import random
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	import PIL

	from transformers import FlavaImageProcessor
	from transformers.image_utils import PILImageResampling
	from transformers.models.flava.image_processing_flava import (
	    FLAVA_CODEBOOK_MEAN,
	    FLAVA_CODEBOOK_STD,
	    FLAVA_IMAGE_MEAN,
	    FLAVA_IMAGE_STD,
	)
else:
	_snake_case			=						_snake_case			=						_snake_case			=						_snake_case			=						None




class 					_snake_case						(						unittest.TestCase				):
					def __init__(  self:				Tuple	,  __lowerCamelCase:				Any	,  __lowerCamelCase:				Tuple=7	,  __lowerCamelCase:				str=3	,  __lowerCamelCase:				Optional[int]=30	,  __lowerCamelCase:				Any=4_00	,  __lowerCamelCase:				Any=True	,  __lowerCamelCase:				Optional[Any]=None	,  __lowerCamelCase:				Optional[int]=True	,  __lowerCamelCase:				Union[str, Any]=None	,  __lowerCamelCase:				List[str]=None	,  __lowerCamelCase:				Union[str, Any]=True	,  __lowerCamelCase:				List[Any]=1 / 2_55	,  __lowerCamelCase:				int=True	,  __lowerCamelCase:				int=FLAVA_IMAGE_MEAN	,  __lowerCamelCase:				Any=FLAVA_IMAGE_STD	,  __lowerCamelCase:				str=14	,  __lowerCamelCase:				int=75	,  __lowerCamelCase:				str=None	,  __lowerCamelCase:				Any=16	,  __lowerCamelCase:				List[str]=0.3	,  __lowerCamelCase:				Dict=None	,  __lowerCamelCase:				Dict=True	,  __lowerCamelCase:				List[Any]=None	,  __lowerCamelCase:				List[Any]=None	,  __lowerCamelCase:				str=True	,  __lowerCamelCase:				int=None	,  __lowerCamelCase:				Optional[int]=True	,  __lowerCamelCase:				Dict=True	,  __lowerCamelCase:				Union[str, Any]=FLAVA_CODEBOOK_MEAN	,  __lowerCamelCase:				Optional[int]=FLAVA_CODEBOOK_STD	,  )    ->				int:
												__UpperCAmelCase						:			List[str]         =   size if size is not None else {"height": 2_24, "width": 2_24}
												__UpperCAmelCase						:			List[str]         =   crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
												__UpperCAmelCase						:			Optional[Any]         =   codebook_size if codebook_size is not None else {"height": 1_12, "width": 1_12}
												__UpperCAmelCase						:			List[Any]         =   codebook_crop_size if codebook_crop_size is not None else {"height": 1_12, "width": 1_12}

												__UpperCAmelCase						:			Union[str, Any]         =   parent
												__UpperCAmelCase						:			Tuple         =   batch_size
												__UpperCAmelCase						:			Optional[int]         =   num_channels
												__UpperCAmelCase						:			Optional[int]         =   do_resize
												__UpperCAmelCase						:			Any         =   do_rescale
												__UpperCAmelCase						:			Optional[int]         =   rescale_factor
												__UpperCAmelCase						:			Any         =   min_resolution
												__UpperCAmelCase						:			Optional[int]         =   max_resolution
												__UpperCAmelCase						:			List[Any]         =   size
												__UpperCAmelCase						:			Optional[int]         =   resample if resample is not None else PILImageResampling.BICUBIC
												__UpperCAmelCase						:			List[Any]         =   do_normalize
												__UpperCAmelCase						:			Union[str, Any]         =   image_mean
												__UpperCAmelCase						:			str         =   image_std
												__UpperCAmelCase						:			int         =   do_center_crop
												__UpperCAmelCase						:			Union[str, Any]         =   crop_size

