



'''simple docstring'''




import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MobileNetVaForImageClassification, MobileNetVaModel
						from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor







class 	_lowerCAmelCase							(			__UpperCAmelCase ):


	def     _a	(self							):
				A_	:						List[str]	=     self.config_class(**self.inputs_dict							)
				self.parent.assertTrue(hasattr(lowercase       ,      """tf_padding"""							)							)
				self.parent.assertTrue(hasattr(lowercase       ,      """depth_multiplier"""							)							)







class 	_lowerCAmelCase							:


	def __init__(self       ,      lowercase       ,      lowercase=13       ,      lowercase=3       ,      lowercase=32       ,      lowercase=0.25       ,      lowercase=8       ,      lowercase=True       ,      lowercase=1024       ,      lowercase=32       ,      lowercase="relu6"       ,      lowercase=0.1       ,      lowercase=0.02       ,      lowercase=True       ,      lowercase=True       ,      lowercase=10       ,      lowercase=None       ,      ):
				A_	:						Any	=     parent
				A_	:						int	=     batch_size
				A_	:						Any	=     num_channels
				A_	:						Tuple	=     image_size
				A_	:						List[str]	=     depth_multiplier
				A_	:						int	=     min_depth
				A_	:						Any	=     tf_padding
				A_	:						Dict	=     int(last_hidden_size * depth_multiplier							)
				A_	:						Tuple	=     output_stride
				A_	:						List[str]	=     hidden_act
				A_	:						Tuple	=     classifier_dropout_prob
				A_	:						Tuple	=     use_labels
				A_	:						int	=     is_training
				A_	:						str	=     num_labels
				A_	:						int	=     initializer_range
				A_	:						int	=     scope


	def     _a	(self							):
				A_	:						Tuple	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

				A_	:						List[str]	=     None
				A_	:						Tuple	=     None
				if self.use_labels:
							A_	:						Dict	=     ids_tensor([self.batch_size]       ,      self.num_labels							)
							A_	:						Dict	=     ids_tensor([self.batch_size, self.image_size, self.image_size]       ,      self.num_labels							)

				A_	:						int	=     self.get_config()

				return config, pixel_values, labels, pixel_labels


	def     _a	(self							):
				return MobileNetVaConfig(
				    num_channels=self.num_channels       ,      image_size=self.image_size       ,      depth_multiplier=self.depth_multiplier       ,      min_depth=self.min_depth       ,      tf_padding=self.tf_padding       ,      hidden_act=self.hidden_act       ,      classifier_dropout_prob=self.classifier_dropout_prob       ,      initializer_range=self.initializer_range       ,      )


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						Optional[Any]	=     MobileNetVaModel(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						str	=     model(lowercase							)
				self.parent.assertEqual(
				    result.last_hidden_state.shape       ,      (
				        self.batch_size,
				        self.last_hidden_size,
				        self.image_size // self.output_stride,
				        self.image_size // self.output_stride,
				    )       ,      )


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						Any	=     self.num_labels
				A_	:						List[Any]	=     MobileNetVaForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Union[str, Any]	=     model(lowercase       ,      labels=lowercase							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)


	def     _a	(self							):
				A_	:						List[Any]	=     self.prepare_config_and_inputs()
				A_,    A_,    A_,    A_	:						str	=     config_and_inputs
				A_	:						List[str]	=     {"""pixel_values""": pixel_values}
				return config, inputs_dict







@require_torch
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		int					=					(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE   :		Any					=					(
	    {'feature-extraction': MobileNetVaModel, 'image-classification': MobileNetVaForImageClassification}
	    if is_torch_available()
	    else {}
	)

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


	def     _a	(self							):
				A_	:						Union[str, Any]	=     MobileNetVaModelTester(self							)
				A_	:						str	=     MobileNetVaConfigTester(self       ,      config_class=lowercase       ,      has_text_modality=lowercase							)


	def     _a	(self							):
				self.config_tester.run_common_tests()


