





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 ,							__magic_name__ ,							__magic_name__=7 ,							__magic_name__=3 ,							__magic_name__=18 ,							__magic_name__=30 ,							__magic_name__=400 ,							__magic_name__=True ,							__magic_name__=None ,							__magic_name__=True ,							)					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Dict           =  size if size is not None else {'''height''': 18, '''width''': 18}
							snake_case_						:   Optional[Any]           =  parent
							snake_case_						:   Union[str, Any]           =  batch_size
							snake_case_						:   Optional[Any]           =  num_channels
							snake_case_						:   int           =  image_size
							snake_case_						:   List[str]           =  min_resolution
							snake_case_						:   Tuple           =  max_resolution
							snake_case_						:   Optional[Any]           =  do_resize
							snake_case_						:   Optional[Any]           =  size
							snake_case_						:   List[str]           =  do_normalize






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


							'''simple docstring'''



							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					):
			lowerCamelCase_					:					int	      =				ImageGPTImageProcessor if is_vision_available() else None






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


							'''simple docstring'''



							snake_case_						:   List[str]           =  ImageGPTImageProcessingTester(self   )






			@property
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(__magic_name__ ,							'''clusters'''   )   )
							self.assertTrue(hasattr(__magic_name__ ,							'''do_resize'''   )   )
							self.assertTrue(hasattr(__magic_name__ ,							'''size'''   )   )
							self.assertTrue(hasattr(__magic_name__ ,							'''do_normalize'''   )   )






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  self.image_processing_class.from_dict(self.image_processor_dict   )
							self.assertEqual(image_processor.size ,							{'''height''': 18, '''width''': 18}   )

							snake_case_						:   Tuple           =  self.image_processing_class.from_dict(self.image_processor_dict ,							size=42   )
							self.assertEqual(image_processor.size ,							{'''height''': 42, '''width''': 42}   )






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  self.image_processing_class(**self.image_processor_dict   )
							snake_case_						:   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(__magic_name__ ,							obj[key]   )   )
											else:
															self.assertEqual(obj[key] ,							__magic_name__   )






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   str           =  self.image_processing_class(**self.image_processor_dict   )

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

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






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  self.image_processing_class(**self.image_processor_dict   )

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

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






			@unittest.skip('''ImageGPT requires clusters at initialization'''   )
			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							pass






def 					lowerCamelCase_						(    )		->						int:






				"""simple docstring"""


				snake_case_						:   Dict           =  load_dataset('''hf-internal-testing/fixtures_image_utils'''			,    split='''test'''			)

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

				snake_case_						:   Any           =  [imagea, imagea]

				return images





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






			@slow
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   int           =  ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''   )

							snake_case_						:   Dict           =  prepare_images()

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

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

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

							# test batched
							snake_case_						:   Optional[Any]           =  image_processing(__magic_name__ ,							return_tensors='''pt'''   )

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

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







from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowerCAmelCase_    =    TypeVar('''KEY''')
lowerCAmelCase_    =    TypeVar('''VAL''')



@dataclass(frozen=_a,					slots=_a					)
class  __lowerCAmelCase	(      Generic[KEY, VAL]					):
			lowerCamelCase_					:					KEY
			lowerCamelCase_					:					VAL




class  __lowerCAmelCase	(      _Item					):






			def __init__(self   )					->       None:


							'''simple docstring'''



							super().__init__(__magic_name__ ,							__magic_name__   )






			def __bool__(self   )					->       bool:


							'''simple docstring'''



							return False




lowerCAmelCase_    =    _DeletedItem()



class  __lowerCAmelCase	(      MutableMapping[KEY, VAL]					):






			def __init__(self ,							__magic_name__ = 8 ,							__magic_name__ = 0.75   )					->       None:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  initial_block_size
							snake_case_						:   list[_Item | None]           =  [None] * initial_block_size
							assert 0.0 < capacity_factor < 1.0
							snake_case_						:   List[str]           =  capacity_factor
							snake_case_						:   int           =  0






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return hash(__magic_name__   ) % len(self._buckets   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return (ind + 1) % len(self._buckets   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       bool:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  self._buckets[ind]
							if not stored:
											snake_case_						:   Optional[Any]           =  _Item(__magic_name__ ,							__magic_name__   )
											self._len += 1
											return True
							elif stored.key == key:
											snake_case_						:   List[Any]           =  _Item(__magic_name__ ,							__magic_name__   )
											return True
							else:
											return False






			def       lowerCamelCase  (self   )					->       bool:


							'''simple docstring'''



							snake_case_						:   Any           =  len(self._buckets   ) * self._capacity_factor
							return len(self   ) >= int(__magic_name__   )






			def       lowerCamelCase  (self   )					->       bool:


							'''simple docstring'''



							if len(self._buckets   ) <= self._initial_block_size:
											return False
							snake_case_						:   int           =  len(self._buckets   ) * self._capacity_factor / 2
							return len(self   ) < limit






			def       lowerCamelCase  (self ,							__magic_name__   )					->       None:


							'''simple docstring'''



							snake_case_						:   List[str]           =  self._buckets
							snake_case_						:   int           =  [None] * new_size
							snake_case_						:   Optional[int]           =  0
							for item in old_buckets:
											if item:
															self._add_item(item.key ,							item.val   )






			def       lowerCamelCase  (self   )					->       None:


							'''simple docstring'''



							self._resize(len(self._buckets   ) * 2   )






			def       lowerCamelCase  (self   )					->       None:


							'''simple docstring'''



							self._resize(len(self._buckets   ) // 2   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Iterator[int]:


							'''simple docstring'''



							snake_case_						:   Dict           =  self._get_bucket_index(__magic_name__   )
							for _ in range(len(self._buckets   )   ):
											yield ind
											snake_case_						:   Tuple           =  self._get_next_ind(__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       None:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											if self._try_set(__magic_name__ ,							__magic_name__ ,							__magic_name__   ):
															break






			def __setitem__(self ,							__magic_name__ ,							__magic_name__   )					->       None:


							'''simple docstring'''



							if self._is_full():
											self._size_up()

							self._add_item(__magic_name__ ,							__magic_name__   )






			def __delitem__(self ,							__magic_name__   )					->       None:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											snake_case_						:   Optional[Any]           =  self._buckets[ind]
											if item is None:
															raise KeyError(__magic_name__   )
											if item is _deleted:
															continue
											if item.key == key:
															snake_case_						:   Union[str, Any]           =  _deleted
															self._len -= 1
															break
							if self._is_sparse():
											self._size_down()






			def __getitem__(self ,							__magic_name__   )					->       VAL:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											snake_case_						:   List[Any]           =  self._buckets[ind]
											if item is None:
															break
											if item is _deleted:
															continue
											if item.key == key:
															return item.val
							raise KeyError(__magic_name__   )






			def __len__(self   )					->       int:


							'''simple docstring'''



							return self._len






			def __iter__(self   )					->       Iterator[KEY]:


							'''simple docstring'''



							yield from (item.key for item in self._buckets if item)






			def __repr__(self   )					->       str:


							'''simple docstring'''



							snake_case_						:   List[str]           =  ''' ,'''.join(
							    F'''{item.key}: {item.val}''' for item in self._buckets if item   )
							return F'''HashMap({val_string})'''
