


"""simple docstring"""





import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class 			lowercase_	(			unittest.TestCase    ):







      def __init__(   self			:				Any		,	_lowercase			:				List[str]		,	_lowercase			:				str=3		,	_lowercase			:				Optional[int]=3_2		,	_lowercase			:				List[str]=3		,	_lowercase			:				Tuple=1_0		,	_lowercase			:				Dict=[1_0, 2_0, 3_0, 4_0]		,	_lowercase			:				List[Any]=[1, 1, 2, 1]		,	_lowercase			:				Optional[Any]=True		,	_lowercase			:				Tuple=True		,	_lowercase			:				Dict="relu"		,	_lowercase			:				str=3		,	_lowercase			:				Dict=None		,	):
        lowerCAmelCase__     :     List[str]										=       parent
        lowerCAmelCase__     :     List[str]										=       batch_size
        lowerCAmelCase__     :     Tuple										=       image_size
        lowerCAmelCase__     :     Optional[int]										=       num_channels
        lowerCAmelCase__     :     List[str]										=       embeddings_size
        lowerCAmelCase__     :     str										=       hidden_sizes
        lowerCAmelCase__     :     int										=       depths
        lowerCAmelCase__     :     Union[str, Any]										=       is_training
        lowerCAmelCase__     :     int										=       use_labels
        lowerCAmelCase__     :     Any										=       hidden_act
        lowerCAmelCase__     :     Optional[int]										=       num_labels
        lowerCAmelCase__     :     int										=       scope
        lowerCAmelCase__     :     str										=       len(_lowercase     )







      def 					_lowerCAmelCase							(   self			:				List[Any]     ):
        lowerCAmelCase__     :     Optional[int]										=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

        lowerCAmelCase__     :     Optional[Any]										=       self.get_config()

        return config, pixel_values







      def 					_lowerCAmelCase							(   self			:				Optional[Any]     ):
        return RegNetConfig(
            num_channels=self.num_channels		,	embeddings_size=self.embeddings_size		,	hidden_sizes=self.hidden_sizes		,	depths=self.depths		,	hidden_act=self.hidden_act		,	num_labels=self.num_labels		,	image_size=self.image_size		,	)







      def 					_lowerCAmelCase							(   self			:				Union[str, Any]		,	_lowercase			:				List[str]		,	_lowercase			:				Optional[Any]     ):
        lowerCAmelCase__     :     Optional[int]										=       FlaxRegNetModel(config=_lowercase     )
        lowerCAmelCase__     :     Union[str, Any]										=       model(_lowercase     )

        # Output shape (b, c, h, w)
        self.parent.assertEqual(
            result.last_hidden_state.shape		,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)		,	)







      def 					_lowerCAmelCase							(   self			:				Tuple		,	_lowercase			:				Union[str, Any]		,	_lowercase			:				str     ):
        lowerCAmelCase__     :     Optional[int]										=       self.num_labels
        lowerCAmelCase__     :     str										=       FlaxRegNetForImageClassification(config=_lowercase     )
        lowerCAmelCase__     :     List[Any]										=       model(_lowercase     )
        self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)     )







      def 					_lowerCAmelCase							(   self			:				str     ):
        lowerCAmelCase__     :     Union[str, Any]										=       self.prepare_config_and_inputs()
        lowerCAmelCase__					,		lowerCAmelCase__     :     int										=       config_and_inputs
        lowerCAmelCase__     :     int										=       {"pixel_values": pixel_values}
        return config, inputs_dict

@require_flax
class 			lowercase_	(			a_				,				unittest.TestCase    ):
      __magic_name__  :				Union[str, Any] 							=					(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

      __magic_name__  :				str 							=					False
      __magic_name__  :				Any 							=					False
      __magic_name__  :				Optional[int] 							=					False







      def 					_lowerCAmelCase							(   self			:				Dict     ):
        lowerCAmelCase__     :     Optional[Any]										=       FlaxRegNetModelTester(self     )
        lowerCAmelCase__     :     int										=       ConfigTester(self		,	config_class=_lowercase		,	has_text_modality=_lowercase     )







      def 					_lowerCAmelCase							(   self			:				Optional[int]     ):
        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()







      def 					_lowerCAmelCase							(   self			:				Union[str, Any]     ):
        return







      def 					_lowerCAmelCase							(   self			:				List[Any]     ):
        lowerCAmelCase__     :     List[Any]										=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_lowercase     )







      def 					_lowerCAmelCase							(   self			:				str     ):
        lowerCAmelCase__     :     Dict										=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_lowercase     )







      @unittest.skip(reason="RegNet does not use inputs_embeds"     )
      def 					_lowerCAmelCase							(   self			:				Union[str, Any]     ):
        pass







