




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 		UpperCAmelCase   (				UpperCAmelCase__			):







						'''simple docstring'''





						def        snake_case__						(						self							:     List[str]						):






							"""simple docstring"""






							snake_case_		    =     self.config_class(**self.inputs_dict						)
							self.parent.assertTrue(hasattr(__lowercase       ,	"tf_padding"						)						)
							self.parent.assertTrue(hasattr(__lowercase       ,	"depth_multiplier"						)						)







class 		UpperCAmelCase   :







						'''simple docstring'''





						def __init__(						self							:     Union[str, Any]       ,	__lowercase							:     Tuple       ,	__lowercase							:     Dict=13       ,	__lowercase							:     int=3       ,	__lowercase							:     List[Any]=32       ,	__lowercase							:     int=0.25       ,	__lowercase							:     Dict=8       ,	__lowercase							:     str=True       ,	__lowercase							:     Tuple=10_24       ,	__lowercase							:     Union[str, Any]=32       ,	__lowercase							:     Tuple="relu6"       ,	__lowercase							:     int=0.1       ,	__lowercase							:     Optional[Any]=0.02       ,	__lowercase							:     Tuple=True       ,	__lowercase							:     Any=True       ,	__lowercase							:     List[Any]=10       ,	__lowercase							:     Union[str, Any]=None       ,	):






							"""simple docstring"""






							snake_case_		    =     parent
							snake_case_		    =     batch_size
							snake_case_		    =     num_channels
							snake_case_		    =     image_size
							snake_case_		    =     depth_multiplier
							snake_case_		    =     min_depth
							snake_case_		    =     tf_padding
							snake_case_		    =     int(last_hidden_size * depth_multiplier						)
							snake_case_		    =     output_stride
							snake_case_		    =     hidden_act
							snake_case_		    =     classifier_dropout_prob
							snake_case_		    =     use_labels
							snake_case_		    =     is_training
							snake_case_		    =     num_labels
							snake_case_		    =     initializer_range
							snake_case_		    =     scope






						def        snake_case__						(						self							:     str						):






							"""simple docstring"""






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

							snake_case_		    =     None
							snake_case_		    =     None
							if self.use_labels:
								snake_case_		    =     ids_tensor([self.batch_size]       ,	self.num_labels						)
								snake_case_		    =     ids_tensor([self.batch_size, self.image_size, self.image_size]       ,	self.num_labels						)

							snake_case_		    =     self.get_config()

							return config, pixel_values, labels, pixel_labels






						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							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        snake_case__						(						self							:     str       ,	__lowercase							:     List[str]       ,	__lowercase							:     str       ,	__lowercase							:     Dict       ,	__lowercase							:     Optional[int]						):






							"""simple docstring"""






							snake_case_		    =     MobileNetVaModel(config=__lowercase						)
							model.to(__lowercase						)
							model.eval()
							snake_case_		    =     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        snake_case__						(						self							:     Any       ,	__lowercase							:     Tuple       ,	__lowercase							:     Optional[int]       ,	__lowercase							:     int       ,	__lowercase							:     Tuple						):






							"""simple docstring"""






							snake_case_		    =     self.num_labels
							snake_case_		    =     MobileNetVaForImageClassification(__lowercase						)
							model.to(__lowercase						)
							model.eval()
							snake_case_		    =     model(__lowercase       ,	labels=__lowercase						)
							self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)						)






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     self.prepare_config_and_inputs()
							snake_case_    ,      snake_case_    ,      snake_case_    ,      snake_case_		    =     config_and_inputs
							snake_case_		    =     {"pixel_values": pixel_values}
							return config, inputs_dict







@require_torch
class 		UpperCAmelCase   (				UpperCAmelCase__       ,   UpperCAmelCase__       ,   unittest.TestCase			):







						'''simple docstring'''





						lowerCAmelCase_     		=   (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
						lowerCAmelCase_     		=   (
						    {'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
						    if is_torch_available()
						    else {}
						)

						lowerCAmelCase_     		=   False
						lowerCAmelCase_     		=   False
						lowerCAmelCase_     		=   False
						lowerCAmelCase_     		=   False






						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							snake_case_		    =     MobileNetVaModelTester(self						)
							snake_case_		    =     MobileNetVaConfigTester(self       ,	config_class=__lowercase       ,	has_text_modality=__lowercase						)






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							self.config_tester.run_common_tests()






						@unittest.skip(reason="MobileNetV1 does not use inputs_embeds"						)
						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							pass






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






							"""simple docstring"""






							pass






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






							"""simple docstring"""






							pass






						def        snake_case__						(						self							:     List[Any]						):






							"""simple docstring"""






							snake_case_    ,      snake_case_		    =     self.model_tester.prepare_config_and_inputs_for_common()

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

								snake_case_		    =     ["pixel_values"]
								self.assertListEqual(arg_names[:1]       ,	__lowercase						)






