

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_       	=	{
    '''configuration_blip_2''': [
        '''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Blip2Config''',
        '''Blip2QFormerConfig''',
        '''Blip2VisionConfig''',
    ],
    '''processing_blip_2''': ['''Blip2Processor'''],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''Blip2Model''',
					    '''Blip2QFormerModel''',
					    '''Blip2PreTrainedModel''',
					    '''Blip2ForConditionalGeneration''',
					    '''Blip2VisionModel''',
					]

if TYPE_CHECKING:
					from .configuration_blip_a import (
					    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BlipaConfig,
					    BlipaQFormerConfig,
					    BlipaVisionConfig,
					)
					from .processing_blip_a import BlipaProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blip_a import (
										    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlipaForConditionalGeneration,
										    BlipaModel,
										    BlipaPreTrainedModel,
										    BlipaQFormerModel,
										    BlipaVisionModel,
										)

else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


# 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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				"philschmid/bart-large-cnn-samsum"
			SCREAMING_SNAKE_CASE     :   Tuple									=				(
			    "This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
			    "and returns a summary of the text."
			)
			SCREAMING_SNAKE_CASE     :   str									=				"summarizer"
			SCREAMING_SNAKE_CASE     :   str									=				AutoTokenizer
			SCREAMING_SNAKE_CASE     :   str									=				AutoModelForSeqaSeqLM

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				int							)      ->Optional[int]:
									return self.pre_processor(_UpperCamelCase							,   return_tensors='''pt'''							,   truncation=_UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]							)      ->Tuple:
									return self.model.generate(**_UpperCamelCase							)[0]







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[int]							)      ->Any:
									return self.pre_processor.decode(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							,   clean_up_tokenization_spaces=_UpperCamelCase							)