"""simple docstring"""






import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__lowerCAmelCase    :       Dict					     =logging.getLogger(__name__)





@dataclass
class  _A						:
      snake_case__			:   str
      snake_case__			:   List[str]
      snake_case__			:   Optional[List[str]]






@dataclass
class  _A						:
      snake_case__			:   List[int]
      snake_case__			:   List[int]
      snake_case__			:   Optional[List[int]]							  =	None
      snake_case__			:   Optional[List[int]]							  =	None






class  _A						(       lowerCAmelCase	):
      snake_case__			:   int							  =	'train'
      snake_case__			:   Union[str, Any]							  =	'dev'
      snake_case__			:   str							  =	'test'






class  _A						:

      @staticmethod
      def 				A__       (						__lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            raise NotImplementedError

      @staticmethod
      def 				A__       (						__lowerCAmelCase						):







            """simple docstring"""



            raise NotImplementedError







      @staticmethod
      def 				A__       (						__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=False		,       __lowerCAmelCase="[CLS]"		,       __lowerCAmelCase=1		,       __lowerCAmelCase="[SEP]"		,       __lowerCAmelCase=False		,       __lowerCAmelCase=False		,       __lowerCAmelCase=0		,       __lowerCAmelCase=0		,       __lowerCAmelCase=-100		,       __lowerCAmelCase=0		,       __lowerCAmelCase=True		,       ):







            """simple docstring"""



            lowercase														=	{label: i for i, label in enumerate(__lowerCAmelCase						)}

            lowercase														=	[]
            for ex_index, example in enumerate(__lowerCAmelCase						):
                  if ex_index % 1_0000 == 0:
                        logger.info("""Writing example %d of %d"""		,       __lowerCAmelCase		,       len(__lowerCAmelCase						)						)

                  lowercase														=	[]
                  lowercase														=	[]
                  for word, label in zip(example.words		,       example.labels						):
                        lowercase														=	tokenizer.tokenize(__lowerCAmelCase						)

                        # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                        if len(__lowerCAmelCase						) > 0:
                              tokens.extend(__lowerCAmelCase						)
                              # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                              label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__lowerCAmelCase						) - 1)						)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                  lowercase														=	tokenizer.num_special_tokens_to_add()
                  if len(__lowerCAmelCase						) > max_seq_length - special_tokens_count:
                        lowercase														=	tokens[: (max_seq_length - special_tokens_count)]
                        lowercase														=	label_ids[: (max_seq_length - special_tokens_count)]

                  # The convention in BERT is:
                  # (a) For sequence pairs:
                  #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
                  #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
                  # (b) For single sequences:
                  #  tokens:   [CLS] the dog is hairy . [SEP]
                  #  type_ids:   0   0   0   0  0     0   0
                  #
                  # Where "type_ids" are used to indicate whether this is the first
                  # sequence or the second sequence. The embedding vectors for `type=0` and
                  # `type=1` were learned during pre-training and are added to the wordpiece
                  # embedding vector (and position vector). This is not *strictly* necessary
                  # since the [SEP] token unambiguously separates the sequences, but it makes
                  # it easier for the model to learn the concept of sequences.
                  #
                  # For classification tasks, the first vector (corresponding to [CLS]) is
                  # used as the "sentence vector". Note that this only makes sense because
                  # the entire model is fine-tuned.
                  tokens += [sep_token]
                  label_ids += [pad_token_label_id]
                  if sep_token_extra:
                        # roberta uses an extra separator b/w pairs of sentences
                        tokens += [sep_token]
                        label_ids += [pad_token_label_id]
                  lowercase														=	[sequence_a_segment_id] * len(__lowerCAmelCase						)

                  if cls_token_at_end:
                        tokens += [cls_token]
                        label_ids += [pad_token_label_id]
                        segment_ids += [cls_token_segment_id]
                  else:
                        lowercase														=	[cls_token] + tokens
                        lowercase														=	[pad_token_label_id] + label_ids
                        lowercase														=	[cls_token_segment_id] + segment_ids

                  lowercase														=	tokenizer.convert_tokens_to_ids(__lowerCAmelCase						)

                  # The mask has 1 for real tokens and 0 for padding tokens. Only real
                  # tokens are attended to.
                  lowercase														=	[1 if mask_padding_with_zero else 0] * len(__lowerCAmelCase						)

