
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 				UpperCAmelCase (    unittest.TestCase    ):




	'''simple docstring'''





	@slow
	def 							UpperCamelCase_    (			self   :					Any					):
					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   :					Tuple					):
					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   :					Any					):
					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[int]					):
													return model(**A					)

									eval(**A					).block_until_ready()






	@slow
	def 							UpperCamelCase_    (			self   :					Any					):
					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   :					Optional[int]					):
													return model(**A					)

									eval(**A					).block_until_ready()






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					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   :					List[str]					):
					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   :					List[str]					):
					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   :					Any					):
					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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     '''mobilenet_v1'''


						def __init__(			self  :   Optional[int]	,A_  :   Optional[int]=3	,A_  :   Any=224	,A_  :   List[Any]=1.0	,A_  :   Union[str, Any]=8	,A_  :   Union[str, Any]="relu6"	,A_  :   Optional[Any]=True	,A_  :   List[str]=0.9_99	,A_  :   int=0.02	,A_  :   int=0.0_01	,**A_  :   Union[str, Any]	,)  -> Dict:
							super().__init__(**A_   )

							if depth_multiplier <= 0:
								raise ValueError('depth_multiplier must be greater than zero.'   )

							A     = num_channels
							A     = image_size
							A     = depth_multiplier
							A     = min_depth
							A     = hidden_act
							A     = tf_padding
							A     = classifier_dropout_prob
							A     = initializer_range
							A     = layer_norm_eps







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     version.parse('''1.11'''     )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Mapping[str, Mapping[int, str]]:
							return OrderedDict([('pixel_values', {0: 'batch'})]   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Mapping[str, Mapping[int, str]]:
							if self.task == "image-classification":
								return OrderedDict([('logits', {0: 'batch'})]   )
							else:
								return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]   )




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> float:
							return 1e-4