






'''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			):




				def __init__(			self  :       List[str]  ,     a  :       List[str]  ,     a  :       str=7  ,     a  :       str=3  ,     a  :       int=1_8  ,     a  :       Tuple=3_0  ,     a  :       Tuple=4_0_0  ,     a  :       Union[str, Any]=True  ,     a  :       Optional[int]=None  ,     a  :       Optional[int]=True  ,     ):






							'''simple docstring'''






							lowercase_	:    str           =     size if size is not None else {"height": 1_8, "width": 1_8}
							lowercase_	:    Tuple           =     parent
							lowercase_	:    Union[str, Any]           =     batch_size
							lowercase_	:    Tuple           =     num_channels
							lowercase_	:    int           =     image_size
							lowercase_	:    int           =     min_resolution
							lowercase_	:    Tuple           =     max_resolution
							lowercase_	:    Dict           =     do_resize
							lowercase_	:    Union[str, Any]           =     size
							lowercase_	:    str           =     do_normalize





				def 				lowerCAmelCase__				(			self  :       Any			):






							'''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        _UpperCAmelCase						(		lowerCAmelCase__				,					unittest.TestCase			):
				__lowerCamelCase:   Any		       =    ImageGPTImageProcessor if is_vision_available() else None




				def 				lowerCAmelCase__				(			self  :       Tuple			):






							'''simple docstring'''






							lowercase_	:    Tuple           =     ImageGPTImageProcessingTester(self			)




				@property
				def 				lowerCAmelCase__				(			self  :       List[Any]			):






							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()




				def 				lowerCAmelCase__				(			self  :       Any			):






							'''simple docstring'''






							lowercase_	:    Optional[int]           =     self.image_processing_class(**self.image_processor_dict			)
							self.assertTrue(hasattr(_lowerCamelCase  ,     "clusters"			)			)
							self.assertTrue(hasattr(_lowerCamelCase  ,     "do_resize"			)			)
							self.assertTrue(hasattr(_lowerCamelCase  ,     "size"			)			)
							self.assertTrue(hasattr(_lowerCamelCase  ,     "do_normalize"			)			)




				def 				lowerCAmelCase__				(			self  :       Any			):






							'''simple docstring'''






							lowercase_	:    Union[str, Any]           =     self.image_processing_class.from_dict(self.image_processor_dict			)
							self.assertEqual(image_processor.size  ,     {"height": 1_8, "width": 1_8}			)

							lowercase_	:    Tuple           =     self.image_processing_class.from_dict(self.image_processor_dict  ,     size=4_2			)
							self.assertEqual(image_processor.size  ,     {"height": 4_2, "width": 4_2}			)




				def 				lowerCAmelCase__				(			self  :       Any			):






							'''simple docstring'''






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




				def 				lowerCAmelCase__				(			self  :       List[Any]			):






							'''simple docstring'''






							lowercase_	:    Union[str, Any]           =     self.image_processing_class(**self.image_processor_dict			)

							with tempfile.TemporaryDirectory() as tmpdirname:
										lowercase_	:    Tuple           =     os.path.join(_lowerCamelCase  ,     "image_processor.json"			)
										image_processor_first.to_json_file(_lowerCamelCase			)
										lowercase_	:    List[str]           =     self.image_processing_class.from_json_file(_lowerCamelCase			).to_dict()

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




				def 				lowerCAmelCase__				(			self  :       int			):






							'''simple docstring'''






							lowercase_	:    Optional[int]           =     self.image_processing_class(**self.image_processor_dict			)

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

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





				@unittest.skip("ImageGPT requires clusters at initialization"			)
				def 				lowerCAmelCase__				(			self  :       Optional[int]			):






							'''simple docstring'''






							pass




def 		__SCREAMING_SNAKE_CASE					(  ):


