





'''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     :					Optional[int]					):
								for model_name in ["bert-base-cased", "bert-large-uncased"]:
												with self.subTest(__a					):
																_a								= AutoConfig.from_pretrained(__a					)
																self.assertIsNotNone(__a					)
																self.assertIsInstance(__a					,				__a					)

																_a								= FlaxAutoModel.from_pretrained(__a					)
																self.assertIsNotNone(__a					)
																self.assertIsInstance(__a					,				__a					)




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

																_a								= FlaxAutoModel.from_pretrained(__a					)
																self.assertIsNotNone(__a					)
																self.assertIsInstance(__a					,				__a					)




				@slow
				def   UpperCamelCase__   (  self     :					Optional[int]					):
								for model_name in ["bert-base-cased", "bert-large-uncased"]:
												_a								= AutoTokenizer.from_pretrained(__a					)
												_a								= FlaxBertModel.from_pretrained(__a					)
												_a								= tokenizer("Do you support jax jitted function?"					,				return_tensors=TensorType.JAX					)

												@jax.jit
												def eval(**__a     :					Optional[Any]					):
																return model(**__a					)

												eval(**__a					).block_until_ready()




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

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

												eval(**__a					).block_until_ready()




				def   UpperCamelCase__   (  self     :					Any					):
								with self.assertRaisesRegex(
								    __a					,				"bert-base is not a local folder and is not a valid model identifier"					):
												_a								= FlaxAutoModel.from_pretrained("bert-base"					)




				def   UpperCamelCase__   (  self     :					int					):
								with self.assertRaisesRegex(
								    __a					,				r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"					):
												_a								= FlaxAutoModel.from_pretrained(__a					,				revision="aaaaaa"					)




				def   UpperCamelCase__   (  self     :					Dict					):
								with self.assertRaisesRegex(
								    __a					,				"hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack"					,				):
												_a								= FlaxAutoModel.from_pretrained("hf-internal-testing/config-no-model"					)




				def   UpperCamelCase__   (  self     :					str					):
								with self.assertRaisesRegex(__a					,				"Use `from_pt=True` to load this model"					):
												_a								= 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


lowerCAmelCase_					:    int      	=			{'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    Optional[int]      	=			['GPTNeoXTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    List[str]      	=			[
					    '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

					lowerCAmelCase_					:    List[str]      	=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
