


"""simple docstring"""




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

import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
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


a		:				Any  			=    get_tests_dir('''fixtures/dummy-config.json''')


class  __UpperCamelCase       (		unittest.TestCase		):






							def 					__a     (     self   )			->				List[Any]:
								a   :	Optional[Any]			=       0






							def 					__a     (     self   )			->				Dict:
								self.assertIsNotNone(transformers.models.auto.__spec__   )
								self.assertIsNotNone(importlib.util.find_spec("transformers.models.auto"   )   )






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Dict			=       AutoConfig.from_pretrained("bert-base-uncased"   )
								self.assertIsInstance(lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				int:
								a   :	Tuple			=       AutoConfig.from_pretrained(lowerCAmelCase__   )
								self.assertIsInstance(lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				Any:
								a   :	List[str]			=       AutoConfig.from_pretrained(lowerCAmelCase__   )
								self.assertIsInstance(lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				Dict:
								a   :	Any			=       AutoConfig.for_model("roberta"   )
								self.assertIsInstance(lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				List[Any]:
								with tempfile.TemporaryDirectory() as tmp_dir:
									# This model name contains bert and roberta, but roberta ends up being picked.
									a   :	List[Any]			=       os.path.join(lowerCAmelCase__   ,      "fake-roberta"   )
									os.makedirs(lowerCAmelCase__   ,      exist_ok=lowerCAmelCase__   )
									with open(os.path.join(lowerCAmelCase__   ,      "config.json"   )   ,      "w"   ) as f:
										f.write(json.dumps({}   )   )
									a   :	str			=       AutoConfig.from_pretrained(lowerCAmelCase__   )
									self.assertEqual(type(lowerCAmelCase__   )   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				List[str]:
								try:
									AutoConfig.register("custom"   ,      lowerCAmelCase__   )
									# Wrong model type will raise an error
									with self.assertRaises(lowerCAmelCase__   ):
										AutoConfig.register("model"   ,      lowerCAmelCase__   )
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(lowerCAmelCase__   ):
										AutoConfig.register("bert"   ,      lowerCAmelCase__   )

									# Now that the config is registered, it can be used as any other config with the auto-API
									a   :	List[str]			=       CustomConfig()
									with tempfile.TemporaryDirectory() as tmp_dir:
										config.save_pretrained(lowerCAmelCase__   )
										a   :	Tuple			=       AutoConfig.from_pretrained(lowerCAmelCase__   )
										self.assertIsInstance(lowerCAmelCase__   ,      lowerCAmelCase__   )

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






							def 					__a     (     self   )			->				Optional[int]:
								with self.assertRaisesRegex(
								    lowerCAmelCase__   ,      "bert-base is not a local folder and is not a valid model identifier"   ):
									a   :	List[str]			=       AutoConfig.from_pretrained("bert-base"   )






							def 					__a     (     self   )			->				int:
								with self.assertRaisesRegex(
								    lowerCAmelCase__   ,      R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"   ):
									a   :	Any			=       AutoConfig.from_pretrained(lowerCAmelCase__   ,      revision="aaaaaa"   )






							def 					__a     (     self   )			->				Optional[int]:
								with self.assertRaisesRegex(
								    lowerCAmelCase__   ,      "hf-internal-testing/no-config-test-repo does not appear to have a file named config.json."   ,      ):
									a   :	Any			=       AutoConfig.from_pretrained("hf-internal-testing/no-config-test-repo"   )






							def 					__a     (     self   )			->				Any:
								# If remote code is not set, we will time out when asking whether to load the model.
								with self.assertRaises(lowerCAmelCase__   ):
									a   :	List[str]			=       AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model"   )
								# If remote code is disabled, we can't load this config.
								with self.assertRaises(lowerCAmelCase__   ):
									a   :	str			=       AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model"   ,      trust_remote_code=lowerCAmelCase__   )

								a   :	int			=       AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model"   ,      trust_remote_code=lowerCAmelCase__   )
								self.assertEqual(config.__class__.__name__   ,      "NewModelConfig"   )

								# Test config can be reloaded.
								with tempfile.TemporaryDirectory() as tmp_dir:
									config.save_pretrained(lowerCAmelCase__   )
									a   :	Dict			=       AutoConfig.from_pretrained(lowerCAmelCase__   ,      trust_remote_code=lowerCAmelCase__   )
								self.assertEqual(reloaded_config.__class__.__name__   ,      "NewModelConfig"   )






							def 					__a     (     self   )			->				Tuple:


								class  __UpperCamelCase       (		_snake_case		):
															lowerCamelCase     :      str         ="""new-model"""

								try:
									AutoConfig.register("new-model"   ,      lowerCAmelCase__   )
									# If remote code is not set, the default is to use local
									a   :	Dict			=       AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model"   )
									self.assertEqual(config.__class__.__name__   ,      "NewModelConfigLocal"   )

