

'''simple docstring'''






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      lowercase_      (snake_case__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Optional[Any]							     =     VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE       :    str							     =     CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE       :    str							     =     CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE       :    Optional[Any]							     =     CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class      lowercase_      (snake_case__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Any							     =     VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE       :    List[Any]							     =     QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE       :    Optional[Any]							     =     QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE       :    Tuple							     =     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(snake_case__ )
class      lowercase_      :




  """simple docstring"""




  def __call__( self  :	Tuple   ,lowercase__  :	int   ,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__  :	List[Any]   ,):
   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:
    __lowercase				    =					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   ,)
   __lowercase				    =					titles if not isinstance(__lowercase   ,__lowercase     ) else [titles]
   __lowercase				    =					texts if not isinstance(__lowercase   ,__lowercase     ) else [texts]
   __lowercase				    =					len(__lowercase     )
   __lowercase				    =					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."     )
   __lowercase				    =					super().__call__(__lowercase   ,__lowercase   ,padding=__lowercase   ,truncation=__lowercase     )['''input_ids''']
   __lowercase				    =					super().__call__(__lowercase   ,add_special_tokens=__lowercase   ,padding=__lowercase   ,truncation=__lowercase     )['''input_ids''']
   __lowercase				    =					{
       '''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:
    __lowercase				    =					[]
    for input_ids in encoded_inputs["input_ids"]:
     attention_mask.append([int(input_id != self.pad_token_id     ) for input_id in input_ids]     )
    __lowercase				    =					attention_mask
   return self.pad(__lowercase   ,padding=__lowercase   ,max_length=__lowercase   ,return_tensors=__lowercase     )






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

    __lowercase				    =					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    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	List[int]   ,lowercase__  :	List[int]   ,lowercase__  :	int   ,lowercase__  :	int   ,):
   __lowercase				    =					[]
   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)     )
   __lowercase				    =					sorted(__lowercase   ,key=lambda lowercase__     : x[1]   ,reverse=__lowercase     )
   __lowercase				    =					[]
   for (start_index, end_index), score in scores:
    if start_index > end_index:
     raise ValueError(F"Wrong span indices: [{start_index}:{end_index}]"     )
    __lowercase				    =					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(snake_case__ )
class      lowercase_      (snake_case__							,			snake_case__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Optional[int]							     =     VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE       :    Any							     =     READER_PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE       :    Tuple							     =     READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE       :    str							     =     READER_PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE       :    List[str]							     =     ["""input_ids""", """attention_mask"""]








"""simple docstring"""





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_UpperCamelCase					      =   logging.get_logger(__name__)
logging.set_verbosity_info()



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							if "xprophetnet" in prophetnet_checkpoint_path:
														__lowerCamelCase   :   Optional[Any]       =XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
														__lowerCamelCase     ,    __lowerCamelCase   :   List[Any]       =XLMProphetNetForConditionalGeneration.from_pretrained(
														    SCREAMING_SNAKE_CASE     ,    output_loading_info=SCREAMING_SNAKE_CASE )
							else:
														__lowerCamelCase   :   int       =ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
														__lowerCamelCase     ,    __lowerCamelCase   :   int       =ProphetNetForConditionalGeneration.from_pretrained(
														    SCREAMING_SNAKE_CASE     ,    output_loading_info=SCREAMING_SNAKE_CASE )

							__lowerCamelCase   :   Optional[Any]       =['''key_proj''', '''value_proj''', '''query_proj''']

							__lowerCamelCase   :   Tuple       ={
							    '''self_attn''': '''ngram_self_attn''',
							    '''cross_attn''': '''encoder_attn''',
							    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
							    '''feed_forward_layer_norm''': '''final_layer_norm''',
							    '''feed_forward''': '''''',
							    '''intermediate''': '''fc1''',
							    '''output''': '''fc2''',
							    '''key_proj''': '''k_proj''',
							    '''query_proj''': '''q_proj''',
							    '''value_proj''': '''v_proj''',
							    '''word_embeddings''': '''embed_tokens''',
							    '''embeddings_layer_norm''': '''emb_layer_norm''',
							    '''relative_pos_embeddings''': '''relative_linear''',
							    '''ngram_embeddings''': '''ngram_input_embed''',
							    '''position_embeddings''': '''embed_positions''',
							}

							for key in loading_info["missing_keys"]:
														__lowerCamelCase   :   int       =key.split('''.''' )

														if attributes[0] == "lm_head":
																					__lowerCamelCase   :   int       =prophet
																					__lowerCamelCase   :   Optional[int]       =prophet_old
														else:
																					__lowerCamelCase   :   Any       =prophet.prophetnet
																					__lowerCamelCase   :   Union[str, Any]       =prophet_old.model

														__lowerCamelCase   :   Optional[Any]       =False
														for attribute in attributes:
																					if attribute in mapping:
																												__lowerCamelCase   :   Optional[Any]       =mapping[attribute]
																												if not hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ) and len(SCREAMING_SNAKE_CASE ) > 0:
																																			__lowerCamelCase   :   Any       =attribute
																					elif hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ):
																												__lowerCamelCase   :   Any       =attribute

																					if attribute == "weight":
																												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																												__lowerCamelCase   :   str       =old_model.weight
																												logger.info(F'{attribute} is initialized.' )
																												__lowerCamelCase   :   Any       =True
																												break
																					elif attribute == "bias":
																												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																												__lowerCamelCase   :   Union[str, Any]       =old_model.bias
																												logger.info(F'{attribute} is initialized' )
																												__lowerCamelCase   :   str       =True
																												break
																					elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE     ,    '''in_proj_weight''' ):
																												__lowerCamelCase   :   int       =old_model.in_proj_weight.shape[0] // 3
																												__lowerCamelCase   :   Union[str, Any]       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
																												param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																												param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																												if attribute == "query_proj":
																																			__lowerCamelCase   :   List[str]       =nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
																																			__lowerCamelCase   :   str       =nn.Parameter(old_model.in_proj_bias[:embed_dim] )

																												elif attribute == "key_proj":
																																			__lowerCamelCase   :   List[str]       =nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
																																			__lowerCamelCase   :   Tuple       =nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
																												elif attribute == "value_proj":
																																			__lowerCamelCase   :   Optional[Any]       =nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
																																			__lowerCamelCase   :   int       =nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
																												__lowerCamelCase   :   Dict       =True
																												break
																					elif attribute == "position_embeddings":
																												assert (
																												    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																												), "Hidden size has to match"
																												assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																												__lowerCamelCase   :   str       =nn.Parameter(old_model.embed_positions.weight[:512, :] )
																												__lowerCamelCase   :   Dict       =True
																												break

																					if attribute.isdigit():
																												__lowerCamelCase   :   List[str]       =model[int(SCREAMING_SNAKE_CASE )]
																												__lowerCamelCase   :   Optional[Any]       =old_model[int(SCREAMING_SNAKE_CASE )]
																					else:
																												__lowerCamelCase   :   int       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

																												if old_attribute == "":
																																			__lowerCamelCase   :   Dict       =old_model
																												else:
																																			if not hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ):
																																										raise ValueError(F'{old_model} does not have {old_attribute}' )
																																			__lowerCamelCase   :   Tuple       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

														if not is_key_init:
																					raise ValueError(F'{key} was not correctly initialized!' )

							print(F'Saving model to {pytorch_dump_folder_path}' )
							prophet.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							_UpperCamelCase					      =   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							_UpperCamelCase					      =   parser.parse_args()
							convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




