


'''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 _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Optional[Any]:
			__magic_name__	:   Any					     =					tempfile.mkdtemp()

			# fmt: off
			__magic_name__	:   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
			__magic_name__	:   Optional[Any]					     =					dict(zip(_A  ,					range(len(_A      )      )      )      )
			__magic_name__	:   int					     =					['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
			__magic_name__	:   Union[str, Any]					     =					{'unk_token': '<unk>'}

			__magic_name__	:   List[Any]					     =					os.path.join(self.tmpdirname  ,					VOCAB_FILES_NAMES['vocab_file']      )
			__magic_name__	:   Dict					     =					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      )      )

			__magic_name__	:   Any					     =					{
			    'do_resize': True,
			    'size': 20,
			    'do_center_crop': True,
			    'crop_size': 18,
			    'do_normalize': True,
			    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
			    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
			}
			__magic_name__	:   Union[str, Any]					     =					os.path.join(self.tmpdirname  ,					_A      )
			with open(self.image_processor_file  ,					'w'  ,					encoding='utf-8'      ) as fp:
				json.dump(_A  ,					_A      )







		def        __lowerCAmelCase		( self		:						List[str]  ,					**_A		:						int      )  ->			Optional[int]:
			return CLIPTokenizer.from_pretrained(self.tmpdirname  ,					pad_token='!'  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					**_A		:						List[str]      )  ->			Tuple:
			return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,					pad_token='!'  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					**_A		:						Optional[int]      )  ->			Tuple:
			return OwlViTImageProcessor.from_pretrained(self.tmpdirname  ,					**_A      )







		def        __lowerCAmelCase		( self		:						str      )  ->			Optional[Any]:
			shutil.rmtree(self.tmpdirname      )







		def        __lowerCAmelCase		( self		:						Any      )  ->			str:
			__magic_name__	:   Union[str, Any]					     =					[np.random.randint(255  ,					size=(3, 30, 400)  ,					dtype=np.uinta      )]

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

			return image_inputs







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			int:
			__magic_name__	:   Any					     =					self.get_tokenizer()
			__magic_name__	:   List[Any]					     =					self.get_rust_tokenizer()
			__magic_name__	:   Optional[Any]					     =					self.get_image_processor()

			__magic_name__	:   Union[str, Any]					     =					OwlViTProcessor(tokenizer=_A  ,					image_processor=_A      )
			processor_slow.save_pretrained(self.tmpdirname      )
			__magic_name__	:   Tuple					     =					OwlViTProcessor.from_pretrained(self.tmpdirname  ,					use_fast=_A      )

			__magic_name__	:   List[Any]					     =					OwlViTProcessor(tokenizer=_A  ,					image_processor=_A      )
			processor_fast.save_pretrained(self.tmpdirname      )
			__magic_name__	:   str					     =					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        __lowerCAmelCase		( self		:						int      )  ->			Tuple:
			__magic_name__	:   List[Any]					     =					OwlViTProcessor(tokenizer=self.get_tokenizer()  ,					image_processor=self.get_image_processor()      )
			processor.save_pretrained(self.tmpdirname      )

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

			__magic_name__	:   Optional[int]					     =					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        __lowerCAmelCase		( self		:						str      )  ->			Any:
			__magic_name__	:   Any					     =					self.get_image_processor()
			__magic_name__	:   str					     =					self.get_tokenizer()

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

			__magic_name__	:   str					     =					self.prepare_image_inputs()

			__magic_name__	:   int					     =					image_processor(_A  ,					return_tensors='np'      )
			__magic_name__	:   Tuple					     =					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        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			List[str]:
			__magic_name__	:   int					     =					self.get_image_processor()
			__magic_name__	:   Tuple					     =					self.get_tokenizer()

			__magic_name__	:   int					     =					OwlViTProcessor(tokenizer=_A  ,					image_processor=_A      )

			__magic_name__	:   Optional[int]					     =					'lower newer'

			__magic_name__	:   Optional[int]					     =					processor(text=_A  ,					return_tensors='np'      )

			__magic_name__	:   Optional[Any]					     =					tokenizer(_A  ,					return_tensors='np'      )

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







		def        __lowerCAmelCase		( self		:						Tuple      )  ->			Union[str, Any]:
			__magic_name__	:   int					     =					self.get_image_processor()
			__magic_name__	:   Any					     =					self.get_tokenizer()