                  # Zero-pad up to the sequence length.
                  lowercase														=	max_seq_length - len(__lowerCAmelCase						)
                  if pad_on_left:
                        lowercase														=	([pad_token] * padding_length) + input_ids
                        lowercase														=	([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                        lowercase														=	([pad_token_segment_id] * padding_length) + segment_ids
                        lowercase														=	([pad_token_label_id] * padding_length) + label_ids
                  else:
                        input_ids += [pad_token] * padding_length
                        input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                        segment_ids += [pad_token_segment_id] * padding_length
                        label_ids += [pad_token_label_id] * padding_length

                  assert len(__lowerCAmelCase						) == max_seq_length
                  assert len(__lowerCAmelCase						) == max_seq_length
                  assert len(__lowerCAmelCase						) == max_seq_length
                  assert len(__lowerCAmelCase						) == max_seq_length

                  if ex_index < 5:
                        logger.info("""*** Example ***"""						)
                        logger.info("""guid: %s"""		,       example.guid						)
                        logger.info("""tokens: %s"""		,       """ """.join([str(__lowerCAmelCase						) for x in tokens]						)						)
                        logger.info("""input_ids: %s"""		,       """ """.join([str(__lowerCAmelCase						) for x in input_ids]						)						)
                        logger.info("""input_mask: %s"""		,       """ """.join([str(__lowerCAmelCase						) for x in input_mask]						)						)
                        logger.info("""segment_ids: %s"""		,       """ """.join([str(__lowerCAmelCase						) for x in segment_ids]						)						)
                        logger.info("""label_ids: %s"""		,       """ """.join([str(__lowerCAmelCase						) for x in label_ids]						)						)

                  if "token_type_ids" not in tokenizer.model_input_names:
                        lowercase														=	None

                  features.append(
                      InputFeatures(
                          input_ids=__lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       token_type_ids=__lowerCAmelCase		,       label_ids=__lowerCAmelCase						)						)
            return features


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset





       class  _A						(       lowerCAmelCase	):
             snake_case__			:   List[InputFeatures]
             snake_case__			:   int							  =	nn.CrossEntropyLoss().ignore_index

             def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = None		,       __lowerCAmelCase=False		,       __lowerCAmelCase = Split.train		,       ):







                   """simple docstring"""



                   lowercase														=	os.path.join(
                       __lowerCAmelCase		,       """cached_{}_{}_{}""".format(mode.value		,       tokenizer.__class__.__name__		,       str(__lowerCAmelCase						)						)		,       )

                   # Make sure only the first process in distributed training processes the dataset,
                   # and the others will use the cache.
                   lowercase														=	cached_features_file + """.lock"""
                   with FileLock(__lowerCAmelCase						):
                         if os.path.exists(__lowerCAmelCase						) and not overwrite_cache:
                               logger.info(f'Loading features from cached file {cached_features_file}'						)
                               lowercase														=	torch.load(__lowerCAmelCase						)
                         else:
                               logger.info(f'Creating features from dataset file at {data_dir}'						)
                               lowercase														=	token_classification_task.read_examples_from_file(__lowerCAmelCase		,       __lowerCAmelCase						)
                               # TODO clean up all this to leverage built-in features of tokenizers
                               lowercase														=	token_classification_task.convert_examples_to_features(
                                   __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       cls_token_at_end=bool(model_type in ["""xlnet"""]						)		,       cls_token=tokenizer.cls_token		,       cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0		,       sep_token=tokenizer.sep_token		,       sep_token_extra=__lowerCAmelCase		,       pad_on_left=bool(tokenizer.padding_side == """left"""						)		,       pad_token=tokenizer.pad_token_id		,       pad_token_segment_id=tokenizer.pad_token_type_id		,       pad_token_label_id=self.pad_token_label_id		,       )
                               logger.info(f'Saving features into cached file {cached_features_file}'						)
                               torch.save(self.features		,       __lowerCAmelCase						)

             def __len__(						self						):







                   """simple docstring"""



                   return len(self.features						)







             def __getitem__(						self		,       __lowerCAmelCase						):







                   """simple docstring"""



                   return self.features[i]


if is_tf_available():
       import tensorflow as tf





       class  _A						:
             snake_case__			:   List[InputFeatures]
             snake_case__			:   int							  =	-100

             def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = None		,       __lowerCAmelCase=False		,       __lowerCAmelCase = Split.train		,       ):







                   """simple docstring"""



                   lowercase														=	token_classification_task.read_examples_from_file(__lowerCAmelCase		,       __lowerCAmelCase						)
                   # TODO clean up all this to leverage built-in features of tokenizers
                   lowercase														=	token_classification_task.convert_examples_to_features(
                       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       cls_token_at_end=bool(model_type in ["""xlnet"""]						)		,       cls_token=tokenizer.cls_token		,       cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0		,       sep_token=tokenizer.sep_token		,       sep_token_extra=__lowerCAmelCase		,       pad_on_left=bool(tokenizer.padding_side == """left"""						)		,       pad_token=tokenizer.pad_token_id		,       pad_token_segment_id=tokenizer.pad_token_type_id		,       pad_token_label_id=self.pad_token_label_id		,       )

                   def gen():
                         for ex in self.features:
                               if ex.token_type_ids is None:
                                     yield (
                                         {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                         ex.label_ids,
                                     )
                               else:
                                     yield (
                                         {
                                             "input_ids": ex.input_ids,
                                             "attention_mask": ex.attention_mask,
                                             "token_type_ids": ex.token_type_ids,
                                         },
                                         ex.label_ids,
                                     )

