




'''simple docstring'''



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 	lowercase_					(				UpperCamelCase_     ):


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.config_class(**self.inputs_dict					)
        self.parent.assertTrue(hasattr(__UpperCamelCase						,   "hidden_sizes"					)					)
        self.parent.assertTrue(hasattr(__UpperCamelCase						,   "neck_hidden_sizes"					)					)
        self.parent.assertTrue(hasattr(__UpperCamelCase						,   "num_attention_heads"					)					)







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			int						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			int=1_3						,   __lowerCamelCase      :			List[str]=3_2						,   __lowerCamelCase      :			List[str]=2						,   __lowerCamelCase      :			Optional[int]=3						,   __lowerCamelCase      :			Union[str, Any]=6_4_0						,   __lowerCamelCase      :			Dict=4						,   __lowerCamelCase      :			str="silu"						,   __lowerCamelCase      :			List[str]=3						,   __lowerCamelCase      :			Union[str, Any]=3_2						,   __lowerCamelCase      :			List[Any]=0.1						,   __lowerCamelCase      :			Union[str, Any]=0.1						,   __lowerCamelCase      :			Union[str, Any]=0.1						,   __lowerCamelCase      :			str=0.0_2						,   __lowerCamelCase      :			Dict=True						,   __lowerCamelCase      :			Optional[Any]=True						,   __lowerCamelCase      :			int=1_0						,   __lowerCamelCase      :			int=None						,   ):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   parent
        _SCREAMING_SNAKE_CASE =   batch_size
        _SCREAMING_SNAKE_CASE =   image_size
        _SCREAMING_SNAKE_CASE =   patch_size
        _SCREAMING_SNAKE_CASE =   num_channels
        _SCREAMING_SNAKE_CASE =   last_hidden_size
        _SCREAMING_SNAKE_CASE =   num_attention_heads
        _SCREAMING_SNAKE_CASE =   hidden_act
        _SCREAMING_SNAKE_CASE =   conv_kernel_size
        _SCREAMING_SNAKE_CASE =   output_stride
        _SCREAMING_SNAKE_CASE =   hidden_dropout_prob
        _SCREAMING_SNAKE_CASE =   attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE =   classifier_dropout_prob
        _SCREAMING_SNAKE_CASE =   use_labels
        _SCREAMING_SNAKE_CASE =   is_training
        _SCREAMING_SNAKE_CASE =   num_labels
        _SCREAMING_SNAKE_CASE =   initializer_range
        _SCREAMING_SNAKE_CASE =   scope



   def 							lowerCAmelCase_    (					self      :			Dict					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

        _SCREAMING_SNAKE_CASE =   self.get_config()

        return config, pixel_values, labels, pixel_labels



   def 							lowerCAmelCase_    (					self      :			Any					):

        """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 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			Any						,   __lowerCamelCase      :			Optional[int]						,   __lowerCamelCase      :			Tuple					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   TFMobileViTModel(config=__UpperCamelCase					)
        _SCREAMING_SNAKE_CASE =   model(__UpperCamelCase						,   training=__UpperCamelCase					)
        _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			List[Any]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.num_labels
        _SCREAMING_SNAKE_CASE =   TFMobileViTForImageClassification(__UpperCamelCase					)
        _SCREAMING_SNAKE_CASE =   model(__UpperCamelCase						,   labels=__UpperCamelCase						,   training=__UpperCamelCase					)
        self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_labels)					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			List[str]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.num_labels
        _SCREAMING_SNAKE_CASE =   TFMobileViTForSemanticSegmentation(__UpperCamelCase					)
        _SCREAMING_SNAKE_CASE =   self.image_size // self.output_stride

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

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



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.prepare_config_and_inputs()
        _SCREAMING_SNAKE_CASE =   config_and_inputs
        _SCREAMING_SNAKE_CASE =   {'''pixel_values''': pixel_values}
        return config, inputs_dict







@require_tf
class 	lowercase_					(				UpperCamelCase_  ,						UpperCamelCase_  ,						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 							lowerCAmelCase_    (					self      :			Any					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   TFMobileViTModelTester(self					)
        _SCREAMING_SNAKE_CASE =   TFMobileViTConfigTester(self						,   config_class=__UpperCamelCase						,   has_text_modality=__UpperCamelCase					)



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

        """simple docstring"""
        self.config_tester.run_common_tests()



   @unittest.skip(reason="MobileViT does not use inputs_embeds"					)
   def 							lowerCAmelCase_    (					self      :			int					):

        """simple docstring"""
        pass



   @unittest.skip(reason="MobileViT does not support input and output embeddings"					)
   def 							lowerCAmelCase_    (					self      :			Any					):

        """simple docstring"""
        pass



   @unittest.skip(reason="MobileViT does not output attentions"					)
   def 							lowerCAmelCase_    (					self      :			Dict					):

        """simple docstring"""
        pass



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs_for_common()

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

             _SCREAMING_SNAKE_CASE =   ['''pixel_values''']
             self.assertListEqual(arg_names[:1]						,   __UpperCamelCase					)



   def 							lowerCAmelCase_    (					self      :			int					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

        """simple docstring"""


        def check_hidden_states_output(__lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Any					):
             _SCREAMING_SNAKE_CASE =   model_class(__UpperCamelCase					)

             _SCREAMING_SNAKE_CASE =   model(**self._prepare_for_class(__UpperCamelCase						,   __UpperCamelCase					)					)

             _SCREAMING_SNAKE_CASE =   outputs.hidden_states

             _SCREAMING_SNAKE_CASE =   5
             self.assertEqual(len(__UpperCamelCase					)						,   __UpperCamelCase					)

             # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
             # with the width and height being successively divided by 2.
             _SCREAMING_SNAKE_CASE =   2
             for i in range(len(__UpperCamelCase					)					):
                  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					)

        _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             _SCREAMING_SNAKE_CASE =   True
             check_hidden_states_output(__UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase					)

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

             check_hidden_states_output(__UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase					)



   def 							lowerCAmelCase_    (					self      :			int					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Any					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase					)



   @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 							lowerCAmelCase_    (					self      :			str					):

        """simple docstring"""
        super().test_dataset_conversion()



   def 							lowerCAmelCase_    (					self      :			str						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			Any=2e-1						,   __lowerCamelCase      :			Union[str, Any]=2e-1					):

        """simple docstring"""
        self.assertTrue(np.allclose(__UpperCamelCase						,   __UpperCamelCase						,   atol=__UpperCamelCase						,   rtol=__UpperCamelCase					)					)



   @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 							lowerCAmelCase_    (					self      :			List[Any]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   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":
                  _SCREAMING_SNAKE_CASE =   model_class(__UpperCamelCase					)
                  if getattr(__UpperCamelCase						,   "hf_compute_loss"						,   __UpperCamelCase					):
                       super().test_keras_fit()



   def 							lowerCAmelCase_    (					self      :			str					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   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":
                  _SCREAMING_SNAKE_CASE =   5

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

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

                  _SCREAMING_SNAKE_CASE =   model(__UpperCamelCase						,   **__UpperCamelCase					)[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
                  _SCREAMING_SNAKE_CASE =   self._prepare_for_class(inputs_dict.copy()						,   __UpperCamelCase						,   return_labels=__UpperCamelCase					)
                  _SCREAMING_SNAKE_CASE =   {'''input_ids''', '''pixel_values''', '''input_features'''}
                  _SCREAMING_SNAKE_CASE =   possible_input_names.intersection(set(__UpperCamelCase					)					).pop()
                  _SCREAMING_SNAKE_CASE =   prepared_for_class.pop(__UpperCamelCase					)
                  if "labels" in prepared_for_class:
                       _SCREAMING_SNAKE_CASE =   prepared_for_class['''labels'''].numpy()
                       if len(labels.shape					) > 1 and labels.shape[1] != 1:
                            # labels[0] = -100
                            _SCREAMING_SNAKE_CASE =   tf.convert_to_tensor(__UpperCamelCase					)
                            _SCREAMING_SNAKE_CASE =   model(__UpperCamelCase						,   **__UpperCamelCase					)[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
                  _SCREAMING_SNAKE_CASE =   self._prepare_for_class(inputs_dict.copy()						,   __UpperCamelCase						,   return_labels=__UpperCamelCase					)
                  _SCREAMING_SNAKE_CASE =   model(__UpperCamelCase					)[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
                  _SCREAMING_SNAKE_CASE =   self._prepare_for_class(inputs_dict.copy()						,   __UpperCamelCase						,   return_labels=__UpperCamelCase					)

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

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

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

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

                  _SCREAMING_SNAKE_CASE =   tuple(__UpperCamelCase					)

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

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



   @slow
   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

        """simple docstring"""


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


def 				SCREAMING_SNAKE_CASE_				(							)				->				Optional[int]:
     _SCREAMING_SNAKE_CASE =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
     return image







@require_tf
class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   @slow
   def 							lowerCAmelCase_    (					self      :			Any					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"					)

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

        # forward pass
        _SCREAMING_SNAKE_CASE =   model(**__UpperCamelCase						,   training=__UpperCamelCase					)

        # verify the logits
        _SCREAMING_SNAKE_CASE =   tf.TensorShape((1, 1_0_0_0)					)
        self.assertEqual(outputs.logits.shape						,   __UpperCamelCase					)

        _SCREAMING_SNAKE_CASE =   tf.constant([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]					)

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



   @slow
   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

        """simple docstring"""
        _SCREAMING_SNAKE_CASE =   TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"					)

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

        _SCREAMING_SNAKE_CASE =   prepare_img()
        _SCREAMING_SNAKE_CASE =   image_processor(images=__UpperCamelCase						,   return_tensors="tf"					)

        # forward pass
        _SCREAMING_SNAKE_CASE =   model(inputs.pixel_values						,   training=__UpperCamelCase					)
        _SCREAMING_SNAKE_CASE =   outputs.logits

        # verify the logits
        _SCREAMING_SNAKE_CASE =   tf.TensorShape((1, 2_1, 3_2, 3_2)					)
        self.assertEqual(logits.shape						,   __UpperCamelCase					)

        _SCREAMING_SNAKE_CASE =   tf.constant(
            [
                [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
                [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
                [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
            ]					)

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







'''simple docstring'''



import math
def 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple						)				->				List[Any]:
   _SCREAMING_SNAKE_CASE =   0
   if n % 2 == 0:
      count += 1
      while n % 2 == 0:
         _SCREAMING_SNAKE_CASE =   int(n / 2						)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

   _SCREAMING_SNAKE_CASE =   3

   while i <= int(math.sqrt(__A						)						):
      if n % i == 0:
         count += 1
         while n % i == 0:
            _SCREAMING_SNAKE_CASE =   int(n / i						)
      _SCREAMING_SNAKE_CASE =   i + 2

   # this condition checks the prime
   # number n is greater than 2

   if n > 2:
      count += 1
   return count


if __name__ == "__main__":
 lowerCamelCase_	 =		51_24_21_83
 print(f'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
 print(f'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')

