






from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
					from transformers import (
					    TFAutoModel,
					    TFAutoModelForCausalLM,
					    TFAutoModelForMaskedLM,
					    TFAutoModelForPreTraining,
					    TFAutoModelForQuestionAnswering,
					    TFAutoModelForSeqaSeqLM,
					    TFAutoModelForSequenceClassification,
					    TFAutoModelForTableQuestionAnswering,
					    TFAutoModelForTokenClassification,
					    TFAutoModelWithLMHead,
					    TFBertForMaskedLM,
					    TFBertForPreTraining,
					    TFBertForQuestionAnswering,
					    TFBertForSequenceClassification,
					    TFBertModel,
					    TFFunnelBaseModel,
					    TFFunnelModel,
					    TFGPTaLMHeadModel,
					    TFRobertaForMaskedLM,
					    TFTaForConditionalGeneration,
					    TFTapasForQuestionAnswering,
					)
					from transformers.models.auto.modeling_tf_auto import (
					    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
					    TF_MODEL_FOR_MASKED_LM_MAPPING,
					    TF_MODEL_FOR_PRETRAINING_MAPPING,
					    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    TF_MODEL_MAPPING,
					)
					from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
					from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST




class 			snake_case__   (							lowercase_):

							'''simple docstring'''
							lowerCamelCase							:				int 				= "new-model"


if is_tf_available():




					class 			snake_case__   (							lowercase_):

												'''simple docstring'''
												lowerCamelCase							:				int 				= NewModelConfig




@require_tf
class 			snake_case__   (							unittest.TestCase):

							'''simple docstring'''



							@slow
							def 					__lowercase						(							self    ) ->      Tuple:






												'''simple docstring'''





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

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



							@slow
							def 					__lowercase						(							self    ) ->      Dict:






												'''simple docstring'''





												__snake_case			:List[str]          =						"""bert-base-cased"""
												__snake_case			:Union[str, Any]          =						AutoConfig.from_pretrained(a__    )
												self.assertIsNotNone(a__    )
												self.assertIsInstance(a__			,						a__    )

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



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






												'''simple docstring'''





												for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__snake_case			:List[str]          =						AutoConfig.from_pretrained(a__    )
																	self.assertIsNotNone(a__    )
																	self.assertIsInstance(a__			,						a__    )

																	__snake_case			:List[str]          =						TFAutoModelForCausalLM.from_pretrained(a__    )
																	__snake_case			:Any          =						TFAutoModelForCausalLM.from_pretrained(a__			,						output_loading_info=a__    )
																	self.assertIsNotNone(a__    )
																	self.assertIsInstance(a__			,						a__    )



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






												'''simple docstring'''





												for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__snake_case			:Any          =						AutoConfig.from_pretrained(a__    )
																	self.assertIsNotNone(a__    )
																	self.assertIsInstance(a__			,						a__    )

																	__snake_case			:int          =						TFAutoModelWithLMHead.from_pretrained(a__    )
																	self.assertIsNotNone(a__    )
																	self.assertIsInstance(a__			,						a__    )



							@slow
							def 					__lowercase						(							self    ) ->      Any:






												'''simple docstring'''





												for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__snake_case			:List[Any]          =						AutoConfig.from_pretrained(a__    )
																	self.assertIsNotNone(a__    )
																	self.assertIsInstance(a__			,						a__    )

																	__snake_case			:Union[str, Any]          =						TFAutoModelForMaskedLM.from_pretrained(a__    )
																	__snake_case			:int          =						TFAutoModelForMaskedLM.from_pretrained(a__			,						output_loading_info=a__    )
																	self.assertIsNotNone(a__    )
																	self.assertIsInstance(a__			,						a__    )



							@slow
							def 					__lowercase						(							self    ) ->      Tuple:






												'''simple docstring'''





												for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__snake_case			:Any          =						AutoConfig.from_pretrained(a__    )
																	self.assertIsNotNone(a__    )
																	self.assertIsInstance(a__			,						a__    )

																	__snake_case			:str          =						TFAutoModelForSeqaSeqLM.from_pretrained(a__    )
																	__snake_case			:Tuple          =						TFAutoModelForSeqaSeqLM.from_pretrained(a__			,						output_loading_info=a__    )
																	self.assertIsNotNone(a__    )
																	self.assertIsInstance(a__			,						a__    )



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






												'''simple docstring'''





												for model_name in ["bert-base-uncased"]:
																	__snake_case			:Optional[int]          =						AutoConfig.from_pretrained(a__    )
																	self.assertIsNotNone(a__    )
																	self.assertIsInstance(a__			,						a__    )

																	__snake_case			:Optional[Any]          =						TFAutoModelForSequenceClassification.from_pretrained(a__    )
																	self.assertIsNotNone(a__    )
																	self.assertIsInstance(a__			,						a__    )



							@slow
							def 					__lowercase						(							self    ) ->      Dict:






												'''simple docstring'''





												for model_name in ["bert-base-uncased"]:
																	__snake_case			:int          =						AutoConfig.from_pretrained(a__    )
																	self.assertIsNotNone(a__    )
																	self.assertIsInstance(a__			,						a__    )

																	__snake_case			:Optional[Any]          =						TFAutoModelForQuestionAnswering.from_pretrained(a__    )
																	self.assertIsNotNone(a__    )
																	self.assertIsInstance(a__			,						a__    )



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






												'''simple docstring'''





												for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
																	__snake_case			:Any          =						AutoConfig.from_pretrained(a__    )
																	self.assertIsNotNone(a__    )
																	self.assertIsInstance(a__			,						a__    )

