




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        UpperCamelCase_					:

    '''simple docstring'''






    def __init__(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			Tuple=13      ,				UpperCAmelCase__   :			List[str]=64      ,				UpperCAmelCase__   :			int=2      ,				UpperCAmelCase__   :			Dict=3      ,				UpperCAmelCase__   :			Any=True      ,				UpperCAmelCase__   :			Dict=True      ,				UpperCAmelCase__   :			Any=32      ,				UpperCAmelCase__   :			Tuple=5      ,				UpperCAmelCase__   :			List[Any]=4      ,				UpperCAmelCase__   :			List[str]=37      ,				UpperCAmelCase__   :			List[Any]="gelu"      ,				UpperCAmelCase__   :			int=0.1      ,				UpperCAmelCase__   :			int=0.1      ,				UpperCAmelCase__   :			Optional[int]=10      ,				UpperCAmelCase__   :			List[str]=0.02      ,				UpperCAmelCase__   :			str=[1, 16, 4, 4]      ,				UpperCAmelCase__   :			str=None      ,				)		->List[Any]:





           '''simple docstring'''

           A__   				=					parent
           A__   				=					batch_size
           A__   				=					image_size
           A__   				=					patch_size
           A__   				=					num_channels
           A__   				=					is_training
           A__   				=					use_labels
           A__   				=					hidden_size
           A__   				=					num_hidden_layers
           A__   				=					num_attention_heads
           A__   				=					intermediate_size
           A__   				=					hidden_act
           A__   				=					hidden_dropout_prob
           A__   				=					attention_probs_dropout_prob
           A__   				=					type_sequence_label_size
           A__   				=					initializer_range
           A__   				=					scope
           A__   				=					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
           A__   				=					(self.image_size // 32) ** 2
           A__   				=					num_patches + 1


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Optional[int]:





           '''simple docstring'''

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

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

           A__   				=					self.get_config()

           return config, pixel_values, labels


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Optional[Any]:





           '''simple docstring'''

           A__   				=					{
               '''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=__UpperCamelCase      ,				initializer_range=self.initializer_range      ,				backbone_featmap_shape=self.backbone_featmap_shape      ,				backbone_config=__UpperCamelCase      ,				)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			Tuple      ,				UpperCAmelCase__   :			Tuple)		->List[str]:





           '''simple docstring'''

           A__   				=					ViTHybridModel(config=__UpperCamelCase)
           model.to(__UpperCamelCase)
           model.eval()
           A__   				=					model(__UpperCamelCase)
           self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, self.seq_length, self.hidden_size))


    def  SCREAMING_SNAKE_CASE							(    self   :			Any      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Dict)		->Dict:





           '''simple docstring'''

           A__   				=					self.type_sequence_label_size
           A__   				=					ViTHybridForImageClassification(__UpperCamelCase)
           model.to(__UpperCamelCase)
           model.eval()
           A__   				=					model(__UpperCamelCase      ,				labels=__UpperCamelCase)
           self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.type_sequence_label_size))





    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Tuple:





           '''simple docstring'''

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



@require_torch
class        UpperCamelCase_					(				_UpperCAmelCase	,      _UpperCAmelCase	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
    UpperCAmelCase__													=   (
        {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
        if is_torch_available()
        else {}
    )
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Optional[int]:





           '''simple docstring'''

           A__   				=					ViTHybridModelTester(self)
           A__   				=					ConfigTester(self      ,				config_class=__UpperCamelCase      ,				has_text_modality=__UpperCamelCase      ,				hidden_size=37)


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Optional[int]:





           '''simple docstring'''

           self.config_tester.run_common_tests()


    @unittest.skip(reason='''ViT does not use inputs_embeds''')
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Optional[Any]:





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->int:





           '''simple docstring'''

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Optional[int]:





           '''simple docstring'''

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

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

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


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Any:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__UpperCamelCase)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Optional[int]:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase)


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Tuple:





           '''simple docstring'''

