'''simple docstring'''

import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
							import jax

							from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
							from transformers.models.bert.modeling_flax_bert import FlaxBertModel
							from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		@slow
		def 		UpperCamelCase__       (   self				) ->     int:
								for model_name in ["bert-base-cased", "bert-large-uncased"]:
														with self.subTest(__A				):
																				_lowerCAmelCase										=AutoConfig.from_pretrained(__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A		,						__A				)

																				_lowerCAmelCase										=FlaxAutoModel.from_pretrained(__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A		,						__A				)






		@slow
		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								for model_name in ["roberta-base", "roberta-large"]:
														with self.subTest(__A				):
																				_lowerCAmelCase										=AutoConfig.from_pretrained(__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A		,						__A				)

																				_lowerCAmelCase										=FlaxAutoModel.from_pretrained(__A				)
																				self.assertIsNotNone(__A				)
																				self.assertIsInstance(__A		,						__A				)






		@slow
		def 		UpperCamelCase__       (   self				) ->     List[str]:
								for model_name in ["bert-base-cased", "bert-large-uncased"]:
														_lowerCAmelCase										=AutoTokenizer.from_pretrained(__A				)
														_lowerCAmelCase										=FlaxBertModel.from_pretrained(__A				)
														_lowerCAmelCase										=tokenizer('Do you support jax jitted function?'		,						return_tensors=TensorType.JAX				)

														@jax.jit
														def eval(**__A				):
																				return model(**__A				)

														eval(**__A				).block_until_ready()






		@slow
		def 		UpperCamelCase__       (   self				) ->     Any:
								for model_name in ["roberta-base", "roberta-large"]:
														_lowerCAmelCase										=AutoTokenizer.from_pretrained(__A				)
														_lowerCAmelCase										=FlaxRobertaModel.from_pretrained(__A				)
														_lowerCAmelCase										=tokenizer('Do you support jax jitted function?'		,						return_tensors=TensorType.JAX				)

														@jax.jit
														def eval(**__A				):
																				return model(**__A				)

														eval(**__A				).block_until_ready()






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								with self.assertRaisesRegex(
								    __A		,						'bert-base is not a local folder and is not a valid model identifier'				):
														_lowerCAmelCase										=FlaxAutoModel.from_pretrained('bert-base'				)






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								with self.assertRaisesRegex(
								    __A		,						r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'				):
														_lowerCAmelCase										=FlaxAutoModel.from_pretrained(__A		,						revision='aaaaaa'				)






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								with self.assertRaisesRegex(
								    __A		,						'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'		,						):
														_lowerCAmelCase										=FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'				)






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								with self.assertRaisesRegex(__A		,						'Use `from_pt=True` to load this model'				):
														_lowerCAmelCase										=FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'				)






'''simple docstring'''

from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_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_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)





