

from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import numpy as np
	import tensorflow as tf

	from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
	from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import MobileViTImageProcessor

class    a__  (   snake_case     ):
			"""simple docstring"""







			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     self.config_class(**self.inputs_dict    )
							self.parent.assertTrue(hasattr(lowercase , "hidden_sizes"    )    )
							self.parent.assertTrue(hasattr(lowercase , "neck_hidden_sizes"    )    )
							self.parent.assertTrue(hasattr(lowercase , "num_attention_heads"    )    )

class    a__  :
			"""simple docstring"""







			def __init__(      self , lowercase , lowercase=13 , lowercase=32 , lowercase=2 , lowercase=3 , lowercase=640 , lowercase=4 , lowercase="silu" , lowercase=3 , lowercase=32 , lowercase=0.1 , lowercase=0.1 , lowercase=0.1 , lowercase=0.02 , lowercase=True , lowercase=True , lowercase=10 , lowercase=None , )					->  Any:
							'''simple docstring'''






							A__            =     parent
							A__            =     batch_size
							A__            =     image_size
							A__            =     patch_size
							A__            =     num_channels
							A__            =     last_hidden_size
							A__            =     num_attention_heads
							A__            =     hidden_act
							A__            =     conv_kernel_size
							A__            =     output_stride
							A__            =     hidden_dropout_prob
							A__            =     attention_probs_dropout_prob
							A__            =     classifier_dropout_prob
							A__            =     use_labels
							A__            =     is_training
							A__            =     num_labels
							A__            =     initializer_range
							A__            =     scope


			def        UpperCamelCase						(      self    )					->  List[Any]:
							'''simple docstring'''






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

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

							A__            =     self.get_config()

							return config, pixel_values, labels, pixel_labels


			def        UpperCamelCase						(      self    )					->  Optional[int]:
							'''simple docstring'''






							return MobileViTConfig(
							    image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase , lowercase    )					->  Dict:
							'''simple docstring'''






							A__            =     TFMobileViTModel(config=lowercase    )
							A__            =     model(lowercase , training=lowercase    )
							A__            =     A__            =     self.image_size // self.output_stride
							self.parent.assertEqual(
							    result.last_hidden_state.shape , (self.batch_size, self.last_hidden_size, expected_height, expected_width)    )


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase , lowercase    )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     self.num_labels
							A__            =     TFMobileViTForImageClassification(lowercase    )
							A__            =     model(lowercase , labels=lowercase , training=lowercase    )
							self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels)    )


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase , lowercase    )					->  int:
							'''simple docstring'''






							A__            =     self.num_labels
							A__            =     TFMobileViTForSemanticSegmentation(lowercase    )
							A__            =     A__            =     self.image_size // self.output_stride

							A__            =     model(lowercase , training=lowercase    )
							self.parent.assertEqual(
							    result.logits.shape , (self.batch_size, self.num_labels, expected_height, expected_width)    )

							A__            =     model(lowercase , labels=lowercase , training=lowercase    )
							self.parent.assertEqual(
							    result.logits.shape , (self.batch_size, self.num_labels, expected_height, expected_width)    )


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






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

@require_tf
class    a__  (   snake_case							,		snake_case							,		unittest.TestCase     ):
			"""simple docstring"""







			__lowerCamelCase	    =    (
			    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
			    if is_tf_available()
			    else ()
			)
			__lowerCamelCase	    =    (
			    {'feature-extraction': TFMobileViTModel, 'image-classification': TFMobileViTForImageClassification}
			    if is_tf_available()
			    else {}
			)

			__lowerCamelCase	    =    False
			__lowerCamelCase	    =    False
			__lowerCamelCase	    =    False
			__lowerCamelCase	    =    False
			__lowerCamelCase	    =    False


			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							A__            =     TFMobileViTModelTester(self    )
							A__            =     TFMobileViTConfigTester(self , config_class=lowercase , has_text_modality=lowercase    )


