


'''simple docstring'''







import copy

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


lowerCAmelCase     :  Tuple											=    r"""
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

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



     """simple docstring"""






     __magic_name__           =     "rag"
     __magic_name__           =     True







     def __init__(			self   ,						snake_case__=None   ,						snake_case__=True   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=" / "   ,						snake_case__=" // "   ,						snake_case__=5   ,						snake_case__=300   ,						snake_case__=768   ,						snake_case__=8   ,						snake_case__="wiki_dpr"   ,						snake_case__="train"   ,						snake_case__="compressed"   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=False   ,						snake_case__=False   ,						snake_case__=0.0   ,						snake_case__=True   ,						snake_case__=False   ,						snake_case__=False   ,						snake_case__=False   ,						snake_case__=True   ,						snake_case__=None   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(
                bos_token_id=snake_case__   ,						pad_token_id=snake_case__   ,						eos_token_id=snake_case__   ,						decoder_start_token_id=snake_case__   ,						forced_eos_token_id=snake_case__   ,						is_encoder_decoder=snake_case__   ,						prefix=snake_case__   ,						vocab_size=snake_case__   ,						**snake_case__   ,						)
            assert (
                "question_encoder" in kwargs and "generator" in kwargs
            ), "Config has to be initialized with question_encoder and generator config"
            _lowerCAmelCase     :				Optional[Any]       =							kwargs.pop('question_encoder' )
            _lowerCAmelCase     :				List[Any]       =							question_encoder_config.pop('model_type' )
            _lowerCAmelCase     :				List[str]       =							kwargs.pop('generator' )
            _lowerCAmelCase     :				Tuple       =							decoder_config.pop('model_type' )

            from ..auto.configuration_auto import AutoConfig

            _lowerCAmelCase     :				Union[str, Any]       =							AutoConfig.for_model(snake_case__   ,						**snake_case__ )
            _lowerCAmelCase     :				List[str]       =							AutoConfig.for_model(snake_case__   ,						**snake_case__ )

            _lowerCAmelCase     :				Optional[int]       =							reduce_loss
            _lowerCAmelCase     :				Optional[Any]       =							label_smoothing
            _lowerCAmelCase     :				Dict       =							exclude_bos_score
            _lowerCAmelCase     :				Optional[int]       =							do_marginalize

            _lowerCAmelCase     :				List[str]       =							title_sep
            _lowerCAmelCase     :				Optional[Any]       =							doc_sep
            _lowerCAmelCase     :				str       =							n_docs
            _lowerCAmelCase     :				Optional[int]       =							max_combined_length

            _lowerCAmelCase     :				Dict       =							dataset
            _lowerCAmelCase     :				Optional[int]       =							dataset_split
            _lowerCAmelCase     :				str       =							index_name

            _lowerCAmelCase     :				Tuple       =							retrieval_vector_size
            _lowerCAmelCase     :				Any       =							retrieval_batch_size
            _lowerCAmelCase     :				Any       =							passages_path
            _lowerCAmelCase     :				Tuple       =							index_path
            _lowerCAmelCase     :				Any       =							use_dummy_dataset

            _lowerCAmelCase     :				Union[str, Any]       =							output_retrieved

            _lowerCAmelCase     :				List[str]       =							do_deduplication

            _lowerCAmelCase     :				Optional[int]       =							use_cache

            if self.forced_eos_token_id is None:
                   _lowerCAmelCase     :				Tuple       =							getattr(self.generator   ,						'forced_eos_token_id'   ,						snake_case__ )







     @classmethod
     def a						(			cls   ,						snake_case__   ,						snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

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







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							copy.deepcopy(self.__dict__ )
            _lowerCAmelCase     :				List[str]       =							self.question_encoder.to_dict()
            _lowerCAmelCase     :				Optional[Any]       =							self.generator.to_dict()
            _lowerCAmelCase     :				Optional[int]       =							self.__class__.model_type
            return output





