import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:int        =		ArgumentParser(
        description=(
            '''PyTorch TPU distributed training launch '''
            '''helper utility that will spawn up '''
            '''multiple distributed processes'''
        )     )

    # Optional arguments for the launch helper
    parser.add_argument('''--num_cores'''  ,      type=SCREAMING_SNAKE_CASE  ,      default=1  ,      help='''Number of TPU cores to use (1 or 8).'''     )

    # positional
    parser.add_argument(
        '''training_script'''  ,      type=SCREAMING_SNAKE_CASE  ,      help=(
            '''The full path to the single TPU training '''
            '''program/script to be launched in parallel, '''
            '''followed by all the arguments for the '''
            '''training script'''
        )  ,      )

    # rest from the training program
    parser.add_argument('''training_script_args'''  ,      nargs=SCREAMING_SNAKE_CASE     )

    return parser.parse_args()



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:Tuple        =		parse_args()

    # Import training_script as a module.
    __UpperCamelCase					:Dict        =		Path(args.training_script     )
    sys.path.append(str(script_fpath.parent.resolve()     )     )
    __UpperCamelCase					:Any        =		script_fpath.stem
    __UpperCamelCase					:Union[str, Any]        =		importlib.import_module(SCREAMING_SNAKE_CASE     )

    # Patch sys.argv
    __UpperCamelCase					:Dict        =		[args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores     )]
    xmp.spawn(mod._mp_fn  ,      args=()  ,      nprocs=args.num_cores     )


if __name__ == "__main__":
    main()






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      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    def       UpperCamelCase__       (     self)	->     int:
        __UpperCamelCase					:str        =		0







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''')
        self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:int        =		Path(__lowercase) / '''preprocessor_config.json'''
            __UpperCamelCase					:Dict        =		Path(__lowercase) / '''config.json'''
            json.dump(
                {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
            json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

            __UpperCamelCase					:Union[str, Any]        =		AutoImageProcessor.from_pretrained(__lowercase)
            self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        # Ensure we can load the image processor from the feature extractor config
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:str        =		Path(__lowercase) / '''preprocessor_config.json'''
            __UpperCamelCase					:Union[str, Any]        =		Path(__lowercase) / '''config.json'''
            json.dump(
                {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
            json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

            __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained(__lowercase)
            self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:int        =		CLIPConfig()

            # Create a dummy config file with image_proceesor_type
            __UpperCamelCase					:Tuple        =		Path(__lowercase) / '''preprocessor_config.json'''
            __UpperCamelCase					:Optional[Any]        =		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
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained(__lowercase).to_dict()

            config_dict.pop('''image_processor_type''')
            __UpperCamelCase					:List[str]        =		CLIPImageProcessor(**__lowercase)

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

            __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained(__lowercase)

            # make sure private variable is not incorrectly saved
            __UpperCamelCase					:Union[str, Any]        =		json.loads(config.to_json_string())
            self.assertTrue('''_processor_class''' not in dict_as_saved)

        self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     List[str]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:Tuple        =		Path(__lowercase) / '''preprocessor_config.json'''
            json.dump(
                {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)

            __UpperCamelCase					:List[str]        =		AutoImageProcessor.from_pretrained(__lowercase)
            self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        with self.assertRaisesRegex(
            __lowercase			,			'''clip-base is not a local folder and is not a valid model identifier'''):
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained('''clip-base''')







    def       UpperCamelCase__       (     self)	->     List[Any]:
        with self.assertRaisesRegex(
            __lowercase			,			r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''):
            __UpperCamelCase					:str        =		AutoImageProcessor.from_pretrained(__lowercase			,			revision='''aaaaaa''')







    def       UpperCamelCase__       (     self)	->     List[str]:
        with self.assertRaisesRegex(
            __lowercase			,			'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''			,			):
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''')







    def       UpperCamelCase__       (     self)	->     str:
        # If remote code is not set, we will time out when asking whether to load the model.
        with self.assertRaises(__lowercase):
            __UpperCamelCase					:Dict        =		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):
            __UpperCamelCase					:List[Any]        =		AutoImageProcessor.from_pretrained(
                '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=__lowercase)

        __UpperCamelCase					:Optional[Any]        =		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)
            __UpperCamelCase					:List[Any]        =		AutoImageProcessor.from_pretrained(__lowercase			,			trust_remote_code=__lowercase)
        self.assertEqual(reloaded_image_processor.__class__.__name__			,			'''NewImageProcessor''')







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        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:
                __UpperCamelCase					:int        =		Path(__lowercase) / '''preprocessor_config.json'''
                __UpperCamelCase					:List[str]        =		Path(__lowercase) / '''config.json'''
                json.dump(
                    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
                json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

                __UpperCamelCase					:int        =		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)
                __UpperCamelCase					:int        =		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       UpperCamelCase__       (     self)	->     List[Any]:


        class      lowerCamelCase_					(       UpperCAmelCase_			):






            '''simple docstring'''






            a__     :						List[str]      =				True

        try:
            AutoConfig.register('''custom'''			,			__lowercase)
            AutoImageProcessor.register(__lowercase			,			__lowercase)
            # If remote code is not set, the default is to use local
            __UpperCamelCase					:str        =		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.
            __UpperCamelCase					:Optional[Any]        =		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
            __UpperCamelCase					:List[str]        =		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]





