
import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
    from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image




class      __A:

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=13	,      SCREAMING_SNAKE_CASE_=64	,      SCREAMING_SNAKE_CASE_=2	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=32	,      SCREAMING_SNAKE_CASE_=5	,      SCREAMING_SNAKE_CASE_=4	,      SCREAMING_SNAKE_CASE_=37	,      SCREAMING_SNAKE_CASE_="gelu"	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=10	,      SCREAMING_SNAKE_CASE_=0.02	,      SCREAMING_SNAKE_CASE_=[1, 16, 4, 4]	,      SCREAMING_SNAKE_CASE_=None	,      ):
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    image_size
       UpperCamelCase__				=    patch_size
       UpperCamelCase__				=    num_channels
       UpperCamelCase__				=    is_training
       UpperCamelCase__				=    use_labels
       UpperCamelCase__				=    hidden_size
       UpperCamelCase__				=    num_hidden_layers
       UpperCamelCase__				=    num_attention_heads
       UpperCamelCase__				=    intermediate_size
       UpperCamelCase__				=    hidden_act
       UpperCamelCase__				=    hidden_dropout_prob
       UpperCamelCase__				=    attention_probs_dropout_prob
       UpperCamelCase__				=    type_sequence_label_size
       UpperCamelCase__				=    initializer_range
       UpperCamelCase__				=    scope
       UpperCamelCase__				=    backbone_featmap_shape

       # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       # the number of patches is based on the feature map of the backbone, which by default uses an output stride
       # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
       UpperCamelCase__				=    (self.image_size // 32) ** 2
       UpperCamelCase__				=    num_patches + 1







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       UpperCamelCase__				=    None
       if self.use_labels:
            UpperCamelCase__				=    ids_tensor([self.batch_size]	,      self.type_sequence_label_size						)

       UpperCamelCase__				=    self.get_config()

       return config, pixel_values, labels







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    {
           """global_padding""": """same""",
           """layer_type""": """bottleneck""",
           """depths""": [3, 4, 9],
           """out_features""": ["""stage1""", """stage2""", """stage3"""],
           """embedding_dynamic_padding""": True,
           """hidden_sizes""": [4, 8, 16, 32],
           """num_groups""": 2,
       }

       return ViTHybridConfig(
           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=SCREAMING_SNAKE_CASE_	,      initializer_range=self.initializer_range	,      backbone_featmap_shape=self.backbone_featmap_shape	,      backbone_config=SCREAMING_SNAKE_CASE_	,      )







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    ViTHybridModel(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.type_sequence_label_size
       UpperCamelCase__				=    ViTHybridForImageClassification(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_	,      labels=SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size)						)







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






@require_torch
class      __A(    __lowerCamelCase    ,				__lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE__         =      (
      {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE__         =      False
  SCREAMING_SNAKE_CASE__         =      False
  SCREAMING_SNAKE_CASE__         =      False







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    ViTHybridModelTester(self						)
       UpperCamelCase__				=    ConfigTester(self	,      config_class=SCREAMING_SNAKE_CASE_	,      has_text_modality=SCREAMING_SNAKE_CASE_	,      hidden_size=37						)







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







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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            self.assertIsInstance(model.get_input_embeddings()	,      (nn.Module)						)
            UpperCamelCase__				=    model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_	,      nn.Linear						)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

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

            UpperCamelCase__				=    ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase__				=    _config_zero_init(SCREAMING_SNAKE_CASE_						)
       for model_class in self.all_model_classes:
            UpperCamelCase__				=    model_class(config=SCREAMING_SNAKE_CASE_						)
            # Skip the check for the backbone
            for name, module in model.named_modules():
                 if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                      UpperCamelCase__				=    [F"{name}.{key}" for key in module.state_dict().keys()]
                      break

            for name, param in model.named_parameters():
                 if param.requires_grad:
                      if name in backbone_params:
                           continue
                      self.assertIn(
                          ((param.data.mean() * 1E9).round() / 1E9).item()	,      [0.0, 1.0]	,      msg=F"Parameter {name} of model {model_class} seems not properly initialized"	,      )







  @slow
  def 	UpperCAmelCase_ (self						):
       for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCamelCase__				=    ViTHybridModel.from_pretrained(SCREAMING_SNAKE_CASE_						)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_						)






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
     return image




@require_torch
@require_vision
class      __A(    unittest.TestCase			):

  """simple docstring"""







  @cached_property
  def 	UpperCAmelCase_ (self						):
       return (
           ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
           if is_vision_available()
           else None
       )







  @slow
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
           SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    self.default_image_processor
       UpperCamelCase__				=    prepare_img()
       UpperCamelCase__				=    image_processor(images=SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""						).to(SCREAMING_SNAKE_CASE_						)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__				=    model(**SCREAMING_SNAKE_CASE_						)

       # verify the logits
       UpperCamelCase__				=    torch.Size((1, 10_00)						)
       self.assertEqual(outputs.logits.shape	,      SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    torch.tensor([-1.9090, -0.4993, -0.2389]						).to(SCREAMING_SNAKE_CASE_						)

       self.assertTrue(torch.allclose(outputs.logits[0, :3]	,      SCREAMING_SNAKE_CASE_	,      atol=1E-4						)						)







  @slow
  @require_accelerate
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    ViTHybridImageProcessor.from_pretrained("""google/vit-hybrid-base-bit-384"""						)
       UpperCamelCase__				=    ViTHybridForImageClassification.from_pretrained("""google/vit-hybrid-base-bit-384"""	,      device_map="""auto"""						)

       UpperCamelCase__				=    prepare_img()

       UpperCamelCase__				=    image_processor(images=SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""						)
       UpperCamelCase__				=    model(**SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    outputs.logits
       # model predicts one of the 1000 ImageNet classes
       UpperCamelCase__				=    logits.argmax(-1						).item()

       self.assertTrue(model.config.idalabel[predicted_class_idx]	,      """tabby, tabby cat"""						)







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 						__magic_name__			(   __a      :	dict						):



     '''simple docstring'''




     return (data["data"], data["target"])






def 						__magic_name__			(   __a      :	np.ndarray	,		__a      :	np.ndarray	,		__a      :	np.ndarray						):



     '''simple docstring'''




     UpperCamelCase__				=    XGBRegressor(verbosity=0	,		random_state=42						)
     xgb.fit(__a	,		__a						)
     # Predict target for test data
     UpperCamelCase__				=    xgb.predict(__a						)
     UpperCamelCase__				=    predictions.reshape(len(__a						)	,		1						)
     return predictions






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    fetch_california_housing()
     UpperCamelCase__		,	UpperCamelCase__				=    data_handling(__a						)
     UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    train_test_split(
         __a	,		__a	,		test_size=0.25	,		random_state=1						)
     UpperCamelCase__				=    xgboost(__a	,		__a	,		__a						)
     # Error printing
     print(f"Mean Absolute Error : {mean_absolute_error(__a	,		__a						)}"						)
     print(f"Mean Square Error  : {mean_squared_error(__a	,		__a						)}"						)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    main()





