





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
    import PIL

_UpperCAmelCase							:			Dict        =							logging.get_logger(__name__)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	List[List[ImageInput]]:
    if isinstance(_UpperCAmelCase		,      (list, tuple)					) and isinstance(videos[0]		,      (list, tuple)					) and is_valid_image(videos[0][0]					):
        return videos

    elif isinstance(_UpperCAmelCase		,      (list, tuple)					) and is_valid_image(videos[0]					):
        return [videos]

    elif is_valid_image(_UpperCAmelCase					):
        return [[videos]]

    raise ValueError(F"""Could not make batched video from {videos}"""					)


class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						["""pixel_values"""]
 def __init__( self    : Optional[Any]     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BILINEAR     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Union[int, float] = 1 / 255     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	**UpperCAmelCase    : int     ,	)     ->      None:
     super().__init__(**UpperCAmelCase      )
     lowerCamelCase__       : Any   					=						size if size is not None else {'shortest_edge': 256}
     lowerCamelCase__       : Optional[Any]   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     lowerCamelCase__       : str   					=						crop_size if crop_size is not None else {'height': 224, 'width': 224}
     lowerCamelCase__       : List[Any]   					=						get_size_dict(UpperCAmelCase     ,	param_name='crop_size'      )

     lowerCamelCase__       : List[Any]   					=						do_resize
     lowerCamelCase__       : int   					=						size
     lowerCamelCase__       : str   					=						do_center_crop
     lowerCamelCase__       : Optional[Any]   					=						crop_size
     lowerCamelCase__       : List[Any]   					=						resample
     lowerCamelCase__       : Optional[int]   					=						do_rescale
     lowerCamelCase__       : Optional[int]   					=						rescale_factor
     lowerCamelCase__       : Any   					=						offset
     lowerCamelCase__       : List[Any]   					=						do_normalize
     lowerCamelCase__       : int   					=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowerCamelCase__       : Any   					=						image_std if image_std is not None else IMAGENET_STANDARD_STD
 def 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Dict[str, int]     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BILINEAR     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : int     ,	)     ->      np.ndarray:
     lowerCamelCase__       : int   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     if "shortest_edge" in size:
         lowerCamelCase__       : Tuple   					=						get_resize_output_image_size(UpperCAmelCase     ,	size['shortest_edge']     ,	default_to_square=UpperCAmelCase      )
     elif "height" in size and "width" in size:
         lowerCamelCase__       : Tuple   					=						(size['height'], size['width'])
     else:
         raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""      )
     return resize(UpperCAmelCase     ,	size=UpperCAmelCase     ,	resample=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : List[str]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Dict[str, int]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Optional[int]     ,	)     ->      np.ndarray:
     lowerCamelCase__       : str   					=						get_size_dict(UpperCAmelCase      )
     if "height" not in size or "width" not in size:
         raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""      )
     return center_crop(UpperCAmelCase     ,	size=(size['height'], size['width'])     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Any     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[int, float]     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Dict     ,	)     ->      Any:
     lowerCamelCase__       : int   					=						image.astype(np.floataa      )
     if offset:
         lowerCamelCase__       : List[Any]   					=						image - (scale / 2)
     return rescale(UpperCAmelCase     ,	scale=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : List[str]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Any     ,	)     ->      np.ndarray:
     return normalize(UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : List[str]     ,	UpperCAmelCase    : ImageInput     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : PILImageResampling = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : float = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[ChannelDimension] = ChannelDimension.FIRST     ,	)     ->      np.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_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.'      )

     if offset and not do_rescale:
         raise ValueError('For offset, do_rescale must also be set to True.'      )

     # All transformations expect numpy arrays.
     lowerCamelCase__       : List[Any]   					=						to_numpy_array(UpperCAmelCase      )

     if do_resize:
         lowerCamelCase__       : Any   					=						self.resize(image=UpperCAmelCase     ,	size=UpperCAmelCase     ,	resample=UpperCAmelCase      )

     if do_center_crop:
         lowerCamelCase__       : Dict   					=						self.center_crop(UpperCAmelCase     ,	size=UpperCAmelCase      )

     if do_rescale:
         lowerCamelCase__       : Union[str, Any]   					=						self.rescale(image=UpperCAmelCase     ,	scale=UpperCAmelCase     ,	offset=UpperCAmelCase      )

     if do_normalize:
         lowerCamelCase__       : str   					=						self.normalize(image=UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase      )

     lowerCamelCase__       : Optional[Any]   					=						to_channel_dimension_format(UpperCAmelCase     ,	UpperCAmelCase      )
     return image







