

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				,       snake_case				,       snake_case=7				,       snake_case=3				,       snake_case=18				,       snake_case=30				,       snake_case=400				,       snake_case=True				,       snake_case=None				,       snake_case=True				,       ):
											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	):
											return {
											    # here we create 2 clusters for the sake of simplicity
											    "clusters": np.asarray(
											        [
											            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
											            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
											        ]	),
											    "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 :			List[Any]	      =      ImageGPTImageProcessor if is_vision_available() else None




				def    a							( self	):
											snake_case_          =   ImageGPTImageProcessingTester(self	)




				@property
				def    a							( self	):
											return self.image_processor_tester.prepare_image_processor_dict()




				def    a							( self	):
											snake_case_          =   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    a							( self	):
											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	):
											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(snake_case				,       obj[key]	)	)
																		else:
																									self.assertEqual(obj[key]				,       snake_case	)




				def    a							( self	):
											snake_case_          =   self.image_processing_class(**self.image_processor_dict	)

											with tempfile.TemporaryDirectory() as tmpdirname:
																		snake_case_          =   os.path.join(snake_case				,       'image_processor.json'	)
																		image_processor_first.to_json_file(snake_case	)
																		snake_case_          =   self.image_processing_class.from_json_file(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(snake_case				,       image_processor_second[key]	)	)
																		else:
																									self.assertEqual(image_processor_first[key]				,       snake_case	)




				def    a							( self	):
											snake_case_          =   self.image_processing_class(**self.image_processor_dict	)

											with tempfile.TemporaryDirectory() as tmpdirname:
																		image_processor_first.save_pretrained(snake_case	)
																		snake_case_          =   self.image_processing_class.from_pretrained(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(snake_case				,       image_processor_second[key]	)	)
																		else:
																									self.assertEqual(image_processor_first[key]				,       snake_case	)




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





def  __lowerCamelCase     (			):







							'''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	):
											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()				,       snake_case	)

											# test batched
											snake_case_          =   image_processing(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()				,       snake_case	)







def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							global f  # a global dp table for knapsack
							if f[i][j] < 0:
														if j < wt[i - 1]:
																					snake_case_          =   mf_knapsack(i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
														else:
																					snake_case_          =   max(
																					    mf_knapsack(i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)       ,							mf_knapsack(i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							j - wt[i - 1]				) + val[i - 1]       ,							)
														snake_case_          =   val
							return f[i][j]





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   [[0] * (w + 1) for _ in range(n + 1				)]

							for i in range(1       ,							n + 1				):
														for w_ in range(1       ,							w + 1				):
																					if wt[i - 1] <= w_:
																												snake_case_          =   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]       ,							dp[i - 1][w_]				)
																					else:
																												snake_case_          =   dp[i - 1][w_]

							return dp[n][w_], dp





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							if not (isinstance(UpperCamelCase__       ,							(list, tuple)				) and isinstance(UpperCamelCase__       ,							(list, tuple)				)):
														raise ValueError(
														    'Both the weights and values vectors must be either lists or tuples'				)

							snake_case_          =   len(UpperCamelCase__				)
							if num_items != len(UpperCamelCase__				):
														snake_case_          =   (
														    'The number of weights must be the same as the number of values.\n'
														    F'''But got {num_items} weights and {len(UpperCamelCase__				)} values'''
														)
														raise ValueError(UpperCamelCase__				)
							for i in range(UpperCamelCase__				):
														if not isinstance(wt[i]       ,							UpperCamelCase__				):
																					snake_case_          =   (
																					    'All weights must be integers but got weight of '
																					    F'''type {type(wt[i]				)} at index {i}'''
																					)
																					raise TypeError(UpperCamelCase__				)

							snake_case_    ,   snake_case_          =   knapsack(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
							snake_case_          =   set()
							_construct_solution(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)

							return optimal_val, example_optional_set





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							if i > 0 and j > 0:
														if dp[i - 1][j] == dp[i][j]:
																					_construct_solution(UpperCamelCase__       ,							UpperCamelCase__       ,							i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__				)
														else:
																					optimal_set.add(UpperCamelCase__				)
																					_construct_solution(UpperCamelCase__       ,							UpperCamelCase__       ,							i - 1       ,							j - wt[i - 1]       ,							UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       int						    =	[3, 2, 4, 4]
					_UpperCAmelCase		:       Tuple						    =	[4, 3, 2, 3]
					_UpperCAmelCase		:       Dict						    =	4
					_UpperCAmelCase		:       int						    =	6
					_UpperCAmelCase		:       Union[str, Any]						    =	[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					_UpperCAmelCase    ,						_UpperCAmelCase		:       Optional[int]						    =	knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					_UpperCAmelCase    ,						_UpperCAmelCase		:       Optional[int]						    =	knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)




