


'''simple docstring'''

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      Union[List[PIL.Image.Image], np.ndarray]
		A_     :      Optional[List[bool]]


if is_transformers_available() and is_torch_available():
							from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline



'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
							from PIL import Image
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      Any				   =    """Salesforce/blip-image-captioning-base"""
		A_     :      Any				   =    (
		    """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
		    """image to caption, and returns a text that contains the description in English."""
		)
		A_     :      List[str]				   =    """image_captioner"""
		A_     :      Optional[Any]				   =    AutoModelForVisionaSeq

		A_     :      Any				   =    ["""image"""]
		A_     :      Union[str, Any]				   =    ["""text"""]







		def __init__( self		:						Dict  ,					*_A		:						Dict  ,					**_A		:						Any      )  ->			Dict:
			requires_backends(self  ,					['vision']      )
			super().__init__(*_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						"Image"      )  ->			Union[str, Any]:
			return self.pre_processor(images=_A  ,					return_tensors='pt'      )







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						str      )  ->			List[Any]:
			return self.model.generate(**_A      )







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						Optional[int]      )  ->			Union[str, Any]:
			return self.pre_processor.batch_decode(_A  ,					skip_special_tokens=_A      )[0].strip()