			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






							self.config_tester.run_common_tests()


			@unittest.skip(reason="MobileViT does not use inputs_embeds"    )
			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							pass


			@unittest.skip(reason="MobileViT does not support input and output embeddings"    )
			def        UpperCamelCase						(      self    )					->  Any:
							'''simple docstring'''






							pass


			@unittest.skip(reason="MobileViT does not output attentions"    )
			def        UpperCamelCase						(      self    )					->  List[str]:
							'''simple docstring'''






							pass


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							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    )					->  Any:
							'''simple docstring'''






							A__            =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase    )


			def        UpperCamelCase						(      self    )					->  Optional[int]:
							'''simple docstring'''






							def check_hidden_states_output(lowercase , lowercase , lowercase    ):
											A__            =     model_class(lowercase    )

											A__            =     model(**self._prepare_for_class(lowercase , lowercase    )    )

											A__            =     outputs.hidden_states

											A__            =     5
											self.assertEqual(len(lowercase    ) , lowercase    )

											# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
											# with the width and height being successively divided by 2.
											A__            =     2
											for i in range(len(lowercase    )    ):
															self.assertListEqual(
															    list(hidden_states[i].shape[-2:]    ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
															divisor *= 2

											self.assertEqual(self.model_tester.output_stride , divisor // 2    )

							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    )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowercase    )


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							A__            =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*lowercase    )


			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices("GPU"    )    ) == 0 , reason="TF does not support backprop for grouped convolutions on CPU." , )
			def        UpperCamelCase						(      self    )					->  Optional[int]:
							'''simple docstring'''






							super().test_dataset_conversion()


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase=2e-1 , lowercase=2e-1    )					->  List[Any]:
							'''simple docstring'''






							self.assertTrue(np.allclose(lowercase , lowercase , atol=lowercase , rtol=lowercase    )    )


			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices("GPU"    )    ) == 0 , reason="TF does not support backprop for grouped convolutions on CPU." , )
			@slow
			def        UpperCamelCase						(      self    )					->  List[Any]:
							'''simple docstring'''






							A__      ,			A__            =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
											if model_class.__name__ != "TFMobileViTModel":
															A__            =     model_class(lowercase    )
															if getattr(lowercase , "hf_compute_loss" , lowercase    ):
																			super().test_keras_fit()


			def        UpperCamelCase						(      self    )					->  List[str]:
							'''simple docstring'''






							A__      ,			A__            =     self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
											# set an ignore index to correctly test the masked loss used in
											# `TFMobileViTForSemanticSegmentation`.
											if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
															A__            =     5

											A__            =     model_class(lowercase    )
											if getattr(lowercase , "hf_compute_loss" , lowercase    ):
															# The number of elements in the loss should be the same as the number of elements in the label
															A__            =     self._prepare_for_class(inputs_dict.copy() , lowercase , return_labels=lowercase    )
															A__            =     prepared_for_class[
															    sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=lowercase    )[0]
															]
															A__            =     added_label.shape.as_list()[:1]

															# Test that model correctly compute the loss with kwargs
															A__            =     self._prepare_for_class(inputs_dict.copy() , lowercase , return_labels=lowercase    )
															A__            =     {"input_ids", "pixel_values", "input_features"}
															A__            =     possible_input_names.intersection(set(lowercase    )    ).pop()
															A__            =     prepared_for_class.pop(lowercase    )

															A__            =     model(lowercase , **lowercase    )[0]
															self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]    )