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowercase						)






						def        snake_case__						(						self							:     List[str]						):






							"""simple docstring"""






							def check_hidden_states_output(__lowercase							:     List[Any]       ,	__lowercase							:     Optional[Any]       ,	__lowercase							:     str						):
								snake_case_		    =     model_class(__lowercase						)
								model.to(__lowercase						)
								model.eval()

								with torch.no_grad():
									snake_case_		    =     model(**self._prepare_for_class(__lowercase       ,	__lowercase						)						)

								snake_case_		    =     outputs.hidden_states

								snake_case_		    =     26
								self.assertEqual(len(__lowercase						)       ,	__lowercase						)

							snake_case_    ,      snake_case_		    =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								snake_case_		    =     True
								check_hidden_states_output(__lowercase       ,	__lowercase       ,	__lowercase						)

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

								check_hidden_states_output(__lowercase       ,	__lowercase       ,	__lowercase						)






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowercase						)






						@slow
						def        snake_case__						(						self							:     List[Any]						):






							"""simple docstring"""






							for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_		    =     MobileNetVaModel.from_pretrained(__lowercase						)
								self.assertIsNotNone(__lowercase						)







def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
	return image






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







						'''simple docstring'''





						@cached_property
						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							return (
							    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224"						) if is_vision_available() else None
							)






						@slow
						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






							snake_case_		    =     MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224"						).to(__lowercase						)

							snake_case_		    =     self.default_image_processor
							snake_case_		    =     prepare_img()
							snake_case_		    =     image_processor(images=__lowercase       ,	return_tensors="pt"						).to(__lowercase						)

							# forward pass
							with torch.no_grad():
								snake_case_		    =     model(**__lowercase						)

							# verify the logits
							snake_case_		    =     torch.Size((1, 10_01)						)
							self.assertEqual(outputs.logits.shape       ,	__lowercase						)

							snake_case_		    =     torch.tensor([-4.1739, -1.1233, 3.1205]						).to(__lowercase						)

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






class 		UpperCAmelCase   :







						'''simple docstring'''





						def __init__(						self							:     Dict						):






							"""simple docstring"""






							snake_case_		    =     {}  # Mapping from char to TrieNode
							snake_case_		    =     False






						def        snake_case__						(						self							:     Dict       ,	__lowercase							:     list[str]						):






							"""simple docstring"""






							for word in words:
								self.insert(__lowercase						)






						def        snake_case__						(						self							:     List[str]       ,	__lowercase							:     str						):






							"""simple docstring"""






							snake_case_		    =     self
							for char in word:
								if char not in curr.nodes:
									snake_case_		    =     TrieNode()
								snake_case_		    =     curr.nodes[char]
							snake_case_		    =     True






						def        snake_case__						(						self							:     List[Any]       ,	__lowercase							:     str						):






							"""simple docstring"""






							snake_case_		    =     self
							for char in word:
								if char not in curr.nodes:
									return False
								snake_case_		    =     curr.nodes[char]
							return curr.is_leaf






						def        snake_case__						(						self							:     Optional[Any]       ,	__lowercase							:     str						):






							"""simple docstring"""






							def _delete(__lowercase							:     TrieNode       ,	__lowercase							:     str       ,	__lowercase							:     int						) -> bool:
								if index == len(__lowercase						):
									# If word does not exist
									if not curr.is_leaf:
										return False
									snake_case_		    =     False
									return len(curr.nodes						) == 0
								snake_case_		    =     word[index]
								snake_case_		    =     curr.nodes.get(__lowercase						)
								# If char not in current trie node
								if not char_node:
									return False
								# Flag to check if node can be deleted
								snake_case_		    =     _delete(__lowercase       ,	__lowercase       ,	index + 1						)
								if delete_curr:
									del curr.nodes[char]
									return len(curr.nodes						) == 0
								return delete_curr

							_delete(self       ,	__lowercase       ,	0						)







def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	if node.is_leaf:
		print(_A     ,	end=" "							)

	for key, value in node.nodes.items():
		print_words(_A     ,	word + key							)


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     "banana bananas bandana band apple all beast".split()
	snake_case_		    =     TrieNode()
	root.insert_many(_A							)
	# print_words(root, "")
	assert all(root.find(_A							) for word in words							)
	assert root.find("banana"							)
	assert not root.find("bandanas"							)
	assert not root.find("apps"							)
	assert root.find("apple"							)
	assert root.find("all"							)
	root.delete("all"							)
	assert not root.find("all"							)
	root.delete("banana"							)
	assert not root.find("banana"							)
	assert root.find("bananas"							)
	return True


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	print(str(_A							)     ,	"works!" if passes else "doesn't work :("							)


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	assert test_trie()


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	print_results("Testing trie functionality"     ,	test_trie()							)


if __name__ == "__main__":
		main()
