




"""simple docstring"""
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 __lowercase(   unittest.TestCase					):




					'''simple docstring'''


					def      snake_case_					(     self  ):
								__lowerCamelCase       :						List[Any]													=					tempfile.mkdtemp()

								# fmt: off
								__lowerCamelCase       :						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
								__lowerCamelCase       :						Tuple													=					dict(zip(__lowerCAmelCase    ,						range(len(__lowerCAmelCase  )  )  )  )
								__lowerCamelCase       :						Optional[int]													=					['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
								__lowerCamelCase       :						Dict													=					{'unk_token': '<unk>'}

								__lowerCamelCase       :						str													=					os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES['vocab_file']  )
								__lowerCamelCase       :						List[str]													=					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(__lowerCAmelCase  ) + '\n'  )
								with open(self.merges_file    ,						'w'    ,						encoding='utf-8'  ) as fp:
											fp.write('\n'.join(__lowerCAmelCase  )  )

								__lowerCamelCase       :						int													=					{
								    'do_resize': True,
								    'size': 20,
								    'do_center_crop': True,
								    'crop_size': 18,
								    'do_normalize': True,
								    'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
								    'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
								}
								__lowerCamelCase       :						int													=					os.path.join(self.tmpdirname    ,						__lowerCAmelCase  )
								with open(self.image_processor_file    ,						'w'    ,						encoding='utf-8'  ) as fp:
											json.dump(__lowerCAmelCase    ,						__lowerCAmelCase  )
					def      snake_case_					(     self    ,						**__a  ):
								return CLIPTokenizer.from_pretrained(self.tmpdirname    ,						pad_token='!'    ,						**__lowerCAmelCase  )
					def      snake_case_					(     self    ,						**__a  ):
								return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,						pad_token='!'    ,						**__lowerCAmelCase  )
					def      snake_case_					(     self    ,						**__a  ):
								return OwlViTImageProcessor.from_pretrained(self.tmpdirname    ,						**__lowerCAmelCase  )
					def      snake_case_					(     self  ):
								shutil.rmtree(self.tmpdirname  )
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						int													=					[np.random.randint(255    ,						size=(3, 30, 400)    ,						dtype=np.uinta  )]

								__lowerCamelCase       :						List[str]													=					[Image.fromarray(np.moveaxis(__lowerCAmelCase    ,						0    ,						-1  )  ) for x in image_inputs]

								return image_inputs
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Tuple													=					self.get_tokenizer()
								__lowerCamelCase       :						str													=					self.get_rust_tokenizer()
								__lowerCamelCase       :						List[str]													=					self.get_image_processor()

								__lowerCamelCase       :						int													=					OwlViTProcessor(tokenizer=__lowerCAmelCase    ,						image_processor=__lowerCAmelCase  )
								processor_slow.save_pretrained(self.tmpdirname  )
								__lowerCamelCase       :						List[str]													=					OwlViTProcessor.from_pretrained(self.tmpdirname    ,						use_fast=__lowerCAmelCase  )

								__lowerCamelCase       :						List[str]													=					OwlViTProcessor(tokenizer=__lowerCAmelCase    ,						image_processor=__lowerCAmelCase  )
								processor_fast.save_pretrained(self.tmpdirname  )
								__lowerCamelCase       :						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    ,						__lowerCAmelCase  )
								self.assertIsInstance(processor_fast.tokenizer    ,						__lowerCAmelCase  )

								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    ,						__lowerCAmelCase  )
								self.assertIsInstance(processor_fast.image_processor    ,						__lowerCAmelCase  )
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						str													=					OwlViTProcessor(tokenizer=self.get_tokenizer()    ,						image_processor=self.get_image_processor()  )
								processor.save_pretrained(self.tmpdirname  )

								__lowerCamelCase       :						Tuple													=					self.get_tokenizer(bos_token='(BOS)'    ,						eos_token='(EOS)'  )
								__lowerCamelCase       :						Dict													=					self.get_image_processor(do_normalize=__lowerCAmelCase  )

