# 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


UpperCAmelCase							= '''Here is a text containing a lot of information: \'\'\'{text}\'\'\'.

Can you answer this question about the text: \'{question}\''''




class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		List[str] 			=       'google/flan-t5-base'
 _lowerCamelCase  :		Any 			=       (
     '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.'
 )
 _lowerCamelCase  :		List[str] 			=       'text_qa'
 _lowerCamelCase  :		int 			=       AutoTokenizer
 _lowerCamelCase  :		List[Any] 			=       AutoModelForSeqaSeqLM

 _lowerCamelCase  :		Tuple 			=       ['text', 'text']
 _lowerCamelCase  :		Dict 			=       ['text']

 def     __snake_case    (	self				:						Optional[int]				,	lowerCamelCase				:						str				,	lowerCamelCase				:						str     )       ->							Dict:
        _UpperCamelCase   :				int       =     QA_PROMPT.format(text=lowerCamelCase				,	question=lowerCamelCase     )
        return self.pre_processor(lowerCamelCase				,	return_tensors='pt'     )

 def     __snake_case    (	self				:						Optional[Any]				,	lowerCamelCase				:						Dict     )       ->							str:
        _UpperCamelCase   :				Optional[Any]       =     self.model.generate(**lowerCamelCase     )

        _UpperCamelCase  ,					_UpperCamelCase   :				List[str]       =     inputs['input_ids'].shape
        _UpperCamelCase   :				str       =     output_ids.shape[0]

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

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						List[str]     )       ->							List[Any]:
        return self.pre_processor.decode(lowerCamelCase				,	skip_special_tokens=lowerCamelCase				,	clean_up_tokenization_spaces=lowerCamelCase     )




from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
 import torch




class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Dict 			=       ['image_processor', 'tokenizer']
 _lowerCamelCase  :		Tuple 			=       'OneFormerImageProcessor'
 _lowerCamelCase  :		Dict 			=       ('CLIPTokenizer', 'CLIPTokenizerFast')

 def __init__(	self				:						str				,	lowerCamelCase				:						Any=None				,	lowerCamelCase				:						List[Any]=None				,	lowerCamelCase				:						int = 7_7				,	lowerCamelCase				:						int = 7_7				,	**lowerCamelCase				:						Union[str, Any]     )       ->							Any:
        if image_processor is None:
               raise ValueError('You need to specify an `image_processor`.'     )
        if tokenizer is None:
               raise ValueError('You need to specify a `tokenizer`.'     )

        _UpperCamelCase   :				int       =     max_seq_length
        _UpperCamelCase   :				Optional[Any]       =     task_seq_length

        super().__init__(lowerCamelCase				,	lowerCamelCase     )

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						int=None				,	lowerCamelCase				:						Optional[int]=7_7     )       ->							Tuple:
        if text_list is None:
               raise ValueError('tokens cannot be None.'     )

        _UpperCamelCase   :				Dict       =     self.tokenizer(lowerCamelCase				,	padding='max_length'				,	max_length=lowerCamelCase				,	truncation=lowerCamelCase     )

        _UpperCamelCase  ,					_UpperCamelCase   :				List[str]       =     tokens['attention_mask'], tokens['input_ids']

        _UpperCamelCase   :				Optional[int]       =     []
        for attn_mask, input_id in zip(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     torch.tensor(lowerCamelCase     ) * torch.tensor(lowerCamelCase     )
               token_inputs.append(token.unsqueeze(0     )     )

        _UpperCamelCase   :				Dict       =     torch.cat(lowerCamelCase				,	dim=0     )
        return token_inputs

 def __call__(	self				:						Union[str, Any]				,	lowerCamelCase				:						int=None				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						Any=None				,	**lowerCamelCase				:						List[Any]     )       ->							List[Any]:

        if task_inputs is None:
               raise ValueError('You have to specify the task_input. Found None.'     )
        elif images is None:
               raise ValueError('You have to specify the image. Found None.'     )

        if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs     ):
               raise ValueError('task_inputs must be semantic, instance, or panoptic.'     )

