"""simple docstring"""




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class 			UpperCamelCase__(	unittest.TestCase       ):





						def  snake_case__		(				self   )    -> List[str]:
													A__		   =							tempfile.mkdtemp()

													A__		   =							BlipImageProcessor()
													A__		   =							GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'   )

													A__		   =							BlipaProcessor(__UpperCAmelCase						,__UpperCAmelCase   )

													processor.save_pretrained(self.tmpdirname   )





						def  snake_case__		(				self						,**__UpperCAmelCase   )    -> str:
													return AutoProcessor.from_pretrained(self.tmpdirname						,**__UpperCAmelCase   ).tokenizer





						def  snake_case__		(				self						,**__UpperCAmelCase   )    -> Any:
													return AutoProcessor.from_pretrained(self.tmpdirname						,**__UpperCAmelCase   ).image_processor





						def  snake_case__		(				self   )    -> Dict:
													shutil.rmtree(self.tmpdirname   )





						def  snake_case__		(				self   )    -> Any:
													A__		   =							[np.random.randint(2_55						,size=(3, 30, 4_00)						,dtype=np.uinta   )]

													A__		   =							[Image.fromarray(np.moveaxis(__UpperCAmelCase						,0						,-1   )   ) for x in image_inputs]

													return image_inputs





						def  snake_case__		(				self   )    -> Optional[Any]:
													A__		   =							BlipaProcessor(tokenizer=self.get_tokenizer()						,image_processor=self.get_image_processor()   )
													processor.save_pretrained(self.tmpdirname   )

													A__		   =							self.get_tokenizer(bos_token='(BOS)'						,eos_token='(EOS)'   )
													A__		   =							self.get_image_processor(do_normalize=__UpperCAmelCase						,padding_value=1.0   )

													A__		   =							BlipaProcessor.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						,__UpperCAmelCase   )

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





						def  snake_case__		(				self   )    -> Tuple:
													A__		   =							self.get_image_processor()
													A__		   =							self.get_tokenizer()

													A__		   =							BlipaProcessor(tokenizer=__UpperCAmelCase						,image_processor=__UpperCAmelCase   )

													A__		   =							self.prepare_image_inputs()

													A__		   =							image_processor(__UpperCAmelCase						,return_tensors='np'   )
													A__		   =							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  snake_case__		(				self   )    -> str:
													A__		   =							self.get_image_processor()
													A__		   =							self.get_tokenizer()

													A__		   =							BlipaProcessor(tokenizer=__UpperCAmelCase						,image_processor=__UpperCAmelCase   )

													A__		   =							'lower newer'

													A__		   =							processor(text=__UpperCAmelCase   )

													A__		   =							tokenizer(__UpperCAmelCase						,return_token_type_ids=__UpperCAmelCase   )

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





						def  snake_case__		(				self   )    -> int:
													A__		   =							self.get_image_processor()
													A__		   =							self.get_tokenizer()

													A__		   =							BlipaProcessor(tokenizer=__UpperCAmelCase						,image_processor=__UpperCAmelCase   )

													A__		   =							'lower newer'
													A__		   =							self.prepare_image_inputs()

													A__		   =							processor(text=__UpperCAmelCase						,images=__UpperCAmelCase   )

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

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





						def  snake_case__		(				self   )    -> Dict:
													A__		   =							self.get_image_processor()
													A__		   =							self.get_tokenizer()

													A__		   =							BlipaProcessor(tokenizer=__UpperCAmelCase						,image_processor=__UpperCAmelCase   )

													A__		   =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													A__		   =							processor.batch_decode(__UpperCAmelCase   )
													A__		   =							tokenizer.batch_decode(__UpperCAmelCase   )

													self.assertListEqual(__UpperCAmelCase						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[Any]:
													A__		   =							self.get_image_processor()
													A__		   =							self.get_tokenizer()

													A__		   =							BlipaProcessor(tokenizer=__UpperCAmelCase						,image_processor=__UpperCAmelCase   )

													A__		   =							'lower newer'
													A__		   =							self.prepare_image_inputs()

													A__		   =							processor(text=__UpperCAmelCase						,images=__UpperCAmelCase   )

													# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
													self.assertListEqual(list(inputs.keys()   )						,['pixel_values', 'input_ids', 'attention_mask']   )






"""simple docstring"""




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

__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							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 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  List[Any]  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = 1 / 2_55						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'shortest_edge': 2_56}
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

													A__		   =							do_resize
													A__		   =							size
													A__		   =							do_center_crop
													A__		   =							crop_size
													A__		   =							resample
													A__		   =							do_rescale
													A__		   =							rescale_factor
													A__		   =							offset
													A__		   =							do_normalize
													A__		   =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													A__		   =							image_std if image_std is not None else IMAGENET_STANDARD_STD





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													if "shortest_edge" in size:
																				A__		   =							get_resize_output_image_size(__UpperCAmelCase						,size['shortest_edge']						,default_to_square=__UpperCAmelCase   )
													elif "height" in size and "width" in size:
																				A__		   =							(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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> Optional[Any]:
													A__		   =							image.astype(np.floataa   )
													if offset:
																				A__		   =							image - (scale / 2)
													return rescale(__UpperCAmelCase						,scale=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													return normalize(__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = 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.
													A__		   =							to_numpy_array(__UpperCAmelCase   )

													if do_resize:
																				A__		   =							self.resize(image=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase   )

													if do_center_crop:
																				A__		   =							self.center_crop(__UpperCAmelCase						,size=__UpperCAmelCase   )

													if do_rescale:
																				A__		   =							self.rescale(image=__UpperCAmelCase						,scale=__UpperCAmelCase						,offset=__UpperCAmelCase   )

													if do_normalize:
																				A__		   =							self.normalize(image=__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase   )

													A__		   =							to_channel_dimension_format(__UpperCAmelCase						,__UpperCAmelCase   )
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													A__		   =							do_resize if do_resize is not None else self.do_resize
													A__		   =							resample if resample is not None else self.resample
													A__		   =							do_center_crop if do_center_crop is not None else self.do_center_crop
													A__		   =							do_rescale if do_rescale is not None else self.do_rescale
													A__		   =							rescale_factor if rescale_factor is not None else self.rescale_factor
													A__		   =							offset if offset is not None else self.offset
													A__		   =							do_normalize if do_normalize is not None else self.do_normalize
													A__		   =							image_mean if image_mean is not None else self.image_mean
													A__		   =							image_std if image_std is not None else self.image_std

													A__		   =							size if size is not None else self.size
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else self.crop_size
													A__		   =							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.'   )

													A__		   =							make_batched(__UpperCAmelCase   )

													A__		   =							[
													    [
													        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
													]

													A__		   =							{'pixel_values': videos}
													return BatchFeature(data=__UpperCAmelCase						,tensor_type=__UpperCAmelCase   )