      @unittest.skip(reason="RegNet does not support input and output embeddings"     )
      def 					_lowerCAmelCase							(   self			:				Any     ):
        pass







      def 					_lowerCAmelCase							(   self			:				Tuple     ):
        lowerCAmelCase__					,		lowerCAmelCase__     :     List[str]										=       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowerCAmelCase__     :     Optional[Any]										=       model_class(_lowercase     )
          lowerCAmelCase__     :     str										=       inspect.signature(model.__call__     )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowerCAmelCase__     :     str										=       [*signature.parameters.keys()]

          lowerCAmelCase__     :     Dict										=       ["pixel_values"]
          self.assertListEqual(arg_names[:1]		,	_lowercase     )







      def 					_lowerCAmelCase							(   self			:				Union[str, Any]     ):







        def check_hidden_states_output(_lowercase			:				Any		,	_lowercase			:				int		,	_lowercase			:				Optional[int]     ):
          lowerCAmelCase__     :     List[Any]										=       model_class(_lowercase     )

          lowerCAmelCase__     :     Any										=       model(**self._prepare_for_class(_lowercase		,	_lowercase     )     )

          lowerCAmelCase__     :     Dict										=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          lowerCAmelCase__     :     List[str]										=       self.model_tester.num_stages
          self.assertEqual(len(_lowercase     )		,	expected_num_stages + 1     )

        lowerCAmelCase__					,		lowerCAmelCase__     :     int										=       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowerCAmelCase__     :     Dict										=       True
          check_hidden_states_output(_lowercase		,	_lowercase		,	_lowercase     )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          lowerCAmelCase__     :     Union[str, Any]										=       True

          check_hidden_states_output(_lowercase		,	_lowercase		,	_lowercase     )







      def 					_lowerCAmelCase							(   self			:				int     ):
        lowerCAmelCase__					,		lowerCAmelCase__     :     Optional[int]										=       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__     ):
            lowerCAmelCase__     :     List[Any]										=       self._prepare_for_class(_lowercase		,	_lowercase     )
            lowerCAmelCase__     :     Tuple										=       model_class(_lowercase     )

            @jax.jit
            def model_jitted(_lowercase			:				Optional[Any]		,	**_lowercase			:				str     ):
              return model(pixel_values=_lowercase		,	**_lowercase     )

            with self.subTest("JIT Enabled"     ):
              lowerCAmelCase__     :     Optional[int]										=       model_jitted(**_lowercase     ).to_tuple()

            with self.subTest("JIT Disabled"     ):
              with jax.disable_jit():
                lowerCAmelCase__     :     Tuple										=       model_jitted(**_lowercase     ).to_tuple()

            self.assertEqual(len(_lowercase     )		,	len(_lowercase     )     )
            for jitted_output, output in zip(_lowercase		,	_lowercase     ):
              self.assertEqual(jitted_output.shape		,	output.shape     )





def      lowercase__						(	)       ->  int:
  lowerCAmelCase__     :     Dict										=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
  return image







@require_flax
class 			lowercase_	(			unittest.TestCase    ):







      @cached_property
      def 					_lowerCAmelCase							(   self			:				int     ):
        return AutoImageProcessor.from_pretrained("facebook/regnet-y-040"     ) if is_vision_available() else None







      @slow
      def 					_lowerCAmelCase							(   self			:				Optional[Any]     ):
        lowerCAmelCase__     :     str										=       FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"     )

        lowerCAmelCase__     :     Tuple										=       self.default_image_processor
        lowerCAmelCase__     :     Tuple										=       prepare_img()
        lowerCAmelCase__     :     Dict										=       image_processor(images=_lowercase		,	return_tensors="np"     )

        lowerCAmelCase__     :     List[Any]										=       model(**_lowercase     )

        # verify the logits
        lowerCAmelCase__     :     Union[str, Any]										=       (1, 1_0_0_0)
        self.assertEqual(outputs.logits.shape		,	_lowercase     )

        lowerCAmelCase__     :     Any										=       jnp.array([-0.4180, -1.5051, -3.4836]     )

        self.assertTrue(jnp.allclose(outputs.logits[0, :3]		,	_lowercase		,	atol=1e-4     )     )




from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase   :    Union[str, Any]     =		logging.get_logger(__name__)

UpperCAmelCase   :    List[Any]     =		{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					List[str]			       =		"lilt"







		def __init__( self		,      A=3_05_22		,      A=7_68		,      A=12		,      A=12		,      A=30_72		,      A="gelu"		,      A=0.1		,      A=0.1		,      A=5_12		,      A=2		,      A=0.02		,      A=1e-1_2		,      A=0		,      A="absolute"		,      A=None		,      A=4		,      A=10_24		,      **A		,      )  -> List[Any]:







					'''simple docstring'''







					super().__init__(pad_token_id=A		,      **A	)

					lowerCamelCase    		=  vocab_size
					lowerCamelCase    		=  hidden_size
					lowerCamelCase    		=  num_hidden_layers
					lowerCamelCase    		=  num_attention_heads
					lowerCamelCase    		=  hidden_act
					lowerCamelCase    		=  intermediate_size
					lowerCamelCase    		=  hidden_dropout_prob
					lowerCamelCase    		=  attention_probs_dropout_prob
					lowerCamelCase    		=  max_position_embeddings
					lowerCamelCase    		=  type_vocab_size
					lowerCamelCase    		=  initializer_range
					lowerCamelCase    		=  layer_norm_eps
					lowerCamelCase    		=  position_embedding_type
					lowerCamelCase    		=  classifier_dropout
					lowerCamelCase    		=  channel_shrink_ratio
					lowerCamelCase    		=  max_ad_position_embeddings

