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       snake_case					(      unittest.TestCase						):




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




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




		def 	__lowercase(      self     :	List[Any]				)->       Tuple:




							"""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       snake_case					(      _snake_case   ,						unittest.TestCase						):
		lowercase_			=     ImageGPTImageProcessor if is_vision_available() else None




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




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				List[str]			=		ImageGPTImageProcessingTester(self				)




		@property
		def 	__lowercase(      self     :	int				)->       List[Any]:




							"""simple docstring"""
							return self.image_processor_tester.prepare_image_processor_dict()




		def 	__lowercase(      self     :	List[str]				)->       str:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(snake_case_     , 'clusters'				)				)
							self.assertTrue(hasattr(snake_case_     , 'do_resize'				)				)
							self.assertTrue(hasattr(snake_case_     , 'size'				)				)
							self.assertTrue(hasattr(snake_case_     , 'do_normalize'				)				)




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




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

							SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.image_processing_class.from_dict(self.image_processor_dict     , size=42				)
							self.assertEqual(image_processor.size     , {'height': 42, 'width': 42}				)




		def 	__lowercase(      self     :	Any				)->       str:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Dict			=		self.image_processing_class(**self.image_processor_dict				)
							SCREAMING_SNAKE_CASE__				:				Dict			=		json.loads(image_processor.to_json_string()				)
							for key, value in self.image_processor_dict.items():
												if key == "clusters":
																	self.assertTrue(np.array_equal(snake_case_     , obj[key]				)				)
												else:
																	self.assertEqual(obj[key]     , snake_case_				)




		def 	__lowercase(      self     :	str				)->       str:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.image_processing_class(**self.image_processor_dict				)

							with tempfile.TemporaryDirectory() as tmpdirname:
												SCREAMING_SNAKE_CASE__				:				List[Any]			=		os.path.join(snake_case_     , 'image_processor.json'				)
												image_processor_first.to_json_file(snake_case_				)
												SCREAMING_SNAKE_CASE__				:				str			=		self.image_processing_class.from_json_file(snake_case_				).to_dict()

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




		def 	__lowercase(      self     :	Union[str, Any]				)->       Dict:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Tuple			=		self.image_processing_class(**self.image_processor_dict				)

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

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




		@unittest.skip('ImageGPT requires clusters at initialization'				)
		def 	__lowercase(      self     :	Tuple				)->       List[Any]:




							"""simple docstring"""
							pass







def     _a				(      ):







					'''simple docstring'''


					SCREAMING_SNAKE_CASE__				:				Optional[int]			=		load_dataset('hf-internal-testing/fixtures_image_utils'     ,  split='test' )

					SCREAMING_SNAKE_CASE__				:				List[str]			=		Image.open(dataset[4]['file'] )
					SCREAMING_SNAKE_CASE__				:				List[str]			=		Image.open(dataset[5]['file'] )

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

					return images







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




		@slow
		def 	__lowercase(      self     :	Optional[Any]				)->       int:




							"""simple docstring"""
							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'				)

							SCREAMING_SNAKE_CASE__				:				Any			=		prepare_images()

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

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

							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()     , snake_case_				)

							# test batched
							SCREAMING_SNAKE_CASE__				:				Dict			=		image_processing(snake_case_     , return_tensors='pt'				)

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

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




from __future__ import annotations







def     _a				(      lowercase__  :			list[int | float]     ,  lowercase__  :			int     ,  lowercase__  :			int ):







     '''simple docstring'''


     if len(lowercase__ ) == 0:
          raise ValueError('find_max() arg is an empty sequence' )
     if (
         left >= len(lowercase__ )
         or left < -len(lowercase__ )
         or right >= len(lowercase__ )
         or right < -len(lowercase__ )
     ):
          raise IndexError('list index out of range' )
     if left == right:
          return nums[left]
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(left + right) >> 1  # the middle
     SCREAMING_SNAKE_CASE__				:				int			=		find_max(lowercase__     ,  lowercase__     ,  lowercase__ )  # find max in range[left, mid]
     SCREAMING_SNAKE_CASE__				:				Tuple			=		find_max(lowercase__     ,  mid + 1     ,  lowercase__ )  # find max in range[mid + 1, right]

     return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)



