


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 						A__							(		unittest.TestCase  ):




						def __init__(       self					:      Any				,    _a					:      List[str]				,    _a					:      Dict=7				,    _a					:      Union[str, Any]=3				,    _a					:      List[str]=18				,    _a					:      str=30				,    _a					:      Dict=400				,    _a					:      Optional[int]=True				,    _a					:      Union[str, Any]=None				,    _a					:      str=True				,    )							->     Optional[int]:


										"""simple docstring"""





										_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 							__UpperCamelCase				(       self					:      List[str]  )							->     List[str]:


										"""simple docstring"""





										return {
										    # here we create 2 clusters for the sake of simplicity
										    "clusters": np.asarray(
										        [
										            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
										            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
										        ]  ),
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										}






@require_torch
@require_vision
class 						A__							(		snake_case__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						ImageGPTImageProcessor if is_vision_available() else None




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =ImageGPTImageProcessingTester(self  )




						@property
						def 							__UpperCamelCase				(       self					:      int  )							->     Any:


										"""simple docstring"""





										return self.image_processor_tester.prepare_image_processor_dict()




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     List[Any]:


										"""simple docstring"""





										_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 							__UpperCamelCase				(       self					:      Tuple  )							->     List[Any]:


										"""simple docstring"""





										_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 							__UpperCamelCase				(       self					:      str  )							->     List[Any]:


										"""simple docstring"""





										_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 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     Dict:


										"""simple docstring"""





										_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 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Tuple:


										"""simple docstring"""





										_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 							__UpperCamelCase				(       self					:      int  )							->     Tuple:


										"""simple docstring"""





										pass







def 						lowerCamelCase(   ):
				_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 						A__							(		unittest.TestCase  ):




						@slow
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Union[str, Any]:


										"""simple docstring"""





										_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_  )



from collections import OrderedDict
from typing import Mapping

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


snake_case_						:			str													=							logging.get_logger(__name__)

snake_case_						:			str													=							{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"data2vec-text"




						def __init__(       self					:      Optional[int]				,    _a					:      Union[str, Any]=3_0522				,    _a					:      Any=768				,    _a					:      List[Any]=12				,    _a					:      Any=12				,    _a					:      List[str]=3072				,    _a					:      Optional[Any]="gelu"				,    _a					:      List[str]=0.1				,    _a					:      Optional[Any]=0.1				,    _a					:      Optional[Any]=512				,    _a					:      Any=2				,    _a					:      Optional[Any]=0.02				,    _a					:      Dict=1E-12				,    _a					:      int=1				,    _a					:      Any=0				,    _a					:      List[Any]=2				,    _a					:      Dict="absolute"				,    _a					:      Optional[Any]=True				,    _a					:      Dict=None				,    **_a					:      Optional[Any]				,    )							->     Union[str, Any]:


										"""simple docstring"""





										super().__init__(pad_token_id=_a				,    bos_token_id=_a				,    eos_token_id=_a				,    **_a  )

										_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 						A__							(		UpperCamelCase__  ):




						@property
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										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),
										    ]  )