"""simple docstring"""



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

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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


__lowercase		:		int														=							get_tests_dir("fixtures")
__lowercase		:		str														=							get_tests_dir("fixtures/dummy_feature_extractor_config.json")
__lowercase		:		List[Any]														=							get_tests_dir("fixtures/dummy-config.json")



class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Dict    )    ->       str:
         __snake_case				=							0
    def  lowercase					(		self							:     Dict    )    ->       Any:
         __snake_case				=							AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''    )
         self.assertIsInstance(A_	,      A_    )
    def  lowercase					(		self							:     List[str]    )    ->       str:
         __snake_case				=							AutoFeatureExtractor.from_pretrained(A_    )
         self.assertIsInstance(A_	,      A_    )
    def  lowercase					(		self							:     Dict    )    ->       List[str]:
         with tempfile.TemporaryDirectory() as tmpdirname:
              __snake_case				=							WavaVecaConfig()

              # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
              __snake_case				=							AutoFeatureExtractor.from_pretrained(A_    ).to_dict()

              config_dict.pop('''feature_extractor_type'''    )
              __snake_case				=							WavaVecaFeatureExtractor(**A_    )

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

              __snake_case				=							AutoFeatureExtractor.from_pretrained(A_    )

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

         self.assertIsInstance(A_	,      A_    )
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         __snake_case				=							AutoFeatureExtractor.from_pretrained(A_    )
         self.assertIsInstance(A_	,      A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       Dict:
         with self.assertRaisesRegex(
             A_	,      '''bert-base is not a local folder and is not a valid model identifier'''    ):
              __snake_case				=							AutoFeatureExtractor.from_pretrained('''bert-base'''    )
    def  lowercase					(		self							:     str    )    ->       List[str]:
         with self.assertRaisesRegex(
             A_	,      R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''    ):
              __snake_case				=							AutoFeatureExtractor.from_pretrained(A_	,      revision='''aaaaaa'''    )
    def  lowercase					(		self							:     List[str]    )    ->       Any:
         with self.assertRaisesRegex(
             A_	,      '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''	,      ):
              __snake_case				=							AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''    )
    def  lowercase					(		self							:     int    )    ->       int:
         # If remote code is not set, we will time out when asking whether to load the model.
         with self.assertRaises(A_    ):
              __snake_case				=							AutoFeatureExtractor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_feature_extractor'''    )
         # If remote code is disabled, we can't load this config.
         with self.assertRaises(A_    ):
              __snake_case				=							AutoFeatureExtractor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_feature_extractor'''	,      trust_remote_code=A_    )

         __snake_case				=							AutoFeatureExtractor.from_pretrained(
             '''hf-internal-testing/test_dynamic_feature_extractor'''	,      trust_remote_code=A_    )
         self.assertEqual(feature_extractor.__class__.__name__	,      '''NewFeatureExtractor'''    )

         # Test feature extractor can be reloaded.
         with tempfile.TemporaryDirectory() as tmp_dir:
              feature_extractor.save_pretrained(A_    )
              __snake_case				=							AutoFeatureExtractor.from_pretrained(A_	,      trust_remote_code=A_    )
         self.assertEqual(reloaded_feature_extractor.__class__.__name__	,      '''NewFeatureExtractor'''    )
    def  lowercase					(		self							:     Optional[int]    )    ->       List[Any]:
         try:
              AutoConfig.register('''custom'''	,      A_    )
              AutoFeatureExtractor.register(A_	,      A_    )
              # Trying to register something existing in the Transformers library will raise an error
              with self.assertRaises(A_    ):
                   AutoFeatureExtractor.register(A_	,      A_    )

              # Now that the config is registered, it can be used as any other config with the auto-API
              __snake_case				=							CustomFeatureExtractor.from_pretrained(A_    )
              with tempfile.TemporaryDirectory() as tmp_dir:
                   feature_extractor.save_pretrained(A_    )
                   __snake_case				=							AutoFeatureExtractor.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]







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



         class      _A	(						_UpperCAmelCase					):




             """simple docstring"""




             UpperCamelCase_	:					Optional[int]							=					True

         try:
              AutoConfig.register('''custom'''	,      A_    )
              AutoFeatureExtractor.register(A_	,      A_    )
              # If remote code is not set, the default is to use local
              __snake_case				=							AutoFeatureExtractor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_feature_extractor'''    )
              self.assertEqual(feature_extractor.__class__.__name__	,      '''NewFeatureExtractor'''    )
              self.assertTrue(feature_extractor.is_local    )

              # If remote code is disabled, we load the local one.
              __snake_case				=							AutoFeatureExtractor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_feature_extractor'''	,      trust_remote_code=A_    )
              self.assertEqual(feature_extractor.__class__.__name__	,      '''NewFeatureExtractor'''    )
              self.assertTrue(feature_extractor.is_local    )

              # If remote is enabled, we load from the Hub
              __snake_case				=							AutoFeatureExtractor.from_pretrained(
                  '''hf-internal-testing/test_dynamic_feature_extractor'''	,      trust_remote_code=A_    )
              self.assertEqual(feature_extractor.__class__.__name__	,      '''NewFeatureExtractor'''    )
              self.assertTrue(not hasattr(A_	,      '''is_local'''    )    )

         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]

"""simple docstring"""



import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowercase		:		int														=							version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case,      snake_case,      snake_case,      snake_case,      snake_case=False,      ):
     output_path.parent.mkdir(parents=snake_case,      exist_ok=snake_case)
     # 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(
              snake_case,      snake_case,      f=output_path.as_posix(),      input_names=snake_case,      output_names=snake_case,      dynamic_axes=snake_case,      do_constant_folding=snake_case,      use_external_data_format=snake_case,      enable_onnx_checker=snake_case,      opset_version=snake_case,      )
     else:
          export(
              snake_case,      snake_case,      f=output_path.as_posix(),      input_names=snake_case,      output_names=snake_case,      dynamic_axes=snake_case,      do_constant_folding=snake_case,      opset_version=snake_case,      )





@torch.no_grad()
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case = False):
     __snake_case				=							torch.floataa if fpaa else torch.floataa
     if fpaa and torch.cuda.is_available():
          __snake_case				=							'''cuda'''
     elif fpaa and not torch.cuda.is_available():
          raise ValueError('''`float16` model export is only supported on GPUs with CUDA''')
     else:
          __snake_case				=							'''cpu'''
     __snake_case				=							Path(snake_case)

     # VAE DECODER
     __snake_case				=							AutoencoderKL.from_pretrained(model_path + '''/vae''')
     __snake_case				=							vae_decoder.config.latent_channels
     # forward only through the decoder part
     __snake_case				=							vae_decoder.decode
     onnx_export(
         snake_case,      model_args=(
             torch.randn(1,      snake_case,      25,      25).to(device=snake_case,      dtype=snake_case),
             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=snake_case,      )
     del vae_decoder


if __name__ == "__main__":
       __lowercase		:		Union[str, Any]														=							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")

       __lowercase		:		List[Any]														=							parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("SD: Done: ONNX")