
'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import PIL.Image

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


_snake_case				:   List[str]     =				logging.get_logger(__name__)


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					List[str]       =					["""pixel_values"""]




		def __init__(				self					,						UpperCamelCase = True					,						UpperCamelCase = 32					,						UpperCamelCase=PILImageResampling.BILINEAR					,						UpperCamelCase = True					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= do_resize
								_SCREAMING_SNAKE_CASE				= do_rescale
								_SCREAMING_SNAKE_CASE				= size_divisor
								_SCREAMING_SNAKE_CASE				= resample
								super().__init__(**UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = None					,						**UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= get_image_size(UpperCamelCase      )
								# Rounds the height and width down to the closest multiple of size_divisor
								_SCREAMING_SNAKE_CASE				= height // size_divisor * size_divisor
								_SCREAMING_SNAKE_CASE				= width // size_divisor * size_divisor
								_SCREAMING_SNAKE_CASE				= resize(UpperCamelCase					,						(new_h, new_w)					,						resample=UpperCamelCase					,						data_format=UpperCamelCase					,						**UpperCamelCase      )
								return image




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = None					,						**UpperCamelCase      ):
								return rescale(image=UpperCamelCase					,						scale=UpperCamelCase					,						data_format=UpperCamelCase					,						**UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase=None					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = ChannelDimension.FIRST					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= do_resize if do_resize is not None else self.do_resize
								_SCREAMING_SNAKE_CASE				= do_rescale if do_rescale is not None else self.do_rescale
								_SCREAMING_SNAKE_CASE				= size_divisor if size_divisor is not None else self.size_divisor
								_SCREAMING_SNAKE_CASE				= resample if resample is not None else self.resample

								if do_resize and size_divisor is None:
														raise ValueError("size_divisor is required for resizing"      )

								_SCREAMING_SNAKE_CASE				= make_list_of_images(UpperCamelCase      )

								if not valid_images(UpperCamelCase      ):
														raise ValueError("Invalid image(s)"      )

								# All transformations expect numpy arrays.
								_SCREAMING_SNAKE_CASE				= [to_numpy_array(UpperCamelCase      ) for img in images]

								if do_resize:
														_SCREAMING_SNAKE_CASE				= [self.resize(UpperCamelCase					,						size_divisor=UpperCamelCase					,						resample=UpperCamelCase      ) for image in images]

								if do_rescale:
														_SCREAMING_SNAKE_CASE				= [self.rescale(UpperCamelCase					,						scale=1 / 255      ) for image in images]

								_SCREAMING_SNAKE_CASE				= [to_channel_dimension_format(UpperCamelCase					,						UpperCamelCase      ) for image in images]

								_SCREAMING_SNAKE_CASE				= {"pixel_values": images}
								return BatchFeature(data=UpperCamelCase					,						tensor_type=UpperCamelCase      )

'''simple docstring'''







import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Optional[int]       =					["""image_processor""", """tokenizer"""]
		a							:					Optional[int]       =					"""BlipImageProcessor"""
		a							:					Optional[int]       =					"""AutoTokenizer"""




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								super().__init__(UpperCamelCase					,						UpperCamelCase      )

								# add QFormer tokenizer
								_SCREAMING_SNAKE_CASE				= qformer_tokenizer




		def __call__(				self					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = True					,						UpperCamelCase = False					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = 0					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = False					,						UpperCamelCase = True					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								if images is None and text is None:
														raise ValueError("You have to specify at least images or text."      )

								_SCREAMING_SNAKE_CASE				= BatchFeature()

								if text is not None:
														_SCREAMING_SNAKE_CASE				= self.tokenizer(
														    text=UpperCamelCase					,						add_special_tokens=UpperCamelCase					,						padding=UpperCamelCase					,						truncation=UpperCamelCase					,						max_length=UpperCamelCase					,						stride=UpperCamelCase					,						pad_to_multiple_of=UpperCamelCase					,						return_attention_mask=UpperCamelCase					,						return_overflowing_tokens=UpperCamelCase					,						return_special_tokens_mask=UpperCamelCase					,						return_offsets_mapping=UpperCamelCase					,						return_token_type_ids=UpperCamelCase					,						return_length=UpperCamelCase					,						verbose=UpperCamelCase					,						return_tensors=UpperCamelCase					,						**UpperCamelCase					,						)
														encoding.update(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.qformer_tokenizer(
														    text=UpperCamelCase					,						add_special_tokens=UpperCamelCase					,						padding=UpperCamelCase					,						truncation=UpperCamelCase					,						max_length=UpperCamelCase					,						stride=UpperCamelCase					,						pad_to_multiple_of=UpperCamelCase					,						return_attention_mask=UpperCamelCase					,						return_overflowing_tokens=UpperCamelCase					,						return_special_tokens_mask=UpperCamelCase					,						return_offsets_mapping=UpperCamelCase					,						return_token_type_ids=UpperCamelCase					,						return_length=UpperCamelCase					,						verbose=UpperCamelCase					,						return_tensors=UpperCamelCase					,						**UpperCamelCase					,						)
														_SCREAMING_SNAKE_CASE				= qformer_text_encoding.pop("input_ids"      )
														_SCREAMING_SNAKE_CASE				= qformer_text_encoding.pop("attention_mask"      )

								if images is not None:
														_SCREAMING_SNAKE_CASE				= self.image_processor(UpperCamelCase					,						return_tensors=UpperCamelCase      )
														encoding.update(UpperCamelCase      )

								return encoding




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.tokenizer.batch_decode(*UpperCamelCase					,						**UpperCamelCase      )




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.tokenizer.decode(*UpperCamelCase					,						**UpperCamelCase      )




		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer.model_input_names
								_SCREAMING_SNAKE_CASE				= self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )




		def     lowercase       (				self					,						UpperCamelCase					,						**UpperCamelCase      ):
								if os.path.isfile(UpperCamelCase      ):
														raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'      )
								os.makedirs(UpperCamelCase					,						exist_ok=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= os.path.join(UpperCamelCase					,						"qformer_tokenizer"      )
								self.qformer_tokenizer.save_pretrained(UpperCamelCase      )
								return super().save_pretrained(UpperCamelCase					,						**UpperCamelCase      )




		@classmethod
		def     lowercase       (				cls					,						UpperCamelCase					,						**UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= AutoTokenizer.from_pretrained(UpperCamelCase					,						subfolder="qformer_tokenizer"      )
								_SCREAMING_SNAKE_CASE				= cls._get_arguments_from_pretrained(UpperCamelCase					,						**UpperCamelCase      )
								args.append(UpperCamelCase      )
								return cls(*UpperCamelCase      )