import os
import re
import shutil
import sys
import tempfile
import unittest

import black


A     :  Optional[Any]					     =       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
A     :  Dict					     =       "    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       Union[str, Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir  ,     "models/bert/"   )   )
        SCREAMING_SNAKE_CASE_          =  self.transformer_dir
        shutil.copy(
            os.path.join(__magic_name__  ,     "src/transformers/models/bert/modeling_bert.py"   )  ,     os.path.join(self.transformer_dir  ,     "models/bert/modeling_bert.py"   )  ,     )







  def    __A     (							self :       Dict   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  "src/transformers"
        shutil.rmtree(self.transformer_dir   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Any  ,     __magic_name__ :       str  ,     __magic_name__ :       Optional[int]=None   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
        if overwrite_result is not None:
              SCREAMING_SNAKE_CASE_          =  comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
        SCREAMING_SNAKE_CASE_          =  black.Mode(target_versions={black.TargetVersion.PYaa}  ,     line_length=119   )
        SCREAMING_SNAKE_CASE_          =  black.format_str(__magic_name__  ,     mode=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  os.path.join(self.transformer_dir  ,     "new_code.py"   )
        with open(__magic_name__  ,     "w"  ,     newline="\n"   ) as f:
              f.write(__magic_name__   )
        if overwrite_result is None:
              self.assertTrue(len(check_copies.is_copy_consistent(__magic_name__   )   ) == 0   )
        else:
              check_copies.is_copy_consistent(f.name  ,     overwrite=__magic_name__   )
              with open(__magic_name__  ,     "r"   ) as f:
                    self.assertTrue(f.read()  ,     __magic_name__   )







  def    __A     (							self :       int   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"   )
        self.assertEqual(__magic_name__  ,     __magic_name__   )







  def    __A     (							self :       int   )  ->	Union[str, Any]:
        # Base copy consistency
        self.check_copy_consistency(
            "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"  ,     "BertLMPredictionHead"  ,     REFERENCE_CODE + "\n"  ,     )

        # With no empty line at the end
        self.check_copy_consistency(
            "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"  ,     "BertLMPredictionHead"  ,     __magic_name__  ,     )

        # Copy consistency with rename
        self.check_copy_consistency(
            "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"  ,     "TestModelLMPredictionHead"  ,     re.sub("Bert"  ,     "TestModel"  ,     __magic_name__   )  ,     )

        # Copy consistency with a really long name
        SCREAMING_SNAKE_CASE_          =  "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
        self.check_copy_consistency(
            F'''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'''  ,     F'''{long_class_name}LMPredictionHead'''  ,     re.sub("Bert"  ,     __magic_name__  ,     __magic_name__   )  ,     )

        # Copy consistency with overwrite
        self.check_copy_consistency(
            "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"  ,     "TestModelLMPredictionHead"  ,     __magic_name__  ,     overwrite_result=re.sub("Bert"  ,     "TestModel"  ,     __magic_name__   )  ,     )







  def    __A     (							self :       str   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  check_copies.LOCALIZED_READMES["README_zh-hans.md"]

        SCREAMING_SNAKE_CASE_          =  (
            "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
            " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
            " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
            " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
            " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
            " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
            " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
            " method has been applied to compress GPT2 into"
            " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
            " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
            " Multilingual BERT into"
            " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
            " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
            " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
            " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
            " Luong, Quoc V. Le, Christopher D. Manning."
        )
        SCREAMING_SNAKE_CASE_          =  (
            "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
            " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
            " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
            " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
        )
        SCREAMING_SNAKE_CASE_          =  (
            "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
            " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
            " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
            " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
            " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
            " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
            " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
            " method has been applied to compress GPT2 into"
            " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
            " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
            " Multilingual BERT into"
            " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
            " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
            " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
            " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
            " Christopher D. Manning 发布。\n"
        )

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  check_copies.convert_to_localized_md(
            __magic_name__  ,     __magic_name__  ,     localized_readme["format_model_list"]   )

        self.assertFalse(__magic_name__   )
        self.assertEqual(__magic_name__  ,     __magic_name__   )

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  check_copies.convert_to_localized_md(
            __magic_name__  ,     __magic_name__  ,     localized_readme["format_model_list"]   )

        # Check whether the number of models is equal to README.md after conversion.
        self.assertTrue(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  (
            "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
            " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
            " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
            " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
        )
        SCREAMING_SNAKE_CASE_          =  (
            "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
            " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
            " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
            " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
        )
        SCREAMING_SNAKE_CASE_          =  (
            "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
            " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
            " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
            " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
        )

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  check_copies.convert_to_localized_md(
            __magic_name__  ,     __magic_name__  ,     localized_readme["format_model_list"]   )

