# 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__					(       _a		):
			A__    :       Tuple        =      """philschmid/bart-large-cnn-samsum"""
			A__    :       Optional[Any]        =      (
			    """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."""
			)
			A__    :       Optional[int]        =      """summarizer"""
			A__    :       Any        =      AutoTokenizer
			A__    :       str        =      AutoModelForSeqaSeqLM

			A__    :       str        =      ["""text"""]
			A__    :       Union[str, Any]        =      ["""text"""]



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[str]:
								return self.pre_processor(__lowerCamelCase				,						return_tensors='pt'				,						truncation=__lowerCamelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[str]:
								return self.model.generate(**__lowerCamelCase   )[0]







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Tuple:
								return self.pre_processor.decode(__lowerCamelCase				,						skip_special_tokens=__lowerCamelCase				,						clean_up_tokenization_spaces=__lowerCamelCase   )





from datetime import datetime

import matplotlib.pyplot as plt
import torch
def 							lowerCAmelCase(     __lowerCamelCase						):
					for param in module.parameters():
										__a		 =       False
def 							lowerCAmelCase(     ):
					__a		 =       'cuda' if torch.cuda.is_available() else 'cpu'
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										__a		 =       'mps'
					if device == "mps":
										print(
										    'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
										    ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
										    ' with generations.'						)
					return device
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       plt.imshow(__lowerCamelCase						)
					fig.axes.get_xaxis().set_visible(__lowerCamelCase						)
					fig.axes.get_yaxis().set_visible(__lowerCamelCase						)
					plt.show()



def 							lowerCAmelCase(     ):
					__a		 =       datetime.now()
					__a		 =       current_time.strftime('%H:%M:%S'						)
					return timestamp




