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


lowerCAmelCase			:							Union[str, Any]     =logging.get_logger(__name__)

lowerCAmelCase			:							Any     ={"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase			:							Tuple     ={
    "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			:							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"
        ),
    },
}
lowerCAmelCase			:							Optional[Any]     ={
    "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			:							List[Any]     ={
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
lowerCAmelCase			:							Tuple     ={
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
lowerCAmelCase			:							Dict     ={
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


lowerCAmelCase			:							List[Any]     ={
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
lowerCAmelCase			:							Optional[Any]     ={
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
lowerCAmelCase			:							int     ={
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case    		=       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      _snake_case    		=       DPRContextEncoderTokenizer





class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case    		=       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      _snake_case    		=       DPRQuestionEncoderTokenizer





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

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


lowerCAmelCase			:							List[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(__lowerCAmelCase		)
class 	__snake_case	:





      '''simple docstring'''






      def __call__(					self     :				Tuple    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Optional[str] = None    ,  _UpperCamelCase     :				Optional[str] = None    ,  _UpperCamelCase     :				Union[bool, str] = False    ,  _UpperCamelCase     :				Union[bool, str] = False    ,  _UpperCamelCase     :				Optional[int] = None    ,  _UpperCamelCase     :				Optional[Union[str, TensorType]] = None    ,  _UpperCamelCase     :				Optional[bool] = None    ,  **_UpperCamelCase     :				List[Any]    ,  )				->BatchEncoding:


          """simple docstring"""


          if titles is None and texts is None:
              return super().__call__(
                  _UpperCamelCase    ,  padding=_UpperCamelCase    ,  truncation=_UpperCamelCase    ,  max_length=_UpperCamelCase    ,  return_tensors=_UpperCamelCase    ,  return_attention_mask=_UpperCamelCase    ,  **_UpperCamelCase    ,  )
          elif titles is None or texts is None:
              _lowerCamelCase     :		int 						=						titles if texts is None else texts
              return super().__call__(
                  _UpperCamelCase    ,  _UpperCamelCase    ,  padding=_UpperCamelCase    ,  truncation=_UpperCamelCase    ,  max_length=_UpperCamelCase    ,  return_tensors=_UpperCamelCase    ,  return_attention_mask=_UpperCamelCase    ,  **_UpperCamelCase    ,  )
          _lowerCamelCase     :		Union[str, Any] 						=						titles if not isinstance(_UpperCamelCase    ,  _UpperCamelCase) else [titles]
          _lowerCamelCase     :		List[str] 						=						texts if not isinstance(_UpperCamelCase    ,  _UpperCamelCase) else [texts]
          _lowerCamelCase     :		Tuple 						=						len(_UpperCamelCase)
          _lowerCamelCase     :		List[str] 						=						questions if not isinstance(_UpperCamelCase    ,  _UpperCamelCase) else [questions] * n_passages
          assert len(_UpperCamelCase) == len(
              _UpperCamelCase), F"""There should be as many titles than texts but got {len(_UpperCamelCase)} titles and {len(_UpperCamelCase)} texts."""
          _lowerCamelCase     :		Any 						=						super().__call__(_UpperCamelCase    ,  _UpperCamelCase    ,  padding=_UpperCamelCase    ,  truncation=_UpperCamelCase)["""input_ids"""]
          _lowerCamelCase     :		Optional[int] 						=						super().__call__(_UpperCamelCase    ,  add_special_tokens=_UpperCamelCase    ,  padding=_UpperCamelCase    ,  truncation=_UpperCamelCase)["""input_ids"""]
          _lowerCamelCase     :		str 						=						{
              """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(_UpperCamelCase    ,  _UpperCamelCase)
              ]
          }
          if return_attention_mask is not False:
              _lowerCamelCase     :		List[str] 						=						[]
              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     :		str 						=						attention_mask
          return self.pad(_UpperCamelCase    ,  padding=_UpperCamelCase    ,  max_length=_UpperCamelCase    ,  return_tensors=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				BatchEncoding    ,  _UpperCamelCase     :				DPRReaderOutput    ,  _UpperCamelCase     :				int = 16    ,  _UpperCamelCase     :				int = 64    ,  _UpperCamelCase     :				int = 4    ,  )				->List[DPRSpanPrediction]:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						reader_input["""input_ids"""]
          _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		List[str] 						=						reader_output[:3]
          _lowerCamelCase     :		Optional[int] 						=						len(_UpperCamelCase)
          _lowerCamelCase     :		List[Any] 						=						sorted(range(_UpperCamelCase)    ,  reverse=_UpperCamelCase    ,  key=relevance_logits.__getitem__)
          _lowerCamelCase     :		List[DPRReaderOutput] 						=						[]
          for doc_id in sorted_docs:
              _lowerCamelCase     :		List[Any] 						=						list(input_ids[doc_id])
              # assuming question & title information is at the beginning of the sequence
              _lowerCamelCase     :		List[str] 						=						sequence_ids.index(self.sep_token_id    ,  2) + 1  # second sep id
              if sequence_ids[-1] == self.pad_token_id:
                  _lowerCamelCase     :		Union[str, Any] 						=						sequence_ids.index(self.pad_token_id)
              else:
                  _lowerCamelCase     :		Dict 						=						len(_UpperCamelCase)

