






from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 tensorflow as tf

   from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor




class     _lowerCAmelCase:





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase=1_3  ,      _lowerCamelCase=3_0  ,      _lowerCamelCase=2  ,      _lowerCamelCase=3  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=3_2  ,      _lowerCamelCase=2  ,      _lowerCamelCase=4  ,      _lowerCamelCase=3_7  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=1_0  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=3  ,      _lowerCamelCase=None  ,      ):
         UpperCamelCase_: str   				=    parent
         UpperCamelCase_: str   				=    batch_size
         UpperCamelCase_: int   				=    image_size
         UpperCamelCase_: Any   				=    patch_size
         UpperCamelCase_: Dict   				=    num_channels
         UpperCamelCase_: Dict   				=    is_training
         UpperCamelCase_: Any   				=    use_labels
         UpperCamelCase_: Optional[int]   				=    hidden_size
         UpperCamelCase_: str   				=    num_hidden_layers
         UpperCamelCase_: int   				=    num_attention_heads
         UpperCamelCase_: Optional[int]   				=    intermediate_size
         UpperCamelCase_: List[str]   				=    hidden_act
         UpperCamelCase_: Optional[Any]   				=    hidden_dropout_prob
         UpperCamelCase_: List[str]   				=    attention_probs_dropout_prob
         UpperCamelCase_: Tuple   				=    type_sequence_label_size
         UpperCamelCase_: Optional[int]   				=    initializer_range
         UpperCamelCase_: Tuple   				=    scope

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         UpperCamelCase_: Tuple   				=    (image_size // patch_size) ** 2
         UpperCamelCase_: List[str]   				=    num_patches + 1





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         UpperCamelCase_: List[Any]   				=    None
         if self.use_labels:
           UpperCamelCase_: str   				=    ids_tensor([self.batch_size]  ,      self.type_sequence_label_size							)

         UpperCamelCase_: List[Any]   				=    self.get_config()

         return config, pixel_values, labels





       def       _a   ( self							):
         return ViTConfig(
             image_size=self.image_size  ,      patch_size=self.patch_size  ,      num_channels=self.num_channels  ,      hidden_size=self.hidden_size  ,      num_hidden_layers=self.num_hidden_layers  ,      num_attention_heads=self.num_attention_heads  ,      intermediate_size=self.intermediate_size  ,      hidden_act=self.hidden_act  ,      hidden_dropout_prob=self.hidden_dropout_prob  ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,      is_decoder=_lowerCamelCase  ,      initializer_range=self.initializer_range  ,      )





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Union[str, Any]   				=    TFViTModel(config=_lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    model(_lowerCamelCase  ,      training=_lowerCamelCase							)
         self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)							)

         # Test with an image with different size than the one specified in config.
         UpperCamelCase_: int   				=    self.image_size // 2
         UpperCamelCase_: List[str]   				=    pixel_values[:, :, :image_size, :image_size]
         UpperCamelCase_: List[Any]   				=    model(_lowerCamelCase  ,      interpolate_pos_encoding=_lowerCamelCase  ,      training=_lowerCamelCase							)
         UpperCamelCase_: Optional[int]   				=    (image_size // self.patch_size) ** 2 + 1
         self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, seq_length, self.hidden_size)							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Dict   				=    self.type_sequence_label_size
         UpperCamelCase_: Tuple   				=    TFViTForImageClassification(_lowerCamelCase							)
         UpperCamelCase_: Optional[Any]   				=    model(_lowerCamelCase  ,      labels=_lowerCamelCase  ,      training=_lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.type_sequence_label_size)							)

         # Test with an image with different size than the one specified in config.
         UpperCamelCase_: List[Any]   				=    self.image_size // 2
         UpperCamelCase_: Union[str, Any]   				=    pixel_values[:, :, :image_size, :image_size]
         UpperCamelCase_: int   				=    model(_lowerCamelCase  ,      interpolate_pos_encoding=_lowerCamelCase  ,      training=_lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.type_sequence_label_size)							)

