



"""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


snake_case_								=			"""Here is a text containing a lot of information: '''{text}'''.

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







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				"""google/flan-t5-base"""
						__UpperCamelCase													=				(
						    """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."""
						)
						__UpperCamelCase													=				"""text_qa"""
						__UpperCamelCase													=				AutoTokenizer
						__UpperCamelCase													=				AutoModelForSeqaSeqLM

						__UpperCamelCase													=				["""text""", """text"""]
						__UpperCamelCase													=				["""text"""]






						def       UpperCAmelCase__	(   self					:Tuple  ,    lowercase_					:str  ,    lowercase_					:str   )							->     Dict:
												UpperCAmelCase			 =					QA_PROMPT.format(text=lowercase_  ,    question=lowercase_   )
												return self.pre_processor(lowercase_  ,    return_tensors='pt'   )






						def       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:Any   )							->     Any:
												UpperCAmelCase			 =					self.model.generate(**lowercase_   )

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

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






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






"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 							__snake_case(					A_				,	A_				,	unittest.TestCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			IFImgaImgPipeline
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    _UpperCAmelCase       =			PipelineTesterMixin.required_optional_params - {"latents"}
    def 						_a							(  self	):




         '''simple docstring'''



         return self._get_dummy_components()
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase=0	):




         '''simple docstring'''



         if str(__lowerCamelCase	).startswith('mps'	):
              __A					:					Tuple								=		torch.manual_seed(__lowerCamelCase	)
         else:
              __A					:					int								=		torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)

         __A					:					str								=		floats_tensor((1, 3, 32, 32)					,       rng=random.Random(__lowerCamelCase	)	).to(__lowerCamelCase	)

         __A					:					str								=		{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_optional_components()
    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()					,       reason='XFormers attention is only available with CUDA and `xformers` installed'					,       )
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_save_load_floataa(expected_max_diff=1e-1	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_floataa_inference(expected_max_diff=1e-1	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_local()

    def 						_a							(  self	):




         '''simple docstring'''



         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2					,       )



