tensors="pt"   )

        # verify pixel values
        lowerCAmelCase__   :				Dict 			=				torch.Size([1, 3, 800, 1_066]   )
        self.assertEqual(encoding["pixel_values"].shape			,					snake_case   )

        lowerCAmelCase__   :				Dict 			=				torch.tensor([0.2_796, 0.3_138, 0.3_481]   )
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]			,					snake_case			,					atol=1e-4   )   )

        # verify area
        lowerCAmelCase__   :				str 			=				torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"]			,					snake_case   )   )
        # verify boxes
        lowerCAmelCase__   :				int 			=				torch.Size([6, 4]   )
        self.assertEqual(encoding["labels"][0]["boxes"].shape			,					snake_case   )
        lowerCAmelCase__   :				Optional[int] 			=				torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]			,					snake_case			,					atol=1e-3   )   )
        # verify image_id
        lowerCAmelCase__   :				int 			=				torch.tensor([39_769]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]			,					snake_case   )   )
        # verify is_crowd
        lowerCAmelCase__   :				Optional[int] 			=				torch.tensor([0, 0, 0, 0, 0, 0]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]			,					snake_case   )   )
        # verify class_labels
        lowerCAmelCase__   :				Union[str, Any] 			=				torch.tensor([75, 75, 63, 65, 17, 17]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]			,					snake_case   )   )
        # verify orig_size
        lowerCAmelCase__   :				Optional[int] 			=				torch.tensor([480, 640]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]			,					snake_case   )   )
        # verify size
        lowerCAmelCase__   :				Dict 			=				torch.tensor([800, 1_066]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]			,					snake_case   )   )






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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

        lowerCAmelCase__   :				Tuple 			=				{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

        lowerCAmelCase__   :				Optional[Any] 			=				pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"   )

        # encode them
        lowerCAmelCase__   :				Any 			=				YolosImageProcessor(format="coco_panoptic"   )
        lowerCAmelCase__   :				Tuple 			=				image_processing(images=snake_case			,					annotations=snake_case			,					masks_path=snake_case			,					return_tensors="pt"   )

        # verify pixel values
        lowerCAmelCase__   :				Dict 			=				torch.Size([1, 3, 800, 1_066]   )
        self.assertEqual(encoding["pixel_values"].shape			,					snake_case   )

        lowerCAmelCase__   :				Any 			=				torch.tensor([0.2_796, 0.3_138, 0.3_481]   )
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]			,					snake_case			,					atol=1e-4   )   )

        # verify area
        lowerCAmelCase__   :				Union[str, Any] 			=				torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"]			,					snake_case   )   )
        # verify boxes
        lowerCAmelCase__   :				List[Any] 			=				torch.Size([6, 4]   )
        self.assertEqual(encoding["labels"][0]["boxes"].shape			,					snake_case   )
        lowerCAmelCase__   :				Dict 			=				torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]			,					snake_case			,					atol=1e-3   )   )
        # verify image_id
        lowerCAmelCase__   :				Tuple 			=				torch.tensor([39_769]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]			,					snake_case   )   )
        # verify is_crowd
        lowerCAmelCase__   :				Any 			=				torch.tensor([0, 0, 0, 0, 0, 0]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]			,					snake_case   )   )
        # verify class_labels
        lowerCAmelCase__   :				Optional[int] 			=				torch.tensor([17, 17, 63, 75, 75, 93]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]			,					snake_case   )   )
        # verify masks
        lowerCAmelCase__   :				List[Any] 			=				822_873
        self.assertEqual(encoding["labels"][0]["masks"].sum().item()			,					snake_case   )
        # verify orig_size
        lowerCAmelCase__   :				str 			=				torch.tensor([480, 640]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]			,					snake_case   )   )
        # verify size
        lowerCAmelCase__   :				Union[str, Any] 			=				torch.tensor([800, 1_066]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]			,					snake_case   )   )









"""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
_UpperCamelCase		     =    sys.version_info >= (3, 1_0)



def       SCREAMING_SNAKE_CASE							( lowercase__=None					,					lowercase__=None   )			->						List[Any]:
 return field(default_factory=lambda: default					,					metadata=lowercase__   )






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      int
       __UpperCamelCase					:      float
       __UpperCamelCase					:      str
       __UpperCamelCase					:      bool






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      int    =						42
       __UpperCamelCase					:      str    =						field(default='toto'       ,	metadata={'help': 'help message'}       )






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      bool    =						False
       __UpperCamelCase					:      bool    =						True
       __UpperCamelCase					:      Optional[bool]    =						None






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      List[str]    =						'titi'
       __UpperCamelCase					:      Tuple    =						'toto'






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      Tuple    =						'titi'
       __UpperCamelCase					:      List[Any]    =						'toto'
       __UpperCamelCase					:      List[str]    =						42






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      BasicEnum    =						"toto"






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				BasicEnum(self.foo   )






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      MixedTypeEnum    =						"toto"






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				MixedTypeEnum(self.foo   )






@dataclass
class 			__a					:





       """simple docstring"""


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






@dataclass
class 			__a					:





       """simple docstring"""


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






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      List[int]    =						field()
       __UpperCamelCase					:      str    =						field()
       __UpperCamelCase					:      BasicEnum    =						field()






