

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


class 			A						(							unittest.TestCase			):







    """simple docstring"""





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





        '''simple docstring'''







        A__   				=       0


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





        '''simple docstring'''







        A__   				=       AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32'		)
        self.assertIsInstance(lowercase_,lowercase_		)


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





        '''simple docstring'''







        with tempfile.TemporaryDirectory() as tmpdirname:
            A__   				=       Path(lowercase_		) / 'preprocessor_config.json'
            A__   				=       Path(lowercase_		) / 'config.json'
            json.dump(
                {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'},open(lowercase_,'w'		),)
            json.dump({'model_type': 'clip'},open(lowercase_,'w'		)		)

            A__   				=       AutoImageProcessor.from_pretrained(lowercase_		)
            self.assertIsInstance(lowercase_,lowercase_		)


    def 						snake_case__					(				self			:	int		)->   int:





        '''simple docstring'''







        with tempfile.TemporaryDirectory() as tmpdirname:
            A__   				=       Path(lowercase_		) / 'preprocessor_config.json'
            A__   				=       Path(lowercase_		) / 'config.json'
            json.dump(
                {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'},open(lowercase_,'w'		),)
            json.dump({'model_type': 'clip'},open(lowercase_,'w'		)		)

            A__   				=       AutoImageProcessor.from_pretrained(lowercase_		)
            self.assertIsInstance(lowercase_,lowercase_		)


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





        '''simple docstring'''







        with tempfile.TemporaryDirectory() as tmpdirname:
            A__   				=       CLIPConfig()

            # Create a dummy config file with image_proceesor_type
            A__   				=       Path(lowercase_		) / 'preprocessor_config.json'
            A__   				=       Path(lowercase_		) / 'config.json'
            json.dump(
                {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'},open(lowercase_,'w'		),)
            json.dump({'model_type': 'clip'},open(lowercase_,'w'		)		)

            # remove image_processor_type to make sure config.json alone is enough to load image processor locally
            A__   				=       AutoImageProcessor.from_pretrained(lowercase_		).to_dict()

            config_dict.pop('image_processor_type'		)
            A__   				=       CLIPImageProcessor(**lowercase_		)

            # save in new folder
            model_config.save_pretrained(lowercase_		)
            config.save_pretrained(lowercase_		)

            A__   				=       AutoImageProcessor.from_pretrained(lowercase_		)

            # make sure private variable is not incorrectly saved
            A__   				=       json.loads(config.to_json_string()		)
            self.assertTrue('_processor_class' not in dict_as_saved		)

        self.assertIsInstance(lowercase_,lowercase_		)


    def 						snake_case__					(				self			:	Any		)->   str:





        '''simple docstring'''







        with tempfile.TemporaryDirectory() as tmpdirname:
            A__   				=       Path(lowercase_		) / 'preprocessor_config.json'
            json.dump(
                {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'},open(lowercase_,'w'		),)

            A__   				=       AutoImageProcessor.from_pretrained(lowercase_		)
            self.assertIsInstance(lowercase_,lowercase_		)


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





        '''simple docstring'''







        with self.assertRaisesRegex(
            lowercase_,'clip-base is not a local folder and is not a valid model identifier'		):
            A__   				=       AutoImageProcessor.from_pretrained('clip-base'		)


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





        '''simple docstring'''







        with self.assertRaisesRegex(
            lowercase_,r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'		):
            A__   				=       AutoImageProcessor.from_pretrained(lowercase_,revision='aaaaaa'		)


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





        '''simple docstring'''







        with self.assertRaisesRegex(
            lowercase_,'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.',):
            A__   				=       AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model'		)


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





        '''simple docstring'''







        with self.assertRaises(lowercase_		):
            A__   				=       AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'		)
        # If remote code is disabled, we can't load this config.
        with self.assertRaises(lowercase_		):
            A__   				=       AutoImageProcessor.from_pretrained(
                'hf-internal-testing/test_dynamic_image_processor',trust_remote_code=lowercase_		)

        A__   				=       AutoImageProcessor.from_pretrained(
            'hf-internal-testing/test_dynamic_image_processor',trust_remote_code=lowercase_		)
        self.assertEqual(image_processor.__class__.__name__,'NewImageProcessor'		)

