



'''simple docstring'''






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 							lowercase       (  UpperCamelCase_				):


 """simple docstring"""


 UpperCAmelCase							=  ["""image_processor""", """tokenizer"""]
 UpperCAmelCase							=  """BlipImageProcessor"""
 UpperCAmelCase							=  ("""BertTokenizer""", """BertTokenizerFast""")







 def __init__(							self  ,a_  ,a_							)		->   List[str]:
        _UpperCAmelCase  :						Any			     = False
        super().__init__(UpperCamelCase__  ,UpperCamelCase__							)
        _UpperCAmelCase  :						Any			     = self.image_processor







 def __call__(							self  ,a_ = None  ,a_ = None  ,a_ = True  ,a_ = False  ,a_ = None  ,a_ = None  ,a_ = 0  ,a_ = None  ,a_ = None  ,a_ = False  ,a_ = False  ,a_ = False  ,a_ = False  ,a_ = False  ,a_ = True  ,a_ = None  ,**a_  ,)		->   List[Any]:





        if images is None and text is None:
               raise ValueError("""You have to specify either images or text."""							)

        # Get only text
        if images is None:
               _UpperCAmelCase  :						List[str]			     = self.tokenizer
               _UpperCAmelCase  :						List[str]			     = 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__  ,)
               return text_encoding

        # add pixel_values
        _UpperCAmelCase  :						Any			     = self.image_processor(UpperCamelCase__  ,return_tensors=UpperCamelCase__							)

        if text is not None:
               _UpperCAmelCase  :						Dict			     = 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__  ,)
        else:
               _UpperCAmelCase  :						Any			     = None

        if text_encoding is not None:
               encoding_image_processor.update(UpperCamelCase__							)

        return encoding_image_processor







 def _snake_case (							self  ,*a_  ,**a_							)		->   Union[str, Any]:
        return self.tokenizer.batch_decode(*UpperCamelCase__  ,**UpperCamelCase__							)







 def _snake_case (							self  ,*a_  ,**a_							)		->   int:
        return self.tokenizer.decode(*UpperCamelCase__  ,**UpperCamelCase__							)







 @property
 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						List[Any]			     = self.tokenizer.model_input_names
        _UpperCAmelCase  :						Union[str, Any]			     = self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size'''		,  [None, 400 * 2**20, 600 * 2**20]						)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,  ['''default''', 0, 100 * 2**20, 900 * 2**20]						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						if input_in_memory_max_size != "default":
												monkeypatch.setattr(datasets.config		,  '''IN_MEMORY_MAX_SIZE'''		,  _lowercase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							datasets.config.IN_MEMORY_MAX_SIZE
						if input_in_memory_max_size == "default":
												assert in_memory_max_size == 0
						else:
												assert in_memory_max_size == input_in_memory_max_size
						if dataset_size and in_memory_max_size:
												SCREAMING_SNAKE_CASE							:    str							=							dataset_size < in_memory_max_size
						else:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							False
						SCREAMING_SNAKE_CASE							:    Any							=							is_small_dataset(_lowercase						)
						assert result == expected