        # Check if the model link is synchronized.
        self.assertEqual(__magic_name__  ,     __magic_name__   )



import copy

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


A     :  Tuple					     =       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(SCREAMING_SNAKE_CASE__					)
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						'''rag'''
  lowerCamelCase__     =						True







  def __init__(							self :       Any  ,     __magic_name__ :       Optional[int]=None  ,     __magic_name__ :       Dict=True  ,     __magic_name__ :       List[Any]=None  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       List[Any]=None  ,     __magic_name__ :       Any=None  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       Any=" / "  ,     __magic_name__ :       int=" // "  ,     __magic_name__ :       Any=5  ,     __magic_name__ :       Dict=300  ,     __magic_name__ :       Optional[Any]=768  ,     __magic_name__ :       str=8  ,     __magic_name__ :       List[Any]="wiki_dpr"  ,     __magic_name__ :       Any="train"  ,     __magic_name__ :       Any="compressed"  ,     __magic_name__ :       List[Any]=None  ,     __magic_name__ :       Optional[int]=None  ,     __magic_name__ :       Optional[int]=False  ,     __magic_name__ :       Union[str, Any]=False  ,     __magic_name__ :       List[str]=0.0  ,     __magic_name__ :       Dict=True  ,     __magic_name__ :       str=False  ,     __magic_name__ :       int=False  ,     __magic_name__ :       Tuple=False  ,     __magic_name__ :       Tuple=True  ,     __magic_name__ :       Dict=None  ,     **__magic_name__ :       int  ,     )  ->	List[str]:
        super().__init__(
            bos_token_id=__magic_name__  ,     pad_token_id=__magic_name__  ,     eos_token_id=__magic_name__  ,     decoder_start_token_id=__magic_name__  ,     forced_eos_token_id=__magic_name__  ,     is_encoder_decoder=__magic_name__  ,     prefix=__magic_name__  ,     vocab_size=__magic_name__  ,     **__magic_name__  ,     )
        assert (
            "question_encoder" in kwargs and "generator" in kwargs
        ), "Config has to be initialized with question_encoder and generator config"
        SCREAMING_SNAKE_CASE_          =  kwargs.pop("question_encoder"   )
        SCREAMING_SNAKE_CASE_          =  question_encoder_config.pop("model_type"   )
        SCREAMING_SNAKE_CASE_          =  kwargs.pop("generator"   )
        SCREAMING_SNAKE_CASE_          =  decoder_config.pop("model_type"   )

        from ..auto.configuration_auto import AutoConfig

        SCREAMING_SNAKE_CASE_          =  AutoConfig.for_model(__magic_name__  ,     **__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  AutoConfig.for_model(__magic_name__  ,     **__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  reduce_loss
        SCREAMING_SNAKE_CASE_          =  label_smoothing
        SCREAMING_SNAKE_CASE_          =  exclude_bos_score
        SCREAMING_SNAKE_CASE_          =  do_marginalize

        SCREAMING_SNAKE_CASE_          =  title_sep
        SCREAMING_SNAKE_CASE_          =  doc_sep
        SCREAMING_SNAKE_CASE_          =  n_docs
        SCREAMING_SNAKE_CASE_          =  max_combined_length

        SCREAMING_SNAKE_CASE_          =  dataset
        SCREAMING_SNAKE_CASE_          =  dataset_split
        SCREAMING_SNAKE_CASE_          =  index_name

        SCREAMING_SNAKE_CASE_          =  retrieval_vector_size
        SCREAMING_SNAKE_CASE_          =  retrieval_batch_size
        SCREAMING_SNAKE_CASE_          =  passages_path
        SCREAMING_SNAKE_CASE_          =  index_path
        SCREAMING_SNAKE_CASE_          =  use_dummy_dataset

        SCREAMING_SNAKE_CASE_          =  output_retrieved

        SCREAMING_SNAKE_CASE_          =  do_deduplication

        SCREAMING_SNAKE_CASE_          =  use_cache

        if self.forced_eos_token_id is None:
              SCREAMING_SNAKE_CASE_          =  getattr(self.generator  ,     "forced_eos_token_id"  ,     __magic_name__   )







  @classmethod
  def    __A     (							cls :       Dict  ,     __magic_name__ :       PretrainedConfig  ,     __magic_name__ :       PretrainedConfig  ,     **__magic_name__ :       List[str]   )  ->	PretrainedConfig:
        return cls(question_encoder=question_encoder_config.to_dict()  ,     generator=generator_config.to_dict()  ,     **__magic_name__   )







  def    __A     (							self :       Any   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  copy.deepcopy(self.__dict__   )
        SCREAMING_SNAKE_CASE_          =  self.question_encoder.to_dict()
        SCREAMING_SNAKE_CASE_          =  self.generator.to_dict()
        SCREAMING_SNAKE_CASE_          =  self.__class__.model_type
        return output