																	__snake_case			:Union[str, Any]          =						TFAutoModelForTableQuestionAnswering.from_pretrained(a__    )
																	__snake_case			:Tuple          =						TFAutoModelForTableQuestionAnswering.from_pretrained(
																	    a__			,						output_loading_info=a__    )
																	self.assertIsNotNone(a__    )
																	self.assertIsInstance(a__			,						a__    )



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






												'''simple docstring'''





												__snake_case			:Dict          =						TFAutoModelWithLMHead.from_pretrained(a__    )
												self.assertIsInstance(a__			,						a__    )
												self.assertEqual(model.num_parameters()			,						1_44_10    )
												self.assertEqual(model.num_parameters(only_trainable=a__    )			,						1_44_10    )



							def 					__lowercase						(							self    ) ->      Dict:






												'''simple docstring'''





												__snake_case			:Optional[int]          =						TFAutoModelWithLMHead.from_pretrained(a__    )
												self.assertIsInstance(a__			,						a__    )
												self.assertEqual(model.num_parameters()			,						1_44_10    )
												self.assertEqual(model.num_parameters(only_trainable=a__    )			,						1_44_10    )



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






												'''simple docstring'''





												__snake_case			:str          =						TFAutoModel.from_pretrained("""sgugger/funnel-random-tiny"""    )
												self.assertIsInstance(a__			,						a__    )

												__snake_case			:List[str]          =						copy.deepcopy(model.config    )
												__snake_case			:Tuple          =						["""FunnelBaseModel"""]
												__snake_case			:int          =						TFAutoModel.from_config(a__    )
												self.assertIsInstance(a__			,						a__    )

												with tempfile.TemporaryDirectory() as tmp_dir:
																	model.save_pretrained(a__    )
																	__snake_case			:Union[str, Any]          =						TFAutoModel.from_pretrained(a__    )
																	self.assertIsInstance(a__			,						a__    )



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






												'''simple docstring'''





												try:
																	AutoConfig.register("""new-model"""			,						a__    )

																	__snake_case			:List[Any]          =						[
																	    TFAutoModel,
																	    TFAutoModelForCausalLM,
																	    TFAutoModelForMaskedLM,
																	    TFAutoModelForPreTraining,
																	    TFAutoModelForQuestionAnswering,
																	    TFAutoModelForSequenceClassification,
																	    TFAutoModelForTokenClassification,
																	]

																	for auto_class in auto_classes:
																						with self.subTest(auto_class.__name__    ):
																											# Wrong config class will raise an error
																											with self.assertRaises(a__    ):
																																auto_class.register(a__			,						a__    )
																											auto_class.register(a__			,						a__    )
																											# Trying to register something existing in the Transformers library will raise an error
																											with self.assertRaises(a__    ):
																																auto_class.register(a__			,						a__    )

																											# Now that the config is registered, it can be used as any other config with the auto-API
																											__snake_case			:Optional[int]          =						BertModelTester(self    ).get_config()
																											__snake_case			:Dict          =						NewModelConfig(**tiny_config.to_dict()    )
																											__snake_case			:Tuple          =						auto_class.from_config(a__    )
																											self.assertIsInstance(a__			,						a__    )

																											with tempfile.TemporaryDirectory() as tmp_dir:
																																model.save_pretrained(a__    )
																																__snake_case			:Any          =						auto_class.from_pretrained(a__    )
																																self.assertIsInstance(a__			,						a__    )

												finally:
																	if "new-model" in CONFIG_MAPPING._extra_content:
																						del CONFIG_MAPPING._extra_content["new-model"]
																	for mapping in (
																	    TF_MODEL_MAPPING,
																	    TF_MODEL_FOR_PRETRAINING_MAPPING,
																	    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
																	    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
																	    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
																	    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
																	    TF_MODEL_FOR_MASKED_LM_MAPPING,
																	):
																						if NewModelConfig in mapping._extra_content:
																											del mapping._extra_content[NewModelConfig]



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






												'''simple docstring'''





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



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






												'''simple docstring'''





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



							def 					__lowercase						(							self    ) ->      str:






												'''simple docstring'''





												with self.assertRaisesRegex(
												    a__			,						"""hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin"""			,						):
																	__snake_case			:Tuple          =						TFAutoModel.from_pretrained("""hf-internal-testing/config-no-model"""    )



							def 					__lowercase						(							self    ) ->      str:






												'''simple docstring'''





												with self.assertRaisesRegex(a__			,						"""Use `from_pt=True` to load this model"""    ):
																	__snake_case			:str          =						TFAutoModel.from_pretrained("""hf-internal-testing/tiny-bert-pt-only"""    )



							def 					__lowercase						(							self    ) ->      Any:






												'''simple docstring'''





												__snake_case			:int          =						TFAutoModel.from_pretrained("""hf-internal-testing/tiny-random-bert"""    )
												with RequestCounter() as counter:
																	__snake_case			:Optional[int]          =						TFAutoModel.from_pretrained("""hf-internal-testing/tiny-random-bert"""    )
																	self.assertEqual(counter.get_request_count			,						0    )
																	self.assertEqual(counter.head_request_count			,						1    )
																	self.assertEqual(counter.other_request_count			,						0    )

												# With a sharded checkpoint
												__snake_case			:Tuple          =						TFAutoModel.from_pretrained("""ArthurZ/tiny-random-bert-sharded"""    )
												with RequestCounter() as counter:
																	__snake_case			:str          =						TFAutoModel.from_pretrained("""ArthurZ/tiny-random-bert-sharded"""    )
																	self.assertEqual(counter.get_request_count			,						0    )
																	self.assertEqual(counter.head_request_count			,						1    )
																	self.assertEqual(counter.other_request_count			,						0    )










# 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."""  )


