



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


SCREAMING_SNAKE_CASE_:Union[str, Any]             =						get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
SCREAMING_SNAKE_CASE_:Dict             =						get_tests_dir("""fixtures/vocab.json""")
SCREAMING_SNAKE_CASE_:Union[str, Any]             =						get_tests_dir("""fixtures""")






class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[Any]					=      ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]






    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   0






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[Any]          =   AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""		)
        self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        with tempfile.TemporaryDirectory() as tmpdirname:
            A :       List[str]          =   WavaVecaConfig()
            A :       Tuple          =   AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""		)

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

            A :       List[str]          =   AutoProcessor.from_pretrained(lowerCamelCase__		)

        self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        with tempfile.TemporaryDirectory() as tmpdirname:
            # copy relevant files
            copyfile(lowerCamelCase__,		os.path.join(lowerCamelCase__,		lowerCamelCase__		)		)
            copyfile(lowerCamelCase__,		os.path.join(lowerCamelCase__,		"""vocab.json"""		)		)

            A :       Optional[int]          =   AutoProcessor.from_pretrained(lowerCamelCase__		)

        self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)






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

            A :       List[str]          =   WavaVecaProcessor(lowerCamelCase__,		lowerCamelCase__		)

            # save in new folder
            processor.save_pretrained(lowerCamelCase__		)

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

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

            A :       Union[str, Any]          =   AutoProcessor.from_pretrained(lowerCamelCase__		)

        self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        with tempfile.TemporaryDirectory() as tmpdirname:
            A :       Dict          =   WavaVecaFeatureExtractor()
            A :       Dict          =   AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""		)

            A :       str          =   WavaVecaProcessor(lowerCamelCase__,		lowerCamelCase__		)

            # save in new folder
            processor.save_pretrained(lowerCamelCase__		)

            # drop `processor_class` in feature extractor
            with open(os.path.join(lowerCamelCase__,		lowerCamelCase__		),		"""r"""		) as f:
                A :       Union[str, Any]          =   json.load(lowerCamelCase__		)
                config_dict.pop("""processor_class"""		)

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

            A :       Tuple          =   AutoProcessor.from_pretrained(lowerCamelCase__		)

        self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        with tempfile.TemporaryDirectory() as tmpdirname:
            A :       str          =   WavaVecaConfig(processor_class="""Wav2Vec2Processor"""		)
            model_config.save_pretrained(lowerCamelCase__		)
            # copy relevant files
            copyfile(lowerCamelCase__,		os.path.join(lowerCamelCase__,		"""vocab.json"""		)		)
            # create emtpy sample processor
            with open(os.path.join(lowerCamelCase__,		lowerCamelCase__		),		"""w"""		) as f:
                f.write("""{}"""		)

            A :       List[str]          =   AutoProcessor.from_pretrained(lowerCamelCase__		)

        self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        # If remote code is not set, we will time out when asking whether to load the model.
        with self.assertRaises(lowerCamelCase__		):
            A :       Optional[int]          =   AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""		)
        # If remote code is disabled, we can't load this config.
        with self.assertRaises(lowerCamelCase__		):
            A :       List[Any]          =   AutoProcessor.from_pretrained(
                """hf-internal-testing/test_dynamic_processor""",		trust_remote_code=lowerCamelCase__		)

        A :       Any          =   AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""",		trust_remote_code=lowerCamelCase__		)
        self.assertTrue(processor.special_attribute_present		)
        self.assertEqual(processor.__class__.__name__,		"""NewProcessor"""		)

        A :       int          =   processor.feature_extractor
        self.assertTrue(feature_extractor.special_attribute_present		)
        self.assertEqual(feature_extractor.__class__.__name__,		"""NewFeatureExtractor"""		)

        A :       Union[str, Any]          =   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
            A :       str          =   AutoProcessor.from_pretrained(
                """hf-internal-testing/test_dynamic_processor""",		trust_remote_code=lowerCamelCase__,		use_fast=lowerCamelCase__		)
            A :       str          =   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 			_lowerCAmelCase					(       self		):
        try:
            AutoConfig.register("""custom""",		lowerCamelCase__		)
            AutoFeatureExtractor.register(lowerCamelCase__,		lowerCamelCase__		)
            AutoTokenizer.register(lowerCamelCase__,		slow_tokenizer_class=lowerCamelCase__		)
            AutoProcessor.register(lowerCamelCase__,		lowerCamelCase__		)
            # Trying to register something existing in the Transformers library will raise an error
            with self.assertRaises(lowerCamelCase__		):
                AutoProcessor.register(lowerCamelCase__,		lowerCamelCase__		)

