"""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  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      ["image_processor", "tokenizer"]
       SCREAMING_SNAKE_CASE_			=      "BlipImageProcessor"
       SCREAMING_SNAKE_CASE_			=      ("BertTokenizer", "BertTokenizerFast")







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__)      ->      List[Any]:
            snake_case_					       =					False
            super().__init__(lowerCAmelCase__,							lowerCAmelCase__)
            snake_case_					       =					self.image_processor







       def __call__( self,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = True,							lowerCAmelCase__ = False,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = 0,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = False,							lowerCAmelCase__ = False,							lowerCAmelCase__ = False,							lowerCAmelCase__ = False,							lowerCAmelCase__ = False,							lowerCAmelCase__ = True,							lowerCAmelCase__ = None,							**lowerCAmelCase__,							)      ->      BatchEncoding:
            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:
                 snake_case_					       =					self.tokenizer
                 snake_case_					       =					self.tokenizer(
                     text=lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__,							padding=lowerCAmelCase__,							truncation=lowerCAmelCase__,							max_length=lowerCAmelCase__,							stride=lowerCAmelCase__,							pad_to_multiple_of=lowerCAmelCase__,							return_attention_mask=lowerCAmelCase__,							return_overflowing_tokens=lowerCAmelCase__,							return_special_tokens_mask=lowerCAmelCase__,							return_offsets_mapping=lowerCAmelCase__,							return_token_type_ids=lowerCAmelCase__,							return_length=lowerCAmelCase__,							verbose=lowerCAmelCase__,							return_tensors=lowerCAmelCase__,							**lowerCAmelCase__,							)
                 return text_encoding

            # add pixel_values
            snake_case_					       =					self.image_processor(lowerCAmelCase__,							return_tensors=lowerCAmelCase__)

            if text is not None:
                 snake_case_					       =					self.tokenizer(
                     text=lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__,							padding=lowerCAmelCase__,							truncation=lowerCAmelCase__,							max_length=lowerCAmelCase__,							stride=lowerCAmelCase__,							pad_to_multiple_of=lowerCAmelCase__,							return_attention_mask=lowerCAmelCase__,							return_overflowing_tokens=lowerCAmelCase__,							return_special_tokens_mask=lowerCAmelCase__,							return_offsets_mapping=lowerCAmelCase__,							return_token_type_ids=lowerCAmelCase__,							return_length=lowerCAmelCase__,							verbose=lowerCAmelCase__,							return_tensors=lowerCAmelCase__,							**lowerCAmelCase__,							)
            else:
                 snake_case_					       =					None

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

            return encoding_image_processor







       def 		a_						( self,							*lowerCAmelCase__,							**lowerCAmelCase__)      ->      Optional[Any]:
            return self.tokenizer.batch_decode(*lowerCAmelCase__,							**lowerCAmelCase__)







       def 		a_						( self,							*lowerCAmelCase__,							**lowerCAmelCase__)      ->      Union[str, Any]:
            return self.tokenizer.decode(*lowerCAmelCase__,							**lowerCAmelCase__)







       @property
       def 		a_						( self)      ->      List[str]:
            snake_case_					       =					self.tokenizer.model_input_names
            snake_case_					       =					self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))







__UpperCamelCase					:       Optional[int]    					=      {
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}

