






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


lowerCamelCase__							=     get_tests_dir("""fixtures/dummy-config.json""")




class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						0



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            self.assertIsNotNone(transformers.models.auto.__spec__    )
            self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto"""    )    )



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						AutoConfig.from_pretrained("""bert-base-uncased"""    )
            self.assertIsInstance(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:List[str]          =						AutoConfig.from_pretrained(a__    )
            self.assertIsInstance(a__			,						a__    )



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






            '''simple docstring'''





            __snake_case			:Tuple          =						AutoConfig.from_pretrained(a__    )
            self.assertIsInstance(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						AutoConfig.for_model("""roberta"""    )
            self.assertIsInstance(a__			,						a__    )



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






            '''simple docstring'''





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



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            try:
                 AutoConfig.register("""custom"""			,						a__    )
                 # Wrong model type will raise an error
                 with self.assertRaises(a__    ):
                      AutoConfig.register("""model"""			,						a__    )
                 # Trying to register something existing in the Transformers library will raise an error
                 with self.assertRaises(a__    ):
                      AutoConfig.register("""bert"""			,						a__    )

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

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



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            with self.assertRaisesRegex(
                a__			,						"""bert-base is not a local folder and is not a valid model identifier"""    ):
                 __snake_case			:str          =						AutoConfig.from_pretrained("""bert-base"""    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            with self.assertRaisesRegex(
                a__			,						R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""    ):
                 __snake_case			:Dict          =						AutoConfig.from_pretrained(a__			,						revision="""aaaaaa"""    )



       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





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



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            with self.assertRaises(a__    ):
                 __snake_case			:Dict          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""    )
            # If remote code is disabled, we can't load this config.
            with self.assertRaises(a__    ):
                 __snake_case			:int          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )

            __snake_case			:Tuple          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )
            self.assertEqual(config.__class__.__name__			,						"""NewModelConfig"""    )

            # Test config can be reloaded.
            with tempfile.TemporaryDirectory() as tmp_dir:
                 config.save_pretrained(a__    )
                 __snake_case			:Union[str, Any]          =						AutoConfig.from_pretrained(a__			,						trust_remote_code=a__    )
            self.assertEqual(reloaded_config.__class__.__name__			,						"""NewModelConfig"""    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            class 			snake_case__   (							lowercase_):

                   '''simple docstring'''
                   lowerCamelCase							:				Optional[Any] 				= "new-model"

            try:
                 AutoConfig.register("""new-model"""			,						a__    )
                 # If remote code is not set, the default is to use local
                 __snake_case			:List[Any]          =						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.
                 __snake_case			:Tuple          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )
                 self.assertEqual(config.__class__.__name__			,						"""NewModelConfigLocal"""    )

                 # If remote is enabled, we load from the Hub
                 __snake_case			:List[Any]          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )
                 self.assertEqual(config.__class__.__name__			,						"""NewModelConfig"""    )

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










# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


lowerCamelCase__							=     """pytorch_model.bin"""
lowerCamelCase__							=     """pytorch_model.bin.index.json"""
lowerCamelCase__							=     """adapter_config.json"""
lowerCamelCase__							=     """adapter_model.bin"""
lowerCamelCase__							=     """adapter_model.safetensors"""
lowerCamelCase__							=     """tf_model.h5"""
lowerCamelCase__							=     """tf_model.h5.index.json"""
lowerCamelCase__							=     """model.ckpt"""
lowerCamelCase__							=     """flax_model.msgpack"""
lowerCamelCase__							=     """flax_model.msgpack.index.json"""
lowerCamelCase__							=     """model.safetensors"""
lowerCamelCase__							=     """model.safetensors.index.json"""
lowerCamelCase__							=     """config.json"""
lowerCamelCase__							=     """preprocessor_config.json"""
lowerCamelCase__							=     FEATURE_EXTRACTOR_NAME
lowerCamelCase__							=     """generation_config.json"""
lowerCamelCase__							=     """modelcard.json"""

lowerCamelCase__							=     """▁"""
lowerCamelCase__							=     SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

lowerCamelCase__							=     [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
lowerCamelCase__							=     [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
lowerCamelCase__							=     [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]






def   UpperCamelCase  (       snake_case__     :   Dict  ):




     '''simple docstring'''






     if version.parse(snake_case__  ) < version.parse(snake_case__  ):
          if "dev" in min_version:
               __snake_case			:Tuple          =						(
                   """This example requires a source install from HuggingFace Transformers (see """
                   """`https://huggingface.co/docs/transformers/installation#install-from-source`),"""
               )
          else:
               __snake_case			:List[str]          =						f'''This example requires a minimum version of {min_version},'''
          error_message += f''' but the version found is {__version__}.\n'''
          raise ImportError(
              error_message
              + """Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other """
              """versions of HuggingFace Transformers."""  )