															# Test that model correctly compute the loss when we mask some positions
															A__            =     self._prepare_for_class(inputs_dict.copy() , lowercase , return_labels=lowercase    )
															A__            =     {"input_ids", "pixel_values", "input_features"}
															A__            =     possible_input_names.intersection(set(lowercase    )    ).pop()
															A__            =     prepared_for_class.pop(lowercase    )
															if "labels" in prepared_for_class:
																			A__            =     prepared_for_class["labels"].numpy()
																			if len(labels.shape    ) > 1 and labels.shape[1] != 1:
																							# labels[0] = -100
																							A__            =     tf.convert_to_tensor(lowercase    )
																							A__            =     model(lowercase , **lowercase    )[0]
																							self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]    )
																							self.assertTrue(not np.any(np.isnan(loss.numpy()    )    )    )

                # Test that model correctly compute the loss with a dict
															A__            =     self._prepare_for_class(inputs_dict.copy() , lowercase , return_labels=lowercase    )
															A__            =     model(lowercase    )[0]
															self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]    )

															# Test that model correctly compute the loss with a tuple
															A__            =     self._prepare_for_class(inputs_dict.copy() , lowercase , return_labels=lowercase    )

															# Get keys that were added with the _prepare_for_class function
															A__            =     prepared_for_class.keys() - inputs_dict.keys()
															A__            =     inspect.signature(model.call    ).parameters
															A__            =     list(signature.keys()    )

															# Create a dictionary holding the location of the tensors in the tuple
															A__            =     {0: input_name}
															for label_key in label_keys:
																			A__            =     signature_names.index(lowercase    )
																			A__            =     label_key
															A__            =     sorted(tuple_index_mapping.items()    )
															# Initialize a list with their default values, update the values and convert to a tuple
															A__            =     []

															for name in signature_names:
																			if name != "kwargs":
																							list_input.append(signature[name].default    )

															for index, value in sorted_tuple_index_mapping:
																			A__            =     prepared_for_class[value]

															A__            =     tuple(lowercase    )

															# Send to model
															A__            =     model(tuple_input[:-1]    )[0]

															self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]    )


			@slow
			def        UpperCamelCase						(      self    )					->  List[Any]:
							'''simple docstring'''






							for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__            =     TFMobileViTModel.from_pretrained(lowercase    )
											self.assertIsNotNone(lowercase    )



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





@require_tf
class    a__  (   unittest.TestCase     ):
			"""simple docstring"""







			@slow
			def        UpperCamelCase						(      self    )					->  List[str]:
							'''simple docstring'''






							A__            =     TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"    )

							A__            =     MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small"    )
							A__            =     prepare_img()
							A__            =     image_processor(images=lowercase , return_tensors="tf"    )

							# forward pass
							A__            =     model(**lowercase , training=lowercase    )

							# verify the logits
							A__            =     tf.TensorShape((1, 1000)    )
							self.assertEqual(outputs.logits.shape , lowercase    )

							A__            =     tf.constant([-1.9364, -1.2327, -0.4653]    )

							tf.debugging.assert_near(outputs.logits[0, :3] , lowercase , atol=1e-4 , rtol=1e-04    )


			@slow
			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"    )

							A__            =     MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"    )

							A__            =     prepare_img()
							A__            =     image_processor(images=lowercase , return_tensors="tf"    )

							# forward pass
							A__            =     model(inputs.pixel_values , training=lowercase    )
							A__            =     outputs.logits

							# verify the logits
							A__            =     tf.TensorShape((1, 21, 32, 32)    )
							self.assertEqual(logits.shape , lowercase    )

							A__            =     tf.constant(
							    [
							        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
							        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
							        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
							    ]    )

							tf.debugging.assert_near(logits[0, :3, :3, :3] , lowercase , rtol=1e-4 , atol=1e-4    )




import math



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int )     -> bool:




				'''simple docstring'''





				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5		,    int(math.sqrt(SCREAMING_SNAKE_CASE_ ) + 1 )		,    6 ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int = 2_0_0_0_0_0_0 )     -> int:




				'''simple docstring'''





				return sum(num for num in range(3		,    SCREAMING_SNAKE_CASE_		,    2 ) if is_prime(SCREAMING_SNAKE_CASE_ ) ) + 2 if n > 2 else 0


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

