


import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 			lowercase_				(lowerCAmelCase_      ):
				snake_case  						=(KDPMaAncestralDiscreteScheduler,)
				snake_case  						=10





				def 	__UpperCamelCase						(							self					,     **lowercase_)     ->   Any:
											a__     						={
											    'num_train_timesteps': 1100,
											    'beta_start': 0.00_01,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**__a)
											return config





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:





											for timesteps in [10, 50, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=__a)





				def 	__UpperCamelCase						(							self)     ->   Optional[int]:





											for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,     [0.00_02, 0.0_02, 0.02]):
																		self.check_over_configs(beta_start=__a					,     beta_end=__a)





				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:





											for schedule in ["linear", "scaled_linear"]:
																		self.check_over_configs(beta_schedule=__a)





				def 	__UpperCamelCase						(							self)     ->   Any:





											if torch_device == "mps":
																		return
											a__     						=self.scheduler_classes[0]
											a__     						=self.get_scheduler_config()
											a__     						=scheduler_class(**__a)

											scheduler.set_timesteps(self.num_inference_steps)

											a__     						=torch.manual_seed(0)

											a__     						=self.dummy_model()
											a__     						=self.dummy_sample_deter * scheduler.init_noise_sigma
											a__     						=sample.to(__a)

											for i, t in enumerate(scheduler.timesteps):
																		a__     						=scheduler.scale_model_input(__a					,     __a)

																		a__     						=model(__a					,     __a)

																		a__     						=scheduler.step(__a					,     __a					,     __a					,     generator=__a)
																		a__     						=output.prev_sample

											a__     						=torch.sum(torch.abs(__a))
											a__     						=torch.mean(torch.abs(__a))

											assert abs(result_sum.item() - 1_38_49.38_77) < 1e-2
											assert abs(result_mean.item() - 18.03_31) < 5e-3





				def 	__UpperCamelCase						(							self)     ->   Optional[int]:





											for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(prediction_type=__a)





				def 	__UpperCamelCase						(							self)     ->   str:





											if torch_device == "mps":
																		return
											a__     						=self.scheduler_classes[0]
											a__     						=self.get_scheduler_config(prediction_type='v_prediction')
											a__     						=scheduler_class(**__a)

											scheduler.set_timesteps(self.num_inference_steps)

											a__     						=self.dummy_model()
											a__     						=self.dummy_sample_deter * scheduler.init_noise_sigma
											a__     						=sample.to(__a)

											a__     						=torch.manual_seed(0)

											for i, t in enumerate(scheduler.timesteps):
																		a__     						=scheduler.scale_model_input(__a					,     __a)

																		a__     						=model(__a					,     __a)

																		a__     						=scheduler.step(__a					,     __a					,     __a					,     generator=__a)
																		a__     						=output.prev_sample

											a__     						=torch.sum(torch.abs(__a))
											a__     						=torch.mean(torch.abs(__a))

											assert abs(result_sum.item() - 3_28.99_70) < 1e-2
											assert abs(result_mean.item() - 0.42_84) < 1e-3





				def 	__UpperCamelCase						(							self)     ->   List[Any]:





											if torch_device == "mps":
																		return
											a__     						=self.scheduler_classes[0]
											a__     						=self.get_scheduler_config()
											a__     						=scheduler_class(**__a)

											scheduler.set_timesteps(self.num_inference_steps					,     device=__a)
											a__     						=torch.manual_seed(0)

											a__     						=self.dummy_model()
											a__     						=self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma

											for t in scheduler.timesteps:
																		a__     						=scheduler.scale_model_input(__a					,     __a)

																		a__     						=model(__a					,     __a)

																		a__     						=scheduler.step(__a					,     __a					,     __a					,     generator=__a)
																		a__     						=output.prev_sample

											a__     						=torch.sum(torch.abs(__a))
											a__     						=torch.mean(torch.abs(__a))

											assert abs(result_sum.item() - 1_38_49.38_18) < 1e-1
											assert abs(result_mean.item() - 18.03_31) < 1e-3





# 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_)

