


'''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.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    "facebook/bart-large-mnli"
     __magic_name__    			=    (
         "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
         "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
         "It returns the most likely label in the list of provided `labels` for the input text."
     )
     __magic_name__    			=    "text_classifier"
     __magic_name__    			=    AutoTokenizer
     __magic_name__    			=    AutoModelForSequenceClassification

     __magic_name__    			=    ["text", ["text"]]
     __magic_name__    			=    ["text"]







     def a	( self   ):

            '''simple docstring'''

            super().setup()
            _lowerCAmelCase    :							Tuple   			=       self.model.config
            _lowerCAmelCase    :							str   			=       -1
            for idx, label in config.idalabel.items():
                   if label.lower().startswith('entail'   ):
                          _lowerCAmelCase    :							Optional[int]   			=       int(snake_case__   )
            if self.entailment_id == -1:
                   raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.'   )







     def a	( self      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       labels
            return self.pre_processor(
                [text] * len(snake_case__   )      ,	[F'This example is {label}' for label in labels]      ,	return_tensors='pt'      ,	padding='max_length'      ,	)







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       outputs.logits
            _lowerCAmelCase    :							Optional[Any]   			=       torch.argmax(logits[:, 2]   ).item()
            return self._labels[label_id]





'''simple docstring'''







import socket






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Tuple   			=       socket.socket(socket.AF_INET   ,						socket.SOCK_STREAM					)
       _lowerCAmelCase    :							Optional[int]   			=       socket.gethostname()
       _lowerCAmelCase    :							Any   			=       1_2_3_1_2

       sock.connect((host, port)					)
       sock.send(B'Hello server!'					)

       with open('Received_file'   ,						'wb'					) as out_file:
              print('File opened'					)
              print('Receiving data...'					)
              while True:
                     _lowerCAmelCase    :							Union[str, Any]   			=       sock.recv(1_0_2_4					)
                     if not data:
                            break
                     out_file.write(_A					)

       print('Successfully received the file'					)
       sock.close()
       print('Connection closed'					)


if __name__ == "__main__":
       main()