        _UpperCamelCase   :				Optional[Any]       =     self.image_processor(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	**lowerCamelCase     )

        if isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     [task_inputs]

        if isinstance(lowerCamelCase				,	lowerCamelCase     ) and all(isinstance(lowerCamelCase				,	lowerCamelCase     ) for task_input in task_inputs     ):
               _UpperCamelCase   :				str       =     []
               for task in task_inputs:
                      _UpperCamelCase   :				Optional[Any]       =     f"the task is {task}"
                      task_token_inputs.append(lowerCamelCase     )
               _UpperCamelCase   :				Dict       =     self._preprocess_text(lowerCamelCase				,	max_length=self.task_seq_length     )
        else:
               raise TypeError('Task Inputs should be a string or a list of strings.'     )

        if hasattr(lowerCamelCase				,	'text_inputs'     ):
               _UpperCamelCase   :				str       =     encoded_inputs.text_inputs

               _UpperCamelCase   :				Optional[Any]       =     []
               for texts in texts_list:
                      _UpperCamelCase   :				Optional[int]       =     self._preprocess_text(lowerCamelCase				,	max_length=self.max_seq_length     )
                      text_inputs.append(text_input_list.unsqueeze(0     )     )

               _UpperCamelCase   :				Optional[int]       =     torch.cat(lowerCamelCase				,	dim=0     )

        return encoded_inputs

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						int=None				,	**lowerCamelCase				:						List[str]     )       ->							Tuple:

        if task_inputs is None:
               raise ValueError('You have to specify the task_input. Found None.'     )
        elif images is None:
               raise ValueError('You have to specify the image. Found None.'     )

        if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs     ):
               raise ValueError('task_inputs must be semantic, instance, or panoptic.'     )

        _UpperCamelCase   :				Any       =     self.image_processor.encode_inputs(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	**lowerCamelCase     )

        if isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     [task_inputs]

        if isinstance(lowerCamelCase				,	lowerCamelCase     ) and all(isinstance(lowerCamelCase				,	lowerCamelCase     ) for task_input in task_inputs     ):
               _UpperCamelCase   :				int       =     []
               for task in task_inputs:
                      _UpperCamelCase   :				List[str]       =     f"the task is {task}"
                      task_token_inputs.append(lowerCamelCase     )
               _UpperCamelCase   :				int       =     self._preprocess_text(lowerCamelCase				,	max_length=self.task_seq_length     )
        else:
               raise TypeError('Task Inputs should be a string or a list of strings.'     )

        if hasattr(lowerCamelCase				,	'text_inputs'     ):
               _UpperCamelCase   :				Optional[int]       =     encoded_inputs.text_inputs

               _UpperCamelCase   :				Any       =     []
               for texts in texts_list:
                      _UpperCamelCase   :				Any       =     self._preprocess_text(lowerCamelCase				,	max_length=self.max_seq_length     )
                      text_inputs.append(text_input_list.unsqueeze(0     )     )

               _UpperCamelCase   :				Optional[Any]       =     torch.cat(lowerCamelCase				,	dim=0     )

        return encoded_inputs

 def     __snake_case    (	self				:						Dict				,	*lowerCamelCase				:						Tuple				,	**lowerCamelCase				:						Any     )       ->							str:
        return self.image_processor.post_process_semantic_segmentation(*lowerCamelCase				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						Union[str, Any]				,	*lowerCamelCase				:						Optional[int]				,	**lowerCamelCase				:						Any     )       ->							Any:
        return self.image_processor.post_process_instance_segmentation(*lowerCamelCase				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						Dict				,	*lowerCamelCase				:						List[str]				,	**lowerCamelCase				:						int     )       ->							List[Any]:
        return self.image_processor.post_process_panoptic_segmentation(*lowerCamelCase				,	**lowerCamelCase     )



