





import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
       import numpy as np
       import torch

if is_vision_available():
       from PIL import Image


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase=False     ,							lowerCAmelCase=False     ,							lowerCAmelCase=False       ):
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       _UpperCamelCase														=    []
       for i in range(image_processor_tester.batch_size       ):
              if equal_resolution:
                     _UpperCamelCase														=    _UpperCamelCase														=    image_processor_tester.max_resolution
              else:
                     # To avoid getting image width/height 0
                     _UpperCamelCase														=    image_processor_tester.min_resolution
                     if getattr(lowerCAmelCase     ,							'''size_divisor'''     ,							lowerCAmelCase       ):
                            # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                            _UpperCamelCase														=    max(image_processor_tester.size_divisor     ,							lowerCAmelCase       )
                     _UpperCamelCase							,		_UpperCamelCase														=    np.random.choice(np.arange(lowerCAmelCase     ,							image_processor_tester.max_resolution       )     ,							2       )
              image_inputs.append(
                  np.random.randint(255     ,							size=(image_processor_tester.num_channels, width, height)     ,							dtype=np.uinta       )       )

       if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              _UpperCamelCase														=    [Image.fromarray(np.moveaxis(lowerCAmelCase     ,							0     ,							-1       )       ) for image in image_inputs]

       if torchify:
              _UpperCamelCase														=    [torch.from_numpy(lowerCAmelCase       ) for image in image_inputs]

       return image_inputs


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase=10     ,							lowerCAmelCase=10     ,							lowerCAmelCase=False     ,							lowerCAmelCase=False       ):
       _UpperCamelCase														=    []
       for i in range(image_processor_tester.num_frames       ):
              video.append(np.random.randint(255     ,							size=(image_processor_tester.num_channels, width, height)     ,							dtype=np.uinta       )       )

       if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              _UpperCamelCase														=    [Image.fromarray(np.moveaxis(lowerCAmelCase     ,							0     ,							-1       )       ) for frame in video]

       if torchify:
              _UpperCamelCase														=    [torch.from_numpy(lowerCAmelCase       ) for frame in video]

       return video


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase=False     ,							lowerCAmelCase=False     ,							lowerCAmelCase=False       ):
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       _UpperCamelCase														=    []
       for i in range(image_processor_tester.batch_size       ):
              if equal_resolution:
                     _UpperCamelCase														=    _UpperCamelCase														=    image_processor_tester.max_resolution
              else:
                     _UpperCamelCase							,		_UpperCamelCase														=    np.random.choice(
                         np.arange(image_processor_tester.min_resolution     ,							image_processor_tester.max_resolution       )     ,							2       )
                     _UpperCamelCase														=    prepare_video(
                         image_processor_tester=lowerCAmelCase     ,							width=lowerCAmelCase     ,							height=lowerCAmelCase     ,							numpify=lowerCAmelCase     ,							torchify=lowerCAmelCase     ,							)
              video_inputs.append(lowerCAmelCase       )

       return video_inputs

class 			__A:
     __A							=							None

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.image_processing_class(**self.image_processor_dict   )
            _UpperCamelCase														=    json.loads(image_processor.to_json_string()   )
            for key, value in self.image_processor_dict.items():
                   self.assertEqual(obj[key],							A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.image_processing_class(**self.image_processor_dict   )

            with tempfile.TemporaryDirectory() as tmpdirname:
                   _UpperCamelCase														=    os.path.join(A,							'''image_processor.json'''   )
                   image_processor_first.to_json_file(A   )
                   _UpperCamelCase														=    self.image_processing_class.from_json_file(A   )

            self.assertEqual(image_processor_second.to_dict(),							image_processor_first.to_dict()   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.image_processing_class(**self.image_processor_dict   )

            with tempfile.TemporaryDirectory() as tmpdirname:
                   _UpperCamelCase														=    image_processor_first.save_pretrained(A   )[0]
                   check_json_file_has_correct_format(A   )
                   _UpperCamelCase														=    self.image_processing_class.from_pretrained(A   )

            self.assertEqual(image_processor_second.to_dict(),							image_processor_first.to_dict()   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.image_processing_class()
            self.assertIsNotNone(A   )



     @require_torch
     @require_vision
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            if self.test_cast_dtype is not None:
                   # Initialize image_processor
                   _UpperCamelCase														=    self.image_processing_class(**self.image_processor_dict   )