			__magic_name__	:   Optional[int]					     =					OwlViTProcessor(tokenizer=_A  ,					image_processor=_A      )

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

			__magic_name__	:   List[str]					     =					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        __lowerCAmelCase		( self		:						List[Any]      )  ->			Dict:
			__magic_name__	:   List[str]					     =					'google/owlvit-base-patch32'
			__magic_name__	:   Optional[int]					     =					OwlViTProcessor.from_pretrained(_A      )

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

			__magic_name__	:   Tuple					     =					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        __lowerCAmelCase		( self		:						List[str]      )  ->			Any:
			__magic_name__	:   Optional[Any]					     =					'google/owlvit-base-patch32'
			__magic_name__	:   Union[str, Any]					     =					OwlViTProcessor.from_pretrained(_A      )

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

			__magic_name__	:   List[Any]					     =					16
			__magic_name__	:   int					     =					len(_A      )
			__magic_name__	:   str					     =					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        __lowerCAmelCase		( self		:						int      )  ->			Dict:
			__magic_name__	:   int					     =					'google/owlvit-base-patch32'
			__magic_name__	:   str					     =					OwlViTProcessor.from_pretrained(_A      )

			__magic_name__	:   List[str]					     =					['cat', 'nasa badge']
			__magic_name__	:   Tuple					     =					processor(text=_A      )

			__magic_name__	:   Optional[int]					     =					16
			__magic_name__	:   int					     =					inputs['input_ids']
			__magic_name__	:   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        __lowerCAmelCase		( self		:						Dict      )  ->			Union[str, Any]:
			__magic_name__	:   List[str]					     =					self.get_image_processor()
			__magic_name__	:   List[str]					     =					self.get_tokenizer()

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

			__magic_name__	:   List[Any]					     =					self.prepare_image_inputs()
			__magic_name__	:   Any					     =					self.prepare_image_inputs()

			__magic_name__	:   Tuple					     =					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        __lowerCAmelCase		( self		:						Dict      )  ->			Union[str, Any]:
			__magic_name__	:   Optional[int]					     =					self.get_image_processor()
			__magic_name__	:   Tuple					     =					self.get_tokenizer()

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

			__magic_name__	:   int					     =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			__magic_name__	:   str					     =					processor.batch_decode(_A      )
			__magic_name__	:   Optional[int]					     =					tokenizer.batch_decode(_A      )

			self.assertListEqual(_A  ,					_A      )



'''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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase				:Tuple        =      logging.get_logger(__name__)
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      int				   =    ["""pixel_values"""]







		def __init__( self		:						Any  ,					_A		:						bool = True  ,					_A		:						Optional[Dict[str, int]] = None  ,					_A		:						PILImageResampling = PILImageResampling.BILINEAR  ,					_A		:						bool = True  ,					_A		:						Dict[str, int] = None  ,					_A		:						bool = True  ,					_A		:						Union[int, float] = 1 / 255  ,					_A		:						bool = True  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					**_A		:						Optional[Any]  ,					)  ->			None:
			super().__init__(**_A      )
			__magic_name__	:   List[str]					     =					size if size is not None else {'shortest_edge': 256}
			__magic_name__	:   str					     =					get_size_dict(_A  ,					default_to_square=_A      )
			__magic_name__	:   List[str]					     =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
			__magic_name__	:   Optional[int]					     =					get_size_dict(_A      )
			__magic_name__	:   Union[str, Any]					     =					do_resize
			__magic_name__	:   List[Any]					     =					size
			__magic_name__	:   List[str]					     =					resample
			__magic_name__	:   Dict					     =					do_center_crop
			__magic_name__	:   List[str]					     =					crop_size
			__magic_name__	:   int					     =					do_rescale
			__magic_name__	:   Tuple					     =					rescale_factor
			__magic_name__	:   List[str]					     =					do_normalize
			__magic_name__	:   Union[str, Any]					     =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			__magic_name__	:   Dict					     =					image_std if image_std is not None else IMAGENET_STANDARD_STD







