





import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


UpperCamelCase_												=	get_tests_dir('fixtures/dummy_feature_extractor_config.json')
UpperCamelCase_												=	get_tests_dir('fixtures/vocab.json')
UpperCamelCase_												=	get_tests_dir('fixtures')

class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 __UpperCamelCase       =      ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']

 def  __UpperCAmelCase						(  self		)	->       Tuple:
    UpperCAmelCase__						=0

 def  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    UpperCAmelCase__						=AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"		)
    self.assertIsInstance(A_,		A_		)

 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    with tempfile.TemporaryDirectory() as tmpdirname:
       UpperCAmelCase__						=WavaVecaConfig()
       UpperCAmelCase__						=AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"		)

       # save in new folder
       model_config.save_pretrained(A_		)
       processor.save_pretrained(A_		)

       UpperCAmelCase__						=AutoProcessor.from_pretrained(A_		)

    self.assertIsInstance(A_,		A_		)

 def  __UpperCAmelCase						(  self		)	->       Tuple:
    with tempfile.TemporaryDirectory() as tmpdirname:
       # copy relevant files
       copyfile(A_,		os.path.join(A_,		A_		)		)
       copyfile(A_,		os.path.join(A_,		"vocab.json"		)		)

       UpperCAmelCase__						=AutoProcessor.from_pretrained(A_		)

    self.assertIsInstance(A_,		A_		)

 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    with tempfile.TemporaryDirectory() as tmpdirname:
       UpperCAmelCase__						=WavaVecaFeatureExtractor()
       UpperCAmelCase__						=AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"		)

       UpperCAmelCase__						=WavaVecaProcessor(A_,		A_		)

       # save in new folder
       processor.save_pretrained(A_		)

       # drop `processor_class` in tokenizer
       with open(os.path.join(A_,		A_		),		"r"		) as f:
          UpperCAmelCase__						=json.load(A_		)
          config_dict.pop("processor_class"		)

       with open(os.path.join(A_,		A_		),		"w"		) as f:
          f.write(json.dumps(A_		)		)

       UpperCAmelCase__						=AutoProcessor.from_pretrained(A_		)

    self.assertIsInstance(A_,		A_		)

 def  __UpperCAmelCase						(  self		)	->       Any:
    with tempfile.TemporaryDirectory() as tmpdirname:
       UpperCAmelCase__						=WavaVecaFeatureExtractor()
       UpperCAmelCase__						=AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"		)

       UpperCAmelCase__						=WavaVecaProcessor(A_,		A_		)

       # save in new folder
       processor.save_pretrained(A_		)

       # drop `processor_class` in feature extractor
       with open(os.path.join(A_,		A_		),		"r"		) as f:
          UpperCAmelCase__						=json.load(A_		)
          config_dict.pop("processor_class"		)

       with open(os.path.join(A_,		A_		),		"w"		) as f:
          f.write(json.dumps(A_		)		)

       UpperCAmelCase__						=AutoProcessor.from_pretrained(A_		)

    self.assertIsInstance(A_,		A_		)

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    with tempfile.TemporaryDirectory() as tmpdirname:
       UpperCAmelCase__						=WavaVecaConfig(processor_class="Wav2Vec2Processor"		)
       model_config.save_pretrained(A_		)
       # copy relevant files
       copyfile(A_,		os.path.join(A_,		"vocab.json"		)		)
       # create emtpy sample processor
       with open(os.path.join(A_,		A_		),		"w"		) as f:
          f.write("{}"		)

       UpperCAmelCase__						=AutoProcessor.from_pretrained(A_		)

    self.assertIsInstance(A_,		A_		)

 def  __UpperCAmelCase						(  self		)	->       int:
    # If remote code is not set, we will time out when asking whether to load the model.
    with self.assertRaises(A_		):
       UpperCAmelCase__						=AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"		)
    # If remote code is disabled, we can't load this config.
    with self.assertRaises(A_		):
       UpperCAmelCase__						=AutoProcessor.from_pretrained(
           "hf-internal-testing/test_dynamic_processor",		trust_remote_code=A_		)

    UpperCAmelCase__						=AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor",		trust_remote_code=A_		)
    self.assertTrue(processor.special_attribute_present		)
    self.assertEqual(processor.__class__.__name__,		"NewProcessor"		)

    UpperCAmelCase__						=processor.feature_extractor
    self.assertTrue(feature_extractor.special_attribute_present		)
    self.assertEqual(feature_extractor.__class__.__name__,		"NewFeatureExtractor"		)

    UpperCAmelCase__						=processor.tokenizer
    self.assertTrue(tokenizer.special_attribute_present		)
    if is_tokenizers_available():
       self.assertEqual(tokenizer.__class__.__name__,		"NewTokenizerFast"		)

       # Test we can also load the slow version
       UpperCAmelCase__						=AutoProcessor.from_pretrained(
           "hf-internal-testing/test_dynamic_processor",		trust_remote_code=A_,		use_fast=A_		)
       UpperCAmelCase__						=new_processor.tokenizer
       self.assertTrue(new_tokenizer.special_attribute_present		)
       self.assertEqual(new_tokenizer.__class__.__name__,		"NewTokenizer"		)
    else:
       self.assertEqual(tokenizer.__class__.__name__,		"NewTokenizer"		)

