
'''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			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=7					,						UpperCamelCase=3					,						UpperCamelCase=18					,						UpperCamelCase=30					,						UpperCamelCase=400					,						UpperCamelCase=True					,						UpperCamelCase=None					,						UpperCamelCase=True					,						):
								_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     lowercase       (				self      ):
								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 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Union[str, Any]       =					ImageGPTImageProcessor if is_vision_available() else None




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= ImageGPTImageProcessingTester(self      )




		@property
		def     lowercase       (				self      ):
								return self.image_processor_tester.prepare_image_processor_dict()




		def     lowercase       (				self      ):
								_SCREAMING_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      ):
								_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     lowercase       (				self      ):
								_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(UpperCamelCase					,						obj[key]      )      )
														else:
																				self.assertEqual(obj[key]					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )

								with tempfile.TemporaryDirectory() as tmpdirname:
														_SCREAMING_SNAKE_CASE				= os.path.join(UpperCamelCase					,						"image_processor.json"      )
														image_processor_first.to_json_file(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.image_processing_class.from_json_file(UpperCamelCase      ).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(UpperCamelCase					,						image_processor_second[key]      )      )
														else:
																				self.assertEqual(image_processor_first[key]					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )

								with tempfile.TemporaryDirectory() as tmpdirname:
														image_processor_first.save_pretrained(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.image_processing_class.from_pretrained(UpperCamelCase      ).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(UpperCamelCase					,						image_processor_second[key]      )      )
														else:
																				self.assertEqual(image_processor_first[key]					,						UpperCamelCase      )




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



def 			_a		(	):
						_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 			lowerCAmelCase (				unittest.TestCase			):




		@slow
		def     lowercase       (				self      ):
								_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, 1_024)      )

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

								# test batched
								_SCREAMING_SNAKE_CASE				= image_processing(UpperCamelCase					,						return_tensors="pt"      )

								self.assertIsInstance(encoding.input_ids					,						torch.LongTensor      )
								self.assertEqual(encoding.input_ids.shape					,						(2, 1_024)      )

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

'''simple docstring'''







import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Optional[int]       =					["""image_processor""", """tokenizer"""]
		a							:					Optional[int]       =					"""BlipImageProcessor"""
		a							:					Optional[int]       =					"""AutoTokenizer"""




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								super().__init__(UpperCamelCase					,						UpperCamelCase      )

								# add QFormer tokenizer
								_SCREAMING_SNAKE_CASE				= qformer_tokenizer




		def __call__(				self					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = True					,						UpperCamelCase = False					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = 0					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = True					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								if images is None and text is None:
														raise ValueError("You have to specify at least images or text."      )

								_SCREAMING_SNAKE_CASE				= BatchFeature()

								if text is not None:
														_SCREAMING_SNAKE_CASE				= self.tokenizer(
														    text=UpperCamelCase					,						add_special_tokens=UpperCamelCase					,						padding=UpperCamelCase					,						truncation=UpperCamelCase					,						max_length=UpperCamelCase					,						stride=UpperCamelCase					,						pad_to_multiple_of=UpperCamelCase					,						return_attention_mask=UpperCamelCase					,						return_overflowing_tokens=UpperCamelCase					,						return_special_tokens_mask=UpperCamelCase					,						return_offsets_mapping=UpperCamelCase					,						return_token_type_ids=UpperCamelCase					,						return_length=UpperCamelCase					,						verbose=UpperCamelCase					,						return_tensors=UpperCamelCase					,						**UpperCamelCase					,						)
														encoding.update(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.qformer_tokenizer(
														    text=UpperCamelCase					,						add_special_tokens=UpperCamelCase					,						padding=UpperCamelCase					,						truncation=UpperCamelCase					,						max_length=UpperCamelCase					,						stride=UpperCamelCase					,						pad_to_multiple_of=UpperCamelCase					,						return_attention_mask=UpperCamelCase					,						return_overflowing_tokens=UpperCamelCase					,						return_special_tokens_mask=UpperCamelCase					,						return_offsets_mapping=UpperCamelCase					,						return_token_type_ids=UpperCamelCase					,						return_length=UpperCamelCase					,						verbose=UpperCamelCase					,						return_tensors=UpperCamelCase					,						**UpperCamelCase					,						)
														_SCREAMING_SNAKE_CASE				= qformer_text_encoding.pop("input_ids"      )
														_SCREAMING_SNAKE_CASE				= qformer_text_encoding.pop("attention_mask"      )

								if images is not None:
														_SCREAMING_SNAKE_CASE				= self.image_processor(UpperCamelCase					,						return_tensors=UpperCamelCase      )
														encoding.update(UpperCamelCase      )

								return encoding




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.tokenizer.batch_decode(*UpperCamelCase					,						**UpperCamelCase      )




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.tokenizer.decode(*UpperCamelCase					,						**UpperCamelCase      )




		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer.model_input_names
								_SCREAMING_SNAKE_CASE				= self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )




		def     lowercase       (				self					,						UpperCamelCase					,						**UpperCamelCase      ):
								if os.path.isfile(UpperCamelCase      ):
														raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'      )
								os.makedirs(UpperCamelCase					,						exist_ok=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= os.path.join(UpperCamelCase					,						"qformer_tokenizer"      )
								self.qformer_tokenizer.save_pretrained(UpperCamelCase      )
								return super().save_pretrained(UpperCamelCase					,						**UpperCamelCase      )




		@classmethod
		def     lowercase       (				cls					,						UpperCamelCase					,						**UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCamelCase					,						subfolder="qformer_tokenizer"      )
								_SCREAMING_SNAKE_CASE				= cls._get_arguments_from_pretrained(UpperCamelCase					,						**UpperCamelCase      )
								args.append(UpperCamelCase      )
								return cls(*UpperCamelCase      )