                   # create random PyTorch tensors
                   _UpperCamelCase														=    prepare_image_inputs(self.image_processor_tester,							equal_resolution=A,							torchify=A   )

                   _UpperCamelCase														=    image_processor(A,							return_tensors='''pt'''   )
                   # for layoutLM compatiblity
                   self.assertEqual(encoding.pixel_values.device,							torch.device('''cpu'''   )   )
                   self.assertEqual(encoding.pixel_values.dtype,							torch.floataa   )

                   _UpperCamelCase														=    image_processor(A,							return_tensors='''pt'''   ).to(torch.floataa   )
                   self.assertEqual(encoding.pixel_values.device,							torch.device('''cpu'''   )   )
                   self.assertEqual(encoding.pixel_values.dtype,							torch.floataa   )

                   _UpperCamelCase														=    image_processor(A,							return_tensors='''pt'''   ).to('''cpu''',							torch.bfloataa   )
                   self.assertEqual(encoding.pixel_values.device,							torch.device('''cpu'''   )   )
                   self.assertEqual(encoding.pixel_values.dtype,							torch.bfloataa   )

                   with self.assertRaises(A   ):
                          _UpperCamelCase														=    image_processor(A,							return_tensors='''pt'''   ).to(torch.bfloataa,							'''cpu'''   )

                   # Try with text + image feature
                   _UpperCamelCase														=    image_processor(A,							return_tensors='''pt'''   )
                   encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]   )}   )
                   _UpperCamelCase														=    encoding.to(torch.floataa   )

                   self.assertEqual(encoding.pixel_values.device,							torch.device('''cpu'''   )   )
                   self.assertEqual(encoding.pixel_values.dtype,							torch.floataa   )
                   self.assertEqual(encoding.input_ids.dtype,							torch.long   )













import numpy as np
import requests


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    requests.get(
           '''https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'''
           '''master/Week1/ADRvsRating.csv'''       )
       _UpperCamelCase														=    response.text.splitlines()
       _UpperCamelCase														=    []
       for item in lines:
              _UpperCamelCase														=    item.split(''','''       )
              data.append(lowerCAmelCase       )
       data.pop(0       )  # This is for removing the labels from the list
       _UpperCamelCase														=    np.matrix(lowerCAmelCase       )
       return dataset


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    len_data

       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							data_x.transpose()       )
       prod -= data_y.transpose()
       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							lowerCAmelCase       )
       _UpperCamelCase														=    theta - (alpha / n) * sum_grad
       return theta


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    np.dot(lowerCAmelCase     ,							data_x.transpose()       )
       prod -= data_y.transpose()
       _UpperCamelCase														=    np.sum(np.square(lowerCAmelCase       )       )
       _UpperCamelCase														=    sum_elem / (2 * len_data)
       return error


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    100_000
       _UpperCamelCase														=    0.0_0_0_1_5_5_0

       _UpperCamelCase														=    data_x.shape[1]
       _UpperCamelCase														=    data_x.shape[0] - 1

       _UpperCamelCase														=    np.zeros((1, no_features)       )

       for i in range(0     ,							lowerCAmelCase       ):
              _UpperCamelCase														=    run_steep_gradient_descent(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )
              _UpperCamelCase														=    sum_of_square_error(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )
              print(f'''At Iteration {i + 1} - Error is {error:.5f}'''       )

       return theta


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    sum(abs(y - predicted_y[i]       ) for i, y in enumerate(lowerCAmelCase       )       )
       return total / len(lowerCAmelCase       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    collect_dataset()

       _UpperCamelCase														=    data.shape[0]
       _UpperCamelCase														=    np.c_[np.ones(lowerCAmelCase       ), data[:, :-1]].astype(lowerCAmelCase       )
       _UpperCamelCase														=    data[:, -1].astype(lowerCAmelCase       )

       _UpperCamelCase														=    run_linear_regression(lowerCAmelCase     ,							lowerCAmelCase       )
       _UpperCamelCase														=    theta.shape[1]
       print('''Resultant Feature vector : '''       )
       for i in range(0     ,							lowerCAmelCase       ):
              print(f'''{theta[0, i]:.5f}'''       )


if __name__ == "__main__":
       main()