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						np.ndarray  ,					_A		:						Dict[str, int]  ,					_A		:						PILImageResampling = PILImageResampling.BICUBIC  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						List[str]  ,					)  ->			np.ndarray:
			__magic_name__	:   Optional[Any]					     =					get_size_dict(_A  ,					default_to_square=_A      )
			if "shortest_edge" not in size:
				raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'      )
			__magic_name__	:   Dict					     =					get_resize_output_image_size(_A  ,					size=size['shortest_edge']  ,					default_to_square=_A      )
			return resize(_A  ,					size=_A  ,					resample=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						np.ndarray  ,					_A		:						Dict[str, int]  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						Optional[int]  ,					)  ->			np.ndarray:
			__magic_name__	:   int					     =					get_size_dict(_A      )
			return center_crop(_A  ,					size=(size['height'], size['width'])  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						np.ndarray  ,					_A		:						float  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						Tuple      )  ->			np.ndarray:
			return rescale(_A  ,					scale=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						np.ndarray  ,					_A		:						Union[float, List[float]]  ,					_A		:						Union[float, List[float]]  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						int  ,					)  ->			np.ndarray:
			return normalize(_A  ,					mean=_A  ,					std=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						ImageInput  ,					_A		:						Optional[bool] = None  ,					_A		:						Dict[str, int] = None  ,					_A		:						PILImageResampling = None  ,					_A		:						bool = None  ,					_A		:						Dict[str, int] = None  ,					_A		:						Optional[bool] = None  ,					_A		:						Optional[float] = None  ,					_A		:						Optional[bool] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[str, TensorType]] = None  ,					_A		:						Union[str, ChannelDimension] = ChannelDimension.FIRST  ,					**_A		:						List[Any]  ,					)  ->			List[str]:
			__magic_name__	:   int					     =					do_resize if do_resize is not None else self.do_resize
			__magic_name__	:   Tuple					     =					size if size is not None else self.size
			__magic_name__	:   Optional[Any]					     =					get_size_dict(_A  ,					default_to_square=_A      )
			__magic_name__	:   Dict					     =					resample if resample is not None else self.resample
			__magic_name__	:   Optional[Any]					     =					do_center_crop if do_center_crop is not None else self.do_center_crop
			__magic_name__	:   Dict					     =					crop_size if crop_size is not None else self.crop_size
			__magic_name__	:   List[str]					     =					get_size_dict(_A      )
			__magic_name__	:   Union[str, Any]					     =					do_rescale if do_rescale is not None else self.do_rescale
			__magic_name__	:   str					     =					rescale_factor if rescale_factor is not None else self.rescale_factor
			__magic_name__	:   Any					     =					do_normalize if do_normalize is not None else self.do_normalize
			__magic_name__	:   Tuple					     =					image_mean if image_mean is not None else self.image_mean
			__magic_name__	:   Union[str, Any]					     =					image_std if image_std is not None else self.image_std

			__magic_name__	:   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:
				raise ValueError('Size must be specified if do_resize is True.'      )

			if do_center_crop and crop_size is None:
				raise ValueError('Crop size must be specified if do_center_crop is True.'      )

			if do_rescale and rescale_factor is None:
				raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

			if do_normalize and (image_mean is None or image_std is None):
				raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

			# All transformations expect numpy arrays.
			__magic_name__	:   List[Any]					     =					[to_numpy_array(_A      ) for image in images]

			if do_resize:
				__magic_name__	:   Union[str, Any]					     =					[self.resize(image=_A  ,					size=_A  ,					resample=_A      ) for image in images]

			if do_center_crop:
				__magic_name__	:   Union[str, Any]					     =					[self.center_crop(image=_A  ,					size=_A      ) for image in images]

			if do_rescale:
				__magic_name__	:   List[Any]					     =					[self.rescale(image=_A  ,					scale=_A      ) for image in images]

			if do_normalize:
				__magic_name__	:   Optional[Any]					     =					[self.normalize(image=_A  ,					mean=_A  ,					std=_A      ) for image in images]

			__magic_name__	:   Union[str, Any]					     =					[to_channel_dimension_format(_A  ,					_A      ) for image in images]

			__magic_name__	:   List[str]					     =					{'pixel_values': images}
			return BatchFeature(data=_A  ,					tensor_type=_A      )