			"""simple docstring"""




			lowercase_	:    Dict           =     load_dataset("hf-internal-testing/fixtures_image_utils"						,		split="test"							)

			lowercase_	:    Tuple           =     Image.open(dataset[4]["file"]							)
			lowercase_	:    Dict           =     Image.open(dataset[5]["file"]							)

			lowercase_	:    List[Any]           =     [imagea, imagea]

			return images





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




				@slow
				def 				lowerCAmelCase__				(			self  :       str			):






							'''simple docstring'''






							lowercase_	:    List[str]           =     ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"			)

							lowercase_	:    Dict           =     prepare_images()

							# test non-batched
							lowercase_	:    Union[str, Any]           =     image_processing(images[0]  ,     return_tensors="pt"			)

							self.assertIsInstance(encoding.input_ids  ,     torch.LongTensor			)
							self.assertEqual(encoding.input_ids.shape  ,     (1, 1_0_2_4)			)

							lowercase_	:    Union[str, Any]           =     [3_0_6, 1_9_1, 1_9_1]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,     _lowerCamelCase			)

							# test batched
							lowercase_	:    str           =     image_processing(_lowerCamelCase  ,     return_tensors="pt"			)

							self.assertIsInstance(encoding.input_ids  ,     torch.LongTensor			)
							self.assertEqual(encoding.input_ids.shape  ,     (2, 1_0_2_4)			)

							lowercase_	:    Tuple           =     [3_0_3, 1_3, 1_3]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,     _lowerCamelCase			)












'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator


class        _UpperCAmelCase						:




    def __init__(			self  :       Optional[Any]  ,     a  :       Any			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     str(id_			)
       lowercase_	:    List[str]           =     None
       lowercase_	:    Tuple           =     None
       lowercase_	:    Dict           =     []
       lowercase_	:    Union[str, Any]           =     {}  # {vertex:distance}




    def __lt__(			self  :       Optional[Any]  ,     a  :       int			):






       '''simple docstring'''






       return self.key < other.key




    def __repr__(			self  :       Union[str, Any]			):






       '''simple docstring'''






       return self.id




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Optional[int]			):






       '''simple docstring'''






       self.neighbors.append(a			)





    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       int  ,     a  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    int           =     weight




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   graph[a - 1].add_neighbor(graph[b - 1]							)
   graph[b - 1].add_neighbor(graph[a - 1]							)
   # add the edges:
   graph[a - 1].add_edge(graph[b - 1]						,		_UpperCamelCase							)
   graph[b - 1].add_edge(graph[a - 1]						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     []
   for u in graph:
      lowercase_	:    List[Any]           =     math.inf
      lowercase_	:    str           =     None
   lowercase_	:    Tuple           =     0
   lowercase_	:    Tuple           =     graph[:]
   while q:
      lowercase_	:    List[Any]           =     min(_UpperCamelCase							)
      q.remove(_UpperCamelCase							)
      for v in u.neighbors:
         if (v in q) and (u.edges[v.id] < v.key):
            lowercase_	:    Optional[int]           =     u
            lowercase_	:    Union[str, Any]           =     u.edges[v.id]
   for i in range(1						,		len(_UpperCamelCase							)							):
      a.append((int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)							)
   return a




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   for u in graph:
      lowercase_	:    str           =     math.inf
      lowercase_	:    int           =     None
   lowercase_	:    List[Any]           =     0

   lowercase_	:    str           =     list(_UpperCamelCase							)
   hq.heapify(_UpperCamelCase							)

   while h:
      lowercase_	:    List[Any]           =     hq.heappop(_UpperCamelCase							)
      for v in u.neighbors:
         if (v in h) and (u.edges[v.id] < v.key):
            lowercase_	:    str           =     u
            lowercase_	:    Optional[int]           =     u.edges[v.id]
            hq.heapify(_UpperCamelCase							)

   for i in range(1						,		len(_UpperCamelCase							)							):
      yield (int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""






if __name__ == "__main__":
 import doctest

 doctest.testmod()