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				BasicEnum(self.required_enum   )






@dataclass
class 			__a					:





       """simple docstring"""


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


if is_python_no_less_than_3_10:






    @dataclass
    class 			__a					:





           """simple docstring"""


           __UpperCamelCase					:      bool    =						False
           __UpperCamelCase					:      bool    =						True
           __UpperCamelCase					:      bool | None    =						None






    @dataclass
    class 			__a					:





           """simple docstring"""


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






class 			__a					(						unittest.TestCase       ):





       """simple docstring"""


       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case   ):





        """simple docstring"""

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

         self.assertEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				HfArgumentParser(snake_case   )

        lowerCAmelCase__   :				Optional[int] 			=				argparse.ArgumentParser()
        expected.add_argument("--foo"			,					type=snake_case			,					required=snake_case   )
        expected.add_argument("--bar"			,					type=snake_case			,					required=snake_case   )
        expected.add_argument("--baz"			,					type=snake_case			,					required=snake_case   )
        expected.add_argument("--flag"			,					type=snake_case			,					default=snake_case			,					const=snake_case			,					nargs="?"   )
        self.argparsersEqual(snake_case			,					snake_case   )

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				HfArgumentParser(snake_case   )

        lowerCAmelCase__   :				Optional[int] 			=				argparse.ArgumentParser()
        expected.add_argument("--foo"			,					default=42			,					type=snake_case   )
        expected.add_argument("--baz"			,					default="toto"			,					type=snake_case			,					help="help message"   )
        self.argparsersEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				argparse.ArgumentParser()
        expected.add_argument("--foo"			,					type=snake_case			,					default=snake_case			,					const=snake_case			,					nargs="?"   )
        expected.add_argument("--baz"			,					type=snake_case			,					default=snake_case			,					const=snake_case			,					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=snake_case			,					dest="baz"   )
        expected.add_argument("--opt"			,					type=snake_case			,					default=snake_case   )

        lowerCAmelCase__   :				Optional[Any] 			=				[WithDefaultBoolExample]
        if is_python_no_less_than_3_10:
         dataclass_types.append(snake_case   )

        for dataclass_type in dataclass_types:
         lowerCAmelCase__   :				Optional[Any] 			=				HfArgumentParser(snake_case   )
         self.argparsersEqual(snake_case			,					snake_case   )

         lowerCAmelCase__   :				Union[str, Any] 			=				parser.parse_args([]   )
         self.assertEqual(snake_case			,					Namespace(foo=snake_case			,					baz=snake_case			,					opt=snake_case   )   )

         lowerCAmelCase__   :				List[str] 			=				parser.parse_args(["--foo", "--no_baz"]   )
         self.assertEqual(snake_case			,					Namespace(foo=snake_case			,					baz=snake_case			,					opt=snake_case   )   )

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

         lowerCAmelCase__   :				List[Any] 			=				parser.parse_args(["--foo", "True", "--baz", "True", "--opt", "True"]   )
         self.assertEqual(snake_case			,					Namespace(foo=snake_case			,					baz=snake_case			,					opt=snake_case   )   )

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				HfArgumentParser(snake_case   )

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

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

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

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        @dataclass
        class 			__a					:





               """simple docstring"""


               __UpperCamelCase					:      Literal["titi", "toto", 42]    =						"toto"




        lowerCAmelCase__   :				Dict 			=				HfArgumentParser(snake_case   )

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

        lowerCAmelCase__   :				int 			=				parser.parse_args([]   )
        self.assertEqual(args.foo			,					"toto"   )

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

        lowerCAmelCase__   :				int 			=				parser.parse_args(["--foo", "42"]   )
        self.assertEqual(args.foo			,					42   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				HfArgumentParser(snake_case   )

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

        self.argparsersEqual(snake_case			,					snake_case   )

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

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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

        lowerCAmelCase__   :				Optional[Any] 			=				[OptionalExample]
        if is_python_no_less_than_3_10:
         dataclass_types.append(snake_case   )

        for dataclass_type in dataclass_types:
         lowerCAmelCase__   :				Tuple 			=				HfArgumentParser(snake_case   )

         self.argparsersEqual(snake_case			,					snake_case   )

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

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				HfArgumentParser(snake_case   )

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				HfArgumentParser(snake_case   )

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				HfArgumentParser(snake_case   )

        lowerCAmelCase__   :				List[Any] 			=				{
            "foo": 12,
            "bar": 3.14,
            "baz": "42",
            "flag": True,
        }

        lowerCAmelCase__   :				Tuple 			=				parser.parse_dict(snake_case   )[0]
        lowerCAmelCase__   :				Any 			=				BasicExample(**snake_case   )
        self.assertEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				HfArgumentParser(snake_case   )

        lowerCAmelCase__   :				List[Any] 			=				{
            "foo": 12,
            "bar": 3.14,
            "baz": "42",
            "flag": True,
            "extra": 42,
        }

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				HfArgumentParser(snake_case   )

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

        lowerCAmelCase__   :				str 			=				BasicExample(**snake_case   )
        self.assertEqual(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				HfArgumentParser(snake_case   )

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				HfArgumentParser(snake_case   )
        self.assertIsNotNone(snake_case   )






