





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


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







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Optional[Any]:
					__A	:						int       =				b.T
					__A	:						List[Any]       =				np.sum(np.square(a  )					,						axis=1  )
					__A	:						int       =				np.sum(np.square(a  )					,						axis=0  )
					__A	:						Any       =				np.matmul(a					,						a  )
					__A	:						Optional[Any]       =				aa[:, None] - 2 * ab + ba[None, :]
					return d







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Any:
					__A	:						Tuple       =				x.reshape(-1					,						3  )
					__A	:						Any       =				squared_euclidean_distance(a					,						a  )
					return np.argmin(a					,						axis=1  )





class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  str					    =	['''pixel_values''']



				def __init__(			self     ,					_A = None     ,					_A = True     ,					_A = None     ,					_A = PILImageResampling.BILINEAR     ,					_A = True     ,					_A = True     ,					**_A     ,					):
									super().__init__(**_A     )
									__A	:						Optional[Any]       =				size if size is not None else {'height': 256, 'width': 256}
									__A	:						Any       =				get_size_dict(_A     )
									__A	:						List[str]       =				np.array(_A     ) if clusters is not None else None
									__A	:						int       =				do_resize
									__A	:						Union[str, Any]       =				size
									__A	:						Dict       =				resample
									__A	:						List[Any]       =				do_normalize
									__A	:						str       =				do_color_quantize



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A = PILImageResampling.BILINEAR     ,					_A = None     ,					**_A     ,					):
									__A	:						Optional[Any]       =				get_size_dict(_A     )
									if "height" not in size or "width" not in size:
														raise ValueError(F"""Size dictionary must contain both height and width keys. Got {size.keys()}"""     )
									return resize(
									    _A     ,					size=(size['height'], size['width'])     ,					resample=_A     ,					data_format=_A     ,					**_A     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = None     ,					):
									__A	:						Dict       =				rescale(image=_A     ,					scale=1 / 1_2_7.5     ,					data_format=_A     )
									__A	:						str       =				image - 1
									return image





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = ChannelDimension.FIRST     ,					**_A     ,					):
									__A	:						Any       =				do_resize if do_resize is not None else self.do_resize
									__A	:						Dict       =				size if size is not None else self.size
									__A	:						Union[str, Any]       =				get_size_dict(_A     )
									__A	:						Optional[Any]       =				resample if resample is not None else self.resample
									__A	:						Optional[Any]       =				do_normalize if do_normalize is not None else self.do_normalize
									__A	:						Optional[int]       =				do_color_quantize if do_color_quantize is not None else self.do_color_quantize
									__A	:						List[str]       =				clusters if clusters is not None else self.clusters
									__A	:						str       =				np.array(_A     )

									__A	:						int       =				make_list_of_images(_A     )

									if not valid_images(_A     ):
														raise ValueError(
														    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
														    'torch.Tensor, tf.Tensor or jax.ndarray.'     )

									if do_resize and size is None or resample is None:
														raise ValueError('Size and resample must be specified if do_resize is True.'     )

									if do_color_quantize and clusters is None:
														raise ValueError('Clusters must be specified if do_color_quantize is True.'     )

									# All transformations expect numpy arrays.
									__A	:						str       =				[to_numpy_array(_A     ) for image in images]

									if do_resize:
														__A	:						int       =				[self.resize(image=_A     ,					size=_A     ,					resample=_A     ) for image in images]

									if do_normalize:
														__A	:						List[Any]       =				[self.normalize(image=_A     ) for image in images]

									if do_color_quantize:
														__A	:						List[Any]       =				[to_channel_dimension_format(_A     ,					ChannelDimension.LAST     ) for image in images]
														# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
														__A	:						List[Any]       =				np.array(_A     )
														__A	:						str       =				color_quantize(_A     ,					_A     ).reshape(images.shape[:-1]     )

														# flatten to (batch_size, height*width)
														__A	:						Dict       =				images.shape[0]
														__A	:						List[Any]       =				images.reshape(_A     ,					-1     )

														# We need to convert back to a list of images to keep consistent behaviour across processors.
														__A	:						Union[str, Any]       =				list(_A     )
									else:
														__A	:						Any       =				[to_channel_dimension_format(_A     ,					_A     ) for image in images]

									__A	:						List[Any]       =				{'input_ids': images}
									return BatchFeature(data=_A     ,					tensor_type=_A     )











import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import OwlViTImageProcessor, OwlViTProcessor





@require_vision
class 						_A(					unittest.TestCase   ):


				"""simple docstring"""


				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				tempfile.mkdtemp()

									# fmt: off
									__A	:						List[str]       =				['', 'l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
									# fmt: on
									__A	:						Union[str, Any]       =				dict(zip(_A     ,					range(len(_A     )     )     )     )
									__A	:						Optional[int]       =				['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
									__A	:						int       =				{'unk_token': '<unk>'}