 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    try:
       AutoConfig.register("custom",		A_		)
       AutoFeatureExtractor.register(A_,		A_		)
       AutoTokenizer.register(A_,		slow_tokenizer_class=A_		)
       AutoProcessor.register(A_,		A_		)
       # Trying to register something existing in the Transformers library will raise an error
       with self.assertRaises(A_		):
          AutoProcessor.register(A_,		A_		)

       # Now that the config is registered, it can be used as any other config with the auto-API
       UpperCAmelCase__						=CustomFeatureExtractor.from_pretrained(A_		)

       with tempfile.TemporaryDirectory() as tmp_dir:
          UpperCAmelCase__						=os.path.join(A_,		"vocab.txt"		)
          with open(A_,		"w",		encoding="utf-8"		) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]		)		)
          UpperCAmelCase__						=CustomTokenizer(A_		)

       UpperCAmelCase__						=CustomProcessor(A_,		A_		)

       with tempfile.TemporaryDirectory() as tmp_dir:
          processor.save_pretrained(A_		)
          UpperCAmelCase__						=AutoProcessor.from_pretrained(A_		)
          self.assertIsInstance(A_,		A_		)

    finally:
       if "custom" in CONFIG_MAPPING._extra_content:
          del CONFIG_MAPPING._extra_content["custom"]
       if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
          del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
       if CustomConfig in TOKENIZER_MAPPING._extra_content:
          del TOKENIZER_MAPPING._extra_content[CustomConfig]
       if CustomConfig in PROCESSOR_MAPPING._extra_content:
          del PROCESSOR_MAPPING._extra_content[CustomConfig]

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

    class     snake_case_		(   a							):






     '''simple docstring'''





     __UpperCamelCase       =      False

    class     snake_case_		(   a							):






     '''simple docstring'''





     __UpperCamelCase       =      False

    class     snake_case_		(   a							):






     '''simple docstring'''





     __UpperCamelCase       =      'AutoFeatureExtractor'
     __UpperCamelCase       =      'AutoTokenizer'
     __UpperCamelCase       =      False

    try:
       AutoConfig.register("custom",		A_		)
       AutoFeatureExtractor.register(A_,		A_		)
       AutoTokenizer.register(A_,		slow_tokenizer_class=A_		)
       AutoProcessor.register(A_,		A_		)
       # If remote code is not set, the default is to use local classes.
       UpperCAmelCase__						=AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"		)
       self.assertEqual(processor.__class__.__name__,		"NewProcessor"		)
       self.assertFalse(processor.special_attribute_present		)
       self.assertFalse(processor.feature_extractor.special_attribute_present		)
       self.assertFalse(processor.tokenizer.special_attribute_present		)

       # If remote code is disabled, we load the local ones.
       UpperCAmelCase__						=AutoProcessor.from_pretrained(
           "hf-internal-testing/test_dynamic_processor",		trust_remote_code=A_		)
       self.assertEqual(processor.__class__.__name__,		"NewProcessor"		)
       self.assertFalse(processor.special_attribute_present		)
       self.assertFalse(processor.feature_extractor.special_attribute_present		)
       self.assertFalse(processor.tokenizer.special_attribute_present		)

       # If remote is enabled, we load from the Hub.
       UpperCAmelCase__						=AutoProcessor.from_pretrained(
           "hf-internal-testing/test_dynamic_processor",		trust_remote_code=A_		)
       self.assertEqual(processor.__class__.__name__,		"NewProcessor"		)
       self.assertTrue(processor.special_attribute_present		)
       self.assertTrue(processor.feature_extractor.special_attribute_present		)
       self.assertTrue(processor.tokenizer.special_attribute_present		)

    finally:
       if "custom" in CONFIG_MAPPING._extra_content:
          del CONFIG_MAPPING._extra_content["custom"]
       if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
          del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
       if CustomConfig in TOKENIZER_MAPPING._extra_content:
          del TOKENIZER_MAPPING._extra_content[CustomConfig]
       if CustomConfig in PROCESSOR_MAPPING._extra_content:
          del PROCESSOR_MAPPING._extra_content[CustomConfig]

 def  __UpperCAmelCase						(  self		)	->       Any:
    UpperCAmelCase__						=AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert"		)
    self.assertEqual(processor.__class__.__name__,		"BertTokenizerFast"		)






 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    UpperCAmelCase__						=AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext"		)
    self.assertEqual(processor.__class__.__name__,		"ConvNextImageProcessor"		)

@is_staging_test
class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 __UpperCamelCase       =      ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']

 @classmethod
 def  __UpperCAmelCase						(  cls		)	->       Union[str, Any]:
    UpperCAmelCase__						=TOKEN
    HfFolder.save_token(A_		)

