

'''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     UpperCAmelCase_ (	unittest.TestCase    ):



	"""simple docstring"""





	def __init__(    self				,			lowerCamelCase				,			lowerCamelCase=7				,			lowerCamelCase=3				,			lowerCamelCase=18				,			lowerCamelCase=30				,			lowerCamelCase=4_00				,			lowerCamelCase=True				,			lowerCamelCase=None				,			lowerCamelCase=True				,			)							->     Dict:




				'''simple docstring'''






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



	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     Optional[int]:




				'''simple docstring'''






				return {
				    # here we create 2 clusters for the sake of simplicity
				    "clusters": np.asarray(
				        [
				            [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
				            [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
				        ]   ),
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				}

@require_torch
@require_vision
class     UpperCAmelCase_ (	_a				,	unittest.TestCase    ):



	"""simple docstring"""





	__SCREAMING_SNAKE_CASE					=       ImageGPTImageProcessor if is_vision_available() else None

	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     int:




				'''simple docstring'''






				UpperCamelCase				:	Dict    						=					ImageGPTImageProcessingTester(self   )

	@property
	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     Optional[int]:




				'''simple docstring'''






				return self.image_processor_tester.prepare_image_processor_dict()

	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     Any:




				'''simple docstring'''






				UpperCamelCase				:	Tuple    						=					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]:




				'''simple docstring'''






				UpperCamelCase				:	Tuple    						=					self.image_processing_class.from_dict(self.image_processor_dict   )
				self.assertEqual(image_processor.size				,			{"height": 18, "width": 18}   )

				UpperCamelCase				:	Any    						=					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   )							->     int:




				'''simple docstring'''






				UpperCamelCase				:	List[Any]    						=					self.image_processing_class(**self.image_processor_dict   )
				UpperCamelCase				:	Tuple    						=					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   )							->     Dict:




				'''simple docstring'''






				UpperCamelCase				:	Dict    						=					self.image_processing_class(**self.image_processor_dict   )

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCamelCase				:	Any    						=					os.path.join(_SCREAMING_SNAKE_CASE				,			"image_processor.json"   )
							image_processor_first.to_json_file(_SCREAMING_SNAKE_CASE   )
							UpperCamelCase				:	Optional[Any]    						=					self.image_processing_class.from_json_file(_SCREAMING_SNAKE_CASE   ).to_dict()

				UpperCamelCase				:	int    						=					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   )							->     Any:




				'''simple docstring'''






				UpperCamelCase				:	str    						=					self.image_processing_class(**self.image_processor_dict   )

				with tempfile.TemporaryDirectory() as tmpdirname:
							image_processor_first.save_pretrained(_SCREAMING_SNAKE_CASE   )
							UpperCamelCase				:	Any    						=					self.image_processing_class.from_pretrained(_SCREAMING_SNAKE_CASE   ).to_dict()

				UpperCamelCase				:	Dict    						=					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:




				'''simple docstring'''






				pass

def       A__	(							):


			'''simple docstring'''


			UpperCamelCase				:	int    						=					load_dataset("hf-internal-testing/fixtures_image_utils"							,       split="test")

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

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

			return images




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



	"""simple docstring"""





	@slow
	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     List[str]:




				'''simple docstring'''






				UpperCamelCase				:	int    						=					ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"   )

				UpperCamelCase				:	Any    						=					prepare_images()

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

				self.assertIsInstance(encoding.input_ids				,			torch.LongTensor   )
				self.assertEqual(encoding.input_ids.shape				,			(1, 10_24)   )

				UpperCamelCase				:	str    						=					[3_06, 1_91, 1_91]
				self.assertEqual(encoding.input_ids[0, :3].tolist()				,			_SCREAMING_SNAKE_CASE   )

				# test batched
				UpperCamelCase				:	List[str]    						=					image_processing(_SCREAMING_SNAKE_CASE				,			return_tensors="pt"   )

				self.assertIsInstance(encoding.input_ids				,			torch.LongTensor   )
				self.assertEqual(encoding.input_ids.shape				,			(2, 10_24)   )

				UpperCamelCase				:	List[str]    						=					[3_03, 13, 13]
				self.assertEqual(encoding.input_ids[1, -3:].tolist()				,			_SCREAMING_SNAKE_CASE   )






from __future__ import annotations

import math

__a							     =					'2020.9.26'
__a							     =					'xcodz-dot, cclaus, dhruvmanila'







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     if not all(isinstance(_lowercase					,		(float, int) ) for val in locals().values() ):
          UpperCAmelCase_     :				Optional[int]  =     f'''Input values must either be float or int: {list(locals().values() )}'''
          raise TypeError(_lowercase )
     UpperCAmelCase_     :				Tuple  =     ((x * distance) / (z + distance)) * scale
     UpperCAmelCase_     :				str  =     ((y * distance) / (z + distance)) * scale
     return projected_x, projected_y







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     if not isinstance(_lowercase					,		_lowercase ):
          raise TypeError('''Axis must be a str''' )
     UpperCAmelCase_     :				Optional[Any]  =     locals()
     del input_variables["axis"]
     if not all(isinstance(_lowercase					,		(float, int) ) for val in input_variables.values() ):
          UpperCAmelCase_     :				List[Any]  =     (
              '''Input values except axis must either be float or int: '''
              f'''{list(input_variables.values() )}'''
          )
          raise TypeError(_lowercase )
     UpperCAmelCase_     :				Dict  =     (angle % 360) / 450 * 180 / math.pi
     if axis == "z":
          UpperCAmelCase_     :				Optional[int]  =     x * math.cos(_lowercase ) - y * math.sin(_lowercase )
          UpperCAmelCase_     :				List[Any]  =     y * math.cos(_lowercase ) + x * math.sin(_lowercase )
          UpperCAmelCase_     :				Optional[int]  =     z
     elif axis == "x":
          UpperCAmelCase_     :				Any  =     y * math.cos(_lowercase ) - z * math.sin(_lowercase )
          UpperCAmelCase_     :				int  =     z * math.cos(_lowercase ) + y * math.sin(_lowercase )
          UpperCAmelCase_     :				Dict  =     x
     elif axis == "y":
          UpperCAmelCase_     :				Union[str, Any]  =     x * math.cos(_lowercase ) - z * math.sin(_lowercase )
          UpperCAmelCase_     :				Optional[int]  =     z * math.cos(_lowercase ) + x * math.sin(_lowercase )
          UpperCAmelCase_     :				Optional[int]  =     y
     else:
          raise ValueError('''not a valid axis, choose one of \'x\', \'y\', \'z\'''' )

     return new_x, new_y, new_z


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
     print(F"""{rotate(1.0, 2.0, 3.0, "y", 90.0) = }""")