									__A	:						Optional[int]       =				os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES['vocab_file']     )
									__A	:						int       =				os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES['merges_file']     )
									with open(self.vocab_file     ,					'w'     ,					encoding='utf-8'     ) as fp:
														fp.write(json.dumps(_A     ) + '\n'     )
									with open(self.merges_file     ,					'w'     ,					encoding='utf-8'     ) as fp:
														fp.write('\n'.join(_A     )     )

									__A	:						List[Any]       =				{
									    'do_resize': True,
									    'size': 20,
									    'do_center_crop': True,
									    'crop_size': 18,
									    'do_normalize': True,
									    'image_mean': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
									    'image_std': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
									}
									__A	:						Optional[int]       =				os.path.join(self.tmpdirname     ,					_A     )
									with open(self.image_processor_file     ,					'w'     ,					encoding='utf-8'     ) as fp:
														json.dump(_A     ,					_A     )



				def 					UpperCAmelCase_		(			self     ,					**_A     ):
									return CLIPTokenizer.from_pretrained(self.tmpdirname     ,					pad_token='!'     ,					**_A     )



				def 					UpperCAmelCase_		(			self     ,					**_A     ):
									return CLIPTokenizerFast.from_pretrained(self.tmpdirname     ,					pad_token='!'     ,					**_A     )



				def 					UpperCAmelCase_		(			self     ,					**_A     ):
									return OwlViTImageProcessor.from_pretrained(self.tmpdirname     ,					**_A     )



				def 					UpperCAmelCase_		(			self     ):
									shutil.rmtree(self.tmpdirname     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				[np.random.randint(255     ,					size=(3, 30, 400)     ,					dtype=np.uinta     )]

									__A	:						Optional[int]       =				[Image.fromarray(np.moveaxis(_A     ,					0     ,					-1     )     ) for x in image_inputs]

									return image_inputs



				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				self.get_tokenizer()
									__A	:						str       =				self.get_rust_tokenizer()
									__A	:						List[str]       =				self.get_image_processor()

									__A	:						Optional[int]       =				OwlViTProcessor(tokenizer=_A     ,					image_processor=_A     )
									processor_slow.save_pretrained(self.tmpdirname     )
									__A	:						int       =				OwlViTProcessor.from_pretrained(self.tmpdirname     ,					use_fast=_A     )

									__A	:						Optional[Any]       =				OwlViTProcessor(tokenizer=_A     ,					image_processor=_A     )
									processor_fast.save_pretrained(self.tmpdirname     )
									__A	:						Optional[Any]       =				OwlViTProcessor.from_pretrained(self.tmpdirname     )

									self.assertEqual(processor_slow.tokenizer.get_vocab()     ,					tokenizer_slow.get_vocab()     )
									self.assertEqual(processor_fast.tokenizer.get_vocab()     ,					tokenizer_fast.get_vocab()     )
									self.assertEqual(tokenizer_slow.get_vocab()     ,					tokenizer_fast.get_vocab()     )
									self.assertIsInstance(processor_slow.tokenizer     ,					_A     )
									self.assertIsInstance(processor_fast.tokenizer     ,					_A     )

									self.assertEqual(processor_slow.image_processor.to_json_string()     ,					image_processor.to_json_string()     )
									self.assertEqual(processor_fast.image_processor.to_json_string()     ,					image_processor.to_json_string()     )
									self.assertIsInstance(processor_slow.image_processor     ,					_A     )
									self.assertIsInstance(processor_fast.image_processor     ,					_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[str]       =				OwlViTProcessor(tokenizer=self.get_tokenizer()     ,					image_processor=self.get_image_processor()     )
									processor.save_pretrained(self.tmpdirname     )

									__A	:						Optional[int]       =				self.get_tokenizer(bos_token='(BOS)'     ,					eos_token='(EOS)'     )
									__A	:						Optional[int]       =				self.get_image_processor(do_normalize=_A     )

									__A	:						Any       =				OwlViTProcessor.from_pretrained(
									    self.tmpdirname     ,					bos_token='(BOS)'     ,					eos_token='(EOS)'     ,					do_normalize=_A     )

									self.assertEqual(processor.tokenizer.get_vocab()     ,					tokenizer_add_kwargs.get_vocab()     )
									self.assertIsInstance(processor.tokenizer     ,					_A     )

									self.assertEqual(processor.image_processor.to_json_string()     ,					image_processor_add_kwargs.to_json_string()     )
									self.assertIsInstance(processor.image_processor     ,					_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[Any]       =				self.get_image_processor()
									__A	:						Optional[Any]       =				self.get_tokenizer()

