





import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


_snake_case  :    Any						    =  logging.get_logger(__name__)

_snake_case  :    Any						    =  {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_snake_case  :    List[str]						    =  {
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
_snake_case  :    List[str]						    =  {
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
_snake_case  :    int						    =  {
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

_snake_case  :    Dict						    =  {
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
_snake_case  :    List[Any]						    =  {
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
_snake_case  :    Tuple						    =  {
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


_snake_case  :    Tuple						    =  {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
_snake_case  :    Any						    =  {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
_snake_case  :    Optional[Any]						    =  {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = VOCAB_FILES_NAMES
  a_        = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  a_        = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_        = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
  a_        = DPRContextEncoderTokenizer





class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = VOCAB_FILES_NAMES
  a_        = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  a_        = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_        = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
  a_        = DPRQuestionEncoderTokenizer





_snake_case  :    str						    =  collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

_snake_case  :    List[Any]						    =  collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


_snake_case  :    Tuple						    =  R'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '



@add_start_docstrings(_UpperCamelCase    )
class 				_UpperCAmelCase    :

  """simple docstring"""


  def __call__(				self :		List[str]					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		Optional[str] = None					,  lowerCAmelCase_ :		Optional[str] = None					,  lowerCAmelCase_ :		Union[bool, str] = False					,  lowerCAmelCase_ :		Union[bool, str] = False					,  lowerCAmelCase_ :		Optional[int] = None					,  lowerCAmelCase_ :		Optional[Union[str, TensorType]] = None					,  lowerCAmelCase_ :		Optional[bool] = None					,  **lowerCAmelCase_ :		Dict					,  )							->						BatchEncoding:
     if titles is None and texts is None:
        return super().__call__(
            lowerCAmelCase_					,  padding=lowerCAmelCase_					,  truncation=lowerCAmelCase_					,  max_length=lowerCAmelCase_					,  return_tensors=lowerCAmelCase_					,  return_attention_mask=lowerCAmelCase_					,  **lowerCAmelCase_					,  )
     elif titles is None or texts is None:
        __lowerCAmelCase								=	titles if texts is None else texts
        return super().__call__(
            lowerCAmelCase_					,  lowerCAmelCase_					,  padding=lowerCAmelCase_					,  truncation=lowerCAmelCase_					,  max_length=lowerCAmelCase_					,  return_tensors=lowerCAmelCase_					,  return_attention_mask=lowerCAmelCase_					,  **lowerCAmelCase_					,  )
     __lowerCAmelCase								=	titles if not isinstance(lowerCAmelCase_					,  lowerCAmelCase_     ) else [titles]
     __lowerCAmelCase								=	texts if not isinstance(lowerCAmelCase_					,  lowerCAmelCase_     ) else [texts]
     __lowerCAmelCase								=	len(lowerCAmelCase_     )
     __lowerCAmelCase								=	questions if not isinstance(lowerCAmelCase_					,  lowerCAmelCase_     ) else [questions] * n_passages
     assert len(lowerCAmelCase_     ) == len(
         lowerCAmelCase_     ), f"""There should be as many titles than texts but got {len(lowerCAmelCase_     )} titles and {len(lowerCAmelCase_     )} texts."""
     __lowerCAmelCase								=	super().__call__(lowerCAmelCase_					,  lowerCAmelCase_					,  padding=lowerCAmelCase_					,  truncation=lowerCAmelCase_     )['input_ids']
     __lowerCAmelCase								=	super().__call__(lowerCAmelCase_					,  add_special_tokens=lowerCAmelCase_					,  padding=lowerCAmelCase_					,  truncation=lowerCAmelCase_     )['input_ids']
     __lowerCAmelCase								=	{
         'input_ids': [
             (encoded_question_and_title + encoded_text)[:max_length]
             if max_length is not None and truncation
             else encoded_question_and_title + encoded_text
             for encoded_question_and_title, encoded_text in zip(lowerCAmelCase_					,  lowerCAmelCase_     )
         ]
     }
     if return_attention_mask is not False:
        __lowerCAmelCase								=	[]
        for input_ids in encoded_inputs["input_ids"]:
           attention_mask.append([int(input_id != self.pad_token_id     ) for input_id in input_ids]     )
        __lowerCAmelCase								=	attention_mask
     return self.pad(lowerCAmelCase_					,  padding=lowerCAmelCase_					,  max_length=lowerCAmelCase_					,  return_tensors=lowerCAmelCase_     )