        # Test image processor can be reloaded.
        with tempfile.TemporaryDirectory() as tmp_dir:
            image_processor.save_pretrained(lowercase_		)
            A__   				=       AutoImageProcessor.from_pretrained(lowercase_,trust_remote_code=lowercase_		)
        self.assertEqual(reloaded_image_processor.__class__.__name__,'NewImageProcessor'		)


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





        '''simple docstring'''







        try:
            AutoConfig.register('custom',lowercase_		)
            AutoImageProcessor.register(lowercase_,lowercase_		)
            # Trying to register something existing in the Transformers library will raise an error
            with self.assertRaises(lowercase_		):
                AutoImageProcessor.register(lowercase_,lowercase_		)

            with tempfile.TemporaryDirectory() as tmpdirname:
                A__   				=       Path(lowercase_		) / 'preprocessor_config.json'
                A__   				=       Path(lowercase_		) / 'config.json'
                json.dump(
                    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'},open(lowercase_,'w'		),)
                json.dump({'model_type': 'clip'},open(lowercase_,'w'		)		)

                A__   				=       CustomImageProcessor.from_pretrained(lowercase_		)

            # Now that the config is registered, it can be used as any other config with the auto-API
            with tempfile.TemporaryDirectory() as tmp_dir:
                image_processor.save_pretrained(lowercase_		)
                A__   				=       AutoImageProcessor.from_pretrained(lowercase_		)
                self.assertIsInstance(lowercase_,lowercase_		)

        finally:
            if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]


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





        '''simple docstring'''







        class 			A						(							_UpperCAmelCase			):







            """simple docstring"""





            lowerCamelCase		     =							True

        try:
            AutoConfig.register('custom',lowercase_		)
            AutoImageProcessor.register(lowercase_,lowercase_		)
            # If remote code is not set, the default is to use local
            A__   				=       AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'		)
            self.assertEqual(image_processor.__class__.__name__,'NewImageProcessor'		)
            self.assertTrue(image_processor.is_local		)

            # If remote code is disabled, we load the local one.
            A__   				=       AutoImageProcessor.from_pretrained(
                'hf-internal-testing/test_dynamic_image_processor',trust_remote_code=lowercase_		)
            self.assertEqual(image_processor.__class__.__name__,'NewImageProcessor'		)
            self.assertTrue(image_processor.is_local		)

            # If remote is enabled, we load from the Hub
            A__   				=       AutoImageProcessor.from_pretrained(
                'hf-internal-testing/test_dynamic_image_processor',trust_remote_code=lowercase_		)
            self.assertEqual(image_processor.__class__.__name__,'NewImageProcessor'		)
            self.assertTrue(not hasattr(lowercase_,'is_local'		)		)

        finally:
            if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

lowercase_			=  (3, 9, -11, 0, 7, 5, 1, -1)
lowercase_			=  (4, 6, 2, 0, 8, 10, 3, -2)


@dataclass
class 			A						:







    """simple docstring"""





    lowerCamelCase		     =							42
    lowerCamelCase		     =							42




class 			A						:







    """simple docstring"""





    def __init__(				self			:	List[str],lowercase_			:	Iterable[int]		)->   None:





        '''simple docstring'''







        A__   				=       None
        for i in sorted(lowercase_,reverse=lowercase_		):
            A__   				=       Node(lowercase_,self.head		)


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





        '''simple docstring'''







        A__   				=       self.head
        while node:
            yield node.data
            A__   				=       node.next_node


    def __len__(				self			:	str		)->   int:





        '''simple docstring'''







        return sum(1 for _ in self		)


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





        '''simple docstring'''







        return " -> ".join([str(lowercase_		) for node in self]		)




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     SortedLinkedList   ,      SCREAMING_SNAKE_CASE__      :     SortedLinkedList				)							->				SortedLinkedList:





    '''simple docstring'''






    return SortedLinkedList(list(SCREAMING_SNAKE_CASE__				) + list(SCREAMING_SNAKE_CASE__				)				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     lowercase_			=  SortedLinkedList
     print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))

