




'''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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


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

a  :				Union[str, Any]									=  {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a  :				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'
        ),
    },
}
a  :				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'
        ),
    },
}
a  :				List[str]									=  {
    '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'
        ),
    },
}

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


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






class 						a				(   SCREAMING_SNAKE_CASE_     ):
				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 						a				(   SCREAMING_SNAKE_CASE_     ):
				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







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

a  :				int									=  collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


a  :				List[str]									=  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(SCREAMING_SNAKE_CASE_     )
class 						a				:


				def __call__(		self    :					List[Any]       ,				lowercase_    :					Union[str, Any]       ,				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_    :					Dict       ,				):
									if titles is None and texts is None:
														return super().__call__(
														    _a       ,				padding=_a       ,				truncation=_a       ,				max_length=_a       ,				return_tensors=_a       ,				return_attention_mask=_a       ,				**_a       ,				)
									elif titles is None or texts is None:
														snake_case_									=				titles if texts is None else texts
														return super().__call__(
														    _a       ,				_a       ,				padding=_a       ,				truncation=_a       ,				max_length=_a       ,				return_tensors=_a       ,				return_attention_mask=_a       ,				**_a       ,				)
									snake_case_									=				titles if not isinstance(_a       ,				_a					) else [titles]
									snake_case_									=				texts if not isinstance(_a       ,				_a					) else [texts]
									snake_case_									=				len(_a					)
									snake_case_									=				questions if not isinstance(_a       ,				_a					) else [questions] * n_passages
									assert len(_a					) == len(
									    _a					), F"There should be as many titles than texts but got {len(_a					)} titles and {len(_a					)} texts."
									snake_case_									=				super().__call__(_a       ,				_a       ,				padding=_a       ,				truncation=_a					)['input_ids']
									snake_case_									=				super().__call__(_a       ,				add_special_tokens=_a       ,				padding=_a       ,				truncation=_a					)['input_ids']
									snake_case_									=				{
									    '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(_a       ,				_a					)
									    ]
									}
									if return_attention_mask is not False:
														snake_case_									=				[]
														for input_ids in encoded_inputs["input_ids"]:
																			attention_mask.append([int(input_id != self.pad_token_id					) for input_id in input_ids]					)
														snake_case_									=				attention_mask
									return self.pad(_a       ,				padding=_a       ,				max_length=_a       ,				return_tensors=_a					)


				def   A_ (		self    :					Tuple       ,				lowercase_    :					BatchEncoding       ,				lowercase_    :					DPRReaderOutput       ,				lowercase_    :					int = 16       ,				lowercase_    :					int = 64       ,				lowercase_    :					int = 4       ,				):
									snake_case_									=				reader_input['input_ids']
									snake_case_									=				reader_output[:3]
									snake_case_									=				len(_a					)
									snake_case_									=				sorted(range(_a					)       ,				reverse=_a       ,				key=relevance_logits.__getitem__					)
									snake_case_									=				[]
									for doc_id in sorted_docs:
														snake_case_									=				list(input_ids[doc_id]					)
														# assuming question & title information is at the beginning of the sequence
														snake_case_									=				sequence_ids.index(self.sep_token_id       ,				2					) + 1  # second sep id
														if sequence_ids[-1] == self.pad_token_id:
																			snake_case_									=				sequence_ids.index(self.pad_token_id					)
														else:
																			snake_case_									=				len(_a					)

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


				def   A_ (		self    :					Dict       ,				lowercase_    :					List[int]       ,				lowercase_    :					List[int]       ,				lowercase_    :					int       ,				lowercase_    :					int       ,				):
									snake_case_									=				[]
									for start_index, start_score in enumerate(_a					):
														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)					)
									snake_case_									=				sorted(_a       ,				key=lambda lowercase_					: x[1]       ,				reverse=_a					)
									snake_case_									=				[]
									for (start_index, end_index), score in scores:
														assert start_index <= end_index, F"Wrong span indices: [{start_index}:{end_index}]"
														snake_case_									=				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(_a					) == top_spans:
																			break
									return chosen_span_intervals







