





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					,							)     ->   int:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''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_     :     List[Any]       			=      ImageGPTImageProcessor if is_vision_available() else None






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  ImageGPTImageProcessingTester(self    )






			@property
			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()






			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    int				=  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    )     ->   Optional[Any]:


							'''simple docstring'''



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

							snake_case_						:    List[str]				=  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[str]				=  self.image_processing_class(**self.image_processor_dict    )
							snake_case_						:    Tuple				=  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    )     ->   List[Any]:


							'''simple docstring'''



							snake_case_						:    Dict				=  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_						:    int				=  self.image_processing_class.from_json_file(__magic_name__    ).to_dict()

							snake_case_						:    Dict				=  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    )     ->   List[Any]:


							'''simple docstring'''



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

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

							snake_case_						:    Optional[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    )     ->   Tuple:


							'''simple docstring'''



							pass






def      lowerCamelCase_						(				)		->						Tuple:






				"""simple docstring"""


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

				snake_case_						:    str				=  Image.open(dataset[4]['''file'''] )
				snake_case_						:    Union[str, Any]				=  Image.open(dataset[5]['''file'''] )

				snake_case_						:    str				=  [imagea, imagea]

				return images





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






			@slow
			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



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

							snake_case_						:    Optional[Any]				=  prepare_images()

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

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

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

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

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

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







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


lowerCAmelCase_    =				logging.get_logger(__name__)

lowerCAmelCase_    =				{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Any       			=      '''megatron-bert'''






			def __init__(self					,							__magic_name__=2_9056					,							__magic_name__=1024					,							__magic_name__=24					,							__magic_name__=16					,							__magic_name__=4096					,							__magic_name__="gelu"					,							__magic_name__=0.1					,							__magic_name__=0.1					,							__magic_name__=512					,							__magic_name__=2					,							__magic_name__=0.02					,							__magic_name__=1e-12					,							__magic_name__=0					,							__magic_name__="absolute"					,							__magic_name__=True					,							**__magic_name__					,							)     ->   Any:


							'''simple docstring'''



							super().__init__(pad_token_id=__magic_name__					,							**__magic_name__    )

							snake_case_						:    Union[str, Any]				=  vocab_size
							snake_case_						:    Dict				=  hidden_size
							snake_case_						:    Dict				=  num_hidden_layers
							snake_case_						:    Optional[int]				=  num_attention_heads
							snake_case_						:    int				=  hidden_act
							snake_case_						:    List[str]				=  intermediate_size
							snake_case_						:    Dict				=  hidden_dropout_prob
							snake_case_						:    str				=  attention_probs_dropout_prob
							snake_case_						:    str				=  max_position_embeddings
							snake_case_						:    Any				=  type_vocab_size
							snake_case_						:    int				=  initializer_range
							snake_case_						:    int				=  layer_norm_eps
							snake_case_						:    List[str]				=  position_embedding_type
							snake_case_						:    Dict				=  use_cache
