






"""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    __lowerCamelCase    (  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 ,				)		->       Any:
       UpperCamelCase__         =    size if size is not None else {'height': 18, 'width': 18}
       UpperCamelCase__         =    parent
       UpperCamelCase__         =    batch_size
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    image_size
       UpperCamelCase__         =    min_resolution
       UpperCamelCase__         =    max_resolution
       UpperCamelCase__         =    do_resize
       UpperCamelCase__         =    size
       UpperCamelCase__         =    do_normalize







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       return {
           # here we create 2 clusters for the sake of simplicity
           "clusters": np.asarray(
               [
                   [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
                   [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
               ]      ),
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
       }







@require_torch
@require_vision
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				Any 						=ImageGPTImageProcessor if is_vision_available() else None

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    ImageGPTImageProcessingTester(self      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return self.image_processor_tester.prepare_image_processor_dict()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    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 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size ,				{'height': 18, 'width': 18}      )

       UpperCamelCase__         =    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      )		->       Optional[Any]:
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       UpperCamelCase__         =    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 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )

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

       UpperCamelCase__         =    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 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )

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

       UpperCamelCase__         =    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 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       pass







def   lowerCAmelCase_( )					->	List[Any]:
    """simple docstring"""

    UpperCamelCase__         =    load_dataset('hf-internal-testing/fixtures_image_utils' ,	split='test'					)

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

    UpperCamelCase__         =    [imagea, imagea]

    return images





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

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

       UpperCamelCase__         =    prepare_images()

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

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

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

       # test batched
       UpperCamelCase__         =    image_processing(snake_case_ ,				return_tensors='pt'      )

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

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












"""simple docstring"""





from __future__ import annotations

from typing import Generic, TypeVar

A__					: Any=	TypeVar("""T""")
class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    data
       UpperCamelCase__         =    self
       UpperCamelCase__         =    0







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # map from node name to the node object
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # create a new set with x as its member
       UpperCamelCase__         =    DisjointSetTreeNode(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       DisjointSetTreeNode[T]:
       # find the set x belongs to (with path-compression)
       UpperCamelCase__         =    self.map[data]
       if elem_ref != elem_ref.parent:
           UpperCamelCase__         =    self.find_set(elem_ref.parent.data      )
       return elem_ref.parent

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # helper function for union operation
       if nodea.rank > nodea.rank:
           UpperCamelCase__         =    nodea
       else:
           UpperCamelCase__         =    nodea
           if nodea.rank == nodea.rank:
               nodea.rank += 1







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # merge 2 disjoint sets
       self.link(self.find_set(snake_case_      ) ,				self.find_set(snake_case_      )      )







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # connections: map from the node to the neighbouring nodes (with weights)
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # add a node ONLY if its not present in the graph
       if node not in self.connections:
           UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       None:
       # add an edge with the given weight
       self.add_node(snake_case_      )
       self.add_node(snake_case_      )
       UpperCamelCase__         =    weight
       UpperCamelCase__         =    weight







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       GraphUndirectedWeighted[T]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    set()
       for start in self.connections:
           for end in self.connections[start]:
               if (start, end) not in seen:
                   seen.add((end, start)      )
                   edges.append((start, end, self.connections[start][end])      )
       edges.sort(key=lambda snake_case_      : x[2]      )

       # creating the disjoint set
       UpperCamelCase__         =    DisjointSetTree[T]()
       for node in self.connections:
           disjoint_set.make_set(snake_case_      )

       # MST generation
       UpperCamelCase__         =    0
       UpperCamelCase__         =    0
       UpperCamelCase__         =    GraphUndirectedWeighted[T]()
       while num_edges < len(self.connections      ) - 1:
           UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    edges[index]
           index += 1
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           if parent_u != parent_v:
               num_edges += 1
               graph.add_edge(snake_case_ ,				snake_case_ ,				snake_case_      )
               disjoint_set.union(snake_case_ ,				snake_case_      )
       return graph