  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		BatchEncoding					,  lowerCAmelCase_ :		DPRReaderOutput					,  lowerCAmelCase_ :		int = 1_6					,  lowerCAmelCase_ :		int = 6_4					,  lowerCAmelCase_ :		int = 4					,  )							->						List[DPRSpanPrediction]:
     __lowerCAmelCase								=	reader_input['input_ids']
     __lowerCAmelCase	,	__lowerCAmelCase	,	__lowerCAmelCase								=	reader_output[:3]
     __lowerCAmelCase								=	len(lowerCAmelCase_     )
     __lowerCAmelCase								=	sorted(range(lowerCAmelCase_     )					,  reverse=lowerCAmelCase_					,  key=relevance_logits.__getitem__     )
     __lowerCAmelCase								=	[]
     for doc_id in sorted_docs:
        __lowerCAmelCase								=	list(input_ids[doc_id]     )
        # assuming question & title information is at the beginning of the sequence
        __lowerCAmelCase								=	sequence_ids.index(self.sep_token_id					,  2     ) + 1  # second sep id
        if sequence_ids[-1] == self.pad_token_id:
           __lowerCAmelCase								=	sequence_ids.index(self.pad_token_id     )
        else:
           __lowerCAmelCase								=	len(lowerCAmelCase_     )

        __lowerCAmelCase								=	self._get_best_spans(
            start_logits=start_logits[doc_id][passage_offset:sequence_len]					,  end_logits=end_logits[doc_id][passage_offset:sequence_len]					,  max_answer_length=lowerCAmelCase_					,  top_spans=lowerCAmelCase_					,  )
        for start_index, end_index in best_spans:
           start_index += passage_offset
           end_index += passage_offset
           nbest_spans_predictions.append(
               DPRSpanPrediction(
                   span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]					,  relevance_score=relevance_logits[doc_id]					,  doc_id=lowerCAmelCase_					,  start_index=lowerCAmelCase_					,  end_index=lowerCAmelCase_					,  text=self.decode(sequence_ids[start_index : end_index + 1]     )					,  )     )
        if len(lowerCAmelCase_     ) >= num_spans:
           break
     return nbest_spans_predictions[:num_spans]






  def 				lowercase		(				self :		str					,  lowerCAmelCase_ :		List[int]					,  lowerCAmelCase_ :		List[int]					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		int					,  )							->						List[DPRSpanPrediction]:
     __lowerCAmelCase								=	[]
     for start_index, start_score in enumerate(lowerCAmelCase_     ):
        for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]     ):
           scores.append(((start_index, start_index + answer_length), start_score + end_score)     )
     __lowerCAmelCase								=	sorted(lowerCAmelCase_					,  key=lambda lowerCAmelCase_     : x[1]					,  reverse=lowerCAmelCase_     )
     __lowerCAmelCase								=	[]
     for (start_index, end_index), score in scores:
        assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
        __lowerCAmelCase								=	end_index - start_index + 1
        assert length <= max_answer_length, f"""Span is too long: {length} > {max_answer_length}"""
        if any(
            start_index <= prev_start_index <= prev_end_index <= end_index
            or prev_start_index <= start_index <= end_index <= prev_end_index
            for (prev_start_index, prev_end_index) in chosen_span_intervals     ):
           continue
        chosen_span_intervals.append((start_index, end_index)     )

        if len(lowerCAmelCase_     ) == top_spans:
           break
     return chosen_span_intervals





@add_end_docstrings(_UpperCamelCase    )
class 				_UpperCAmelCase    (    _UpperCamelCase		,    _UpperCamelCase    ):

  """simple docstring"""


  a_        = VOCAB_FILES_NAMES
  a_        = READER_PRETRAINED_VOCAB_FILES_MAP
  a_        = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_        = READER_PRETRAINED_INIT_CONFIGURATION
  a_        = ["""input_ids""", """attention_mask"""]
  a_        = DPRReaderTokenizer








import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json

def   a_    ( lowerCAmelCase_      :			Optional[int]	):
   __lowerCAmelCase								=	filter(lambda lowerCAmelCase_	: p.requires_grad,	model.parameters()	)
   __lowerCAmelCase								=	sum([np.prod(p.size()	) for p in model_parameters]	)
   return params


_snake_case  :    Dict						    =  logging.getLogger(__name__)

def   a_    ( lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			Optional[int]	):
   if metric == "rouge2":
      __lowerCAmelCase								=	'{val_avg_rouge2:.4f}-{step_count}'
   elif metric == "bleu":
      __lowerCAmelCase								=	'{val_avg_bleu:.4f}-{step_count}'
   elif metric == "em":
      __lowerCAmelCase								=	'{val_avg_em:.4f}-{step_count}'
   else:
      raise NotImplementedError(
          F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
          ' function.'	)

   __lowerCAmelCase								=	ModelCheckpoint(
       dirpath=lowerCAmelCase_,	filename=lowerCAmelCase_,	monitor=F"""val_{metric}""",	mode='max',	save_top_k=3,	every_n_epochs=1,	)
   return checkpoint_callback

def   a_    ( lowerCAmelCase_      :			Union[str, Any],	lowerCAmelCase_      :			Any	):
   return EarlyStopping(
       monitor=F"""val_{metric}""",	mode='min' if 'loss' in metric else 'max',	patience=lowerCAmelCase_,	verbose=lowerCAmelCase_,	)




class 				_UpperCAmelCase    (    pl.Callback    ):

  """simple docstring"""


  def 				lowercase		(				self :		Tuple					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		int     )							->						Any:
     __lowerCAmelCase								=	{f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
     pl_module.logger.log_metrics(lowerCAmelCase_     )






  @rank_zero_only
  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		pl.LightningModule					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		List[Any]=True     )							->						None:
     logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""     )
     __lowerCAmelCase								=	trainer.callback_metrics
     trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}     )
     # Log results
     __lowerCAmelCase								=	Path(pl_module.hparams.output_dir     )
     if type_path == "test":
        __lowerCAmelCase								=	od / 'test_results.txt'
        __lowerCAmelCase								=	od / 'test_generations.txt'
     else:
        # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
        # If people want this it will be easy enough to add back.
        __lowerCAmelCase								=	od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
        __lowerCAmelCase								=	od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
        results_file.parent.mkdir(exist_ok=lowerCAmelCase_     )
        generations_file.parent.mkdir(exist_ok=lowerCAmelCase_     )
     with open(lowerCAmelCase_					,  'a+'     ) as writer:
        for key in sorted(lowerCAmelCase_     ):
           if key in ["log", "progress_bar", "preds"]:
              continue
           __lowerCAmelCase								=	metrics[key]
           if isinstance(lowerCAmelCase_					,  torch.Tensor     ):
              __lowerCAmelCase								=	val.item()
           __lowerCAmelCase								=	f"""{key}: {val:.6f}\n"""
           writer.write(lowerCAmelCase_     )

     if not save_generations:
        return

     if "preds" in metrics:
        __lowerCAmelCase								=	'\n'.join(metrics['preds']     )
        generations_file.open('w+'     ).write(lowerCAmelCase_     )






  @rank_zero_only
  def 				lowercase		(				self :		Union[str, Any]					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		List[str]     )							->						Dict:
     try:
        __lowerCAmelCase								=	pl_module.model.model.num_parameters()
     except AttributeError:
        __lowerCAmelCase								=	pl_module.model.num_parameters()

     __lowerCAmelCase								=	count_trainable_parameters(lowerCAmelCase_     )
     # mp stands for million parameters
     trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}     )






  @rank_zero_only
  def 				lowercase		(				self :		int					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		pl.LightningModule     )							->						Any:
     save_json(pl_module.metrics					,  pl_module.metrics_save_path     )
     return self._write_logs(lowerCAmelCase_					,  lowerCAmelCase_					,  'test'     )






  @rank_zero_only
  def 				lowercase		(				self :		List[Any]					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		Any     )							->						int:
     save_json(pl_module.metrics					,  pl_module.metrics_save_path     )
     # Uncommenting this will save val generations
     # return self._write_logs(trainer, pl_module, "valid")

