






"""simple docstring"""







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  lowerCamelCase__ :






    def __init__(			self     ,A     ,A=13     ,A=64     ,A=2     ,A=3     ,A=True     ,A=True     ,A=32     ,A=5     ,A=4     ,A=37     ,A="gelu"     ,A=0.1     ,A=0.1     ,A=10     ,A=0.02     ,A=[1, 16, 4, 4]     ,A=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=A     ,initializer_range=self.initializer_range     ,backbone_featmap_shape=self.backbone_featmap_shape     ,backbone_config=A     ,)






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          UpperCAmelCase          =	ViTHybridModel(config=A )
          model.to(A )
          model.eval()
          UpperCAmelCase          =	model(A )
          self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size) )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          UpperCAmelCase          =	self.type_sequence_label_size
          UpperCAmelCase          =	ViTHybridForImageClassification(A )
          model.to(A )
          model.eval()
          UpperCAmelCase          =	model(A     ,labels=A )
          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  lowerCamelCase__ (	snake_case   ,					snake_case   ,					unittest.TestCase			):
    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=A     ,has_text_modality=A     ,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(A )
                self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module) )
                UpperCAmelCase          =	model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(A     ,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(A )
                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]     ,A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase      , UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs_for_common()

          UpperCAmelCase          =	_config_zero_init(A )
          for model_class in self.all_model_classes:
                UpperCAmelCase          =	model_class(config=A )
                # 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(A )
                self.assertIsNotNone(A )







def _a			(		):







      """simple docstring"""






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







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






    @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(
              A )

          UpperCAmelCase          =	self.default_image_processor
          UpperCAmelCase          =	prepare_img()
          UpperCAmelCase          =	image_processor(images=A     ,return_tensors="""pt""" ).to(A )

          # forward pass
          with torch.no_grad():
                UpperCAmelCase          =	model(**A )

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

          UpperCAmelCase          =	torch.tensor([-1.9090, -0.4993, -0.2389] ).to(A )

          self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,A     ,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=A     ,return_tensors="""pt""" )
          UpperCAmelCase          =	model(**A )
          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""" )











"""simple docstring"""







import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_UpperCamelCase					      =  pd.read_csv(
    """https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"""
    """position_salaries.csv"""
)
_UpperCamelCase					      =  dataset.iloc[:, 1:2].values
_UpperCamelCase					      =  dataset.iloc[:, 2].values


_UpperCamelCase      ,  _UpperCamelCase      ,  _UpperCamelCase      ,  _UpperCamelCase					      =  train_test_split(X, y, test_size=0.2, random_state=0)


_UpperCamelCase					      =  PolynomialFeatures(degree=4)
_UpperCamelCase					      =  poly_reg.fit_transform(X)
_UpperCamelCase					      =  LinearRegression()
pol_reg.fit(X_poly, y)

def _a			(		):







      """simple docstring"""






      plt.scatter(_snake_case				,	_snake_case				,	color="""red"""							)
      plt.plot(_snake_case				,	pol_reg.predict(poly_reg.fit_transform(_snake_case							)							)				,	color="""blue"""							)
      plt.title("""Truth or Bluff (Linear Regression)"""							)
      plt.xlabel("""Position level"""							)
      plt.ylabel("""Salary"""							)
      plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003