								__lowerCamelCase       :						List[Any]													=					OwlViTProcessor.from_pretrained(
								    self.tmpdirname    ,						bos_token='(BOS)'    ,						eos_token='(EOS)'    ,						do_normalize=__lowerCAmelCase  )

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

								self.assertEqual(processor.image_processor.to_json_string()    ,						image_processor_add_kwargs.to_json_string()  )
								self.assertIsInstance(processor.image_processor    ,						__lowerCAmelCase  )
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						List[Any]													=					self.get_image_processor()
								__lowerCamelCase       :						Dict													=					self.get_tokenizer()

								__lowerCamelCase       :						Tuple													=					OwlViTProcessor(tokenizer=__lowerCAmelCase    ,						image_processor=__lowerCAmelCase  )

								__lowerCamelCase       :						int													=					self.prepare_image_inputs()

								__lowerCamelCase       :						Optional[int]													=					image_processor(__lowerCAmelCase    ,						return_tensors='np'  )
								__lowerCamelCase       :						str													=					processor(images=__lowerCAmelCase    ,						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      snake_case_					(     self  ):
								__lowerCamelCase       :						Dict													=					self.get_image_processor()
								__lowerCamelCase       :						Dict													=					self.get_tokenizer()

								__lowerCamelCase       :						str													=					OwlViTProcessor(tokenizer=__lowerCAmelCase    ,						image_processor=__lowerCAmelCase  )

								__lowerCamelCase       :						Dict													=					'lower newer'

								__lowerCamelCase       :						str													=					processor(text=__lowerCAmelCase    ,						return_tensors='np'  )

								__lowerCamelCase       :						List[str]													=					tokenizer(__lowerCAmelCase    ,						return_tensors='np'  )

								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key][0].tolist()    ,						encoded_processor[key][0].tolist()  )
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						str													=					self.get_image_processor()
								__lowerCamelCase       :						List[Any]													=					self.get_tokenizer()

								__lowerCamelCase       :						Optional[int]													=					OwlViTProcessor(tokenizer=__lowerCAmelCase    ,						image_processor=__lowerCAmelCase  )

								__lowerCamelCase       :						Dict													=					'lower newer'
								__lowerCamelCase       :						Any													=					self.prepare_image_inputs()

								__lowerCamelCase       :						Any													=					processor(text=__lowerCAmelCase    ,						images=__lowerCAmelCase  )

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

								# test if it raises when no input is passed
								with pytest.raises(__lowerCAmelCase  ):
											processor()
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						int													=					'google/owlvit-base-patch32'
								__lowerCamelCase       :						Any													=					OwlViTProcessor.from_pretrained(__lowerCAmelCase  )

								__lowerCamelCase       :						Optional[int]													=					['cat', 'nasa badge']
								__lowerCamelCase       :						str													=					processor(text=__lowerCAmelCase  )

								__lowerCamelCase       :						str													=					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(__lowerCAmelCase  ):
											processor()
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						int													=					'google/owlvit-base-patch32'
								__lowerCamelCase       :						Dict													=					OwlViTProcessor.from_pretrained(__lowerCAmelCase  )

								__lowerCamelCase       :						Union[str, Any]													=					[['cat', 'nasa badge'], ['person']]
								__lowerCamelCase       :						Optional[int]													=					processor(text=__lowerCAmelCase  )

								__lowerCamelCase       :						Dict													=					16
								__lowerCamelCase       :						List[Any]													=					len(__lowerCAmelCase  )
								__lowerCamelCase       :						Optional[Any]													=					max([len(__lowerCAmelCase  ) 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(__lowerCAmelCase  ):
											processor()
					def      snake_case_					(     self  ):
								__lowerCamelCase       :						Tuple													=					'google/owlvit-base-patch32'
								__lowerCamelCase       :						Any													=					OwlViTProcessor.from_pretrained(__lowerCAmelCase  )

								__lowerCamelCase       :						List[str]													=					['cat', 'nasa badge']
								__lowerCamelCase       :						Any													=					processor(text=__lowerCAmelCase  )

