






"""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        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def __init__(   self:     Union[str, Any]				,		__A:     List[Any]				,		__A:     Dict=7				,		__A:     Optional[Any]=3				,		__A:     str=18				,		__A:     List[Any]=30				,		__A:     Tuple=400				,		__A:     Tuple=True				,		__A:     List[str]=None				,		__A:     List[Any]=True				,		):

												'''simple docstring'''

												a__          =  size if size is not None else {'''height''': 18, '''width''': 18}
												a__          =  parent
												a__          =  batch_size
												a__          =  num_channels
												a__          =  image_size
												a__          =  min_resolution
												a__          =  max_resolution
												a__          =  do_resize
												a__          =  size
												a__          =  do_normalize





							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												return {
												    # here we create 2 clusters for the sake of simplicity
												    "clusters": np.asarray(
												        [
												            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
												            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
												        ]    ),
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_normalize": self.do_normalize,
												}







@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE						(  lowercase_       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =ImageGPTImageProcessor if is_vision_available() else None




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  ImageGPTImageProcessingTester(self    )




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

												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  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  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  self.image_processing_class.from_dict(self.image_processor_dict    )
												self.assertEqual(image_processor.size				,		{'''height''': 18, '''width''': 18}    )

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




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  self.image_processing_class(**self.image_processor_dict    )
												a__          =  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  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.image_processing_class(**self.image_processor_dict    )

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

												a__          =  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  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  self.image_processing_class(**self.image_processor_dict    )

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

												a__          =  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  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												pass





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  load_dataset('''hf-internal-testing/fixtures_image_utils''' ,     split='''test''')

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

					a__          =  [imagea, imagea]

					return images







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





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''    )

												a__          =  prepare_images()

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

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

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

												# test batched
												a__          =  image_processing(__A				,		return_tensors='''pt'''    )

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

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










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a       :	List[str]             = logging.get_logger(__name__)

__a       :	int             = {
    'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='vit_mae'




							def __init__(   self:     str				,		__A:     str=768				,		__A:     Union[str, Any]=12				,		__A:     str=12				,		__A:     List[Any]=3072				,		__A:     List[Any]="gelu"				,		__A:     Union[str, Any]=0.0				,		__A:     Tuple=0.0				,		__A:     str=0.0_2				,		__A:     Tuple=1e-12				,		__A:     Dict=224				,		__A:     List[Any]=16				,		__A:     Tuple=3				,		__A:     str=True				,		__A:     Optional[Any]=16				,		__A:     List[Any]=512				,		__A:     Optional[Any]=8				,		__A:     Dict=2048				,		__A:     str=0.7_5				,		__A:     str=False				,		**__A:     Tuple				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  intermediate_size
												a__          =  hidden_act
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  initializer_range
												a__          =  layer_norm_eps
												a__          =  image_size
												a__          =  patch_size
												a__          =  num_channels
												a__          =  qkv_bias
												a__          =  decoder_num_attention_heads
												a__          =  decoder_hidden_size
												a__          =  decoder_num_hidden_layers
												a__          =  decoder_intermediate_size
												a__          =  mask_ratio
												a__          =  norm_pix_loss


