'''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


_A			:      Tuple					      =				logging.get_logger(__name__)

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

_A			:      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"
        ),
    },
}
_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			:      str					      =				{
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
_A			:      int					      =				{
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
_A			:      List[str]					      =				{
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


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







class 				_lowercase     (				lowerCAmelCase_			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					VOCAB_FILES_NAMES
 _SCREAMING_SNAKE_CASE     :       int      	=					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 _SCREAMING_SNAKE_CASE     :       Any      	=					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _SCREAMING_SNAKE_CASE     :       Dict      	=					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class 				_lowercase     (				lowerCAmelCase_			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       int      	=					VOCAB_FILES_NAMES
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 _SCREAMING_SNAKE_CASE     :       List[str]      	=					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _SCREAMING_SNAKE_CASE     :       Any      	=					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







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

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


_A			:      Optional[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    ```\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(lowerCAmelCase_			)
class 				_lowercase     :


 '''simple docstring'''






 def __call__(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	List[Any] = None      ,       SCREAMING_SNAKE_CASE__   :	Tuple = None      ,       SCREAMING_SNAKE_CASE__   :	int = False      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any] = False      ,       SCREAMING_SNAKE_CASE__   :	List[str] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[int] = None      ,       SCREAMING_SNAKE_CASE__   :	Any = None      ,       **SCREAMING_SNAKE_CASE__   :	int      ,       )  ->    BatchEncoding:
  if titles is None and texts is None:
   return super().__call__(
       __snake_case      ,       padding=__snake_case      ,       truncation=__snake_case      ,       max_length=__snake_case      ,       return_tensors=__snake_case      ,       return_attention_mask=__snake_case      ,       **__snake_case      ,       )
  elif titles is None or texts is None:
   __lowerCAmelCase											=						titles if texts is None else texts
   return super().__call__(
       __snake_case      ,       __snake_case      ,       padding=__snake_case      ,       truncation=__snake_case      ,       max_length=__snake_case      ,       return_tensors=__snake_case      ,       return_attention_mask=__snake_case      ,       **__snake_case      ,       )
  __lowerCAmelCase											=						titles if not isinstance(__snake_case      ,       __snake_case							) else [titles]
  __lowerCAmelCase											=						texts if not isinstance(__snake_case      ,       __snake_case							) else [texts]
  __lowerCAmelCase											=						len(__snake_case							)
  __lowerCAmelCase											=						questions if not isinstance(__snake_case      ,       __snake_case							) else [questions] * n_passages
  if len(__snake_case							) != len(__snake_case							):
   raise ValueError(
       f"""There should be as many titles than texts but got {len(__snake_case							)} titles and {len(__snake_case							)} texts."""							)
  __lowerCAmelCase											=						super().__call__(__snake_case      ,       __snake_case      ,       padding=__snake_case      ,       truncation=__snake_case							)["""input_ids"""]
  __lowerCAmelCase											=						super().__call__(__snake_case      ,       add_special_tokens=__snake_case      ,       padding=__snake_case      ,       truncation=__snake_case							)["""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(__snake_case      ,       __snake_case							)
      ]
  }
  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(__snake_case      ,       padding=__snake_case      ,       max_length=__snake_case      ,       return_tensors=__snake_case							)
 def a					(      self   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	List[Any] = 16      ,       SCREAMING_SNAKE_CASE__   :	List[str] = 64      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any] = 4      ,       )  ->    List[DPRSpanPrediction]:
  __lowerCAmelCase											=						reader_input["""input_ids"""]
  __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						reader_output[:3]
  __lowerCAmelCase											=						len(__snake_case							)
  __lowerCAmelCase											=						sorted(range(__snake_case							)      ,       reverse=__snake_case      ,       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(__snake_case							)

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





 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]      ,       )  ->    List[DPRSpanPrediction]:
  __lowerCAmelCase											=						[]
  for start_index, start_score in enumerate(__snake_case							):
   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(__snake_case      ,       key=lambda SCREAMING_SNAKE_CASE__							: x[1]      ,       reverse=__snake_case							)
  __lowerCAmelCase											=						[]
  for (start_index, end_index), score in scores:
   if start_index > end_index:
    raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]"""							)
   __lowerCAmelCase											=						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(__snake_case							) == top_spans:
    break
  return chosen_span_intervals







@add_end_docstrings(lowerCAmelCase_			)
class 				_lowercase     (				lowerCAmelCase_  ,							lowerCAmelCase_			):


 '''simple docstring'''






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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase					:			Tuple  				=			{
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase					:			Optional[int]  				=			["LlamaTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase					:			Optional[int]  				=			["LlamaTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase					:			List[Any]  				=			[
						    "LlamaForCausalLM",
						    "LlamaModel",
						    "LlamaPreTrainedModel",
						    "LlamaForSequenceClassification",
						]


if TYPE_CHECKING:
						from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_llama import LlamaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_llama_fast import LlamaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
						import sys

						_lowerCAmelCase					:			int  				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