												__UpperCAmelCase						:			Any         =   input_size_patches
												__UpperCAmelCase						:			List[str]         =   total_mask_patches
												__UpperCAmelCase						:			Optional[int]         =   mask_group_max_patches
												__UpperCAmelCase						:			List[Any]         =   mask_group_min_patches
												__UpperCAmelCase						:			str         =   mask_group_min_aspect_ratio
												__UpperCAmelCase						:			Optional[int]         =   mask_group_max_aspect_ratio

												__UpperCAmelCase						:			Dict         =   codebook_do_resize
												__UpperCAmelCase						:			Optional[int]         =   codebook_size
												__UpperCAmelCase						:			List[Any]         =   codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
												__UpperCAmelCase						:			List[str]         =   codebook_do_center_crop
												__UpperCAmelCase						:			Optional[int]         =   codebook_crop_size
												__UpperCAmelCase						:			str         =   codebook_do_map_pixels
												__UpperCAmelCase						:			List[str]         =   codebook_do_normalize
												__UpperCAmelCase						:			List[Any]         =   codebook_image_mean
												__UpperCAmelCase						:			Any         =   codebook_image_std
					def    _lowerCamelCase (  self:				Dict     )    ->				List[Any]:
												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "resample": self.resample,
												    "do_rescale": self.do_rescale,
												    "rescale_factor": self.rescale_factor,
												    "do_center_crop": self.do_center_crop,
												    "crop_size": self.crop_size,
												    "input_size_patches": self.input_size_patches,
												    "total_mask_patches": self.total_mask_patches,
												    "mask_group_max_patches": self.mask_group_max_patches,
												    "mask_group_min_patches": self.mask_group_min_patches,
												    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
												    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
												    "codebook_do_resize": self.codebook_do_resize,
												    "codebook_size": self.codebook_size,
												    "codebook_resample": self.codebook_resample,
												    "codebook_do_center_crop": self.codebook_do_center_crop,
												    "codebook_crop_size": self.codebook_crop_size,
												    "codebook_do_map_pixels": self.codebook_do_map_pixels,
												    "codebook_do_normalize": self.codebook_do_normalize,
												    "codebook_image_mean": self.codebook_image_mean,
												    "codebook_image_std": self.codebook_image_std,
												}
					def    _lowerCamelCase (  self:				Union[str, Any]     )    ->				Dict:
												return (self.size["height"], self.size["width"])
					def    _lowerCamelCase (  self:				Any     )    ->				Any:
												return (
												    (self.input_size_patches, self.input_size_patches)
												    if not isinstance(self.input_size_patches	,  __lowerCamelCase     )
												    else self.input_size_patches
												)





					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				List[Any]:
												return (self.codebook_size["height"], self.codebook_size["width"])




@require_torch
@require_vision
class 					_snake_case						(						_lowercase					,      unittest.TestCase				):
					lowerCamelCase__:		Optional[Any]  =     FlavaImageProcessor if is_vision_available() else None
					lowerCamelCase__:		List[Any]  =     None
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Optional[int]:
												__UpperCAmelCase						:			Optional[Any]         =   FlavaImageProcessingTester(self     )
					@property
					def    _lowerCamelCase (  self:				Tuple     )    ->				str:
												return self.image_processor_tester.prepare_image_processor_dict()
					def    _lowerCamelCase (  self:				List[Any]     )    ->				str:
												__UpperCAmelCase						:			Optional[Any]         =   self.image_processing_class(**self.image_processor_dict     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "image_mean"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "image_std"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "do_normalize"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "do_resize"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "resample"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "crop_size"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "do_center_crop"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "do_rescale"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "rescale_factor"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "masking_generator"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "codebook_do_resize"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "codebook_size"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "codebook_resample"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "codebook_do_center_crop"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "codebook_crop_size"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "codebook_do_map_pixels"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "codebook_do_normalize"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "codebook_image_mean"     )     )
												self.assertTrue(hasattr(__lowerCamelCase	,  "codebook_image_std"     )     )
					def    _lowerCamelCase (  self:				Optional[Any]     )    ->				List[str]:
												__UpperCAmelCase						:			List[Any]         =   self.image_processing_class.from_dict(self.image_processor_dict     )
												self.assertEqual(image_processor.size	,  {"height": 2_24, "width": 2_24}     )
												self.assertEqual(image_processor.crop_size	,  {"height": 2_24, "width": 2_24}     )
												self.assertEqual(image_processor.codebook_size	,  {"height": 1_12, "width": 1_12}     )
												self.assertEqual(image_processor.codebook_crop_size	,  {"height": 1_12, "width": 1_12}     )

