
'''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				):







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase=7  ,  UpperCAmelCase=3  ,  UpperCAmelCase=18  ,  UpperCAmelCase=30  ,  UpperCAmelCase=400  ,  UpperCAmelCase=True  ,  UpperCAmelCase=None  ,  UpperCAmelCase=True  ,  )					->       Dict:
														_snake_case       =  size if size is not None else {"""height""": 18, """width""": 18}
														_snake_case       =  parent
														_snake_case       =  batch_size
														_snake_case       =  num_channels
														_snake_case       =  image_size
														_snake_case       =  min_resolution
														_snake_case       =  max_resolution
														_snake_case       =  do_resize
														_snake_case       =  size
														_snake_case       =  do_normalize





							def 	lowercase							(self  )					->       str:
														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       _lowerCAmelCase   (   __snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						ImageGPTImageProcessor if is_vision_available() else None

							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  ImageGPTImageProcessingTester(self  )

							@property
							def 	lowercase							(self  )					->       Optional[Any]:
														return self.image_processor_tester.prepare_image_processor_dict()

							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  self.image_processing_class(**self.image_processor_dict  )
														self.assertTrue(hasattr(UpperCAmelCase  ,  """clusters"""  )  )
														self.assertTrue(hasattr(UpperCAmelCase  ,  """do_resize"""  )  )
														self.assertTrue(hasattr(UpperCAmelCase  ,  """size"""  )  )
														self.assertTrue(hasattr(UpperCAmelCase  ,  """do_normalize"""  )  )

							def 	lowercase							(self  )					->       Dict:
														_snake_case       =  self.image_processing_class.from_dict(self.image_processor_dict  )
														self.assertEqual(image_processor.size  ,  {"""height""": 18, """width""": 18}  )

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

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  self.image_processing_class(**self.image_processor_dict  )
														_snake_case       =  json.loads(image_processor.to_json_string()  )
														for key, value in self.image_processor_dict.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase  ,  obj[key]  )  )
																					else:
																												self.assertEqual(obj[key]  ,  UpperCAmelCase  )

							def 	lowercase							(self  )					->       str:
														_snake_case       =  self.image_processing_class(**self.image_processor_dict  )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					_snake_case       =  os.path.join(UpperCAmelCase  ,  """image_processor.json"""  )
																					image_processor_first.to_json_file(UpperCAmelCase  )
																					_snake_case       =  self.image_processing_class.from_json_file(UpperCAmelCase  ).to_dict()

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

							def 	lowercase							(self  )					->       int:
														_snake_case       =  self.image_processing_class(**self.image_processor_dict  )

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

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





							@unittest.skip("""ImageGPT requires clusters at initialization"""  )
							def 	lowercase							(self  )					->       int:
														pass






def  __SCREAMING_SNAKE_CASE				(  ):
							_snake_case       =  load_dataset("""hf-internal-testing/fixtures_image_utils"""							, split="""test"""				)

							_snake_case       =  Image.open(dataset[4]["""file"""]				)
							_snake_case       =  Image.open(dataset[5]["""file"""]				)

							_snake_case       =  [imagea, imagea]

							return images


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







							'''simple docstring'''

							@slow
							def 	lowercase							(self  )					->       Optional[Any]:
														_snake_case       =  ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""  )

														_snake_case       =  prepare_images()

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

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

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

														# test batched
														_snake_case       =  image_processing(UpperCAmelCase  ,  return_tensors="""pt"""  )

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

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

'''simple docstring'''
import logging

from transformers.configuration_utils import PretrainedConfig


__lowerCAmelCase												=	logging.getLogger(__name__)


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						"masked_bert"

							def __init__(self  ,  UpperCAmelCase=30522  ,  UpperCAmelCase=768  ,  UpperCAmelCase=12  ,  UpperCAmelCase=12  ,  UpperCAmelCase=3072  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=512  ,  UpperCAmelCase=2  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=1e-1_2  ,  UpperCAmelCase=0  ,  UpperCAmelCase="topK"  ,  UpperCAmelCase="constant"  ,  UpperCAmelCase=0.0  ,  **UpperCAmelCase  ,  )					->       int:
														super().__init__(pad_token_id=UpperCAmelCase  ,  **UpperCAmelCase  )

														_snake_case       =  vocab_size
														_snake_case       =  hidden_size
														_snake_case       =  num_hidden_layers
														_snake_case       =  num_attention_heads
														_snake_case       =  hidden_act
														_snake_case       =  intermediate_size
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  max_position_embeddings
														_snake_case       =  type_vocab_size
														_snake_case       =  initializer_range
														_snake_case       =  layer_norm_eps
														_snake_case       =  pruning_method
														_snake_case       =  mask_init
														_snake_case       =  mask_scale