'''simple docstring'''







import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
       if is_torch_available():
              import torch
       if is_tf_available():
              import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


lowerCAmelCase     :  Optional[int]											=    logging.get_logger(__name__)

lowerCAmelCase     :  Union[str, Any]											=    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase     :  Optional[Any]											=    {
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """Salesforce/codegen-350M-mono""": (
            """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCAmelCase     :  List[str]											=    {
    """Salesforce/codegen-350M-mono""": 20_48,
}
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     VOCAB_FILES_NAMES
     __magic_name__           =     PRETRAINED_VOCAB_FILES_MAP
     __magic_name__           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__           =     ["input_ids", "attention_mask"]
     __magic_name__           =     CodeGenTokenizer







     def __init__(			self   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__=False   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(
                snake_case__   ,						snake_case__   ,						tokenizer_file=snake_case__   ,						unk_token=snake_case__   ,						bos_token=snake_case__   ,						eos_token=snake_case__   ,						add_prefix_space=snake_case__   ,						**snake_case__   ,						)

            if kwargs.pop('add_bos_token'   ,						snake_case__ ):
                   _lowerCAmelCase     :				Tuple       =							kwargs.pop('name_or_path'   ,						'' )
                   raise ValueError(
                       'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
                       'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
                       F'`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'
                       F'`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'
                       'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
                       ' so that the fast tokenizer works correctly.' )

            _lowerCAmelCase     :				List[str]       =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
            if pre_tok_state.get('add_prefix_space'   ,						snake_case__ ) != add_prefix_space:
                   _lowerCAmelCase     :				List[Any]       =							getattr(snake_case__   ,						pre_tok_state.pop('type' ) )
                   _lowerCAmelCase     :				Dict       =							add_prefix_space
                   _lowerCAmelCase     :				List[Any]       =							pre_tok_class(**snake_case__ )

            _lowerCAmelCase     :				Tuple       =							add_prefix_space







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							kwargs.get('is_split_into_words'   ,						snake_case__ )
            assert self.add_prefix_space or not is_split_into_words, (
                F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

            return super()._batch_encode_plus(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							kwargs.get('is_split_into_words'   ,						snake_case__ )

            assert self.add_prefix_space or not is_split_into_words, (
                F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

            return super()._encode_plus(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = None ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self._tokenizer.model.save(snake_case__   ,						name=snake_case__ )
            return tuple(snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = False   ,						snake_case__ = None   ,						snake_case__ = None   ,						**snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							super().decode(
                token_ids=snake_case__   ,						skip_special_tokens=snake_case__   ,						clean_up_tokenization_spaces=snake_case__   ,						**snake_case__   ,						)

            if truncate_before_pattern is not None and len(snake_case__ ) > 0:
                   _lowerCAmelCase     :				Optional[int]       =							self.truncate(snake_case__   ,						snake_case__ )

            return decoded_text







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            def find_re(snake_case__   ,						snake_case__   ,						snake_case__ ):
                   _lowerCAmelCase     :				List[Any]       =							pattern.search(snake_case__   ,						snake_case__ )
                   return m.start() if m else -1

            _lowerCAmelCase     :				Any       =							[re.compile(snake_case__   ,						re.MULTILINE ) for pattern in truncate_before_pattern]

            _lowerCAmelCase     :				Dict       =							list(re.finditer('^print'   ,						snake_case__   ,						re.MULTILINE ) )

            if len(snake_case__ ) > 1:
                   _lowerCAmelCase     :				Optional[int]       =							completion[: prints[1].start()]

            _lowerCAmelCase     :				Optional[int]       =							list(re.finditer('^def'   ,						snake_case__   ,						re.MULTILINE ) )

            if len(snake_case__ ) > 1:
                   _lowerCAmelCase     :				Optional[Any]       =							completion[: defs[1].start()]

            _lowerCAmelCase     :				int       =							0

            _lowerCAmelCase     :				Tuple       =							[
                pos for pos in [find_re(snake_case__   ,						snake_case__   ,						snake_case__ ) for terminal in terminals] if pos != -1
            ]

            if len(snake_case__ ) > 0:
                   return completion[: min(snake_case__ )]
            else:
                   return completion