									# If remote code is disabled, we load the local one.
									a   :	List[str]			=       AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model"   ,      trust_remote_code=lowerCAmelCase__   )
									self.assertEqual(config.__class__.__name__   ,      "NewModelConfigLocal"   )

									# If remote is enabled, we load from the Hub
									a   :	int			=       AutoConfig.from_pretrained("hf-internal-testing/test_dynamic_model"   ,      trust_remote_code=lowerCAmelCase__   )
									self.assertEqual(config.__class__.__name__   ,      "NewModelConfig"   )

								finally:
									if "new-model" in CONFIG_MAPPING._extra_content:
										del CONFIG_MAPPING._extra_content["new-model"]









"""simple docstring"""




import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
		import onnxruntime as ort


a		:				Any  			=    logging.get_logger(__name__)

a		:				Tuple  			=    {
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__=None   ,      **lowerCAmelCase__   )			->				str:
								logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."   )
								a   :	Optional[int]			=       model
								a   :	int			=       kwargs.get("model_save_dir"   ,      lowerCAmelCase__   )
								a   :	Tuple			=       kwargs.get("latest_model_name"   ,      lowerCAmelCase__   )






							def __call__(     self   ,      **lowerCAmelCase__   )			->				Dict:
								a   :	List[str]			=       {k: np.array(lowerCAmelCase__   ) for k, v in kwargs.items()}
								return self.model.run(lowerCAmelCase__   ,      lowerCAmelCase__   )






							@staticmethod
							def 					__a     (     lowerCAmelCase__   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   )			->				Union[str, Any]:
								if provider is None:
									logger.info("No onnxruntime provider specified, using CPUExecutionProvider"   )
									a   :	List[str]			=       "CPUExecutionProvider"

								return ort.InferenceSession(lowerCAmelCase__   ,      providers=[provider]   ,      sess_options=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   )			->				int:
								a   :	List[str]			=       file_name if file_name is not None else ONNX_WEIGHTS_NAME

								a   :	Optional[int]			=       self.model_save_dir.joinpath(self.latest_model_name   )
								a   :	List[str]			=       Path(lowerCAmelCase__   ).joinpath(lowerCAmelCase__   )
								try:
									shutil.copyfile(lowerCAmelCase__   ,      lowerCAmelCase__   )
								except shutil.SameFileError:
									pass

								# copy external weights (for models >2GB)
								a   :	str			=       self.model_save_dir.joinpath(lowerCAmelCase__   )
								if src_path.exists():
									a   :	Any			=       Path(lowerCAmelCase__   ).joinpath(lowerCAmelCase__   )
									try:
										shutil.copyfile(lowerCAmelCase__   ,      lowerCAmelCase__   )
									except shutil.SameFileError:
										pass






							def 					__a     (     self   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )			->				str:
								if os.path.isfile(lowerCAmelCase__   ):
									logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""   )
									return

								os.makedirs(lowerCAmelCase__   ,      exist_ok=lowerCAmelCase__   )

								# saving model weights/files
								self._save_pretrained(lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				Optional[int]:
								a   :	Tuple			=       file_name if file_name is not None else ONNX_WEIGHTS_NAME
								# load model from local directory
								if os.path.isdir(lowerCAmelCase__   ):
									a   :	Tuple			=       OnnxRuntimeModel.load_model(
									    os.path.join(lowerCAmelCase__   ,      lowerCAmelCase__   )   ,      provider=lowerCAmelCase__   ,      sess_options=lowerCAmelCase__   )
									a   :	Tuple			=       Path(lowerCAmelCase__   )
								# load model from hub
								else:
									# download model
									a   :	Optional[Any]			=       hf_hub_download(
									    repo_id=lowerCAmelCase__   ,      filename=lowerCAmelCase__   ,      use_auth_token=lowerCAmelCase__   ,      revision=lowerCAmelCase__   ,      cache_dir=lowerCAmelCase__   ,      force_download=lowerCAmelCase__   ,      )
									a   :	Optional[int]			=       Path(lowerCAmelCase__   ).parent
									a   :	List[Any]			=       Path(lowerCAmelCase__   ).name
									a   :	int			=       OnnxRuntimeModel.load_model(lowerCAmelCase__   ,      provider=lowerCAmelCase__   ,      sess_options=lowerCAmelCase__   )
								return cls(model=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				List[str]:
								a   :	Any			=       None
								if len(str(lowerCAmelCase__   ).split("@"   )   ) == 2:
									a, a   :	Tuple			=       model_id.split("@"   )

								return cls._from_pretrained(
								    model_id=lowerCAmelCase__   ,      revision=lowerCAmelCase__   ,      cache_dir=lowerCAmelCase__   ,      force_download=lowerCAmelCase__   ,      use_auth_token=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )


