






"""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     UpperCamelCase_ (unittest.TestCase  ):





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    List[Any]:
				for model_name in ["bert-base-cased", "bert-large-uncased"]:
							with self.subTest(lowerCAmelCase_		):
										UpperCAmelCase_  :				Dict      =      AutoConfig.from_pretrained(lowerCAmelCase_		)
										self.assertIsNotNone(lowerCAmelCase_		)
										self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_		)

										UpperCAmelCase_  :				Any      =      FlaxAutoModel.from_pretrained(lowerCAmelCase_		)
										self.assertIsNotNone(lowerCAmelCase_		)
										self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    int:
				for model_name in ["roberta-base", "roberta-large"]:
							with self.subTest(lowerCAmelCase_		):
										UpperCAmelCase_  :				List[Any]      =      AutoConfig.from_pretrained(lowerCAmelCase_		)
										self.assertIsNotNone(lowerCAmelCase_		)
										self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_		)

										UpperCAmelCase_  :				List[Any]      =      FlaxAutoModel.from_pretrained(lowerCAmelCase_		)
										self.assertIsNotNone(lowerCAmelCase_		)
										self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_		)





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Dict:
				for model_name in ["bert-base-cased", "bert-large-uncased"]:
							UpperCAmelCase_  :				Any      =      AutoTokenizer.from_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_  :				List[str]      =      FlaxBertModel.from_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_  :				int      =      tokenizer("Do you support jax jitted function?"			,			return_tensors=TensorType.JAX		)

							@jax.jit
							def eval(**lowerCAmelCase_      :				List[Any]		):
										return model(**lowerCAmelCase_		)

							eval(**lowerCAmelCase_		).block_until_ready()





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Optional[int]:
				for model_name in ["roberta-base", "roberta-large"]:
							UpperCAmelCase_  :				Tuple      =      AutoTokenizer.from_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_  :				Optional[int]      =      FlaxRobertaModel.from_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_  :				List[Any]      =      tokenizer("Do you support jax jitted function?"			,			return_tensors=TensorType.JAX		)

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

							eval(**lowerCAmelCase_		).block_until_ready()





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Tuple:
				with self.assertRaisesRegex(
				    lowerCAmelCase_			,			"bert-base is not a local folder and is not a valid model identifier"		):
							UpperCAmelCase_  :				Optional[int]      =      FlaxAutoModel.from_pretrained("bert-base"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Any:
				with self.assertRaisesRegex(
				    lowerCAmelCase_			,			R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"		):
							UpperCAmelCase_  :				Dict      =      FlaxAutoModel.from_pretrained(lowerCAmelCase_			,			revision="aaaaaa"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Union[str, Any]:
				with self.assertRaisesRegex(
				    lowerCAmelCase_			,			"hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack"			,			):
							UpperCAmelCase_  :				str      =      FlaxAutoModel.from_pretrained("hf-internal-testing/config-no-model"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Optional[int]:
				with self.assertRaisesRegex(lowerCAmelCase_			,			"Use `from_pt=True` to load this model"		):
							UpperCAmelCase_  :				Union[str, Any]      =      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


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   {
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''levit'''





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				int=224			,			lowerCAmelCase_      :				Union[str, Any]=3			,			lowerCAmelCase_      :				Tuple=3			,			lowerCAmelCase_      :				int=2			,			lowerCAmelCase_      :				str=1			,			lowerCAmelCase_      :				Optional[Any]=16			,			lowerCAmelCase_      :				Tuple=[128, 256, 384]			,			lowerCAmelCase_      :				Optional[int]=[4, 8, 12]			,			lowerCAmelCase_      :				str=[4, 4, 4]			,			lowerCAmelCase_      :				Dict=[16, 16, 16]			,			lowerCAmelCase_      :				int=0			,			lowerCAmelCase_      :				Optional[int]=[2, 2, 2]			,			lowerCAmelCase_      :				Any=[2, 2, 2]			,			lowerCAmelCase_      :				int=0.0_2			,			**lowerCAmelCase_      :				List[Any]			,			)		->    List[str]:
				super().__init__(**lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      image_size
				UpperCAmelCase_  :				List[Any]      =      num_channels
				UpperCAmelCase_  :				str      =      kernel_size
				UpperCAmelCase_  :				List[Any]      =      stride
				UpperCAmelCase_  :				List[str]      =      padding
				UpperCAmelCase_  :				Any      =      hidden_sizes
				UpperCAmelCase_  :				List[str]      =      num_attention_heads
				UpperCAmelCase_  :				List[str]      =      depths
				UpperCAmelCase_  :				int      =      key_dim
				UpperCAmelCase_  :				List[str]      =      drop_path_rate
				UpperCAmelCase_  :				str      =      patch_size
				UpperCAmelCase_  :				Tuple      =      attention_ratio
				UpperCAmelCase_  :				Optional[int]      =      mlp_ratio
				UpperCAmelCase_  :				Union[str, Any]      =      initializer_range
				UpperCAmelCase_  :				List[Any]      =      [
				    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
				    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
				]







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       version.parse('''1.11'''  )





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]		)





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    float:
				return 1e-4