								__lowerCamelCase       :						Union[str, Any]													=					16
								__lowerCamelCase       :						List[Any]													=					inputs['input_ids']
								__lowerCamelCase       :						str													=					[
								    [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      snake_case_					(     self  ):
								__lowerCamelCase       :						Tuple													=					self.get_image_processor()
								__lowerCamelCase       :						Optional[int]													=					self.get_tokenizer()

								__lowerCamelCase       :						Optional[Any]													=					OwlViTProcessor(tokenizer=__lowerCAmelCase    ,						image_processor=__lowerCAmelCase  )

								__lowerCamelCase       :						Tuple													=					self.prepare_image_inputs()
								__lowerCamelCase       :						Any													=					self.prepare_image_inputs()

								__lowerCamelCase       :						Optional[Any]													=					processor(images=__lowerCAmelCase    ,						query_images=__lowerCAmelCase  )

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

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





					def      snake_case_					(     self  ):
								__lowerCamelCase       :						List[str]													=					self.get_image_processor()
								__lowerCamelCase       :						Dict													=					self.get_tokenizer()

								__lowerCamelCase       :						int													=					OwlViTProcessor(tokenizer=__lowerCAmelCase    ,						image_processor=__lowerCAmelCase  )

								__lowerCamelCase       :						Dict													=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								__lowerCamelCase       :						Union[str, Any]													=					processor.batch_decode(__lowerCAmelCase  )
								__lowerCamelCase       :						List[Any]													=					tokenizer.batch_decode(__lowerCAmelCase  )

								self.assertListEqual(__lowerCAmelCase    ,						__lowerCAmelCase  )




'''simple docstring'''




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


__lowercase        =    logging.get_logger(__name__)






def 							snake_case__				(     _A:   Dict ,  _A:   Any					)       ->      Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase     =    b.T
		lowerCAmelCase     =    np.sum(np.square(_A					) ,  axis=1					)
		lowerCAmelCase     =    np.sum(np.square(_A					) ,  axis=0					)
		lowerCAmelCase     =    np.matmul(_A ,  _A					)
		lowerCAmelCase     =    aa[:, None] - 2 * ab + ba[None, :]
		return d






def 							snake_case__				(     _A:   Dict ,  _A:   Dict					)       ->      Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    x.reshape(-1 ,  3					)
		lowerCAmelCase     =    squared_euclidean_distance(_A ,  _A					)
		return np.argmin(_A ,  axis=1					)






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			['''pixel_values''']



			def __init__(  self			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = True			,       __lowerCAmelCase = True			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase     =    size if size is not None else {"""height""": 256, """width""": 256}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    np.array(__lowerCAmelCase) if clusters is not None else None
					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    resample
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    do_color_quantize



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					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(
					    __lowerCAmelCase			,       size=(size["""height"""], size["""width"""])			,       resample=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    rescale(image=__lowerCAmelCase			,       scale=1 / 127.5			,       data_format=__lowerCAmelCase)
					lowerCAmelCase     =    image - 1
					return image







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ChannelDimension.FIRST			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase     =    size if size is not None else self.size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    resample if resample is not None else self.resample
					lowerCAmelCase     =    do_normalize if do_normalize is not None else self.do_normalize
					lowerCAmelCase     =    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					lowerCAmelCase     =    clusters if clusters is not None else self.clusters
					lowerCAmelCase     =    np.array(__lowerCAmelCase)

					lowerCAmelCase     =    make_list_of_images(__lowerCAmelCase)

					if not valid_images(__lowerCAmelCase):
							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.
					lowerCAmelCase     =    [to_numpy_array(__lowerCAmelCase) for image in images]

					if do_resize:
							lowerCAmelCase     =    [self.resize(image=__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase) for image in images]

					if do_normalize:
							lowerCAmelCase     =    [self.normalize(image=__lowerCAmelCase) for image in images]

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

							# flatten to (batch_size, height*width)
							lowerCAmelCase     =    images.shape[0]
							lowerCAmelCase     =    images.reshape(__lowerCAmelCase			,       -1)

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

					lowerCAmelCase     =    {"""input_ids""": images}
					return BatchFeature(data=__lowerCAmelCase			,       tensor_type=__lowerCAmelCase)