 def 	A_      ( self    : int     ,	UpperCAmelCase    : ImageInput     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : PILImageResampling = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : float = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[str, TensorType]] = None     ,	UpperCAmelCase    : ChannelDimension = ChannelDimension.FIRST     ,	**UpperCAmelCase    : List[Any]     ,	)     ->      PIL.Image.Image:
     lowerCamelCase__       : List[Any]   					=						do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__       : Any   					=						resample if resample is not None else self.resample
     lowerCamelCase__       : str   					=						do_center_crop if do_center_crop is not None else self.do_center_crop
     lowerCamelCase__       : Optional[int]   					=						do_rescale if do_rescale is not None else self.do_rescale
     lowerCamelCase__       : Optional[int]   					=						rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCamelCase__       : int   					=						offset if offset is not None else self.offset
     lowerCamelCase__       : List[str]   					=						do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__       : Optional[Any]   					=						image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__       : Dict   					=						image_std if image_std is not None else self.image_std

     lowerCamelCase__       : int   					=						size if size is not None else self.size
     lowerCamelCase__       : Optional[Any]   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     lowerCamelCase__       : Union[str, Any]   					=						crop_size if crop_size is not None else self.crop_size
     lowerCamelCase__       : List[str]   					=						get_size_dict(UpperCAmelCase     ,	param_name='crop_size'      )

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

     lowerCamelCase__       : List[Any]   					=						make_batched(UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						[
         [
             self._preprocess_image(
                 image=UpperCAmelCase     ,	do_resize=UpperCAmelCase     ,	size=UpperCAmelCase     ,	resample=UpperCAmelCase     ,	do_center_crop=UpperCAmelCase     ,	crop_size=UpperCAmelCase     ,	do_rescale=UpperCAmelCase     ,	rescale_factor=UpperCAmelCase     ,	offset=UpperCAmelCase     ,	do_normalize=UpperCAmelCase     ,	image_mean=UpperCAmelCase     ,	image_std=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	)
             for img in video
         ]
         for video in videos
     ]

     lowerCamelCase__       : int   					=						{'pixel_values': videos}
     return BatchFeature(data=UpperCAmelCase     ,	tensor_type=UpperCAmelCase      )












import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class   lowerCAmelCase				(   unittest.TestCase     ):
 def 	A_      ( self    : str      )     ->      Dict:
     lowerCamelCase__       : Optional[Any]   					=						tempfile.mkdtemp()

     # fmt: off
     lowerCamelCase__       : Tuple   					=						['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
     # fmt: on
     lowerCamelCase__       : int   					=						os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES['vocab_file']      )
     with open(self.vocab_file     ,	'w'     ,	encoding='utf-8'      ) as vocab_writer:
         vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )

     lowerCamelCase__       : Any   					=						{
         'do_resize': True,
         'size': {'height': 18, 'width': 18},
         'do_normalize': True,
         'image_mean': [0.5, 0.5, 0.5],
         'image_std': [0.5, 0.5, 0.5],
     }
     lowerCamelCase__       : int   					=						os.path.join(self.tmpdirname     ,	UpperCAmelCase      )
     with open(self.image_processor_file     ,	'w'     ,	encoding='utf-8'      ) as fp:
         json.dump(UpperCAmelCase     ,	UpperCAmelCase      )
 def 	A_      ( self    : List[str]     ,	**UpperCAmelCase    : str      )     ->      int:
     return BertTokenizer.from_pretrained(self.tmpdirname     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Optional[int]     ,	**UpperCAmelCase    : List[str]      )     ->      List[str]:
     return ViTImageProcessor.from_pretrained(self.tmpdirname     ,	**UpperCAmelCase      )
 def 	A_      ( self    : int      )     ->      Any:
     shutil.rmtree(self.tmpdirname      )
 def 	A_      ( self    : str      )     ->      str:
     lowerCamelCase__       : List[Any]   					=						[np.random.randint(255     ,	size=(3, 30, 400)     ,	dtype=np.uinta      )]

     lowerCamelCase__       : Union[str, Any]   					=						[Image.fromarray(np.moveaxis(UpperCAmelCase     ,	0     ,	-1      )      ) for x in image_inputs]

     return image_inputs
 def 	A_      ( self    : Dict      )     ->      Dict:
     lowerCamelCase__       : List[Any]   					=						self.get_tokenizer()
     lowerCamelCase__       : Tuple   					=						self.get_image_processor()

