





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    :       List[str]		       =    logging.get_logger(__name__)

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

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


_snake_case    :       Optional[int]		       =    {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
_snake_case    :       int		       =    {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
_snake_case    :       Tuple		       =    {
    '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    :       Tuple		       =    collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

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


_snake_case    :       Any		       =    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					:						Any   ,    lowerCAmelCase_					:						Any   ,    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_					:						Optional[Any]   ,    )				->		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					:						int   ,    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					:						Any   ,    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



"""simple docstring"""




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     __magic_name__      ( __snake_case							:			List[Any]    )					->							Dict:
       lowercase       :							Optional[int]									=				[
           "decoder.version",
           "decoder.output_projection.weight",
           "_float_tensor",
           "decoder.embed_positions._float_tensor",
       ]
       for k in ignore_keys:
              state_dict.pop(__snake_case	,					__snake_case    )


def     __magic_name__      ( __snake_case							:			str    )					->							Optional[Any]:
       lowercase						,       lowercase       :							Tuple									=				emb.weight.shape
       lowercase       :							str									=				nn.Linear(__snake_case	,					__snake_case	,					bias=__snake_case    )
       lowercase       :							Optional[Any]									=				emb.weight.data
       return lin_layer


def     __magic_name__      ( __snake_case							:			Any    )					->							str:
       lowercase       :							int									=				torch.load(__snake_case	,					map_location="cpu"    )
       lowercase       :							Optional[int]									=				Namespace(**checkpoint["cfg"]["model"]    )
       lowercase       :							Union[str, Any]									=				checkpoint["model"]
       remove_ignore_keys_(__snake_case    )
       lowercase       :							Optional[int]									=				state_dict["decoder.embed_tokens.weight"].shape[0]

       lowercase       :							int									=				{key.replace("decoder"	,					"model"    ): val for key, val in state_dict.items()}

       lowercase       :							List[Any]									=				XGLMConfig(
           vocab_size=__snake_case	,					max_position_embeddings=args.max_target_positions	,					num_layers=args.decoder_layers	,					attention_heads=args.decoder_attention_heads	,					ffn_dim=args.decoder_ffn_embed_dim	,					d_model=args.decoder_embed_dim	,					layerdrop=args.decoder_layerdrop	,					dropout=args.dropout	,					attention_dropout=args.attention_dropout	,					activation_dropout=args.activation_dropout	,					activation_function="gelu"	,					scale_embedding=not args.no_scale_embedding	,					tie_word_embeddings=args.share_decoder_input_output_embed	,					)

       lowercase       :							int									=				XGLMForCausalLM(__snake_case    )
       lowercase       :							Any									=				model.load_state_dict(__snake_case	,					strict=__snake_case    )
       print(__snake_case    )
       lowercase       :							Tuple									=				make_linear_from_emb(model.model.embed_tokens    )

       return model


if __name__ == "__main__":
   _A     :			List[Any]  						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
   parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   _A     :			Tuple  						=			parser.parse_args()
   _A     :			List[str]  						=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
   model.save_pretrained(args.pytorch_dump_folder_path)


