


import torch

from transformers import AutoTokenizer




class 			lowercase_				(lowercase__      ):





				def __init__(							self					,     lowercase_)     ->   List[Any]:
											a__     						=AutoTokenizer.from_pretrained(lowercase_)





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   int:

											if isinstance(lowercase_					,     lowercase_) and all(isinstance(_x					,     lowercase_) for _x in x):
																		a__     						=None
																		for l in x:
																									a__     						=self.tokenizer(
																									    lowercase_					,     padding='max_length'					,     max_length=384					,     truncation=lowercase_					,     return_tensors='pt'					,     )
																									a__     						=torch.tensor([len(lowercase_)])
																									if d is not None:
																																for k in d.keys():
																																							a__     						=torch.cat((d[k], t[k])					,     0)
																									else:
																																a__     						=t

																		a__     						=torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'))
																		a__     						=torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'))

											elif isinstance(lowercase_					,     lowercase_) and all(isinstance(_x					,     lowercase_) for _x in x):
																		a__     						=self.tokenizer(
																		    lowercase_					,     padding='max_length'					,     max_length=384					,     truncation=lowercase_					,     return_tensors='pt'					,     )

											else:
																		raise Exception(
																		    'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
																		    ' strings` for supports are supported.')

											return d





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_=0.70)     ->   str:
											a__     						=[]
											for idx in range(len(W_query['input_ids'])):
																		a__     						=a__     						=range(p_start.shape[1])

																		a__     						=[]
																		for start_id in start_indexes:
																									for end_id in end_indexes:
																																if start_id < end_id:
																																							output.append(
																																							    (
																																							        start_id,
																																							        end_id,
																																							        p_start[idx][start_id].item(),
																																							        p_end[idx][end_id].item(),
																																							    ))

																		output.sort(key=lambda lowercase_: (tup[2] * tup[3])					,     reverse=lowercase_)
																		a__     						=[]
																		for k in range(len(lowercase_)):
																									if output[k][2] * output[k][3] >= thresh:
																																a__						,    a__     						=output[k][0], output[k][1]
																																a__     						=self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos])
																																temp.append((decoded, output[k][2] * output[k][3]))

																		final_outputs.append(lowercase_)

											return final_outputs





# 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


_lowerCAmelCase:	Any      							=					'Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''




class 			lowercase_				(lowercase__      ):
				snake_case  						='google/flan-t5-base'
				snake_case  						=(
				    'This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the '
				    'text where to find the answer, and `question`, which is the question, and returns the answer to the question.'
				)
				snake_case  						='text_qa'
				snake_case  						=AutoTokenizer
				snake_case  						=AutoModelForSeqaSeqLM

				snake_case  						=['text', 'text']
				snake_case  						=['text']





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_)     ->   Optional[int]:
											a__     						=QA_PROMPT.format(text=lowercase_					,     question=lowercase_)
											return self.pre_processor(lowercase_					,     return_tensors='pt')





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Dict:
											a__     						=self.model.generate(**lowercase_)

											a__						,    a__     						=inputs['input_ids'].shape
											a__     						=output_ids.shape[0]

											return output_ids.reshape(lowercase_					,     out_b // in_b					,     *output_ids.shape[1:])[0][0]





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Union[str, Any]:
											return self.pre_processor.decode(lowercase_					,     skip_special_tokens=lowercase_					,     clean_up_tokenization_spaces=lowercase_)