												__UpperCAmelCase						:			Tuple         =   self.image_processing_class.from_dict(
												    self.image_processor_dict	,  size=42	,  crop_size=84	,  codebook_size=33	,  codebook_crop_size=66     )
												self.assertEqual(image_processor.size	,  {"height": 42, "width": 42}     )
												self.assertEqual(image_processor.crop_size	,  {"height": 84, "width": 84}     )
												self.assertEqual(image_processor.codebook_size	,  {"height": 33, "width": 33}     )
												self.assertEqual(image_processor.codebook_crop_size	,  {"height": 66, "width": 66}     )
					def    _lowerCamelCase (  self:				str     )    ->				List[str]:
												pass
					def    _lowerCamelCase (  self:				List[str]     )    ->				List[str]:
												# Initialize image_processing
												__UpperCAmelCase						:			Optional[Any]         =   self.image_processing_class(**self.image_processor_dict     )
												# create random PIL images
												__UpperCAmelCase						:			Any         =   prepare_image_inputs(self.image_processor_tester	,  equal_resolution=__lowerCamelCase     )
												for image in image_inputs:
																			self.assertIsInstance(__lowerCamelCase	,  PIL.Image.Image     )

												# Test not batched input
												__UpperCAmelCase						:			Tuple         =   image_processing(image_inputs[0]	,  return_tensors="pt"     )

												# Test no bool masked pos
												self.assertFalse("bool_masked_pos" in encoded_images     )

												__UpperCAmelCase    ,	__UpperCAmelCase						:			Optional[int]         =   self.image_processor_tester.get_expected_image_size()

												self.assertEqual(
												    encoded_images.pixel_values.shape	,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,  )

												# Test batched
												__UpperCAmelCase						:			List[str]         =   image_processing(__lowerCamelCase	,  return_tensors="pt"     )
												__UpperCAmelCase    ,	__UpperCAmelCase						:			Optional[int]         =   self.image_processor_tester.get_expected_image_size()

												# Test no bool masked pos
												self.assertFalse("bool_masked_pos" in encoded_images     )

												self.assertEqual(
												    encoded_images.pixel_values.shape	,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )	,  )
					def    _lowerCamelCase (  self:				str	,  __lowerCamelCase:				Optional[Any]	,  __lowerCamelCase:				Dict     )    ->				List[Any]:
												# Initialize image_processing
												__UpperCAmelCase						:			List[str]         =   self.image_processing_class(**self.image_processor_dict     )
												# create random tensors
												__UpperCAmelCase						:			Tuple         =   prepare_image_inputs(self.image_processor_tester	,  equal_resolution=__lowerCamelCase	,  **__lowerCamelCase     )
												for image in image_inputs:
																			self.assertIsInstance(__lowerCamelCase	,  __lowerCamelCase     )

												# Test not batched input
												__UpperCAmelCase						:			Dict         =   image_processing(image_inputs[0]	,  return_tensors="pt"     )

												__UpperCAmelCase    ,	__UpperCAmelCase						:			Dict         =   self.image_processor_tester.get_expected_image_size()
												self.assertEqual(
												    encoded_images.pixel_values.shape	,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,  )

