






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 		_snake_case	(	unittest.TestCase       ):






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

												SCREAMING_SNAKE_CASE							=			FlaxAutoModel.from_pretrained(a)
												self.assertIsNotNone(a)
												self.assertIsInstance(a		,       a)






			@slow
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						for model_name in ["roberta-base", "roberta-large"]:
									with self.subTest(a):
												SCREAMING_SNAKE_CASE							=			AutoConfig.from_pretrained(a)
												self.assertIsNotNone(a)
												self.assertIsInstance(a		,       a)

												SCREAMING_SNAKE_CASE							=			FlaxAutoModel.from_pretrained(a)
												self.assertIsNotNone(a)
												self.assertIsInstance(a		,       a)






			@slow
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						for model_name in ["bert-base-cased", "bert-large-uncased"]:
									SCREAMING_SNAKE_CASE							=			AutoTokenizer.from_pretrained(a)
									SCREAMING_SNAKE_CASE							=			FlaxBertModel.from_pretrained(a)
									SCREAMING_SNAKE_CASE							=			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  SCREAMING_SNAKE_CASE__     (							self)   ->							Dict:
						for model_name in ["roberta-base", "roberta-large"]:
									SCREAMING_SNAKE_CASE							=			AutoTokenizer.from_pretrained(a)
									SCREAMING_SNAKE_CASE							=			FlaxRobertaModel.from_pretrained(a)
									SCREAMING_SNAKE_CASE							=			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  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						with self.assertRaisesRegex(
						    a		,       'bert-base is not a local folder and is not a valid model identifier'):
									SCREAMING_SNAKE_CASE							=			FlaxAutoModel.from_pretrained('bert-base')






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						with self.assertRaisesRegex(
						    a		,       R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
									SCREAMING_SNAKE_CASE							=			FlaxAutoModel.from_pretrained(a		,       revision='aaaaaa')






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						with self.assertRaisesRegex(
						    a		,       'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'		,       ):
									SCREAMING_SNAKE_CASE							=			FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model')







			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						with self.assertRaisesRegex(a		,       'Use `from_pt=True` to load this model'):
									SCREAMING_SNAKE_CASE							=			FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only')











'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__UpperCAmelCase            =       {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       ["""GPTNeoXTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       [
							    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GPTNeoXForCausalLM""",
							    """GPTNeoXForQuestionAnswering""",
							    """GPTNeoXForSequenceClassification""",
							    """GPTNeoXForTokenClassification""",
							    """GPTNeoXLayer""",
							    """GPTNeoXModel""",
							    """GPTNeoXPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neox import (
														    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoXForCausalLM,
														    GPTNeoXForQuestionAnswering,
														    GPTNeoXForSequenceClassification,
														    GPTNeoXForTokenClassification,
														    GPTNeoXLayer,
														    GPTNeoXModel,
														    GPTNeoXPreTrainedModel,
														)


else:
							import sys

							__UpperCAmelCase            =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



