



"""simple docstring"""






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ImageGPTImageProcessor



class      A__  (    unittest.TestCase				):




	'''simple docstring'''





	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Tuple=7			,     _SCREAMING_SNAKE_CASE:						Tuple=3			,     _SCREAMING_SNAKE_CASE:						Dict=18			,     _SCREAMING_SNAKE_CASE:						int=30			,     _SCREAMING_SNAKE_CASE:						Dict=400			,     _SCREAMING_SNAKE_CASE:						List[str]=True			,     _SCREAMING_SNAKE_CASE:						str=None			,     _SCREAMING_SNAKE_CASE:						str=True			,     )							->			Tuple:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			size if size is not None else {"height": 18, "width": 18}
				__lowerCAmelCase :    Any				=			parent
				__lowerCAmelCase :    List[Any]				=			batch_size
				__lowerCAmelCase :    Any				=			num_channels
				__lowerCAmelCase :    int				=			image_size
				__lowerCAmelCase :    Optional[int]				=			min_resolution
				__lowerCAmelCase :    str				=			max_resolution
				__lowerCAmelCase :    Union[str, Any]				=			do_resize
				__lowerCAmelCase :    Optional[int]				=			size
				__lowerCAmelCase :    List[str]				=			do_normalize







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

				"""simple docstring"""

				return {
				    # here we create 2 clusters for the sake of simplicity
				    "clusters": np.asarray(
				        [
				            [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
				            [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
				        ]),
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				}






@require_torch
@require_vision
class      A__  (    __SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      ImageGPTImageProcessor if is_vision_available() else None

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

				"""simple docstring"""

				__lowerCAmelCase :    int				=			ImageGPTImageProcessingTester(self)

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			List[Any]:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			self.image_processing_class(**self.image_processor_dict)
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "clusters"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "do_resize"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "size"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "do_normalize"))

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

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			self.image_processing_class.from_dict(self.image_processor_dict)
				self.assertEqual(image_processor.size			,     {"height": 18, "width": 18})

				__lowerCAmelCase :    Dict				=			self.image_processing_class.from_dict(self.image_processor_dict			,     size=42)
				self.assertEqual(image_processor.size			,     {"height": 42, "width": 42})

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.image_processing_class(**self.image_processor_dict)
				__lowerCAmelCase :    Any				=			json.loads(image_processor.to_json_string())
				for key, value in self.image_processor_dict.items():
							if key == "clusters":
										self.assertTrue(np.array_equal(_SCREAMING_SNAKE_CASE			,     obj[key]))
							else:
										self.assertEqual(obj[key]			,     _SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			self.image_processing_class(**self.image_processor_dict)

				with tempfile.TemporaryDirectory() as tmpdirname:
							__lowerCAmelCase :    int				=			os.path.join(_SCREAMING_SNAKE_CASE			,     "image_processor.json")
							image_processor_first.to_json_file(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    int				=			self.image_processing_class.from_json_file(_SCREAMING_SNAKE_CASE).to_dict()

				__lowerCAmelCase :    Tuple				=			image_processor_first.to_dict()
				for key, value in image_processor_first.items():
							if key == "clusters":
										self.assertTrue(np.array_equal(_SCREAMING_SNAKE_CASE			,     image_processor_second[key]))
							else:
										self.assertEqual(image_processor_first[key]			,     _SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.image_processing_class(**self.image_processor_dict)

				with tempfile.TemporaryDirectory() as tmpdirname:
							image_processor_first.save_pretrained(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    Optional[int]				=			self.image_processing_class.from_pretrained(_SCREAMING_SNAKE_CASE).to_dict()

				__lowerCAmelCase :    Any				=			image_processor_first.to_dict()
				for key, value in image_processor_first.items():
							if key == "clusters":
										self.assertTrue(np.array_equal(_SCREAMING_SNAKE_CASE			,     image_processor_second[key]))
							else:
										self.assertEqual(image_processor_first[key]			,     _SCREAMING_SNAKE_CASE)







	@unittest.skip("ImageGPT requires clusters at initialization")
	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			List[str]:

				"""simple docstring"""

				pass






def      _lowercase							(    )		->  Union[str, Any]:
			__lowerCAmelCase :    Any				=			load_dataset("hf-internal-testing/fixtures_image_utils"   ,split="test"      )

			__lowerCAmelCase :    Dict				=			Image.open(dataset[4]["file"]      )
			__lowerCAmelCase :    Optional[Any]				=			Image.open(dataset[5]["file"]      )

			__lowerCAmelCase :    List[str]				=			[imagea, imagea]

			return images





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




	'''simple docstring'''





	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small")

				__lowerCAmelCase :    Union[str, Any]				=			prepare_images()

				# test non-batched
				__lowerCAmelCase :    str				=			image_processing(images[0]			,     return_tensors="pt")

				self.assertIsInstance(encoding.input_ids			,     torch.LongTensor)
				self.assertEqual(encoding.input_ids.shape			,     (1, 1024))

				__lowerCAmelCase :    Tuple				=			[306, 191, 191]
				self.assertEqual(encoding.input_ids[0, :3].tolist()			,     _SCREAMING_SNAKE_CASE)

				# test batched
				__lowerCAmelCase :    Tuple				=			image_processing(_SCREAMING_SNAKE_CASE			,     return_tensors="pt")

				self.assertIsInstance(encoding.input_ids			,     torch.LongTensor)
				self.assertEqual(encoding.input_ids.shape			,     (2, 1024))

				__lowerCAmelCase :    Tuple				=			[303, 13, 13]
				self.assertEqual(encoding.input_ids[1, -3:].tolist()			,     _SCREAMING_SNAKE_CASE)




"""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
__snake_case				:  Optional[Any] =       pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
__snake_case				:  int =       dataset.iloc[:, 1:2].values
__snake_case				:  Tuple =       dataset.iloc[:, 2].values


__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				:  int =       train_test_split(X, y, test_size=0.2, random_state=0)


__snake_case				:  Tuple =       PolynomialFeatures(degree=4)
__snake_case				:  Any =       poly_reg.fit_transform(X)
__snake_case				:  List[str] =       LinearRegression()
pol_reg.fit(X_poly, y)


def      _lowercase							(    )		->  List[Any]:
			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