


'''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 			_lowercase (	unittest.TestCase							):
   def __init__(						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      Any       ,   SCREAMING_SNAKE_CASE_       :      Any=7       ,   SCREAMING_SNAKE_CASE_       :      List[str]=3       ,   SCREAMING_SNAKE_CASE_       :      Tuple=18       ,   SCREAMING_SNAKE_CASE_       :      str=30       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=400       ,   SCREAMING_SNAKE_CASE_       :      str=True       ,   SCREAMING_SNAKE_CASE_       :      List[str]=None       ,   SCREAMING_SNAKE_CASE_       :      List[str]=True       ,   ) ->							int:
        __snake_case           =						size if size is not None else {'height': 18, 'width': 18}
        __snake_case           =						parent
        __snake_case           =						batch_size
        __snake_case           =						num_channels
        __snake_case           =						image_size
        __snake_case           =						min_resolution
        __snake_case           =						max_resolution
        __snake_case           =						do_resize
        __snake_case           =						size
        __snake_case           =						do_normalize





   def a  (						self       :      Optional[int]      ) ->							List[str]:
        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 			_lowercase (	__lowercase						,					unittest.TestCase							):
   _SCREAMING_SNAKE_CASE  :  Tuple          =	ImageGPTImageProcessor if is_vision_available() else None
   def a  (						self       :      str      ) ->							Union[str, Any]:
        __snake_case           =						ImageGPTImageProcessingTester(self      )
   @property
   def a  (						self       :      Optional[int]      ) ->							List[str]:
        return self.image_processor_tester.prepare_image_processor_dict()
   def a  (						self       :      Any      ) ->							List[Any]:
        __snake_case           =						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 a  (						self       :      str      ) ->							List[str]:
        __snake_case           =						self.image_processing_class.from_dict(self.image_processor_dict      )
        self.assertEqual(image_processor.size       ,   {'height': 18, 'width': 18}      )

        __snake_case           =						self.image_processing_class.from_dict(self.image_processor_dict       ,   size=42      )
        self.assertEqual(image_processor.size       ,   {'height': 42, 'width': 42}      )
   def a  (						self       :      Union[str, Any]      ) ->							int:
        __snake_case           =						self.image_processing_class(**self.image_processor_dict      )
        __snake_case           =						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 a  (						self       :      Tuple      ) ->							Dict:
        __snake_case           =						self.image_processing_class(**self.image_processor_dict      )

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

        __snake_case           =						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 a  (						self       :      str      ) ->							List[Any]:
        __snake_case           =						self.image_processing_class(**self.image_processor_dict      )

        with tempfile.TemporaryDirectory() as tmpdirname:
             image_processor_first.save_pretrained(SCREAMING_SNAKE_CASE_      )
             __snake_case           =						self.image_processing_class.from_pretrained(SCREAMING_SNAKE_CASE_      ).to_dict()

        __snake_case           =						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 a  (						self       :      List[Any]      ) ->							List[Any]:
        pass





def   _a		()			->			Any:






     """simple docstring"""
     __snake_case           =						load_dataset('hf-internal-testing/fixtures_image_utils'							,   split='test'						)

     __snake_case           =						Image.open(dataset[4]['file']						)
     __snake_case           =						Image.open(dataset[5]['file']						)

     __snake_case           =						[imagea, imagea]

     return images




@require_vision
@require_torch
class 			_lowercase (	unittest.TestCase							):
   @slow
   def a  (						self       :      Any      ) ->							List[Any]:
        __snake_case           =						ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

        __snake_case           =						prepare_images()

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

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

        __snake_case           =						[306, 191, 191]
        self.assertEqual(encoding.input_ids[0, :3].tolist()       ,   SCREAMING_SNAKE_CASE_      )

        # test batched
        __snake_case           =						image_processing(SCREAMING_SNAKE_CASE_       ,   return_tensors='pt'      )

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

        __snake_case           =						[303, 13, 13]
        self.assertEqual(encoding.input_ids[1, -3:].tolist()       ,   SCREAMING_SNAKE_CASE_      )







'''simple docstring'''

from __future__ import annotations

def 					__UpperCamelCase				(						lowercase__    :	float,       lowercase__    :	float,       lowercase__    :	float			):






     '''simple docstring'''





     if (voltage, current, resistance).count(0			) != 1:
          raise ValueError('One and only one argument must be 0'			)
     if resistance < 0:
          raise ValueError('Resistance cannot be negative'			)
     if voltage == 0:
          return {"voltage": float(current * resistance			)}
     elif current == 0:
          return {"current": voltage / resistance}
     elif resistance == 0:
          return {"resistance": voltage / current}
     else:
          raise ValueError('Exactly one argument must be 0'			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