         # test greyscale images
         UpperCamelCase_: Optional[int]   				=    1
         UpperCamelCase_: Tuple   				=    TFViTForImageClassification(_lowerCamelCase							)
         UpperCamelCase_: str   				=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
         UpperCamelCase_: int   				=    model(_lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.type_sequence_label_size)							)





       def       _a   ( self							):
         UpperCamelCase_: List[str]   				=    self.prepare_config_and_inputs()
         UpperCamelCase_: List[Any]   				=    config_and_inputs
         UpperCamelCase_: int   				=    {'pixel_values': pixel_values}
         return config, inputs_dict







@require_tf
class     _lowerCAmelCase(	UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		List[str]									=(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
       a    :		Tuple									=(
           {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
           if is_tf_available()
           else {}
       )

       a    :		Tuple									=False
       a    :		int									=False
       a    :		int									=False





       def       _a   ( self							):
         UpperCamelCase_: Tuple   				=    TFViTModelTester(self							)
         UpperCamelCase_: List[str]   				=    ConfigTester(self  ,      config_class=_lowerCamelCase  ,      has_text_modality=_lowerCamelCase  ,      hidden_size=3_7							)





       def       _a   ( self							):
         self.config_tester.run_common_tests()





       @unittest.skip(reason='ViT does not use inputs_embeds'							)
       def       _a   ( self							):
         pass





       @unittest.skip(reason='ViT does not use inputs_embeds'							)
       def       _a   ( self							):
         pass





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           UpperCamelCase_: Optional[int]   				=    model_class(_lowerCamelCase							)
           self.assertIsInstance(model.get_input_embeddings()  ,      (tf.keras.layers.Layer)							)
           UpperCamelCase_: Union[str, Any]   				=    model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(_lowerCamelCase  ,      tf.keras.layers.Layer							)							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           UpperCamelCase_: Optional[int]   				=    model_class(_lowerCamelCase							)
           UpperCamelCase_: Optional[int]   				=    inspect.signature(model.call							)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCamelCase_: List[str]   				=    [*signature.parameters.keys()]

           UpperCamelCase_: List[str]   				=    ['pixel_values']
           self.assertListEqual(arg_names[:1]  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase							)





       @slow
       def       _a   ( self							):
         UpperCamelCase_: int   				=    TFViTModel.from_pretrained('google/vit-base-patch16-224'							)
         self.assertIsNotNone(_lowerCamelCase							)







def   snake_case ()	->    Optional[int]:






  """simple docstring"""




  UpperCamelCase_: Any   				=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
  return image




@require_tf
@require_vision
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       @cached_property
       def       _a   ( self							):
         return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'							) if is_vision_available() else None





       @slow
       def       _a   ( self							):
         UpperCamelCase_: str   				=    TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'							)

         UpperCamelCase_: str   				=    self.default_image_processor
         UpperCamelCase_: Any   				=    prepare_img()
         UpperCamelCase_: Optional[Any]   				=    image_processor(images=_lowerCamelCase  ,      return_tensors='tf'							)

         # forward pass
         UpperCamelCase_: Union[str, Any]   				=    model(**_lowerCamelCase							)

         # verify the logits
         UpperCamelCase_: List[Any]   				=    tf.TensorShape((1, 1_0_0_0)							)
         self.assertEqual(outputs.logits.shape  ,      _lowerCamelCase							)

         UpperCamelCase_: str   				=    tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]							)

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







import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def   snake_case (UpperCAmelCase__  )	->    tuple:
  return (data["data"], data["target"])

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    np.ndarray:
  UpperCamelCase_: Dict   				=    XGBRegressor(verbosity=0     ,   random_state=4_2  )
  xgb.fit(UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Predict target for test data
  UpperCamelCase_: int   				=    xgb.predict(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    predictions.reshape(len(UpperCAmelCase__  )     ,   1  )
  return predictions

def   snake_case ()	->    None:
  UpperCamelCase_: Union[str, Any]   				=    fetch_california_housing()
  UpperCamelCase_				,UpperCamelCase_: Tuple   				=    data_handling(UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_: Optional[Any]   				=    train_test_split(
      UpperCAmelCase__     ,   UpperCAmelCase__     ,   test_size=0.25     ,   random_state=1  )
  UpperCamelCase_: Union[str, Any]   				=    xgboost(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Error printing
  print(F'''Mean Absolute Error : {mean_absolute_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )
  print(F'''Mean Square Error  : {mean_squared_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()