





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(    __UpperCAmelCase			):

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.config_class(**self.inputs_dict   )
            self.parent.assertTrue(hasattr(A,							'''hidden_sizes'''   )   )
            self.parent.assertTrue(hasattr(A,							'''neck_hidden_sizes'''   )   )
            self.parent.assertTrue(hasattr(A,							'''num_attention_heads'''   )   )






class 			__A:

     def __init__(   self,							A,							A=13,							A=32,							A=2,							A=3,							A=640,							A=4,							A="silu",							A=3,							A=32,							A=0.1,							A=0.1,							A=0.1,							A=0.02,							A=True,							A=True,							A=10,							A=None,							):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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

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

            _UpperCamelCase														=    self.get_config()

            return config, pixel_values, labels, pixel_labels

     def 						_UpperCamelCase  (   self   ):





            """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,							A,							A,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    TFMobileViTModel(config=A   )
            _UpperCamelCase														=    model(A,							training=A   )
            _UpperCamelCase														=    _UpperCamelCase														=    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,							A,							A,							A,							A   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self,							A,							A,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    self.num_labels
            _UpperCamelCase														=    TFMobileViTForSemanticSegmentation(A   )
            _UpperCamelCase														=    _UpperCamelCase														=    self.image_size // self.output_stride

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

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



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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






@require_tf
class 			__A(    __UpperCAmelCase    ,      __UpperCAmelCase    ,      unittest.TestCase			):
     __A							=							(
         (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
         if is_tf_available()
         else ()
     )
     __A							=							(
         {"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
         if is_tf_available()
         else {}
     )

     __A							=							False
     __A							=							False
     __A							=							False
     __A							=							False
     __A							=							False

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    TFMobileViTModelTester(self   )
            _UpperCamelCase														=    TFMobileViTConfigTester(self,							config_class=A,							has_text_modality=A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            self.config_tester.run_common_tests()

     @unittest.skip(reason='''MobileViT does not use inputs_embeds'''   )
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            pass

     @unittest.skip(reason='''MobileViT does not support input and output embeddings'''   )
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            pass

     @unittest.skip(reason='''MobileViT does not output attentions'''   )
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            pass

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase							,		_UpperCamelCase														=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _UpperCamelCase														=    model_class(A   )
                   _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],							A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            def check_hidden_states_output(A,							A,							A   ):
                   _UpperCamelCase														=    model_class(A   )

                   _UpperCamelCase														=    model(**self._prepare_for_class(A,							A   )   )

                   _UpperCamelCase														=    outputs.hidden_states

                   _UpperCamelCase														=    5
                   self.assertEqual(len(A   ),							A   )

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

            _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(A,							A,							A   )

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

                   check_hidden_states_output(A,							A,							A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*A   )

     @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   ):





            """simple docstring"""

            super().test_dataset_conversion()

     def 						_UpperCamelCase  (   self,							A,							A,							A=2E-1,							A=2E-1   ):





            """simple docstring"""

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

     @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   ):





            """simple docstring"""

            _UpperCamelCase							,		_UpperCamelCase														=    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":
                          _UpperCamelCase														=    model_class(A   )
                          if getattr(A,							'''hf_compute_loss''',							A   ):
                                 super().test_keras_fit()

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase							,		_UpperCamelCase														=    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":
                          _UpperCamelCase														=    5

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

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

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

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

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

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

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

                          _UpperCamelCase														=    tuple(A   )

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

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



     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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






def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
       return image

@require_tf
class 			__A(    unittest.TestCase			):

     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    TFMobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''   )

            _UpperCamelCase														=    MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small'''   )
            _UpperCamelCase														=    prepare_img()
            _UpperCamelCase														=    image_processor(images=A,							return_tensors='''tf'''   )

            # forward pass
            _UpperCamelCase														=    model(**A,							training=A   )

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

            _UpperCamelCase														=    tf.constant([-1.9_364, -1.2_327, -0.4_653]   )

            tf.debugging.assert_near(outputs.logits[0, :3],							A,							atol=1E-4,							rtol=1E-0_4   )



     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    TFMobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''   )

            _UpperCamelCase														=    MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''   )

            _UpperCamelCase														=    prepare_img()
            _UpperCamelCase														=    image_processor(images=A,							return_tensors='''tf'''   )

            # forward pass
            _UpperCamelCase														=    model(inputs.pixel_values,							training=A   )
            _UpperCamelCase														=    outputs.logits

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

            _UpperCamelCase														=    tf.constant(
                [
                    [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
                    [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
                    [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
                ]   )

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













import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowercase     :			Optional[int]     						=	{"""UserAgent""": UserAgent().random}
lowercase     :			Union[str, Any]     						=	"""https://www.mywaifulist.moe/random"""


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    requests.get(lowerCAmelCase     ,							headers=lowerCAmelCase       )
       with open(lowerCAmelCase     ,							'''wb'''       ) as file:
              file.write(image.content       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    BeautifulSoup(requests.get(lowerCAmelCase     ,							headers=lowerCAmelCase       ).text     ,							'''html.parser'''       )
       _UpperCamelCase														=    soup.find('''meta'''     ,							attrs={'''property''': '''og:title'''}       ).attrs['''content''']
       _UpperCamelCase														=    soup.find('''meta'''     ,							attrs={'''property''': '''og:image'''}       ).attrs['''content''']
       _UpperCamelCase														=    soup.find('''p'''     ,							id='''description'''       ).get_text()
       _UpperCamelCase							,		_UpperCamelCase														=    os.path.splitext(os.path.basename(lowerCAmelCase       )       )
       _UpperCamelCase														=    title.strip().replace(''' '''     ,							'''_'''       )
       _UpperCamelCase														=    f'''{image_title}{image_extension}'''
       save_image(lowerCAmelCase     ,							lowerCAmelCase       )
       return (title, description, image_title)


if __name__ == "__main__":
       lowercase						,lowercase						,lowercase     :			str     						=	random_anime_character()
       print(F'''{title}\n\n{desc}\n\nImage saved : {image_title}''')