 @classmethod
 def  __UpperCAmelCase						(  cls		)	->       List[str]:
    try:
       delete_repo(token=cls._token,		repo_id="test-processor"		)
    except HTTPError:
       pass

    try:
       delete_repo(token=cls._token,		repo_id="valid_org/test-processor-org"		)
    except HTTPError:
       pass

    try:
       delete_repo(token=cls._token,		repo_id="test-dynamic-processor"		)
    except HTTPError:
       pass

 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						=WavaVecaProcessor.from_pretrained(A_		)
    with tempfile.TemporaryDirectory() as tmp_dir:
       processor.save_pretrained(
           os.path.join(A_,		"test-processor"		),		push_to_hub=A_,		use_auth_token=self._token		)

       UpperCAmelCase__						=WavaVecaProcessor.from_pretrained(f"""{USER}/test-processor"""		)
       for k, v in processor.feature_extractor.__dict__.items():
          self.assertEqual(A_,		getattr(new_processor.feature_extractor,		A_		)		)
       self.assertDictEqual(new_processor.tokenizer.get_vocab(),		processor.tokenizer.get_vocab()		)

 def  __UpperCAmelCase						(  self		)	->       Optional[int]:
    UpperCAmelCase__						=WavaVecaProcessor.from_pretrained(A_		)

    with tempfile.TemporaryDirectory() as tmp_dir:
       processor.save_pretrained(
           os.path.join(A_,		"test-processor-org"		),		push_to_hub=A_,		use_auth_token=self._token,		organization="valid_org",		)

       UpperCAmelCase__						=WavaVecaProcessor.from_pretrained("valid_org/test-processor-org"		)
       for k, v in processor.feature_extractor.__dict__.items():
          self.assertEqual(A_,		getattr(new_processor.feature_extractor,		A_		)		)
       self.assertDictEqual(new_processor.tokenizer.get_vocab(),		processor.tokenizer.get_vocab()		)






 def  __UpperCAmelCase						(  self		)	->       Dict:
    CustomFeatureExtractor.register_for_auto_class()
    CustomTokenizer.register_for_auto_class()
    CustomProcessor.register_for_auto_class()

    UpperCAmelCase__						=CustomFeatureExtractor.from_pretrained(A_		)

    with tempfile.TemporaryDirectory() as tmp_dir:
       UpperCAmelCase__						=os.path.join(A_,		"vocab.txt"		)
       with open(A_,		"w",		encoding="utf-8"		) as vocab_writer:
          vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]		)		)
       UpperCAmelCase__						=CustomTokenizer(A_		)

    UpperCAmelCase__						=CustomProcessor(A_,		A_		)

    with tempfile.TemporaryDirectory() as tmp_dir:
       create_repo(f"""{USER}/test-dynamic-processor""",		token=self._token		)
       UpperCAmelCase__						=Repository(A_,		clone_from=f"""{USER}/test-dynamic-processor""",		token=self._token		)
       processor.save_pretrained(A_		)

       # This has added the proper auto_map field to the feature extractor config
       self.assertDictEqual(
           processor.feature_extractor.auto_map,		{
               "AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor",
               "AutoProcessor": "custom_processing.CustomProcessor",
           },		)

       # This has added the proper auto_map field to the tokenizer config
       with open(os.path.join(A_,		"tokenizer_config.json"		)		) as f:
          UpperCAmelCase__						=json.load(A_		)
       self.assertDictEqual(
           tokenizer_config["auto_map"],		{
               "AutoTokenizer": ["custom_tokenization.CustomTokenizer", None],
               "AutoProcessor": "custom_processing.CustomProcessor",
           },		)

       # The code has been copied from fixtures
       self.assertTrue(os.path.isfile(os.path.join(A_,		"custom_feature_extraction.py"		)		)		)
       self.assertTrue(os.path.isfile(os.path.join(A_,		"custom_tokenization.py"		)		)		)
       self.assertTrue(os.path.isfile(os.path.join(A_,		"custom_processing.py"		)		)		)

       repo.push_to_hub()

    UpperCAmelCase__						=AutoProcessor.from_pretrained(f"""{USER}/test-dynamic-processor""",		trust_remote_code=A_		)
    # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
    self.assertEqual(new_processor.__class__.__name__,		"CustomProcessor"		)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase_												=	{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_												=	['OwlViTFeatureExtractor']
    UpperCamelCase_												=	['OwlViTImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_												=	[
        'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'OwlViTModel',
        'OwlViTPreTrainedModel',
        'OwlViTTextModel',
        'OwlViTVisionModel',
        'OwlViTForObjectDetection',
    ]

if TYPE_CHECKING:
    from .configuration_owlvit import (
        OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        OwlViTConfig,
        OwlViTOnnxConfig,
        OwlViTTextConfig,
        OwlViTVisionConfig,
    )
    from .processing_owlvit import OwlViTProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_owlvit import OwlViTFeatureExtractor
        from .image_processing_owlvit import OwlViTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_owlvit import (
            OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            OwlViTForObjectDetection,
            OwlViTModel,
            OwlViTPreTrainedModel,
            OwlViTTextModel,
            OwlViTVisionModel,
        )

else:
    import sys

    UpperCamelCase_												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