	@unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""							)
	def     _a	(self							):
				pass


	@unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""							)
	def     _a	(self							):
				pass


	@unittest.skip(reason="""MobileNetV1 does not output attentions"""							)
	def     _a	(self							):
				pass


	def     _a	(self							):
				A_,    A_	:						Optional[Any]	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						List[Any]	=     model_class(lowercase							)
							A_	:						Dict	=     inspect.signature(model.forward							)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							A_	:						str	=     [*signature.parameters.keys()]

							A_	:						Tuple	=     ["""pixel_values"""]
							self.assertListEqual(arg_names[:1]       ,      lowercase							)


	def     _a	(self							):
				A_	:						int	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase							)


	def     _a	(self							):
				def check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							):
							A_	:						Union[str, Any]	=     model_class(lowercase							)
							model.to(lowercase							)
							model.eval()

							with torch.no_grad():
										A_	:						Tuple	=     model(**self._prepare_for_class(lowercase       ,      lowercase							)							)

							A_	:						List[Any]	=     outputs.hidden_states

							A_	:						Union[str, Any]	=     26
							self.assertEqual(len(lowercase							)       ,      lowercase							)

				A_,    A_	:						str	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						Optional[int]	=     True
							check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							A_	:						List[Any]	=     True

							check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							)


	def     _a	(self							):
				A_	:						Any	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase							)


	@slow
	def     _a	(self							):
				for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A_	:						str	=     MobileNetVaModel.from_pretrained(lowercase							)
							self.assertIsNotNone(lowercase							)






def 	a			( ):
			'''simple docstring'''





			A_	:						List[Any]	=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
			return image







@require_torch
@require_vision
class 	_lowerCAmelCase							(			unittest.TestCase ):


	@cached_property
	def     _a	(self							):
				return (
				    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224"""							) if is_vision_available() else None
				)


	@slow
	def     _a	(self							):
				A_	:						int	=     MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""							).to(lowercase							)

				A_	:						Optional[int]	=     self.default_image_processor
				A_	:						int	=     prepare_img()
				A_	:						Union[str, Any]	=     image_processor(images=lowercase       ,      return_tensors="""pt"""							).to(lowercase							)

				# forward pass
				with torch.no_grad():
							A_	:						Any	=     model(**lowercase							)

				# verify the logits
				A_	:						Optional[Any]	=     torch.Size((1, 1001)							)
				self.assertEqual(outputs.logits.shape       ,      lowercase							)

				A_	:						List[str]	=     torch.tensor([-4.17_39, -1.12_33, 3.12_05]							).to(lowercase							)

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




'''simple docstring'''




import re



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			return [char.split() for char in re.split(r"""[^ a-z A-Z 0-9 \s]"""						,      str_    )]



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Optional[int]	=     split_input(str_    )
			return "".join(
			    ["""""".join([char.capitalize() for char in sub_str]    ) for sub_str in string_split]    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			try:
						A_	:						List[Any]	=     split_input(lowerCamelCase__    )
						if upper:
									A_	:						Tuple	=     """""".join(
									    [
									        separator.join([char.upper() for char in sub_str]    )
									        for sub_str in string_split
									    ]    )
						else:
									A_	:						Optional[int]	=     """""".join(
									    [
									        separator.join([char.lower() for char in sub_str]    )
									        for sub_str in string_split
									    ]    )
						return res_str
			except IndexError:
						return "not valid string"



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			return to_simple_case(lowerCamelCase__    )



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			try:
						A_	:						Tuple	=     to_simple_case(lowerCamelCase__    )
						return res_str[0].lower() + res_str[1:]
			except IndexError:
						return "not valid string"



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			return to_complex_case(lowerCamelCase__						,      lowerCamelCase__						,      """_"""    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			return to_complex_case(lowerCamelCase__						,      lowerCamelCase__						,      """-"""    )


if __name__ == "__main__":
						__import__('''doctest''').testmod()