


'''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 						_lowerCamelCase					(    unittest.TestCase				):




	'''simple docstring'''


	def __init__(      self      ,      __lowercase      ,      __lowercase=7      ,      __lowercase=3      ,      __lowercase=18      ,      __lowercase=30      ,      __lowercase=400      ,      __lowercase=True      ,      __lowercase=None      ,      __lowercase=True      ,      ):

		"""simple docstring"""







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







	def  snake_case__   (      self						):

		"""simple docstring"""







		return {
		    # here we create 2 clusters for the sake of simplicity
		    "clusters": np.asarray(
		        [
		            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
		            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
		        ]						),
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "do_normalize": self.do_normalize,
		}


@require_torch
@require_vision
class 						_lowerCamelCase					(    snake_case_    ,		unittest.TestCase				):




	'''simple docstring'''
	__lowercase				:		int    							=						ImageGPTImageProcessor if is_vision_available() else None


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		str       =			ImageGPTImageProcessingTester(self						)


	@property
	def  snake_case__   (      self						):

		"""simple docstring"""







		return self.image_processor_tester.prepare_image_processor_dict()


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Any       =			self.image_processing_class(**self.image_processor_dict						)
		self.assertTrue(hasattr(__lowercase      ,      'clusters'						)						)
		self.assertTrue(hasattr(__lowercase      ,      'do_resize'						)						)
		self.assertTrue(hasattr(__lowercase      ,      'size'						)						)
		self.assertTrue(hasattr(__lowercase      ,      'do_normalize'						)						)


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		List[Any]       =			self.image_processing_class.from_dict(self.image_processor_dict						)
		self.assertEqual(image_processor.size      ,      {'height': 18, 'width': 18}						)

		__A  :		Any       =			self.image_processing_class.from_dict(self.image_processor_dict      ,      size=42						)
		self.assertEqual(image_processor.size      ,      {'height': 42, 'width': 42}						)


	def  snake_case__   (      self						):

		"""simple docstring"""







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


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Optional[Any]       =			self.image_processing_class(**self.image_processor_dict						)

		with tempfile.TemporaryDirectory() as tmpdirname:
			__A  :		Any       =			os.path.join(__lowercase      ,      'image_processor.json'						)
			image_processor_first.to_json_file(__lowercase						)
			__A  :		Any       =			self.image_processing_class.from_json_file(__lowercase						).to_dict()

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


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Any       =			self.image_processing_class(**self.image_processor_dict						)

		with tempfile.TemporaryDirectory() as tmpdirname:
			image_processor_first.save_pretrained(__lowercase						)
			__A  :		List[Any]       =			self.image_processing_class.from_pretrained(__lowercase						).to_dict()

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







	@unittest.skip('ImageGPT requires clusters at initialization'						)
	def  snake_case__   (      self						):

		"""simple docstring"""







		pass


def 			_lowercase       (				):
	__A  :		Optional[Any]       =			load_dataset('hf-internal-testing/fixtures_image_utils',				split='test'			)

	__A  :		Any       =			Image.open(dataset[4]['file']			)
	__A  :		Tuple       =			Image.open(dataset[5]['file']			)

	__A  :		Union[str, Any]       =			[imagea, imagea]

	return images


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




	'''simple docstring'''


	@slow
	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Any       =			ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'						)

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

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

		self.assertIsInstance(encoding.input_ids      ,      torch.LongTensor						)
		self.assertEqual(encoding.input_ids.shape      ,      (1, 1_024)						)

		__A  :		str       =			[306, 191, 191]
		self.assertEqual(encoding.input_ids[0, :3].tolist()      ,      __lowercase						)

		# test batched
		__A  :		str       =			image_processing(__lowercase      ,      return_tensors='pt'						)

		self.assertIsInstance(encoding.input_ids      ,      torch.LongTensor						)
		self.assertEqual(encoding.input_ids.shape      ,      (2, 1_024)						)

		__A  :		Any       =			[303, 13, 13]
		self.assertEqual(encoding.input_ids[1, -3:].tolist()      ,      __lowercase						)










'''simple docstring'''
from collections.abc import Sequence


def 			_lowercase       (				UpperCamelCase__       :					Sequence[float],				UpperCamelCase__       :					float			):
 return sum(c * (x**i) for i, c in enumerate(UpperCamelCase__			)			)


def 			_lowercase       (				UpperCamelCase__       :					Sequence[float],				UpperCamelCase__       :					float			):
 __A  :		Optional[Any]       =			0.0
 for coeff in reversed(UpperCamelCase__			):
  __A  :		List[str]       =			result * x + coeff
 return result


if __name__ == "__main__":
 UpperCAmelCase_ :     Any								=      (0.0, 0.0, 5.0, 9.3, 7.0)
 UpperCAmelCase_ :     Any								=      10.0
 print(evaluate_poly(poly, x))
 print(horner(poly, x))






