


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       ,  lowercase_       ,  lowercase_=3       ,  lowercase_=32       ,  lowercase_=3       ,  lowercase_=10       ,  lowercase_=[10, 20, 30, 40]       ,  lowercase_=[1, 1, 2, 1]       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_="relu"       ,  lowercase_=3       ,  lowercase_=None       ,  )  -> List[str]:
											a__      =parent
											a__      =batch_size
											a__      =image_size
											a__      =num_channels
											a__      =embeddings_size
											a__      =hidden_sizes
											a__      =depths
											a__      =is_training
											a__      =use_labels
											a__      =hidden_act
											a__      =num_labels
											a__      =scope
											a__      =len(lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

											a__      =self.get_config()

											return config, pixel_values





				def 						__UpperCamelCase    (				self)  -> List[str]:
											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 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> Any:
											a__      =FlaxRegNetModel(config=lowercase_)
											a__      =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 // 32, self.image_size // 32)       ,  )





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> str:
											a__      =self.num_labels
											a__      =FlaxRegNetForImageClassification(config=lowercase_)
											a__      =model(lowercase_)
											self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels))





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =self.prepare_config_and_inputs()
											a__    ,     a__      =config_and_inputs
											a__      ={'pixel_values': pixel_values}
											return config, inputs_dict







@require_flax
class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

				snake_case	    =False
				snake_case	    =False
				snake_case	    =False





				def 						__UpperCamelCase    (				self)  -> None:
											a__      =FlaxRegNetModelTester(self)
											a__      =ConfigTester(self       ,  config_class=lowercase_       ,  has_text_modality=lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											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 						__UpperCamelCase    (				self)  -> List[Any]:
											return





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowercase_)





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =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 						__UpperCamelCase    (				self)  -> int:
											pass





				@unittest.skip(reason='RegNet does not support input and output embeddings')
				def 						__UpperCamelCase    (				self)  -> Dict:
											pass





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

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

																		a__      =['pixel_values']
																		self.assertListEqual(arg_names[:1]       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											def check_hidden_states_output(lowercase_       ,  lowercase_       ,  lowercase_):
																		a__      =model_class(lowercase_)

																		a__      =model(**self._prepare_for_class(lowercase_       ,  lowercase_))

																		a__      =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		a__      =self.model_tester.num_stages
																		self.assertEqual(len(lowercase_)       ,  expected_num_stages + 1)

											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		a__      =True
																		check_hidden_states_output(lowercase_       ,  lowercase_       ,  lowercase_)

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		a__      =True

																		check_hidden_states_output(lowercase_       ,  lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__):
																									a__      =self._prepare_for_class(lowercase_       ,  lowercase_)
																									a__      =model_class(lowercase_)

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

																									with self.subTest('JIT Enabled'):
																																a__      =model_jitted(**lowercase_).to_tuple()

																									with self.subTest('JIT Disabled'):
																																with jax.disable_jit():
																																							a__      =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(					):
							a__      =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
							return image







@require_flax
class       lowercase_					(unittest.TestCase   ):





				@cached_property
				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											return AutoImageProcessor.from_pretrained('facebook/regnet-y-040') if is_vision_available() else None





				@slow
				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040')

											a__      =self.default_image_processor
											a__      =prepare_img()
											a__      =image_processor(images=lowercase_       ,  return_tensors='np')

											a__      =model(**lowercase_)

											# verify the logits
											a__      =(1, 1000)
											self.assertEqual(outputs.logits.shape       ,  lowercase_)

											a__      =jnp.array([-0.41_80, -1.50_51, -3.48_36])

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








'''simple docstring'''






def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      if divisor % 5 == 0 or divisor % 2 == 0:
            return 0
      _lowerCAmelCase					=				1
      _lowerCAmelCase					=				1
      while repunit:
            _lowerCAmelCase					=				(10 * repunit + 1) % divisor
            repunit_index += 1
      return repunit_index
def   UpperCamelCase__ (						lowerCAmelCase = 1_00_00_00    ):





      """simple docstring"""


      _lowerCAmelCase					=				limit - 1
      if divisor % 2 == 0:
            divisor += 1
      while least_divisible_repunit(lowerCAmelCase    ) <= limit:
            divisor += 2
      return divisor


if __name__ == "__main__":
      print(F"""{solution() = }""")






