





import copy

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


UpperCamelCase_												=	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(a							)
class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'rag'
 __UpperCamelCase       =      True

 def __init__(  self,		A_=None,		A_=True,		A_=None,		A_=None,		A_=None,		A_=None,		A_=None,		A_=" / ",		A_=" // ",		A_=5,		A_=300,		A_=768,		A_=8,		A_="wiki_dpr",		A_="train",		A_="compressed",		A_=None,		A_=None,		A_=False,		A_=False,		A_=0.0,		A_=True,		A_=False,		A_=False,		A_=False,		A_=True,		A_=None,		**A_,		)	->       Optional[Any]:
    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"
    UpperCAmelCase__						=kwargs.pop("question_encoder"		)
    UpperCAmelCase__						=question_encoder_config.pop("model_type"		)
    UpperCAmelCase__						=kwargs.pop("generator"		)
    UpperCAmelCase__						=decoder_config.pop("model_type"		)

    from ..auto.configuration_auto import AutoConfig

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

    UpperCAmelCase__						=reduce_loss
    UpperCAmelCase__						=label_smoothing
    UpperCAmelCase__						=exclude_bos_score
    UpperCAmelCase__						=do_marginalize

    UpperCAmelCase__						=title_sep
    UpperCAmelCase__						=doc_sep
    UpperCAmelCase__						=n_docs
    UpperCAmelCase__						=max_combined_length

    UpperCAmelCase__						=dataset
    UpperCAmelCase__						=dataset_split
    UpperCAmelCase__						=index_name

    UpperCAmelCase__						=retrieval_vector_size
    UpperCAmelCase__						=retrieval_batch_size
    UpperCAmelCase__						=passages_path
    UpperCAmelCase__						=index_path
    UpperCAmelCase__						=use_dummy_dataset

    UpperCAmelCase__						=output_retrieved

    UpperCAmelCase__						=do_deduplication

    UpperCAmelCase__						=use_cache

    if self.forced_eos_token_id is None:
       UpperCAmelCase__						=getattr(self.generator,		"forced_eos_token_id",		A_		)

 @classmethod
 def  __UpperCAmelCase						(  cls,		A_,		A_,		**A_		)	->       PretrainedConfig:
    return cls(question_encoder=question_encoder_config.to_dict(),		generator=generator_config.to_dict(),		**A_		)






 def  __UpperCAmelCase						(  self		)	->       str:
    UpperCAmelCase__						=copy.deepcopy(self.__dict__		)
    UpperCAmelCase__						=self.question_encoder.to_dict()
    UpperCAmelCase__						=self.generator.to_dict()
    UpperCAmelCase__						=self.__class__.model_type
    return output










import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links






def 				_UpperCAmelCase			(    A				,							A=7     ):




   '''simple docstring'''

   UpperCAmelCase__						=None
   if token is not None:
      UpperCAmelCase__						={"Accept": "application/vnd.github+json", "Authorization": F"""Bearer {token}"""}

   # The id of a workflow (not of a workflow run)
   UpperCAmelCase__						="636036"

   UpperCAmelCase__						=F"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
   # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
   url += F"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

   UpperCAmelCase__						=requests.get(A				,							headers=A     ).json()

   return result["workflow_runs"]






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_daily_ci_runs(A     )
   UpperCAmelCase__						=None
   for workflow_run in workflow_runs:
      if workflow_run["status"] == "completed":
         UpperCAmelCase__						=workflow_run["id"]
         break

   return workflow_run_id






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_last_daily_ci_runs(A     )
   if workflow_run_id is not None:
      UpperCAmelCase__						=get_artifacts_links(worflow_run_id=A				,							token=A     )
      for artifact_name in artifact_names:
         if artifact_name in artifacts_links:
            UpperCAmelCase__						=artifacts_links[artifact_name]
            download_artifact(
                artifact_name=A				,							artifact_url=A				,							output_dir=A				,							token=A     )






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   get_last_daily_ci_artifacts(A				,							A				,							A     )

   UpperCAmelCase__						={}
   for artifact_name in artifact_names:
      UpperCAmelCase__						=os.path.join(A				,							F"""{artifact_name}.zip"""     )
      if os.path.isfile(A     ):
         UpperCAmelCase__						={}
         with zipfile.ZipFile(A     ) as z:
            for filename in z.namelist():
               if not os.path.isdir(A     ):
                  # read the file
                  with z.open(A     ) as f:
                     UpperCAmelCase__						=f.read().decode("UTF-8"     )

   return results