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




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				List[int]    ,  _UpperCamelCase     :				List[int]    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				int    ,  )				->List[DPRSpanPrediction]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						[]
          for start_index, start_score in enumerate(_UpperCamelCase):
              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     :		int 						=						sorted(_UpperCamelCase    ,  key=lambda _UpperCamelCase: x[1]    ,  reverse=_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						[]
          for (start_index, end_index), score in scores:
              assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
              _lowerCamelCase     :		str 						=						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(_UpperCamelCase) == top_spans:
                  break
          return chosen_span_intervals





@add_end_docstrings(__lowerCAmelCase		)
class 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       READER_PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case    		=       READER_PRETRAINED_INIT_CONFIGURATION
      _snake_case    		=       ['input_ids', 'attention_mask']
      _snake_case    		=       DPRReaderTokenizer





import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


lowerCAmelCase			:							int     ={
    "cola": 2,
    "mnli": 3,
    "mrpc": 2,
    "sst-2": 2,
    "sts-b": 1,
    "qqp": 2,
    "qnli": 2,
    "rte": 2,
    "wnli": 2,
}


logging.set_verbosity_info()







def 						A__  (					__A   , __A   , __A   , __A=None	):







    '''simple docstring'''
    # Initialise PyTorch model
    _lowerCamelCase     :		Tuple 						=						XLNetConfig.from_json_file(__A	)

    _lowerCamelCase     :		List[Any] 						=						finetuning_task.lower() if finetuning_task is not None else """"""
    if finetuning_task in GLUE_TASKS_NUM_LABELS:
        print(F"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""	)
        _lowerCamelCase     :		int 						=						finetuning_task
        _lowerCamelCase     :		Union[str, Any] 						=						GLUE_TASKS_NUM_LABELS[finetuning_task]
        _lowerCamelCase     :		int 						=						XLNetForSequenceClassification(__A	)
    elif "squad" in finetuning_task:
        _lowerCamelCase     :		Dict 						=						finetuning_task
        _lowerCamelCase     :		Optional[Any] 						=						XLNetForQuestionAnswering(__A	)
    else:
        _lowerCamelCase     :		Any 						=						XLNetLMHeadModel(__A	)

    # Load weights from tf checkpoint
    load_tf_weights_in_xlnet(__A   , __A   , __A	)

    # Save pytorch-model
    _lowerCamelCase     :		Optional[Any] 						=						os.path.join(__A   , __A	)
    _lowerCamelCase     :		Any 						=						os.path.join(__A   , __A	)
    print(F"""Save PyTorch model to {os.path.abspath(__A	)}"""	)
    torch.save(model.state_dict()   , __A	)
    print(F"""Save configuration file to {os.path.abspath(__A	)}"""	)
    with open(__A   , """w"""   , encoding="""utf-8"""	) as f:
        f.write(config.to_json_string()	)


if __name__ == "__main__":
    lowerCAmelCase			:							Dict     =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--xlnet_config_file",
        default=None,
        type=str,
        required=True,
        help=(
            "The config json file corresponding to the pre-trained XLNet model. \n"
            "This specifies the model architecture."
        ),
    )
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=str,
        required=True,
        help="Path to the folder to store the PyTorch model or dataset/vocab.",
    )
    parser.add_argument(
        "--finetuning_task",
        default=None,
        type=str,
        help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
    )
    lowerCAmelCase			:							Union[str, Any]     =parser.parse_args()
    print(args)

    convert_xlnet_checkpoint_to_pytorch(
        args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
    )