@add_end_docstrings(SCREAMING_SNAKE_CASE_     )
class 						a				(   SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_     ):
				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






'''simple docstring'''





import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_UpperCamelCase											=						data_utils.TransfoXLTokenizer
_UpperCamelCase											=						data_utils.TransfoXLCorpus
_UpperCamelCase											=						data_utils
_UpperCamelCase											=						data_utils

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[Any]:
  if transfo_xl_dataset_file:
    # Convert a pre-processed corpus (see original TensorFlow repo)
    with open(_lowerCAmelCase       ,'rb'       ) as fp:
      __lowerCamelCase       :       Optional[Any]           = pickle.load(_lowerCAmelCase       ,encoding='latin1'       )
    # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
    __lowerCamelCase       :       Tuple           = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
    print(F'Save vocabulary to {pytorch_vocab_dump_path}'       )
    __lowerCamelCase       :       str           = corpus.vocab.__dict__
    torch.save(_lowerCAmelCase       ,_lowerCAmelCase       )

    __lowerCamelCase       :       Optional[Any]           = corpus.__dict__
    corpus_dict_no_vocab.pop('vocab'       ,_lowerCAmelCase       )
    __lowerCamelCase       :       Optional[Any]           = pytorch_dump_folder_path + '/' + CORPUS_NAME
    print(F'Save dataset to {pytorch_dataset_dump_path}'       )
    torch.save(_lowerCAmelCase       ,_lowerCAmelCase       )

  if tf_checkpoint_path:
    # Convert a pre-trained TensorFlow model
    __lowerCamelCase       :       int           = os.path.abspath(_lowerCAmelCase       )
    __lowerCamelCase       :       Any           = os.path.abspath(_lowerCAmelCase       )

    print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'       )
    # Initialise PyTorch model
    if transfo_xl_config_file == "":
      __lowerCamelCase       :       Optional[int]           = TransfoXLConfig()
    else:
      __lowerCamelCase       :       Optional[int]           = TransfoXLConfig.from_json_file(_lowerCAmelCase       )
    print(F'Building PyTorch model from configuration: {config}'       )
    __lowerCamelCase       :       List[str]           = TransfoXLLMHeadModel(_lowerCAmelCase       )

    __lowerCamelCase       :       Dict           = load_tf_weights_in_transfo_xl(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )
    # Save pytorch-model
    __lowerCamelCase       :       List[str]           = os.path.join(_lowerCAmelCase       ,_lowerCAmelCase       )
    __lowerCamelCase       :       int           = os.path.join(_lowerCAmelCase       ,_lowerCAmelCase       )
    print(F'Save PyTorch model to {os.path.abspath(_lowerCAmelCase       )}'       )
    torch.save(model.state_dict()       ,_lowerCAmelCase       )
    print(F'Save configuration file to {os.path.abspath(_lowerCAmelCase       )}'       )
    with open(_lowerCAmelCase       ,'w'       ,encoding='utf-8'       ) as f:
      f.write(config.to_json_string()       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   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(
       '--tf_checkpoint_path',
       default='',
       type=str,
       help='An optional path to a TensorFlow checkpoint path to be converted.',
   )
   parser.add_argument(
       '--transfo_xl_config_file',
       default='',
       type=str,
       help=(
           'An optional config json file corresponding to the pre-trained BERT model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--transfo_xl_dataset_file',
       default='',
       type=str,
       help='An optional dataset file to be converted in a vocabulary.',
   )
   _UpperCamelCase											=						parser.parse_args()
   convert_transfo_xl_checkpoint_to_pytorch(
       args.tf_checkpoint_path,
       args.transfo_xl_config_file,
       args.pytorch_dump_folder_path,
       args.transfo_xl_dataset_file,
   )






