

'''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    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



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





				def   a__					(    self       )     ->							int:
											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    snake_case     (      lowercase_,						unittest.TestCase		):






				"""simple docstring"""
				_a    	=     ImageGPTImageProcessor if is_vision_available() else None



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				ImageGPTImageProcessingTester(self       )



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



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				self.image_processing_class(**self.image_processor_dict       )
											self.assertTrue(hasattr(_lowercase,     'clusters'       )       )
											self.assertTrue(hasattr(_lowercase,     'do_resize'       )       )
											self.assertTrue(hasattr(_lowercase,     'size'       )       )
											self.assertTrue(hasattr(_lowercase,     'do_normalize'       )       )



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

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



				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				self.image_processing_class(**self.image_processor_dict       )
											SCREAMING_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(_lowercase,     obj[key]       )       )
																		else:
																									self.assertEqual(obj[key],     _lowercase       )



				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				self.image_processing_class(**self.image_processor_dict       )

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

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



				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				self.image_processing_class(**self.image_processor_dict       )

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

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





				@unittest.skip('ImageGPT requires clusters at initialization'       )
				def   a__					(    self       )     ->							List[Any]:
											pass




def 							_UpperCamelCase				(   )      -> Tuple:
							SCREAMING_SNAKE_CASE_				=				load_dataset('hf-internal-testing/fixtures_image_utils'		,split='test'   )

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

							SCREAMING_SNAKE_CASE_				=				[imagea, imagea]

							return images






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






				"""simple docstring"""



				@slow
				def   a__					(    self       )     ->							Union[str, Any]:
											SCREAMING_SNAKE_CASE_				=				ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'       )

											SCREAMING_SNAKE_CASE_				=				prepare_images()

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

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

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

											# test batched
											SCREAMING_SNAKE_CASE_				=				image_processing(_lowercase,     return_tensors='pt'       )

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

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








'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE							:				List[Any]     	=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:				Tuple     	=			{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     """roberta"""



				def __init__(    self,     _lowercase=50265,     _lowercase=768,     _lowercase=12,     _lowercase=12,     _lowercase=3072,     _lowercase="gelu",     _lowercase=0.1,     _lowercase=0.1,     _lowercase=512,     _lowercase=2,     _lowercase=0.02,     _lowercase=1E-12,     _lowercase=1,     _lowercase=0,     _lowercase=2,     _lowercase="absolute",     _lowercase=True,     _lowercase=None,     **_lowercase,     )     ->							Dict:
											super().__init__(pad_token_id=_lowercase,     bos_token_id=_lowercase,     eos_token_id=_lowercase,     **_lowercase       )

											SCREAMING_SNAKE_CASE_				=				vocab_size
											SCREAMING_SNAKE_CASE_				=				hidden_size
											SCREAMING_SNAKE_CASE_				=				num_hidden_layers
											SCREAMING_SNAKE_CASE_				=				num_attention_heads
											SCREAMING_SNAKE_CASE_				=				hidden_act
											SCREAMING_SNAKE_CASE_				=				intermediate_size
											SCREAMING_SNAKE_CASE_				=				hidden_dropout_prob
											SCREAMING_SNAKE_CASE_				=				attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE_				=				max_position_embeddings
											SCREAMING_SNAKE_CASE_				=				type_vocab_size
											SCREAMING_SNAKE_CASE_				=				initializer_range
											SCREAMING_SNAKE_CASE_				=				layer_norm_eps
											SCREAMING_SNAKE_CASE_				=				position_embedding_type
											SCREAMING_SNAKE_CASE_				=				use_cache
											SCREAMING_SNAKE_CASE_				=				classifier_dropout






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				@property
				def   a__					(    self       )     ->							Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE_				=				{0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		SCREAMING_SNAKE_CASE_				=				{0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]       )