            # Now that the config is registered, it can be used as any other config with the auto-API
            A :       List[str]          =   CustomFeatureExtractor.from_pretrained(lowerCamelCase__		)

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

            A :       Any          =   CustomProcessor(lowerCamelCase__,		lowerCamelCase__		)

            with tempfile.TemporaryDirectory() as tmp_dir:
                processor.save_pretrained(lowerCamelCase__		)
                A :       str          =   AutoProcessor.from_pretrained(lowerCamelCase__		)
                self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)

        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 			_lowerCAmelCase					(       self		):






        class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




            '''simple docstring'''






            __lowerCamelCase			: Optional[int]					=      False






        class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




            '''simple docstring'''






            __lowerCamelCase			: str					=      False






        class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




            '''simple docstring'''






            __lowerCamelCase			: int					=      "AutoFeatureExtractor"
            __lowerCamelCase			: str					=      "AutoTokenizer"
            __lowerCamelCase			: List[str]					=      False

        try:
            AutoConfig.register("""custom""",		lowerCamelCase__		)
            AutoFeatureExtractor.register(lowerCamelCase__,		lowerCamelCase__		)
            AutoTokenizer.register(lowerCamelCase__,		slow_tokenizer_class=lowerCamelCase__		)
            AutoProcessor.register(lowerCamelCase__,		lowerCamelCase__		)
            # If remote code is not set, the default is to use local classes.
            A :       Optional[int]          =   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.
            A :       Union[str, Any]          =   AutoProcessor.from_pretrained(
                """hf-internal-testing/test_dynamic_processor""",		trust_remote_code=lowerCamelCase__		)
            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.
            A :       List[str]          =   AutoProcessor.from_pretrained(
                """hf-internal-testing/test_dynamic_processor""",		trust_remote_code=lowerCamelCase__		)
            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 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert"""		)
        self.assertEqual(processor.__class__.__name__,		"""BertTokenizerFast"""		)






    def 			_lowerCAmelCase					(       self		):
        A :       int          =   AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext"""		)
        self.assertEqual(processor.__class__.__name__,		"""ConvNextImageProcessor"""		)






@is_staging_test
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Tuple					=      ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]






    @classmethod
    def 			_lowerCAmelCase					(       cls		):
        A :       List[Any]          =   TOKEN
        HfFolder.save_token(lowerCamelCase__		)






    @classmethod
    def 			_lowerCAmelCase					(       cls		):
        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 			_lowerCAmelCase					(       self		):
        A :       Optional[Any]          =   WavaVecaProcessor.from_pretrained(lowerCamelCase__		)
        with tempfile.TemporaryDirectory() as tmp_dir:
            processor.save_pretrained(
                os.path.join(lowerCamelCase__,		"""test-processor"""		),		push_to_hub=lowerCamelCase__,		use_auth_token=self._token		)

            A :       List[str]          =   WavaVecaProcessor.from_pretrained(f'''{USER}/test-processor'''		)
            for k, v in processor.feature_extractor.__dict__.items():
                self.assertEqual(lowerCamelCase__,		getattr(new_processor.feature_extractor,		lowerCamelCase__		)		)
            self.assertDictEqual(new_processor.tokenizer.get_vocab(),		processor.tokenizer.get_vocab()		)






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   WavaVecaProcessor.from_pretrained(lowerCamelCase__		)

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

            A :       Optional[int]          =   WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org"""		)
            for k, v in processor.feature_extractor.__dict__.items():
                self.assertEqual(lowerCamelCase__,		getattr(new_processor.feature_extractor,		lowerCamelCase__		)		)
            self.assertDictEqual(new_processor.tokenizer.get_vocab(),		processor.tokenizer.get_vocab()		)






    def 			_lowerCAmelCase					(       self		):
        CustomFeatureExtractor.register_for_auto_class()
        CustomTokenizer.register_for_auto_class()
        CustomProcessor.register_for_auto_class()

