code: 
  
  
  
  
  
  '''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      UpperCAmelCase			( UpperCamelCase__       ):
   def   UpperCAmelCase_							(       self   :List[str]							)->							Optional[int]:
       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      UpperCAmelCase			:
   def __init__(       self   :Optional[int]		, lowercase_   :Union[str, Any]		, lowercase_   :Union[str, Any]=13		, lowercase_   :str=32		, lowercase_   :str=2		, lowercase_   :Dict=3		, lowercase_   :Tuple=6_40		, lowercase_   :Optional[Any]=4		, lowercase_   :List[Any]="silu"		, lowercase_   :Optional[Any]=3		, lowercase_   :Union[str, Any]=32		, lowercase_   :List[str]=0.1		, lowercase_   :Union[str, Any]=0.1		, lowercase_   :Optional[Any]=0.1		, lowercase_   :Any=0.0_2		, lowercase_   :Any=True		, lowercase_   :Union[str, Any]=True		, lowercase_   :List[str]=10		, lowercase_   :int=None		, )->							Optional[Any]:
       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]							)->							Dict:
       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   :Tuple							)->							List[str]:
       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   :List[Any]		, lowercase_   :List[Any]		, lowercase_   :Optional[Any]		, lowercase_   :str		, lowercase_   :Optional[int]							)->							Optional[Any]:
       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   :Any		, lowercase_   :List[Any]		, lowercase_   :List[Any]		, lowercase_   :int		, lowercase_   :Optional[Any]							)->							int:
       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   :int		, lowercase_   :Any		, lowercase_   :Any		, lowercase_   :Optional[Any]		, lowercase_   :str							)->							str:
       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   :Optional[Any]							)->							int:
       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      UpperCAmelCase			( UpperCamelCase__    , UpperCamelCase__    , unittest.TestCase       ):
   __lowercase     	=       (
       (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
       if is_tf_available()
       else ()
   )
   __lowercase     	=       (
       {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
       if is_tf_available()
       else {}
   )
  
   __lowercase     	=       False
   __lowercase     	=       False
   __lowercase     	=       False
   __lowercase     	=       False
   __lowercase     	=       False
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							int:
       A__       =    TFMobileViTModelTester(self							)
       A__       =    TFMobileViTConfigTester(self		, config_class=lowercase_		, has_text_modality=lowercase_							)
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Dict:
       self.config_tester.run_common_tests()
   @unittest.skip(reason="MobileViT does not use inputs_embeds"							)
   def   UpperCAmelCase_							(       self   :str							)->							List[str]:
       pass
   @unittest.skip(reason="MobileViT does not support input and output embeddings"							)
   def   UpperCAmelCase_							(       self   :Any							)->							List[Any]:
       pass
   @unittest.skip(reason="MobileViT does not output attentions"							)
   def   UpperCAmelCase_							(       self   :str							)->							Tuple:
       pass
   def   UpperCAmelCase_							(       self   :str							)->							Tuple:
       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   :List[Any]							)->							Tuple:
       A__       =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_							)
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							List[str]:
       def check_hidden_states_output(lowercase_   :Union[str, Any]		, lowercase_   :List[Any]		, lowercase_   :Dict							):
           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   :Union[str, Any]							)->							List[Any]:
       A__       =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowercase_							)
   def   UpperCAmelCase_							(       self   :List[str]							)->							int:
       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]							)->							List[Any]:
       super().test_dataset_conversion()
   def   UpperCAmelCase_							(       self   :List[str]		, lowercase_   :Union[str, Any]		, lowercase_   :List[str]		, lowercase_   :Any=2E-1		, lowercase_   :Dict=2E-1							)->							List[Any]:
       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   :Optional[int]							)->							str:
       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   :Union[str, Any]							)->							Dict:
       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]							)->							str:
       for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           A__       =    TFMobileViTModel.from_pretrained(lowercase_							)
           self.assertIsNotNone(lowercase_							)
  
  
  
  
  
  def 		UpperCamelCase       (							):
      A__       =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
      return image
  
  
  
  
  
  
  @require_tf
  class      UpperCAmelCase			( unittest.TestCase       ):
   @slow
   def   UpperCAmelCase_							(       self   :Tuple							)->							str:
       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, 10_00)							)
       self.assertEqual(outputs.logits.shape		, lowercase_							)
  
       A__       =    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]		, lowercase_		, atol=1E-4		, rtol=1E-04							)
  
  
  
   @slow
   def   UpperCAmelCase_							(       self   :Optional[int]							)->							Optional[Any]:
       # `from_pt` will be removed
       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(
           [
               [[6.9_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]		, lowercase_		, rtol=1E-4		, atol=1E-4							)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import bisect
  import copy
  from collections import defaultdict
  
  import numpy as np
  from torch.utils.data import BatchSampler, Sampler
  
  from utils import logger
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      Optional[Any] ):
      A__       =    copy.deepcopy(_lowerCamelCase )
      A__       =    sorted(_lowerCamelCase )
      A__       =    [bisect.bisect_right(_lowerCamelCase		, _lowerCamelCase ) for y in x]
      return quantized
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int]		, _lowerCamelCase      :      Tuple=0 ):
      A__       =    np.arange(start=3		, stop=_lowerCamelCase		, step=4 ).tolist() if k > 0 else [10]
      A__       =    _quantize(_lowerCamelCase		, _lowerCamelCase )
      # count number of elements per group
      A__       =    np.unique(_lowerCamelCase		, return_counts=_lowerCamelCase )[1]
      A__       =    [0] + bins + [np.inf]
      logger.info("Using {} as bins for aspect lengths quantization".format(_lowerCamelCase ) )
      logger.info("Count of instances per bin: {}".format(_lowerCamelCase ) )
      return groups
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :Optional[Any]		, lowercase_   :List[Any]		, lowercase_   :Optional[int]		, lowercase_   :str							)->							Dict:
       if not isinstance(lowercase_		, lowercase_							):
           raise ValueError(
               "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(lowercase_							)							)
       A__       =    sampler
       A__       =    group_ids
       A__       =    batch_size
   def __iter__(       self   :int							)->							Tuple:
       A__       =    defaultdict(lowercase_							)
       A__       =    defaultdict(lowercase_							)
  
       A__       =    0
       for idx in self.sampler:
           A__       =    self.group_ids[idx]
           buffer_per_group[group_id].append(lowercase_							)
           samples_per_group[group_id].append(lowercase_							)
           if len(buffer_per_group[group_id]							) == self.batch_size:
               yield buffer_per_group[group_id]  # TODO
               num_batches += 1
               del buffer_per_group[group_id]
           assert len(buffer_per_group[group_id]							) < self.batch_size
  
       # now we have run out of elements that satisfy
       # the group criteria, let's return the remaining
       # elements so that the size of the sampler is
       # deterministic
       A__       =    len(self							)
       A__       =    expected_num_batches - num_batches
       if num_remaining > 0:
           # for the remaining batches, group the batches by similar lengths
           A__       =    []
           for group_id, idxs in sorted(buffer_per_group.items()		, key=lambda lowercase_							: x[0]							):
               batch_idx.extend(lowercase_							)
               if len(lowercase_							) >= self.batch_size:
                   yield batch_idx[: self.batch_size]
                   A__       =    batch_idx[self.batch_size :]
                   num_remaining -= 1
           if len(lowercase_							) > 0:
               yield batch_idx
               num_remaining -= 1
       assert num_remaining == 0
  
  
  
   def __len__(       self   :int							)->							Tuple:
       return (len(self.sampler							) + self.batch_size - 1) // self.batch_size
  
  
  
  