     lowerCamelCase__       : Union[str, Any]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     processor.save_pretrained(self.tmpdirname      )
     lowerCamelCase__       : List[str]   					=						VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname      )

     self.assertEqual(processor.tokenizer.get_vocab()     ,	tokenizer.get_vocab()      )
     self.assertIsInstance(processor.tokenizer     ,	(BertTokenizer, BertTokenizerFast)      )

     self.assertEqual(processor.image_processor.to_json_string()     ,	image_processor.to_json_string()      )
     self.assertIsInstance(processor.image_processor     ,	UpperCAmelCase      )
 def 	A_      ( self    : List[str]      )     ->      Tuple:
     lowerCamelCase__       : List[Any]   					=						VisionTextDualEncoderProcessor(
         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__       : int   					=						self.get_image_processor(do_normalize=UpperCAmelCase     ,	padding_value=1.0      )

     lowerCamelCase__       : Union[str, Any]   					=						VisionTextDualEncoderProcessor.from_pretrained(
         self.tmpdirname     ,	bos_token='(BOS)'     ,	eos_token='(EOS)'     ,	do_normalize=UpperCAmelCase     ,	padding_value=1.0      )

     self.assertEqual(processor.tokenizer.get_vocab()     ,	tokenizer_add_kwargs.get_vocab()      )
     self.assertIsInstance(processor.tokenizer     ,	(BertTokenizer, BertTokenizerFast)      )

     self.assertEqual(processor.image_processor.to_json_string()     ,	image_processor_add_kwargs.to_json_string()      )
     self.assertIsInstance(processor.image_processor     ,	UpperCAmelCase      )
 def 	A_      ( self    : Any      )     ->      Tuple:
     lowerCamelCase__       : List[Any]   					=						self.get_image_processor()
     lowerCamelCase__       : Any   					=						self.get_tokenizer()

     lowerCamelCase__       : Optional[int]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

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

     lowerCamelCase__       : int   					=						image_processor(UpperCAmelCase     ,	return_tensors='np'      )
     lowerCamelCase__       : List[str]   					=						processor(images=UpperCAmelCase     ,	return_tensors='np'      )

     for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum()     ,	input_processor[key].sum()     ,	delta=1e-2      )
 def 	A_      ( self    : Tuple      )     ->      List[Any]:
     lowerCamelCase__       : Dict   					=						self.get_image_processor()
     lowerCamelCase__       : List[str]   					=						self.get_tokenizer()

     lowerCamelCase__       : str   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : Tuple   					=						'lower newer'

     lowerCamelCase__       : Tuple   					=						processor(text=UpperCAmelCase      )

     lowerCamelCase__       : Any   					=						tokenizer(UpperCAmelCase      )

     for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]     ,	encoded_processor[key]      )
 def 	A_      ( self    : Optional[int]      )     ->      Any:
     lowerCamelCase__       : Optional[int]   					=						self.get_image_processor()
     lowerCamelCase__       : Tuple   					=						self.get_tokenizer()

     lowerCamelCase__       : Tuple   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : str   					=						'lower newer'
     lowerCamelCase__       : Dict   					=						self.prepare_image_inputs()

     lowerCamelCase__       : int   					=						processor(text=UpperCAmelCase     ,	images=UpperCAmelCase      )

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

     # test if it raises when no input is passed
     with self.assertRaises(UpperCAmelCase      ):
         processor()
 def 	A_      ( self    : Optional[Any]      )     ->      List[str]:
     lowerCamelCase__       : Dict   					=						self.get_image_processor()
     lowerCamelCase__       : Union[str, Any]   					=						self.get_tokenizer()

     lowerCamelCase__       : List[str]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : List[str]   					=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase__       : Dict   					=						processor.batch_decode(UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						tokenizer.batch_decode(UpperCAmelCase      )

     self.assertListEqual(UpperCAmelCase     ,	UpperCAmelCase      )







 def 	A_      ( self    : Optional[int]      )     ->      List[str]:
     lowerCamelCase__       : Tuple   					=						self.get_image_processor()
     lowerCamelCase__       : List[str]   					=						self.get_tokenizer()

     lowerCamelCase__       : Union[str, Any]   					=						VisionTextDualEncoderProcessor(tokenizer=UpperCAmelCase     ,	image_processor=UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						'lower newer'
     lowerCamelCase__       : List[Any]   					=						self.prepare_image_inputs()

     lowerCamelCase__       : Union[str, Any]   					=						processor(text=UpperCAmelCase     ,	images=UpperCAmelCase      )

     self.assertListEqual(list(inputs.keys()      )     ,	processor.model_input_names      )