                   if "token_type_ids" not in tokenizer.model_input_names:
                         lowercase														=	tf.data.Dataset.from_generator(
                             __lowerCAmelCase		,       ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)		,       (
                                 {"""input_ids""": tf.TensorShape([None]						), """attention_mask""": tf.TensorShape([None]						)},
                                 tf.TensorShape([None]						),
                             )		,       )
                   else:
                         lowercase														=	tf.data.Dataset.from_generator(
                             __lowerCAmelCase		,       ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)		,       (
                                 {
                                     """input_ids""": tf.TensorShape([None]						),
                                     """attention_mask""": tf.TensorShape([None]						),
                                     """token_type_ids""": tf.TensorShape([None]						),
                                 },
                                 tf.TensorShape([None]						),
                             )		,       )

             def 				A__       (						self						):







                   """simple docstring"""



                   lowercase														=	self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features						)						)						)

                   return self.dataset

             def __len__(						self						):







                   """simple docstring"""



                   return len(self.features						)







             def __getitem__(						self		,       __lowerCAmelCase						):







                   """simple docstring"""



                   return self.features[i]




"""simple docstring"""






import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__lowerCAmelCase    :       int					     =logging.getLogger(__name__)





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	False

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            if not self.initialized:
                  lowercase														=	RagRetriever(
                      __lowerCAmelCase		,       question_encoder_tokenizer=__lowerCAmelCase		,       generator_tokenizer=__lowerCAmelCase		,       index=__lowerCAmelCase		,       init_retrieval=__lowerCAmelCase		,       )
                  lowercase														=	True

      def 				A__       (						self						):







            """simple docstring"""



            self.retriever.index.init_index()







      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase							,							lowercase														=	self.retriever._main_retrieve(__lowerCAmelCase		,       __lowerCAmelCase						)
            return doc_ids, retrieved_doc_embeds






class  _A						(       lowerCAmelCase	):

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None						):







            """simple docstring"""



            if index is not None and index.is_initialized() and len(__lowerCAmelCase						) > 0:
                  raise ValueError(
                      """When using Ray for distributed fine-tuning, """
                      """you'll need to provide the paths instead, """
                      """as the dataset and the index are loaded """
                      """separately. More info in examples/rag/use_own_knowledge_dataset.py """						)
            super().__init__(
                __lowerCAmelCase		,       question_encoder_tokenizer=__lowerCAmelCase		,       generator_tokenizer=__lowerCAmelCase		,       index=__lowerCAmelCase		,       init_retrieval=__lowerCAmelCase		,       )
            lowercase														=	retrieval_workers
            if len(self.retrieval_workers						) > 0:
                  ray.get(
                      [
                          worker.create_rag_retriever.remote(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)
                          for worker in self.retrieval_workers
                      ]						)

      def 				A__       (						self						):







            """simple docstring"""



            logger.info("""initializing retrieval"""						)

            if len(self.retrieval_workers						) > 0:
                  ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]						)
            else:
                  # Non-distributed training. Load index into this same process.
                  self.index.init_index()

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            if len(self.retrieval_workers						) > 0:
                  # Select a random retrieval actor.
                  lowercase														=	self.retrieval_workers[random.randint(0		,       len(self.retrieval_workers						) - 1						)]
                  lowercase							,							lowercase														=	ray.get(random_worker.retrieve.remote(__lowerCAmelCase		,       __lowerCAmelCase						)						)
            else:
                  lowercase							,							lowercase														=	self._main_retrieve(__lowerCAmelCase		,       __lowerCAmelCase						)
            return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCAmelCase						)

      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



            return super(__lowerCAmelCase		,       cls						).get_tokenizers(__lowerCAmelCase		,       __lowerCAmelCase		,       **__lowerCAmelCase						)







      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	kwargs.pop("""config"""		,       __lowerCAmelCase						) or RagConfig.from_pretrained(__lowerCAmelCase		,       **__lowerCAmelCase						)
            lowercase														=	RagTokenizer.from_pretrained(__lowerCAmelCase		,       config=__lowerCAmelCase						)
            lowercase														=	rag_tokenizer.question_encoder
            lowercase														=	rag_tokenizer.generator
            if indexed_dataset is not None:
                  lowercase														=	"""custom"""
                  lowercase														=	CustomHFIndex(config.retrieval_vector_size		,       __lowerCAmelCase						)
            else:
                  lowercase														=	cls._build_index(__lowerCAmelCase						)
            return cls(
                __lowerCAmelCase		,       question_encoder_tokenizer=__lowerCAmelCase		,       generator_tokenizer=__lowerCAmelCase		,       retrieval_workers=__lowerCAmelCase		,       index=__lowerCAmelCase		,       )



