



"""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					:     int					,     A					:     Tuple					,     A					:     Union[str, Any]=7					,     A					:     Optional[Any]=3					,     A					:     Union[str, Any]=1_8					,     A					:     List[str]=3_0					,     A					:     Dict=4_0_0					,     A					:     Dict=True					,     A					:     Union[str, Any]=None					,     A					:     Union[str, Any]=True					,     ):
           _UpperCAmelCase      :     Optional[Any]        =   size if size is not None else {"height": 1_8, "width": 1_8}
           _UpperCAmelCase      :     Tuple        =   parent
           _UpperCAmelCase      :     List[Any]        =   batch_size
           _UpperCAmelCase      :     Optional[Any]        =   num_channels
           _UpperCAmelCase      :     Union[str, Any]        =   image_size
           _UpperCAmelCase      :     Optional[Any]        =   min_resolution
           _UpperCAmelCase      :     List[str]        =   max_resolution
           _UpperCAmelCase      :     Optional[Any]        =   do_resize
           _UpperCAmelCase      :     List[Any]        =   size
           _UpperCAmelCase      :     str        =   do_normalize



      def 						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      UpperCAmelCase_  (    _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       List[str]								=			ImageGPTImageProcessor if is_vision_available() else None



      def 						snake_case_ (					self					:     Union[str, Any]						):
           _UpperCAmelCase      :     Dict        =   ImageGPTImageProcessingTester(self						)



      @property
      def 						snake_case_ (					self					:     Optional[int]						):
           return self.image_processor_tester.prepare_image_processor_dict()



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Any        =   self.image_processing_class(**self.image_processor_dict						)
           self.assertTrue(hasattr(A					,     "clusters"						)						)
           self.assertTrue(hasattr(A					,     "do_resize"						)						)
           self.assertTrue(hasattr(A					,     "size"						)						)
           self.assertTrue(hasattr(A					,     "do_normalize"						)						)



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     int        =   self.image_processing_class.from_dict(self.image_processor_dict						)
           self.assertEqual(image_processor.size					,     {"height": 1_8, "width": 1_8}						)

           _UpperCAmelCase      :     Union[str, Any]        =   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 						snake_case_ (					self					:     Union[str, Any]						):
           _UpperCAmelCase      :     List[Any]        =   self.image_processing_class(**self.image_processor_dict						)
           _UpperCAmelCase      :     str        =   json.loads(image_processor.to_json_string()						)
           for key, value in self.image_processor_dict.items():
                if key == "clusters":
                     self.assertTrue(np.array_equal(A					,     obj[key]						)						)
                else:
                     self.assertEqual(obj[key]					,     A						)



      def 						snake_case_ (					self					:     Union[str, Any]						):
           _UpperCAmelCase      :     Optional[Any]        =   self.image_processing_class(**self.image_processor_dict						)

           with tempfile.TemporaryDirectory() as tmpdirname:
                _UpperCAmelCase      :     Optional[int]        =   os.path.join(A					,     "image_processor.json"						)
                image_processor_first.to_json_file(A						)
                _UpperCAmelCase      :     List[Any]        =   self.image_processing_class.from_json_file(A						).to_dict()

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



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Tuple        =   self.image_processing_class(**self.image_processor_dict						)

           with tempfile.TemporaryDirectory() as tmpdirname:
                image_processor_first.save_pretrained(A						)
                _UpperCAmelCase      :     int        =   self.image_processing_class.from_pretrained(A						).to_dict()

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



      @unittest.skip("ImageGPT requires clusters at initialization"						)
      def 						snake_case_ (					self					:     Optional[Any]						):
           pass







def 						__snake_case						(				)  ->       Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase      :     str        =   load_dataset("hf-internal-testing/fixtures_image_utils"						,  split="test"    )

     _UpperCAmelCase      :     Union[str, Any]        =   Image.open(dataset[4]["file"]    )
     _UpperCAmelCase      :     List[str]        =   Image.open(dataset[5]["file"]    )

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

     return images






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



      @slow
      def 						snake_case_ (					self					:     Tuple						):
           _UpperCAmelCase      :     List[str]        =   ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"						)

           _UpperCAmelCase      :     Union[str, Any]        =   prepare_images()

           # test non-batched
           _UpperCAmelCase      :     int        =   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)						)

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

           # test batched
           _UpperCAmelCase      :     Union[str, Any]        =   image_processing(A					,     return_tensors="pt"						)

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

           _UpperCAmelCase      :     Any        =   [3_0_3, 1_3, 1_3]
           self.assertEqual(encoding.input_ids[1, -3:].tolist()					,     A						)





"""simple docstring"""




from __future__ import annotations

from typing import Any

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						list[Any]    )  ->       None:





     '''simple docstring'''

     create_state_space_tree(SCREAMING_SNAKE_CASE__						,  []						,  0    )

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						list[Any]						,  SCREAMING_SNAKE_CASE__     :						list[Any]						,  SCREAMING_SNAKE_CASE__     :						int    )  ->       None:





     '''simple docstring'''

     if index == len(SCREAMING_SNAKE_CASE__    ):
          print(SCREAMING_SNAKE_CASE__    )
          return

     create_state_space_tree(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  index + 1    )
     current_subsequence.append(sequence[index]    )
     create_state_space_tree(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  index + 1    )
     current_subsequence.pop()


if __name__ == "__main__":
 _lowerCAmelCase    : list[Any]							=							[3, 1, 2, 4]
 generate_all_subsequences(seq)

 seq.clear()
 seq.extend(["A", "B", "C"])
 generate_all_subsequences(seq)