												__UpperCAmelCase						:			List[str]         =   image_processing(__lowerCamelCase	,  return_image_mask=__lowerCamelCase	,  return_tensors="pt"     )

												__UpperCAmelCase    ,	__UpperCAmelCase						:			Optional[Any]         =   self.image_processor_tester.get_expected_image_size()
												self.assertEqual(
												    encoded_images.pixel_values.shape	,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )	,  )

												__UpperCAmelCase    ,	__UpperCAmelCase						:			List[Any]         =   self.image_processor_tester.get_expected_mask_size()
												self.assertEqual(
												    encoded_images.bool_masked_pos.shape	,  (
												        self.image_processor_tester.batch_size,
												        expected_height,
												        expected_width,
												    )	,  )

												# Test batched
												__UpperCAmelCase						:			List[str]         =   image_processing(__lowerCamelCase	,  return_tensors="pt"     ).pixel_values

												__UpperCAmelCase    ,	__UpperCAmelCase						:			int         =   self.image_processor_tester.get_expected_image_size()
												self.assertEqual(
												    encoded_images.shape	,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )	,  )

												# Test masking
												__UpperCAmelCase						:			Tuple         =   image_processing(__lowerCamelCase	,  return_image_mask=__lowerCamelCase	,  return_tensors="pt"     )

												__UpperCAmelCase    ,	__UpperCAmelCase						:			int         =   self.image_processor_tester.get_expected_image_size()
												self.assertEqual(
												    encoded_images.pixel_values.shape	,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )	,  )

												__UpperCAmelCase    ,	__UpperCAmelCase						:			Any         =   self.image_processor_tester.get_expected_mask_size()
												self.assertEqual(
												    encoded_images.bool_masked_pos.shape	,  (
												        self.image_processor_tester.batch_size,
												        expected_height,
												        expected_width,
												    )	,  )
					def    _lowerCamelCase (  self:				Union[str, Any]     )    ->				Union[str, Any]:
												self._test_call_framework(np.ndarray	,  prepare_kwargs={"numpify": True}     )
					def    _lowerCamelCase (  self:				Tuple     )    ->				Optional[Any]:
												self._test_call_framework(torch.Tensor	,  prepare_kwargs={"torchify": True}     )
					def    _lowerCamelCase (  self:				int     )    ->				str:
												# Initialize image_processing
												random.seed(12_34     )
												__UpperCAmelCase						:			Any         =   self.image_processing_class(**self.image_processor_dict     )
												__UpperCAmelCase						:			Union[str, Any]         =   prepare_image_inputs(self.image_processor_tester	,  equal_resolution=__lowerCamelCase	,  torchify=__lowerCamelCase     )

												# Test not batched input
												__UpperCAmelCase						:			Any         =   image_processing(image_inputs[0]	,  return_image_mask=__lowerCamelCase	,  return_tensors="pt"     )
												self.assertEqual(encoded_images.bool_masked_pos.sum().item()	,  75     )





					def    _lowerCamelCase (  self:				List[str]     )    ->				Tuple:
												# Initialize image_processing
												__UpperCAmelCase						:			Tuple         =   self.image_processing_class(**self.image_processor_dict     )
												# create random PIL images
												__UpperCAmelCase						:			Dict         =   prepare_image_inputs(self.image_processor_tester	,  equal_resolution=__lowerCamelCase     )
												for image in image_inputs:
																			self.assertIsInstance(__lowerCamelCase	,  PIL.Image.Image     )

												# Test not batched input
												__UpperCAmelCase						:			Union[str, Any]         =   image_processing(image_inputs[0]	,  return_codebook_pixels=__lowerCamelCase	,  return_tensors="pt"     )
												__UpperCAmelCase    ,	__UpperCAmelCase						:			List[str]         =   self.image_processor_tester.get_expected_codebook_image_size()
												self.assertEqual(
												    encoded_images.codebook_pixel_values.shape	,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,  )

