

'''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


lowerCamelCase :      Optional[int]					 =     logging.get_logger(__name__)

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

lowerCamelCase :      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"
        ),
    },
}
lowerCamelCase :      int					 =     {
    "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[int]					 =     {
    "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 :      Union[str, Any]					 =     {
    "facebook/dpr-ctx_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-ctx_encoder-multiset-base": 5_1_2,
}
lowerCamelCase :      str					 =     {
    "facebook/dpr-question_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-question_encoder-multiset-base": 5_1_2,
}
lowerCamelCase :      Tuple					 =     {
    "facebook/dpr-reader-single-nq-base": 5_1_2,
    "facebook/dpr-reader-multiset-base": 5_1_2,
}


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 :      List[str]					 =     {
    "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 							A__    (       A__  ):
  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 							A__    (       A__  ):
  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

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

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


lowerCamelCase :      Union[str, 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(A__  )
class 							A__    :





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





       '''simple docstring'''






       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:
            _SCREAMING_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							,					)
       _SCREAMING_SNAKE_CASE							    =titles if not isinstance(_a							,					_a ) else [titles]
       _SCREAMING_SNAKE_CASE							    =texts if not isinstance(_a							,					_a ) else [texts]
       _SCREAMING_SNAKE_CASE							    =len(_a )
       _SCREAMING_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."
       _SCREAMING_SNAKE_CASE							    =super().__call__(_a							,					_a							,					padding=_a							,					truncation=_a )['input_ids']
       _SCREAMING_SNAKE_CASE							    =super().__call__(_a							,					add_special_tokens=_a							,					padding=_a							,					truncation=_a )['input_ids']
       _SCREAMING_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:
            _SCREAMING_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] )
            _SCREAMING_SNAKE_CASE							    =attention_mask
       return self.pad(_a							,					padding=_a							,					max_length=_a							,					return_tensors=_a )





  def 						A  (		self      :	Optional[Any]							,					_a      :	BatchEncoding							,					_a      :	DPRReaderOutput							,					_a      :	int = 16							,					_a      :	int = 64							,					_a      :	int = 4							,					)	->    List[DPRSpanPrediction]:





       '''simple docstring'''






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

            _SCREAMING_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      :	List[Any]							,					_a      :	List[int]							,					_a      :	List[int]							,					_a      :	int							,					_a      :	int							,					)	->    List[DPRSpanPrediction]:





       '''simple docstring'''






       _SCREAMING_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) )
       _SCREAMING_SNAKE_CASE							    =sorted(_a							,					key=lambda _a : x[1]							,					reverse=_a )
       _SCREAMING_SNAKE_CASE							    =[]
       for (start_index, end_index), score in scores:
            assert start_index <= end_index, f"Wrong span indices: [{start_index}:{end_index}]"
            _SCREAMING_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(A__  )
class 							A__    (       A__     ,   A__  ):
  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 importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     Any=False				)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =OmegaConf.load(_UpperCamelCase				)
     if display:
          print(yaml.dump(OmegaConf.to_container(_UpperCamelCase				)				)				)
     return config


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     Optional[Any]=None							, _UpperCamelCase						:     Union[str, Any]=None				)      ->			Optional[Any]:
     """simple docstring"""




     if conf_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.yaml'
     _SCREAMING_SNAKE_CASE							    =load_config(_UpperCamelCase							, display=_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =VQModel(**config.model.params				)
     if ckpt_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.pt'
     _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location=_UpperCamelCase				)
     if ".ckpt" in ckpt_path:
          _SCREAMING_SNAKE_CASE							    =sd['state_dict']
     model.load_state_dict(_UpperCamelCase							, strict=_UpperCamelCase				)
     model.to(_UpperCamelCase				)
     del sd
     return model


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     int				)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =model.encode(_UpperCamelCase				)
     print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"				)
     _SCREAMING_SNAKE_CASE							    =model.decode(_UpperCamelCase				)
     return xrec


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     List[str]=False				)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =string.rsplit('.'							, 1				)
     if reload:
          _SCREAMING_SNAKE_CASE							    =importlib.import_module(_UpperCamelCase				)
          importlib.reload(_UpperCamelCase				)
     return getattr(importlib.import_module(_UpperCamelCase							, package=_UpperCamelCase				)							, cls				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			List[str]:
     """simple docstring"""




     if "target" not in config:
          raise KeyError('Expected key `target` to instantiate.'				)
     return get_obj_from_str(config['target']				)(**config.get('params'							, {}				)				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     List[str]=True							, _UpperCamelCase						:     int=True				)      ->			Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =instantiate_from_config(_UpperCamelCase				)
     if sd is not None:
          model.load_state_dict(_UpperCamelCase				)
     if gpu:
          model.cuda()
     if eval_mode:
          model.eval()
     return {"model": model}







def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[int]				)      ->			List[Any]:
     """simple docstring"""




     if ckpt:
          _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location='cpu'				)
          _SCREAMING_SNAKE_CASE							    =pl_sd['global_step']
          print(f"loaded model from global step {global_step}."				)
     else:
          _SCREAMING_SNAKE_CASE							    ={'state_dict': None}
          _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =load_model_from_config(config.model							, pl_sd['state_dict']							, gpu=_UpperCamelCase							, eval_mode=_UpperCamelCase				)['model']
     return model, global_step
