




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						(DDIMParallelScheduler,)
 UpperCamelCase						       =						(('''eta''', 0.0), ('''num_inference_steps''', 50))

 def     a__			(    self :  Union[str, Any]						,							**A_ :  Union[str, Any]  )       ->     List[Any]:

       """simple docstring"""






       lowerCamelCase_							=   {
           'num_train_timesteps': 1000,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
           'clip_sample': True,
       }

       config.update(**A_  )
       return config

 def     a__			(    self :  List[Any]						,							**A_ :  Dict  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   self.scheduler_classes[0]
       lowerCamelCase_							=   self.get_scheduler_config(**A_  )
       lowerCamelCase_							=   scheduler_class(**A_  )

       lowerCamelCase_					,    lowerCamelCase_							=   10, 0.0

       lowerCamelCase_							=   self.dummy_model()
       lowerCamelCase_							=   self.dummy_sample_deter

       scheduler.set_timesteps(A_  )

       for t in scheduler.timesteps:
             lowerCamelCase_							=   model(A_						,							A_  )
             lowerCamelCase_							=   scheduler.step(A_						,							A_						,							A_						,							A_  ).prev_sample

       return sample

 def     a__			(    self :  Optional[int]  )       ->     Optional[Any]:

       """simple docstring"""






       for timesteps in [100, 500, 1000]:
             self.check_over_configs(num_train_timesteps=A_  )

 def     a__			(    self :  int  )       ->     List[str]:

       """simple docstring"""






       for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=A_  )

       lowerCamelCase_							=   self.scheduler_classes[0]
       lowerCamelCase_							=   self.get_scheduler_config(steps_offset=1  )
       lowerCamelCase_							=   scheduler_class(**A_  )
       scheduler.set_timesteps(5  )
       assert torch.equal(scheduler.timesteps						,							torch.LongTensor([801, 601, 401, 201, 1]  )  )

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

       """simple docstring"""






       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]						,							[0.002, 0.02, 0.2, 2]  ):
             self.check_over_configs(beta_start=A_						,							beta_end=A_  )

 def     a__			(    self :  Union[str, Any]  )       ->     Optional[Any]:

       """simple docstring"""






       for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=A_  )

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

       """simple docstring"""






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

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

       """simple docstring"""






       for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=A_  )

 def     a__			(    self :  Optional[Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=A_  )

 def     a__			(    self :  str  )       ->     List[str]:

       """simple docstring"""






       for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=A_  )

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

       """simple docstring"""






       self.check_over_configs(thresholding=A_  )
       for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=A_						,							prediction_type=A_						,							sample_max_value=A_						,							)

 def     a__			(    self :  Union[str, Any]  )       ->     Tuple:

       """simple docstring"""






       for t in [1, 10, 49]:
             self.check_over_forward(time_step=A_  )

 def     a__			(    self :  List[str]  )       ->     str:

       """simple docstring"""






       for t, num_inference_steps in zip([1, 10, 50]						,							[10, 50, 500]  ):
             self.check_over_forward(time_step=A_						,							num_inference_steps=A_  )

 def     a__			(    self :  Optional[int]  )       ->     Optional[Any]:

       """simple docstring"""






       for t, eta in zip([1, 10, 49]						,							[0.0, 0.5, 1.0]  ):
             self.check_over_forward(time_step=A_						,							eta=A_  )

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

       """simple docstring"""






       lowerCamelCase_							=   self.scheduler_classes[0]
       lowerCamelCase_							=   self.get_scheduler_config()
       lowerCamelCase_							=   scheduler_class(**A_  )

       assert torch.sum(torch.abs(scheduler._get_variance(0						,							0  ) - 0.0  )  ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(420						,							400  ) - 0.14771  )  ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(980						,							960  ) - 0.32460  )  ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(0						,							0  ) - 0.0  )  ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(487						,							486  ) - 0.00979  )  ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(999						,							998  ) - 0.02  )  ) < 1E-5

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

       """simple docstring"""






       lowerCamelCase_							=   self.scheduler_classes[0]
       lowerCamelCase_							=   self.get_scheduler_config()
       lowerCamelCase_							=   scheduler_class(**A_  )

       lowerCamelCase_					,    lowerCamelCase_							=   10, 0.0
       scheduler.set_timesteps(A_  )

       lowerCamelCase_							=   self.dummy_model()
       lowerCamelCase_							=   self.dummy_sample_deter
       lowerCamelCase_							=   self.dummy_sample_deter + 0.1
       lowerCamelCase_							=   self.dummy_sample_deter - 0.1

       lowerCamelCase_							=   samplea.shape[0]
       lowerCamelCase_							=   torch.stack([samplea, samplea, samplea]						,							dim=0  )
       lowerCamelCase_							=   torch.arange(A_  )[0:3, None].repeat(1						,							A_  )

       lowerCamelCase_							=   model(samples.flatten(0						,							1  )						,							timesteps.flatten(0						,							1  )  )
       lowerCamelCase_							=   scheduler.batch_step_no_noise(A_						,							timesteps.flatten(0						,							1  )						,							samples.flatten(0						,							1  )						,							A_  )

       lowerCamelCase_							=   torch.sum(torch.abs(A_  )  )
       lowerCamelCase_							=   torch.mean(torch.abs(A_  )  )

       assert abs(result_sum.item() - 1147.7904  ) < 1E-2
       assert abs(result_mean.item() - 0.4982  ) < 1E-3

 def     a__			(    self :  Union[str, Any]  )       ->     Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=   self.full_loop()

       lowerCamelCase_							=   torch.sum(torch.abs(A_  )  )
       lowerCamelCase_							=   torch.mean(torch.abs(A_  )  )

       assert abs(result_sum.item() - 172.0067  ) < 1E-2
       assert abs(result_mean.item() - 0.223967  ) < 1E-3