									__A	:						Union[str, Any]       =				OwlViTProcessor(tokenizer=_A     ,					image_processor=_A     )

									__A	:						Union[str, Any]       =				self.prepare_image_inputs()

									__A	:						int       =				image_processor(_A     ,					return_tensors='np'     )
									__A	:						str       =				processor(images=_A     ,					return_tensors='np'     )

									for key in input_image_proc.keys():
														self.assertAlmostEqual(input_image_proc[key].sum()     ,					input_processor[key].sum()     ,					delta=1e-2     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						str       =				self.get_image_processor()
									__A	:						str       =				self.get_tokenizer()

									__A	:						Tuple       =				OwlViTProcessor(tokenizer=_A     ,					image_processor=_A     )

									__A	:						str       =				'lower newer'

									__A	:						str       =				processor(text=_A     ,					return_tensors='np'     )

									__A	:						List[str]       =				tokenizer(_A     ,					return_tensors='np'     )

									for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key][0].tolist()     ,					encoded_processor[key][0].tolist()     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				self.get_image_processor()
									__A	:						Optional[int]       =				self.get_tokenizer()

									__A	:						List[str]       =				OwlViTProcessor(tokenizer=_A     ,					image_processor=_A     )

									__A	:						Any       =				'lower newer'
									__A	:						Optional[Any]       =				self.prepare_image_inputs()

									__A	:						List[Any]       =				processor(text=_A     ,					images=_A     )

									self.assertListEqual(list(inputs.keys()     )     ,					['input_ids', 'attention_mask', 'pixel_values']     )

									# test if it raises when no input is passed
									with pytest.raises(_A     ):
														processor()



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Any       =				'google/owlvit-base-patch32'
									__A	:						int       =				OwlViTProcessor.from_pretrained(_A     )

									__A	:						Dict       =				['cat', 'nasa badge']
									__A	:						Optional[Any]       =				processor(text=_A     )

									__A	:						Optional[int]       =				16
									self.assertListEqual(list(inputs.keys()     )     ,					['input_ids', 'attention_mask']     )
									self.assertEqual(inputs['input_ids'].shape     ,					(2, seq_length)     )

									# test if it raises when no input is passed
									with pytest.raises(_A     ):
														processor()



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Tuple       =				'google/owlvit-base-patch32'
									__A	:						Any       =				OwlViTProcessor.from_pretrained(_A     )

									__A	:						Dict       =				[['cat', 'nasa badge'], ['person']]
									__A	:						Dict       =				processor(text=_A     )

									__A	:						Optional[int]       =				16
									__A	:						Any       =				len(_A     )
									__A	:						Union[str, Any]       =				max([len(_A     ) for texts in input_texts]     )

									self.assertListEqual(list(inputs.keys()     )     ,					['input_ids', 'attention_mask']     )
									self.assertEqual(inputs['input_ids'].shape     ,					(batch_size * num_max_text_queries, seq_length)     )

									# test if it raises when no input is passed
									with pytest.raises(_A     ):
														processor()



				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				'google/owlvit-base-patch32'
									__A	:						str       =				OwlViTProcessor.from_pretrained(_A     )

									__A	:						Union[str, Any]       =				['cat', 'nasa badge']
									__A	:						Tuple       =				processor(text=_A     )

									__A	:						str       =				16
									__A	:						int       =				inputs['input_ids']
									__A	:						List[Any]       =				[
									    [49406, 2368, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
									    [49406, 6841, 11301, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
									]

									self.assertListEqual(list(inputs.keys()     )     ,					['input_ids', 'attention_mask']     )
									self.assertEqual(inputs['input_ids'].shape     ,					(2, seq_length)     )
									self.assertListEqual(list(input_ids[0]     )     ,					predicted_ids[0]     )
									self.assertListEqual(list(input_ids[1]     )     ,					predicted_ids[1]     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[Any]       =				self.get_image_processor()
									__A	:						List[str]       =				self.get_tokenizer()

									__A	:						Optional[Any]       =				OwlViTProcessor(tokenizer=_A     ,					image_processor=_A     )

									__A	:						Optional[int]       =				self.prepare_image_inputs()
									__A	:						Optional[int]       =				self.prepare_image_inputs()

									__A	:						Optional[int]       =				processor(images=_A     ,					query_images=_A     )

									self.assertListEqual(list(inputs.keys()     )     ,					['query_pixel_values', 'pixel_values']     )

									# test if it raises when no input is passed
									with pytest.raises(_A     ):
														processor()





				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[Any]       =				self.get_image_processor()
									__A	:						Union[str, Any]       =				self.get_tokenizer()

									__A	:						str       =				OwlViTProcessor(tokenizer=_A     ,					image_processor=_A     )

									__A	:						Optional[Any]       =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									__A	:						Any       =				processor.batch_decode(_A     )
									__A	:						Tuple       =				tokenizer.batch_decode(_A     )

									self.assertListEqual(_A     ,					_A     )