           A__				,							A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

           A__   				=					_config_zero_init(__UpperCamelCase)
           for model_class in self.all_model_classes:
                  A__   				=					model_class(config=__UpperCamelCase)
                  # Skip the check for the backbone
                  for name, module in model.named_modules():
                         if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                                A__   				=					[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  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Optional[Any]:





           '''simple docstring'''

           for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__   				=					ViTHybridModel.from_pretrained(__UpperCamelCase)
                  self.assertIsNotNone(__UpperCamelCase)







def SCREAMING_SNAKE_CASE       ( )     -> Optional[int]:



       """simple docstring"""

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



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

    '''simple docstring'''






    @cached_property
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->List[Any]:





           '''simple docstring'''

           return (
               ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
               if is_vision_available()
               else None
           )


    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->int:





           '''simple docstring'''

           A__   				=					ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
               __UpperCamelCase)

           A__   				=					self.default_image_processor
           A__   				=					prepare_img()
           A__   				=					image_processor(images=__UpperCamelCase      ,				return_tensors='''pt''').to(__UpperCamelCase)

           # forward pass
           with torch.no_grad():
                  A__   				=					model(**__UpperCamelCase)

           # verify the logits
           A__   				=					torch.Size((1, 1_000))
           self.assertEqual(outputs.logits.shape      ,				__UpperCamelCase)

           A__   				=					torch.tensor([-1.9090, -0.4993, -0.2389]).to(__UpperCamelCase)

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





    @slow
    @require_accelerate
    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->List[str]:





           '''simple docstring'''

           A__   				=					ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''')
           A__   				=					ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384'''      ,				device_map='''auto''')

           A__   				=					prepare_img()

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

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








# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Dict:



       """simple docstring"""

       return 1 / (1 + np.exp(-z		))







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Optional[int]:



       """simple docstring"""

       return (-y * np.log(lowercase_		) - (1 - y) * np.log(1 - h		)).mean()







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					np.dot(lowercase_				,       lowercase_		)
       return np.sum(y * scores - np.log(1 + np.exp(lowercase_		)		)		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_=70_000		)     -> Dict:



       """simple docstring"""

       A__   				=					np.zeros(x.shape[1]		)

       for iterations in range(lowercase_		):
              A__   				=					np.dot(lowercase_				,       lowercase_		)
              A__   				=					sigmoid_function(lowercase_		)
              A__   				=					np.dot(x.T				,       h - y		) / y.size
              A__   				=					theta - alpha * gradient  # updating the weights
              A__   				=					np.dot(lowercase_				,       lowercase_		)
              A__   				=					sigmoid_function(lowercase_		)
              A__   				=					cost_function(lowercase_				,       lowercase_		)
              if iterations % 100 == 0:
                     print(f"""loss: {j} \t"""		)  # printing the loss after every 100 iterations
       return theta


# In[68]:

if __name__ == "__main__":
  _lowerCamelCase			:	str							 =     datasets.load_iris()
  _lowerCamelCase			:	Any							 =     iris.data[:, :2]
  _lowerCamelCase			:	Dict							 =     (iris.target != 0) * 1

  _lowerCamelCase			:	Union[str, Any]							 =     0.1
  _lowerCamelCase			:	Tuple							 =     logistic_reg(alpha, x, y, max_iterations=70000)
  print("""theta: """, theta)  # printing the theta i.e our weights vector







  def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



         """simple docstring"""

         return sigmoid_function(
             np.dot(lowercase_				,       lowercase_		)		)  # predicting the value of probability from the logistic regression algorithm

  plt.figure(figsize=(10, 6))
  plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
  plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	int							 =     (x[:, 0].min(), x[:, 0].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	Union[str, Any]							 =     (x[:, 1].min(), x[:, 1].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	List[Any]							 =     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  _lowerCamelCase			:	str							 =     np.c_[xxa.ravel(), xxa.ravel()]
  _lowerCamelCase			:	Tuple							 =     predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

  plt.legend()
  plt.show()