        A :       Tuple          =   CustomFeatureExtractor.from_pretrained(lowerCamelCase__		)

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

        A :       Optional[Any]          =   CustomProcessor(lowerCamelCase__,		lowerCamelCase__		)

        with tempfile.TemporaryDirectory() as tmp_dir:
            create_repo(f'''{USER}/test-dynamic-processor''',		token=self._token		)
            A :       List[Any]          =   Repository(lowerCamelCase__,		clone_from=f'''{USER}/test-dynamic-processor''',		token=self._token		)
            processor.save_pretrained(lowerCamelCase__		)

            # 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(lowerCamelCase__,		"""tokenizer_config.json"""		)		) as f:
                A :       Tuple          =   json.load(lowerCamelCase__		)
            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(lowerCamelCase__,		"""custom_feature_extraction.py"""		)		)		)
            self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__,		"""custom_tokenization.py"""		)		)		)
            self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__,		"""custom_processing.py"""		)		)		)

            repo.push_to_hub()

        A :       List[Any]          =   AutoProcessor.from_pretrained(f'''{USER}/test-dynamic-processor''',		trust_remote_code=lowerCamelCase__		)
        # 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"""		)







import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


SCREAMING_SNAKE_CASE_:List[Any]             =						version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase=False      ,     )       ->  int:



    """simple docstring"""






    output_path.parent.mkdir(parents=_lowerCAmelCase      ,     exist_ok=_lowerCAmelCase		)
    # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
    # so we check the torch version for backwards compatibility
    if is_torch_less_than_1_11:
        export(
            _lowerCAmelCase      ,     _lowerCAmelCase      ,     f=output_path.as_posix()      ,     input_names=_lowerCAmelCase      ,     output_names=_lowerCAmelCase      ,     dynamic_axes=_lowerCAmelCase      ,     do_constant_folding=_lowerCAmelCase      ,     use_external_data_format=_lowerCAmelCase      ,     enable_onnx_checker=_lowerCAmelCase      ,     opset_version=_lowerCAmelCase      ,     )
    else:
        export(
            _lowerCAmelCase      ,     _lowerCAmelCase      ,     f=output_path.as_posix()      ,     input_names=_lowerCAmelCase      ,     output_names=_lowerCAmelCase      ,     dynamic_axes=_lowerCAmelCase      ,     do_constant_folding=_lowerCAmelCase      ,     opset_version=_lowerCAmelCase      ,     )




@torch.no_grad()
def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = False		)       ->  List[Any]:



    """simple docstring"""






    A :       Tuple          =   torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        A :       Union[str, Any]          =   """cuda"""
    elif fpaa and not torch.cuda.is_available():
        raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
    else:
        A :       Any          =   """cpu"""
    A :       Any          =   Path(_lowerCAmelCase		)

    # VAE DECODER
    A :       Union[str, Any]          =   AutoencoderKL.from_pretrained(model_path + """/vae"""		)
    A :       Any          =   vae_decoder.config.latent_channels
    # forward only through the decoder part
    A :       Optional[int]          =   vae_decoder.decode
    onnx_export(
        _lowerCAmelCase      ,     model_args=(
            torch.randn(1      ,     _lowerCAmelCase      ,     25      ,     25		).to(device=_lowerCAmelCase      ,     dtype=_lowerCAmelCase		),
            False,
        )      ,     output_path=output_path / """vae_decoder""" / """model.onnx"""      ,     ordered_input_names=["""latent_sample""", """return_dict"""]      ,     output_names=["""sample"""]      ,     dynamic_axes={
            """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
        }      ,     opset=_lowerCAmelCase      ,     )
    del vae_decoder


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Tuple             =						argparse.ArgumentParser()

     parser.add_argument(
         """--model_path""",
         type=str,
         required=True,
         help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
     )

     parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
     parser.add_argument(
         """--opset""",
         default=14,
         type=int,
         help="""The version of the ONNX operator set to use.""",
     )
     parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")

     SCREAMING_SNAKE_CASE_:Tuple             =						parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("""SD: Done: ONNX""")


