


"""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


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







class       __a	(   __snake_case       ):
				lowerCamelCase    :					Tuple 					='google/flan-t5-base'
				lowerCamelCase    :					str 					=(
				    '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[Any] 					='text_qa'
				lowerCamelCase    :					Any 					=AutoTokenizer
				lowerCamelCase    :					Any 					=AutoModelForSeqaSeqLM

				lowerCamelCase    :					Union[str, Any] 					=['text', 'text']
				lowerCamelCase    :					List[Any] 					=['text']







				def     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase      ):

					'''simple docstring'''






					lowerCAmelCase_			       = QA_PROMPT.format(text=UpperCAmelCase			,  question=UpperCAmelCase      )
					return self.pre_processor(UpperCAmelCase			,  return_tensors='''pt'''      )







				def     lowerCamelCase_    (   self			,  UpperCAmelCase      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.model.generate(**UpperCAmelCase      )

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

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







				def     lowerCamelCase_    (   self			,  UpperCAmelCase      ):

					'''simple docstring'''






					return self.pre_processor.decode(UpperCAmelCase			,  skip_special_tokens=UpperCAmelCase			,  clean_up_tokenization_spaces=UpperCAmelCase      )



"""simple docstring"""



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available







@dataclass
class       __a	(   __snake_case       ):
				lowerCamelCase    :					int 					={
				    'sld_warmup_steps': 15,
				    'sld_guidance_scale': 20,
				    'sld_threshold': 0.0,
				    'sld_momentum_scale': 0.0,
				    'sld_mom_beta': 0.0,
				}
				lowerCamelCase    :					Optional[Any] 					={
				    'sld_warmup_steps': 10,
				    'sld_guidance_scale': 1000,
				    'sld_threshold': 0.01,
				    'sld_momentum_scale': 0.3,
				    'sld_mom_beta': 0.4,
				}
				lowerCamelCase    :					List[str] 					={
				    'sld_warmup_steps': 7,
				    'sld_guidance_scale': 2000,
				    'sld_threshold': 0.025,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}
				lowerCamelCase    :					Any 					={
				    'sld_warmup_steps': 0,
				    'sld_guidance_scale': 5000,
				    'sld_threshold': 1.0,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}







@dataclass
class       __a	(   __snake_case       ):
				lowerCamelCase    :					Union[List[PIL.Image.Image], np.ndarray]
				lowerCamelCase    :					Optional[List[bool]]
				lowerCamelCase    :					Optional[Union[List[PIL.Image.Image], np.ndarray]]
				lowerCamelCase    :					Optional[str]


if is_transformers_available() and is_torch_available():
		from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
		from .safety_checker import SafeStableDiffusionSafetyChecker