
'''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 						a   (				__magic_name__    ):
				_snake_case		    = '''Salesforce/blip-image-captioning-base'''
				_snake_case		    = (
				    '''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.'''
				)
				_snake_case		    = '''image_captioner'''
				_snake_case		    = AutoModelForVisionaSeq

				_snake_case		    = ['''image''']
				_snake_case		    = ['''text''']




				def __init__(							self	:				Dict,    *SCREAMING_SNAKE_CASE_	:				Optional[Any],    **SCREAMING_SNAKE_CASE_	:				Tuple      ):
										requires_backends(self,    ['''vision''']      )
										super().__init__(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				"Image"      ):
										return self.pre_processor(images=SCREAMING_SNAKE_CASE_,    return_tensors='''pt'''      )




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Dict      ):
										return self.model.generate(**SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				int      ):
										return self.pre_processor.batch_decode(SCREAMING_SNAKE_CASE_,    skip_special_tokens=SCREAMING_SNAKE_CASE_      )[0].strip()



'''simple docstring'''





from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip

