# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowerCAmelCase__     						=			2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
lowerCAmelCase__     						=			{
    # fairseq:
    'wmt19-ru-en': {'length_penalty': 1.1},
    'wmt19-en-ru': {'length_penalty': 1.15},
    'wmt19-en-de': {'length_penalty': 1.0},
    'wmt19-de-en': {'length_penalty': 1.1},
    # allenai:
    'wmt16-en-de-dist-12-1': {'length_penalty': 0.6},
    'wmt16-en-de-dist-6-1': {'length_penalty': 0.6},
    'wmt16-en-de-12-1': {'length_penalty': 0.8},
    'wmt19-de-en-6-6-base': {'length_penalty': 0.6},
    'wmt19-de-en-6-6-big': {'length_penalty': 0.6},
}

# this remaps the different models to their organization names
lowerCAmelCase__     						=			{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
    lowerCAmelCase__     						=			'facebook'
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
    lowerCAmelCase__     						=			'allenai'






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
       # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
       lowerCAmelCase__					     = dict((re.sub(r'@@$'     ,  ''     ,  lowerCAmelCase__     ), v) if k.endswith('@@'     ) else (re.sub(r'$'     ,  '</w>'     ,  lowerCAmelCase__     ), v) for k, v in d.items()     )
       lowerCAmelCase__					     = '<s> <pad> </s> <unk>'.split()
       # restore the special tokens
       for k in keep_keys:
              del da[F"""{k}</w>"""]
              lowerCAmelCase__					     = d[k]  # restore
       return da






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       # prep
       assert os.path.exists(lowerCAmelCase__     )
       os.makedirs(lowerCAmelCase__     ,  exist_ok=lowerCAmelCase__     )
       print(F"""Writing results to {pytorch_dump_folder_path}"""     )

       # handle various types of models

       lowerCAmelCase__					     = basename(lowerCAmelCase__     )
       lowerCAmelCase__					     = dirname(lowerCAmelCase__     )

       lowerCAmelCase__					     = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
       lowerCAmelCase__					     = cls.hub_models()
       lowerCAmelCase__					     = {'bpe': 'fastbpe', 'tokenizer': 'moses'}
       lowerCAmelCase__					     = '.'
       # note: since the model dump is old, fairseq has upgraded its model some
       # time later, and it does a whole lot of rewrites and splits on the saved
       # weights, therefore we can't use torch.load() directly on the model file.
       # see: upgrade_state_dict(state_dict) in fairseq_model.py
       print(F"""using checkpoint {checkpoint_file}"""     )
       lowerCAmelCase__					     = hub_utils.from_pretrained(
           lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  archive_map=lowerCAmelCase__     ,  **lowerCAmelCase__     )

       lowerCAmelCase__					     = vars(chkpt['args']['model']     )

       lowerCAmelCase__					     = args['source_lang']
       lowerCAmelCase__					     = args['target_lang']

       lowerCAmelCase__					     = dirname(lowerCAmelCase__     )
       lowerCAmelCase__					     = basename(lowerCAmelCase__     )

       # dicts
       lowerCAmelCase__					     = os.path.join(lowerCAmelCase__     ,  F"""dict.{src_lang}.txt"""     )
       lowerCAmelCase__					     = os.path.join(lowerCAmelCase__     ,  F"""dict.{tgt_lang}.txt"""     )

       lowerCAmelCase__					     = Dictionary.load(lowerCAmelCase__     )
       lowerCAmelCase__					     = rewrite_dict_keys(src_dict.indices     )
       lowerCAmelCase__					     = len(lowerCAmelCase__     )
       lowerCAmelCase__					     = os.path.join(lowerCAmelCase__     ,  'vocab-src.json'     )
       print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""     )
       with open(lowerCAmelCase__     ,  'w'     ,  encoding='utf-8'     ) as f:
              f.write(json.dumps(lowerCAmelCase__     ,  ensure_ascii=lowerCAmelCase__     ,  indent=lowerCAmelCase__     )     )

       # detect whether this is a do_lower_case situation, which can be derived by checking whether we
       # have at least one uppercase letter in the source vocab
       lowerCAmelCase__					     = True
       for k in src_vocab.keys():
              if not k.islower():
                     lowerCAmelCase__					     = False
                     break

       lowerCAmelCase__					     = Dictionary.load(lowerCAmelCase__     )
       lowerCAmelCase__					     = rewrite_dict_keys(tgt_dict.indices     )
       lowerCAmelCase__					     = len(lowerCAmelCase__     )
       lowerCAmelCase__					     = os.path.join(lowerCAmelCase__     ,  'vocab-tgt.json'     )
       print(F"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"""     )
       with open(lowerCAmelCase__     ,  'w'     ,  encoding='utf-8'     ) as f:
              f.write(json.dumps(lowerCAmelCase__     ,  ensure_ascii=lowerCAmelCase__     ,  indent=lowerCAmelCase__     )     )

       # merges_file (bpecodes)
       lowerCAmelCase__					     = os.path.join(lowerCAmelCase__     ,  VOCAB_FILES_NAMES['merges_file']     )
       for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
              lowerCAmelCase__					     = os.path.join(lowerCAmelCase__     ,  lowerCAmelCase__     )
              if os.path.exists(lowerCAmelCase__     ):
                     break
       with open(lowerCAmelCase__     ,  encoding='utf-8'     ) as fin:
              lowerCAmelCase__					     = fin.read()
       lowerCAmelCase__					     = re.sub(r' \d+$'     ,  ''     ,  lowerCAmelCase__     ,  0     ,  re.M     )  # remove frequency number
       print(F"""Generating {merges_file}"""     )
       with open(lowerCAmelCase__     ,  'w'     ,  encoding='utf-8'     ) as fout:
              fout.write(lowerCAmelCase__     )

       # model config
       lowerCAmelCase__					     = os.path.join(lowerCAmelCase__     ,  'config.json'     )

       # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
       # may have to modify the tokenizer if a different type is used by a future model
       assert args["bpe"] == "fastbpe", F"""need to extend tokenizer to support bpe={args['bpe']}"""
       assert args["tokenizer"] == "moses", F"""need to extend tokenizer to support bpe={args['tokenizer']}"""

       lowerCAmelCase__					     = {
           'architectures': ['FSMTForConditionalGeneration'],
           'model_type': 'fsmt',
           'activation_dropout': args['activation_dropout'],
           'activation_function': 'relu',
           'attention_dropout': args['attention_dropout'],
           'd_model': args['decoder_embed_dim'],
           'dropout': args['dropout'],
           'init_std': 0.0_2,
           'max_position_embeddings': args['max_source_positions'],
           'num_hidden_layers': args['encoder_layers'],
           'src_vocab_size': src_vocab_size,
           'tgt_vocab_size': tgt_vocab_size,
           'langs': [src_lang, tgt_lang],
           'encoder_attention_heads': args['encoder_attention_heads'],
           'encoder_ffn_dim': args['encoder_ffn_embed_dim'],
           'encoder_layerdrop': args['encoder_layerdrop'],
           'encoder_layers': args['encoder_layers'],
           'decoder_attention_heads': args['decoder_attention_heads'],
           'decoder_ffn_dim': args['decoder_ffn_embed_dim'],
           'decoder_layerdrop': args['decoder_layerdrop'],
           'decoder_layers': args['decoder_layers'],
           'bos_token_id': 0,
           'pad_token_id': 1,
           'eos_token_id': 2,
           'is_encoder_decoder': True,
           'scale_embedding': not args['no_scale_embedding'],
           'tie_word_embeddings': args['share_all_embeddings'],
       }

       # good hparam defaults to start with
       lowerCAmelCase__					     = 5
       lowerCAmelCase__					     = False
       if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
              lowerCAmelCase__					     = best_score_hparams[model_dir]['length_penalty']
       else:
              lowerCAmelCase__					     = 1.0

       print(F"""Generating {fsmt_model_config_file}"""     )
       with open(lowerCAmelCase__     ,  'w'     ,  encoding='utf-8'     ) as f:
              f.write(json.dumps(lowerCAmelCase__     ,  ensure_ascii=lowerCAmelCase__     ,  indent=lowerCAmelCase__     )     )

       # tokenizer config
       lowerCAmelCase__					     = os.path.join(lowerCAmelCase__     ,  lowerCAmelCase__     )

       lowerCAmelCase__					     = {
           'langs': [src_lang, tgt_lang],
           'model_max_length': 1_0_2_4,
           'do_lower_case': do_lower_case,
       }

       print(F"""Generating {fsmt_tokenizer_config_file}"""     )
       with open(lowerCAmelCase__     ,  'w'     ,  encoding='utf-8'     ) as f:
              f.write(json.dumps(lowerCAmelCase__     ,  ensure_ascii=lowerCAmelCase__     ,  indent=lowerCAmelCase__     )     )

       # model
       lowerCAmelCase__					     = chkpt['models'][0]
       lowerCAmelCase__					     = model.state_dict()

       # rename keys to start with 'model.'
       lowerCAmelCase__					     = OrderedDict(('model.' + k, v) for k, v in model_state_dict.items()     )

       # remove unneeded keys
       lowerCAmelCase__					     = [
           'model.model',
           'model.encoder.version',
           'model.decoder.version',
           'model.encoder_embed_tokens.weight',
           'model.decoder_embed_tokens.weight',
           'model.encoder.embed_positions._float_tensor',
           'model.decoder.embed_positions._float_tensor',
       ]
       for k in ignore_keys:
              model_state_dict.pop(lowerCAmelCase__     ,  lowerCAmelCase__     )

       lowerCAmelCase__					     = FSMTConfig.from_pretrained(lowerCAmelCase__     )
       lowerCAmelCase__					     = FSMTForConditionalGeneration(lowerCAmelCase__     )

       # check that it loads ok
       model_new.load_state_dict(lowerCAmelCase__     ,  strict=lowerCAmelCase__     )

       # save
       lowerCAmelCase__					     = os.path.join(lowerCAmelCase__     ,  lowerCAmelCase__     )
       print(F"""Generating {pytorch_weights_dump_path}"""     )
       torch.save(lowerCAmelCase__     ,  lowerCAmelCase__     )

       print('Conversion is done!'     )
       print('\nLast step is to upload the files to s3'     )
       print(F"""cd {data_root}"""     )
       print(F"""transformers-cli upload {model_dir}"""     )


if __name__ == "__main__":
    lowerCAmelCase__     						=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--fsmt_checkpoint_path',
        default=None,
        type=str,
        required=True,
        help=(
            'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
            ' bpecodes, etc.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    lowerCAmelCase__     						=			parser.parse_args()
    convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)

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 import BertTokenizer


lowerCAmelCase__     						=			logging.get_logger(__name__)

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

lowerCAmelCase__     						=			{
    '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'
        ),
    },
}
lowerCAmelCase__     						=			{
    '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'
        ),
    },
}
lowerCAmelCase__     						=			{
    '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'
        ),
    },
}

lowerCAmelCase__     						=			{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
lowerCAmelCase__     						=			{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
lowerCAmelCase__     						=			{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


lowerCAmelCase__     						=			{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__     						=			{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__     						=			{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






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

lowerCAmelCase__     						=			collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


lowerCAmelCase__     						=			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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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(SCREAMING_SNAKE_CASE						)
class     a_			:






 '''simple docstring'''






 def __call__(      self :  Optional[int]      ,   lowercase__ :  List[str]      ,   lowercase__ :  Optional[str] = None      ,   lowercase__ :  Optional[str] = None      ,   lowercase__ :  Union[bool, str] = False      ,   lowercase__ :  Union[bool, str] = False      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   lowercase__ :  Optional[bool] = None      ,   **lowercase__ :  Union[str, Any]      ,   ):







        '''simple docstring'''



        if titles is None and texts is None:
               return super().__call__(
                   lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__      ,   max_length=lowercase__      ,   return_tensors=lowercase__      ,   return_attention_mask=lowercase__      ,   **lowercase__      ,   )
        elif titles is None or texts is None:
               lowerCAmelCase__					     = titles if texts is None else texts
               return super().__call__(
                   lowercase__      ,   lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__      ,   max_length=lowercase__      ,   return_tensors=lowercase__      ,   return_attention_mask=lowercase__      ,   **lowercase__      ,   )
        lowerCAmelCase__					     = titles if not isinstance(lowercase__      ,   lowercase__) else [titles]
        lowerCAmelCase__					     = texts if not isinstance(lowercase__      ,   lowercase__) else [texts]
        lowerCAmelCase__					     = len(lowercase__)
        lowerCAmelCase__					     = questions if not isinstance(lowercase__      ,   lowercase__) else [questions] * n_passages
        if len(lowercase__) != len(lowercase__):
               raise ValueError(
                   F"""There should be as many titles than texts but got {len(lowercase__)} titles and {len(lowercase__)} texts.""")
        lowerCAmelCase__					     = super().__call__(lowercase__      ,   lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__)['input_ids']
        lowerCAmelCase__					     = super().__call__(lowercase__      ,   add_special_tokens=lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__)['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(lowercase__      ,   lowercase__)
            ]
        }
        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(lowercase__      ,   padding=lowercase__      ,   max_length=lowercase__      ,   return_tensors=lowercase__)




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  BatchEncoding      ,   lowercase__ :  DPRReaderOutput      ,   lowercase__ :  int = 16      ,   lowercase__ :  int = 64      ,   lowercase__ :  int = 4      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = reader_input['input_ids']
        lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = reader_output[:3]
        lowerCAmelCase__					     = len(lowercase__)
        lowerCAmelCase__					     = sorted(range(lowercase__)      ,   reverse=lowercase__      ,   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(lowercase__)

               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=lowercase__      ,   top_spans=lowercase__      ,   )
               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=lowercase__      ,   start_index=lowercase__      ,   end_index=lowercase__      ,   text=self.decode(sequence_ids[start_index : end_index + 1])      ,   ))
               if len(lowercase__) >= num_spans:
                      break
        return nbest_spans_predictions[:num_spans]




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  List[int]      ,   lowercase__ :  List[int]      ,   lowercase__ :  int      ,   lowercase__ :  int      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = []
        for start_index, start_score in enumerate(lowercase__):
               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(lowercase__      ,   key=lambda lowercase__: x[1]      ,   reverse=lowercase__)
        lowerCAmelCase__					     = []
        for (start_index, end_index), score in scores:
               if start_index > end_index:
                      raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]""")
               lowerCAmelCase__					     = end_index - start_index + 1
               if length > max_answer_length:
                      raise ValueError(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(lowercase__) == top_spans:
                      break
        return chosen_span_intervals






@add_end_docstrings(SCREAMING_SNAKE_CASE						)
class     a_			(						SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= READER_PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= READER_PRETRAINED_INIT_CONFIGURATION
 UpperCAmelCase_     			= ['input_ids', 'attention_mask']
