


'''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  UpperCamelCase__(     unittest.TestCase ):




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				List[Any]=3   ,					lowerCAmelCase    :				Tuple=32   ,					lowerCAmelCase    :				Optional[int]=3   ,					lowerCAmelCase    :				Optional[Any]=10   ,					lowerCAmelCase    :				Optional[Any]=[10, 20, 30, 40]   ,					lowerCAmelCase    :				Optional[int]=[1, 1, 2, 1]   ,					lowerCAmelCase    :				Union[str, Any]=True   ,					lowerCAmelCase    :				Tuple=True   ,					lowerCAmelCase    :				List[Any]="relu"   ,					lowerCAmelCase    :				List[Any]=3   ,					lowerCAmelCase    :				Union[str, Any]=None   ,					)->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			parent
										UpperCAmelCase         =			batch_size
										UpperCAmelCase         =			image_size
										UpperCAmelCase         =			num_channels
										UpperCAmelCase         =			embeddings_size
										UpperCAmelCase         =			hidden_sizes
										UpperCAmelCase         =			depths
										UpperCAmelCase         =			is_training
										UpperCAmelCase         =			use_labels
										UpperCAmelCase         =			hidden_act
										UpperCAmelCase         =			num_labels
										UpperCAmelCase         =			scope
										UpperCAmelCase         =			len(lowerCAmelCase   )




				def   a__(	self    :				Optional[Any]   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

										UpperCAmelCase         =			self.get_config()

										return config, pixel_values




				def   a__(	self    :				str   )->  Optional[Any]:

										"""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   a__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				Any   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			FlaxRegNetModel(config=lowerCAmelCase   )
										UpperCAmelCase         =			model(lowerCAmelCase   )

										# 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   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Optional[Any]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self.num_labels
										UpperCAmelCase         =			FlaxRegNetForImageClassification(config=lowerCAmelCase   )
										UpperCAmelCase         =			model(lowerCAmelCase   )
										self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels)   )





				def   a__(	self    :				List[str]   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			self.prepare_config_and_inputs()
										UpperCAmelCase       ,       UpperCAmelCase         =			config_and_inputs
										UpperCAmelCase         =			{'''pixel_values''': pixel_values}
										return config, inputs_dict







@require_flax
class  UpperCamelCase__(     lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   Optional[int]							= (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

				__magic_name__       :   Optional[int]							= False
				__magic_name__       :   List[str]							= False
				__magic_name__       :   Dict							= False




				def   a__(	self    :				Union[str, Any]   )->  None:

										"""simple docstring"""




										UpperCAmelCase         =			FlaxRegNetModelTester(self   )
										UpperCAmelCase         =			ConfigTester(self   ,					config_class=lowerCAmelCase   ,					has_text_modality=lowerCAmelCase   )




				def   a__(	self    :				List[str]   )->  List[str]:

										"""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   a__(	self    :				Tuple   )->  Tuple:

										"""simple docstring"""




										return




				def   a__(	self    :				Optional[Any]   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase   )




				def   a__(	self    :				Any   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase   )




				@unittest.skip(reason='''RegNet does not use inputs_embeds'''   )
				def   a__(	self    :				str   )->  Optional[Any]:

										"""simple docstring"""




										pass




				@unittest.skip(reason='''RegNet does not support input and output embeddings'''   )
				def   a__(	self    :				Any   )->  List[str]:

										"""simple docstring"""




										pass




				def   a__(	self    :				Any   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

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

																UpperCAmelCase         =			['''pixel_values''']
																self.assertListEqual(arg_names[:1]   ,					lowerCAmelCase   )




				def   a__(	self    :				Tuple   )->  int:

										"""simple docstring"""




										def check_hidden_states_output(lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				int   ):
																UpperCAmelCase         =			model_class(lowerCAmelCase   )

																UpperCAmelCase         =			model(**self._prepare_for_class(lowerCAmelCase   ,					lowerCAmelCase   )   )

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

																UpperCAmelCase         =			self.model_tester.num_stages
																self.assertEqual(len(lowerCAmelCase   )   ,					expected_num_stages + 1   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase         =			True
																check_hidden_states_output(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )

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

																check_hidden_states_output(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )





				def   a__(	self    :				Union[str, Any]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__   ):
																						UpperCAmelCase         =			self._prepare_for_class(lowerCAmelCase   ,					lowerCAmelCase   )
																						UpperCAmelCase         =			model_class(lowerCAmelCase   )

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

																						with self.subTest('''JIT Enabled'''   ):
																												UpperCAmelCase         =			model_jitted(**lowerCAmelCase   ).to_tuple()

																						with self.subTest('''JIT Disabled'''   ):
																												with jax.disable_jit():
																																		UpperCAmelCase         =			model_jitted(**lowerCAmelCase   ).to_tuple()

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







def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
						return image






@require_flax
class  UpperCamelCase__(     unittest.TestCase ):




				@cached_property
				def   a__(	self    :				Dict   )->  int:

										"""simple docstring"""




										return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''   ) if is_vision_available() else None





				@slow
				def   a__(	self    :				Union[str, Any]   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''   )

										UpperCAmelCase         =			self.default_image_processor
										UpperCAmelCase         =			prepare_img()
										UpperCAmelCase         =			image_processor(images=lowerCAmelCase   ,					return_tensors='''np'''   )

										UpperCAmelCase         =			model(**lowerCAmelCase   )

										# verify the logits
										UpperCAmelCase         =			(1, 1000)
										self.assertEqual(outputs.logits.shape   ,					lowerCAmelCase   )

										UpperCAmelCase         =			jnp.array([-0.4180, -1.5051, -3.4836]   )

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









'''simple docstring'''



def    lowerCamelCase__   ( A     :    str  ):




						'''simple docstring'''






						assert column_title.isupper()
						UpperCAmelCase         =			0
						UpperCAmelCase         =			len(A  ) - 1
						UpperCAmelCase         =			0

						while index >= 0:
												UpperCAmelCase         =			(ord(column_title[index]  ) - 64) * pow(26   ,     A  )
												answer += value
												power += 1
												index -= 1

						return answer


if __name__ == "__main__":
	from doctest import testmod

	testmod()