												# Test batched
												__UpperCAmelCase						:			Optional[Any]         =   image_processing(__lowerCamelCase	,  return_codebook_pixels=__lowerCamelCase	,  return_tensors="pt"     )
												__UpperCAmelCase    ,	__UpperCAmelCase						:			int         =   self.image_processor_tester.get_expected_codebook_image_size()
												self.assertEqual(
												    encoded_images.codebook_pixel_values.shape	,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )	,  )





import numpy as np
import requests




def   _UpperCamelCase   (			)						->   Tuple:
							__UpperCAmelCase						:			Tuple         =   requests.get(
							    "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
							    "master/Week1/ADRvsRating.csv"							)
							__UpperCAmelCase						:			List[Any]         =   response.text.splitlines()
							__UpperCAmelCase						:			Any         =   []
							for item in lines:
														__UpperCAmelCase						:			Any         =   item.split(","							)
														data.append(snake_case__							)
							data.pop(0							)  # This is for removing the labels from the list
							__UpperCAmelCase						:			List[str]         =   np.matrix(snake_case__							)
							return dataset




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__,    snake_case__,    snake_case__							)						->   Optional[Any]:
							__UpperCAmelCase						:			Tuple         =   len_data

							__UpperCAmelCase						:			int         =   np.dot(snake_case__,    data_x.transpose()							)
							prod -= data_y.transpose()
							__UpperCAmelCase						:			Optional[int]         =   np.dot(snake_case__,    snake_case__							)
							__UpperCAmelCase						:			List[Any]         =   theta - (alpha / n) * sum_grad
							return theta




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__,    snake_case__							)						->   Optional[int]:
							__UpperCAmelCase						:			Any         =   np.dot(snake_case__,    data_x.transpose()							)
							prod -= data_y.transpose()
							__UpperCAmelCase						:			Optional[int]         =   np.sum(np.square(snake_case__							)							)
							__UpperCAmelCase						:			List[Any]         =   sum_elem / (2 * len_data)
							return error




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   Tuple:
							__UpperCAmelCase						:			Any         =   10_0000
							__UpperCAmelCase						:			Dict         =   0.000_1550

							__UpperCAmelCase						:			str         =   data_x.shape[1]
							__UpperCAmelCase						:			Any         =   data_x.shape[0] - 1

							__UpperCAmelCase						:			List[str]         =   np.zeros((1, no_features)							)

							for i in range(0,    snake_case__							):
														__UpperCAmelCase						:			Union[str, Any]         =   run_steep_gradient_descent(snake_case__,    snake_case__,    snake_case__,    snake_case__,    snake_case__							)
														__UpperCAmelCase						:			Any         =   sum_of_square_error(snake_case__,    snake_case__,    snake_case__,    snake_case__							)
														print(f'''At Iteration {i + 1} - Error is {error:.5f}'''							)

							return theta




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   Optional[int]:
							__UpperCAmelCase						:			str         =   sum(abs(y - predicted_y[i]							) for i, y in enumerate(snake_case__							)							)
							return total / len(snake_case__							)




def   _UpperCamelCase   (			)						->   List[str]:
							__UpperCAmelCase						:			str         =   collect_dataset()

							__UpperCAmelCase						:			List[Any]         =   data.shape[0]
							__UpperCAmelCase						:			Union[str, Any]         =   np.c_[np.ones(snake_case__							), data[:, :-1]].astype(snake_case__							)
							__UpperCAmelCase						:			int         =   data[:, -1].astype(snake_case__							)

							__UpperCAmelCase						:			List[Any]         =   run_linear_regression(snake_case__,    snake_case__							)
							__UpperCAmelCase						:			List[str]         =   theta.shape[1]
							print("Resultant Feature vector : "							)
							for i in range(0,    snake_case__							):
														print(f'''{theta[0, i]:.5f}'''							)


if __name__ == "__main__":
	main()




