



"""simple docstring"""


import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel







class 				A_      (					__lowercase    ):



						"""simple docstring"""
						__UpperCamelCase		    =							"M-CLIP"






						def __init__( self			:int       ,  lowercase_			:int=10_24       ,  lowercase_			:Union[str, Any]=7_68       ,  **lowercase_			:Optional[Any] )    ->      Optional[int]:
												UpperCAmelCase					=			transformerDimSize
												UpperCAmelCase					=			imageDimSize
												super().__init__(**_a )







class 				A_      (					__lowercase    ):



						"""simple docstring"""
						__UpperCamelCase		    =							MCLIPConfig






						def __init__( self			:Tuple       ,  lowercase_			:Union[str, Any]       ,  *lowercase_			:Optional[int]       ,  **lowercase_			:Tuple )    ->      int:
												super().__init__(_a       ,  *_a       ,  **_a )
												UpperCAmelCase					=			XLMRobertaModel(_a )
												UpperCAmelCase					=			torch.nn.Linear(
												    in_features=config.transformerDimensions       ,  out_features=config.numDims )






						def      UpperCAmelCase__					( self			:int       ,  lowercase_			:Any       ,  lowercase_			:Union[str, Any] )    ->      Tuple:
												UpperCAmelCase					=			self.transformer(input_ids=_a       ,  attention_mask=_a )[0]
												UpperCAmelCase					=			(embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
												return self.LinearTransformation(_a ), embs










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







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							"""philschmid/bart-large-cnn-samsum"""
						__UpperCamelCase		    =							(
						    """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."""
						)
						__UpperCamelCase		    =							"""summarizer"""
						__UpperCamelCase		    =							AutoTokenizer
						__UpperCamelCase		    =							AutoModelForSeqaSeqLM

						__UpperCamelCase		    =							["""text"""]
						__UpperCamelCase		    =							["""text"""]






						def      UpperCAmelCase__					( self			:int       ,  lowercase_			:Tuple )    ->      int:
												return self.pre_processor(lowercase_       ,  return_tensors='pt'       ,  truncation=lowercase_ )






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Dict )    ->      Optional[int]:
												return self.model.generate(**lowercase_ )[0]






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Tuple )    ->      List[str]:
												return self.pre_processor.decode(lowercase_       ,  skip_special_tokens=lowercase_       ,  clean_up_tokenization_spaces=lowercase_ )





