
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 __snake_case		(				unittest.TestCase					):
    def __init__(  self		,snake_case		,snake_case=3		,snake_case=32		,snake_case=3		,snake_case=10		,snake_case=[10, 20, 30, 40]		,snake_case=[1, 1, 2, 1]		,snake_case=True		,snake_case=True		,snake_case="relu"		,snake_case=3		,snake_case=None		,):



           '''simple docstring'''







           lowercase							:					Dict	     =				parent
           lowercase							:					Dict	     =				batch_size
           lowercase							:					int	     =				image_size
           lowercase							:					List[Any]	     =				num_channels
           lowercase							:					List[str]	     =				embeddings_size
           lowercase							:					Tuple	     =				hidden_sizes
           lowercase							:					int	     =				depths
           lowercase							:					Union[str, Any]	     =				is_training
           lowercase							:					Optional[Any]	     =				use_labels
           lowercase							:					List[Any]	     =				hidden_act
           lowercase							:					int	     =				num_labels
           lowercase							:					Any	     =				scope
           lowercase							:					List[str]	     =				len(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

           lowercase							:					Any	     =				self.get_config()

           return config, pixel_values
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Any	     =				FlaxRegNetModel(config=snake_case				)
           lowercase							:					Any	     =				model(snake_case				)

           # Output shape (b, c, h, w)
           self.parent.assertEqual(
               result.last_hidden_state.shape		,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					str	     =				self.num_labels
           lowercase							:					int	     =				FlaxRegNetForImageClassification(config=snake_case				)
           lowercase							:					Tuple	     =				model(snake_case				)
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels)				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.prepare_config_and_inputs()
           lowercase     ,      lowercase							:					Optional[Any]	     =				config_and_inputs
           lowercase							:					Optional[int]	     =				{"""pixel_values""": pixel_values}
           return config, inputs_dict






@require_flax
class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    _a		:						Any=    (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

    _a		:						Optional[int]=    False
    _a		:						Tuple=    False
    _a		:						List[Any]=    False
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				FlaxRegNetModelTester(self				)
           lowercase							:					List[Any]	     =				ConfigTester(self		,config_class=snake_case		,has_text_modality=snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*snake_case				)
    @unittest.skip(reason="""RegNet does not use inputs_embeds"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase     ,      lowercase							:					Optional[int]	     =				self.model_tester.prepare_config_and_inputs_for_common()

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

                  lowercase							:					str	     =				["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           def check_hidden_states_output(snake_case		,snake_case		,snake_case				):
                  lowercase							:					Dict	     =				model_class(snake_case				)

                  lowercase							:					List[Any]	     =				model(**self._prepare_for_class(snake_case		,snake_case				)				)

                  lowercase							:					Union[str, Any]	     =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                  lowercase							:					Tuple	     =				self.model_tester.num_stages
                  self.assertEqual(len(snake_case				)		,expected_num_stages + 1				)

           lowercase     ,      lowercase							:					Optional[int]	     =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  lowercase							:					str	     =				True
                  check_hidden_states_output(snake_case		,snake_case		,snake_case				)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  lowercase							:					int	     =				True

                  check_hidden_states_output(snake_case		,snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase     ,      lowercase							:					List[Any]	     =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__				):
                         lowercase							:					Optional[int]	     =				self._prepare_for_class(snake_case		,snake_case				)
                         lowercase							:					Optional[int]	     =				model_class(snake_case				)

                         @jax.jit
                         def model_jitted(snake_case		,**snake_case				):
                                return model(pixel_values=snake_case		,**snake_case				)

                         with self.subTest("""JIT Enabled"""				):
                                lowercase							:					List[Any]	     =				model_jitted(**snake_case				).to_tuple()

                         with self.subTest("""JIT Disabled"""				):
                                with jax.disable_jit():
                                       lowercase							:					int	     =				model_jitted(**snake_case				).to_tuple()

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







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




@require_flax
class __snake_case		(				unittest.TestCase					):
    @cached_property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:					int	     =				FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""				)

           lowercase							:					str	     =				self.default_image_processor
           lowercase							:					List[str]	     =				prepare_img()
           lowercase							:					str	     =				image_processor(images=snake_case		,return_tensors="""np"""				)

           lowercase							:					Optional[int]	     =				model(**snake_case				)

           # verify the logits
           lowercase							:					List[Any]	     =				(1, 1000)
           self.assertEqual(outputs.logits.shape		,snake_case				)

           lowercase							:					int	     =				jnp.array([-0.4_180, -1.5_051, -3.4_836]				)

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


from bisect import bisect
from itertools import accumulate







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       lowercase							:					Dict	     =				sorted(zip(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) ,     key=lambda SCREAMING_SNAKE_CASE__    : x[0] / x[1] ,     reverse=SCREAMING_SNAKE_CASE__    )
       lowercase     ,      lowercase							:					Optional[Any]	     =				[i[0] for i in r], [i[1] for i in r]
       lowercase							:					Any	     =				list(accumulate(SCREAMING_SNAKE_CASE__    )    )
       lowercase							:					int	     =				bisect(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       return (
           0
           if k == 0
           else sum(vl[:k]    ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
           if k != n
           else sum(vl[:k]    )
       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