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

       """simple docstring"""






       lowerCamelCase_							=   self.full_loop(prediction_type='v_prediction'  )

       lowerCamelCase_							=   torch.sum(torch.abs(A_  )  )
       lowerCamelCase_							=   torch.mean(torch.abs(A_  )  )

       assert abs(result_sum.item() - 52.5302  ) < 1E-2
       assert abs(result_mean.item() - 0.0684  ) < 1E-3

 def     a__			(    self :  Tuple  )       ->     Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=   self.full_loop(set_alpha_to_one=A_						,							beta_start=0.01  )
       lowerCamelCase_							=   torch.sum(torch.abs(A_  )  )
       lowerCamelCase_							=   torch.mean(torch.abs(A_  )  )

       assert abs(result_sum.item() - 149.8295  ) < 1E-2
       assert abs(result_mean.item() - 0.1951  ) < 1E-3





 def     a__			(    self :  Any  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   self.full_loop(set_alpha_to_one=A_						,							beta_start=0.01  )
       lowerCamelCase_							=   torch.sum(torch.abs(A_  )  )
       lowerCamelCase_							=   torch.mean(torch.abs(A_  )  )

       assert abs(result_sum.item() - 149.0784  ) < 1E-2
       assert abs(result_mean.item() - 0.1941  ) < 1E-3









import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


lowerCamelCase    :		Any			   =       r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"







@add_start_docstrings(UpperCamelCase       )
class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''rag'''
 UpperCamelCase						       =						True

 def __init__(    self :  Optional[Any]						,							A_ :  Optional[Any]=None						,							A_ :  Any=True						,							A_ :  Dict=None						,							A_ :  Optional[int]=None						,							A_ :  str=None						,							A_ :  int=None						,							A_ :  List[Any]=None						,							A_ :  List[str]=" / "						,							A_ :  Tuple=" // "						,							A_ :  Union[str, Any]=5						,							A_ :  Optional[Any]=300						,							A_ :  int=768						,							A_ :  Dict=8						,							A_ :  int="wiki_dpr"						,							A_ :  int="train"						,							A_ :  List[str]="compressed"						,							A_ :  Tuple=None						,							A_ :  Optional[Any]=None						,							A_ :  Optional[int]=False						,							A_ :  str=False						,							A_ :  Optional[Any]=0.0						,							A_ :  Union[str, Any]=True						,							A_ :  List[Any]=False						,							A_ :  Union[str, Any]=False						,							A_ :  Dict=False						,							A_ :  str=True						,							A_ :  List[str]=None						,							**A_ :  Optional[Any]						,							)       ->     str:

       """simple docstring"""






       super().__init__(
           bos_token_id=A_						,							pad_token_id=A_						,							eos_token_id=A_						,							decoder_start_token_id=A_						,							forced_eos_token_id=A_						,							is_encoder_decoder=A_						,							prefix=A_						,							vocab_size=A_						,							**A_						,							)
       assert (
           "question_encoder" in kwargs and "generator" in kwargs
       ), "Config has to be initialized with question_encoder and generator config"
       lowerCamelCase_							=   kwargs.pop('question_encoder'  )
       lowerCamelCase_							=   question_encoder_config.pop('model_type'  )
       lowerCamelCase_							=   kwargs.pop('generator'  )
       lowerCamelCase_							=   decoder_config.pop('model_type'  )

       from ..auto.configuration_auto import AutoConfig

       lowerCamelCase_							=   AutoConfig.for_model(A_						,							**A_  )
       lowerCamelCase_							=   AutoConfig.for_model(A_						,							**A_  )

       lowerCamelCase_							=   reduce_loss
       lowerCamelCase_							=   label_smoothing
       lowerCamelCase_							=   exclude_bos_score
       lowerCamelCase_							=   do_marginalize

       lowerCamelCase_							=   title_sep
       lowerCamelCase_							=   doc_sep
       lowerCamelCase_							=   n_docs
       lowerCamelCase_							=   max_combined_length

       lowerCamelCase_							=   dataset
       lowerCamelCase_							=   dataset_split
       lowerCamelCase_							=   index_name

       lowerCamelCase_							=   retrieval_vector_size
       lowerCamelCase_							=   retrieval_batch_size
       lowerCamelCase_							=   passages_path
       lowerCamelCase_							=   index_path
       lowerCamelCase_							=   use_dummy_dataset

       lowerCamelCase_							=   output_retrieved

       lowerCamelCase_							=   do_deduplication

       lowerCamelCase_							=   use_cache

       if self.forced_eos_token_id is None:
             lowerCamelCase_							=   getattr(self.generator						,							'forced_eos_token_id'						,							A_  )

 @classmethod
 def     a__			(    cls :  str						,							A_ :  PretrainedConfig						,							A_ :  PretrainedConfig						,							**A_ :  List[str]  )       ->     PretrainedConfig:

       """simple docstring"""






       return cls(question_encoder=question_encoder_config.to_dict()						,							generator=generator_config.to_dict()						,							**A_  )





 def     a__			(    self :  Tuple  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   copy.deepcopy(self.__dict__  )
       lowerCamelCase_							=   self.question_encoder.to_dict()
       lowerCamelCase_							=   self.generator.to_dict()
       lowerCamelCase_							=   self.__class__.model_type
       return output



