





import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()



class 				_UpperCamelCase       (						unittest.TestCase  ):

       @property
       def lowercase			(		self:			Tuple		)							->			Union[str, Any]:




              """simple docstring"""


              torch.manual_seed(0		)
              UpperCamelCase_ 							=	UNetaDModel(
                  block_out_channels=(32, 64)       , layers_per_block=2       , sample_size=32       , in_channels=3       , out_channels=3       , down_block_types=("DownBlock2D", "AttnDownBlock2D")       , up_block_types=("AttnUpBlock2D", "UpBlock2D")       , )
              return model

       def lowercase			(		self:			List[Any]		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	"cpu"
              UpperCamelCase_ 							=	self.dummy_uncond_unet
              UpperCamelCase_ 							=	DDPMScheduler()

              UpperCamelCase_ 							=	DDPMPipeline(unet=lowerCamelCase__       , scheduler=lowerCamelCase__		)
              ddpm.to(lowerCamelCase__		)
              ddpm.set_progress_bar_config(disable=lowerCamelCase__		)

              UpperCamelCase_ 							=	torch.Generator(device=lowerCamelCase__		).manual_seed(0		)
              UpperCamelCase_ 							=	ddpm(generator=lowerCamelCase__       , num_inference_steps=2       , output_type="numpy"		).images

              UpperCamelCase_ 							=	torch.Generator(device=lowerCamelCase__		).manual_seed(0		)
              UpperCamelCase_ 							=	ddpm(generator=lowerCamelCase__       , num_inference_steps=2       , output_type="numpy"       , return_dict=lowerCamelCase__		)[0]

              UpperCamelCase_ 							=	image[0, -3:, -3:, -1]
              UpperCamelCase_ 							=	image_from_tuple[0, -3:, -3:, -1]

              assert image.shape == (1, 32, 32, 3)
              UpperCamelCase_ 							=	np.array(
                  [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.0_00, 1.199e-03, 2.648e-04, 5.101e-04]		)

              assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
              assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2



       def lowercase			(		self:			Union[str, Any]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.dummy_uncond_unet
              UpperCamelCase_ 							=	DDPMScheduler(prediction_type="sample"		)

              UpperCamelCase_ 							=	DDPMPipeline(unet=lowerCamelCase__       , scheduler=lowerCamelCase__		)
              ddpm.to(lowerCamelCase__		)
              ddpm.set_progress_bar_config(disable=lowerCamelCase__		)

              UpperCamelCase_ 							=	torch.manual_seed(0		)
              UpperCamelCase_ 							=	ddpm(generator=lowerCamelCase__       , num_inference_steps=2       , output_type="numpy"		).images

              UpperCamelCase_ 							=	torch.manual_seed(0		)
              UpperCamelCase_ 							=	ddpm(generator=lowerCamelCase__       , num_inference_steps=2       , output_type="numpy"		)[0]

              UpperCamelCase_ 							=	image[0, -3:, -3:, -1]
              UpperCamelCase_ 							=	image_eps[0, -3:, -3:, -1]

              assert image.shape == (1, 32, 32, 3)
              UpperCamelCase_ 							=	1e-2 if torch_device != "mps" else 3e-2
              assert np.abs(image_slice.flatten() - image_eps_slice.flatten()		).max() < tolerance



@slow
@require_torch_gpu
class 				_UpperCamelCase       (						unittest.TestCase  ):

       def lowercase			(		self:			Any		)							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	"google/ddpm-cifar10-32"

              UpperCamelCase_ 							=	UNetaDModel.from_pretrained(lowerCamelCase__		)
              UpperCamelCase_ 							=	DDPMScheduler.from_pretrained(lowerCamelCase__		)

              UpperCamelCase_ 							=	DDPMPipeline(unet=lowerCamelCase__       , scheduler=lowerCamelCase__		)
              ddpm.to(lowerCamelCase__		)
              ddpm.set_progress_bar_config(disable=lowerCamelCase__		)

              UpperCamelCase_ 							=	torch.manual_seed(0		)
              UpperCamelCase_ 							=	ddpm(generator=lowerCamelCase__       , output_type="numpy"		).images

              UpperCamelCase_ 							=	image[0, -3:, -3:, -1]

              assert image.shape == (1, 32, 32, 3)
              UpperCamelCase_ 							=	np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85]		)
              assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2





"""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}'''.

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


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     List[Any]				  =  '''google/flan-t5-base'''
							__magic_name__					:     Dict				  =  (
							    '''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.'''
							)
							__magic_name__					:     Union[str, Any]				  =  '''text_qa'''
							__magic_name__					:     Union[str, Any]				  =  AutoTokenizer
							__magic_name__					:     List[Any]				  =  AutoModelForSeqaSeqLM

							__magic_name__					:     Tuple				  =  ['''text''', '''text''']
							__magic_name__					:     Tuple				  =  ['''text''']




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						str		=      QA_PROMPT.format(text=lowerCamelCase__	,					question=lowerCamelCase__)
								return self.pre_processor(lowerCamelCase__	,					return_tensors="pt")




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Tuple		=      self.model.generate(**lowerCamelCase__)

								snake_case__,					snake_case__  :						Dict		=      inputs["input_ids"].shape
								snake_case__  :						List[Any]		=      output_ids.shape[0]

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







							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	List[Any]:







								'''simple docstring'''




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





