
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				=logging.get_logger(__name__)

__A				={'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A				={
    '''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				={
    '''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				={
    '''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				={
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_1_2,
}
__A				={
    '''facebook/dpr-question_encoder-single-nq-base''': 5_1_2,
    '''facebook/dpr-question_encoder-multiset-base''': 5_1_2,
}
__A				={
    '''facebook/dpr-reader-single-nq-base''': 5_1_2,
    '''facebook/dpr-reader-multiset-base''': 5_1_2,
}


__A				={
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
__A				={
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
__A				={
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase__         = DPRContextEncoderTokenizer




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase__         = DPRQuestionEncoderTokenizer



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

__A				=collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


__A				=R'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''




@add_start_docstrings(snake_case_						)
class     _SCREAMING_SNAKE_CASE     :



	def __call__(    self						,  lowercase						,  lowercase = None						,  lowercase = None						,  lowercase = False						,  lowercase = False						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  **lowercase						,  )				->       BatchEncoding:
					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:
									lowerCamelCase_						=			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						,  )
					lowerCamelCase_						=			titles if not isinstance(lowercase						,  lowercase ) else [titles]
					lowerCamelCase_						=			texts if not isinstance(lowercase						,  lowercase ) else [texts]
					lowerCamelCase_						=			len(lowercase )
					lowerCamelCase_						=			questions if not isinstance(lowercase						,  lowercase ) else [questions] * n_passages
					assert len(lowercase ) == len(
					    lowercase ), f'There should be as many titles than texts but got {len(lowercase )} titles and {len(lowercase )} texts.'
					lowerCamelCase_						=			super().__call__(lowercase						,  lowercase						,  padding=lowercase						,  truncation=lowercase )["input_ids"]
					lowerCamelCase_						=			super().__call__(lowercase						,  add_special_tokens=lowercase						,  padding=lowercase						,  truncation=lowercase )["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(lowercase						,  lowercase )
					    ]
					}
					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(lowercase						,  padding=lowercase						,  max_length=lowercase						,  return_tensors=lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase = 16						,  lowercase = 64						,  lowercase = 4						,  )				->       List[DPRSpanPrediction]:
					lowerCamelCase_						=			reader_input["input_ids"]
					lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			reader_output[:3]
					lowerCamelCase_						=			len(lowercase )
					lowerCamelCase_						=			sorted(range(lowercase )						,  reverse=lowercase						,  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(lowercase )

									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=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						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       List[DPRSpanPrediction]:
					lowerCamelCase_						=			[]
					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) )
					lowerCamelCase_						=			sorted(lowercase						,  key=lambda lowercase : x[1]						,  reverse=lowercase )
					lowerCamelCase_						=			[]
					for (start_index, end_index), score in scores:
									assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
									lowerCamelCase_						=			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(lowercase ) == top_spans:
													break
					return chosen_span_intervals




@add_end_docstrings(snake_case_						)
class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = READER_PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = READER_PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase__         = ['input_ids', 'attention_mask']
	lowerCAmelCase__         = DPRReaderTokenizer






"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				str   ="llama"
       a  :				List[str]   =["past_key_values"]

       def __init__(						self		,     snake_case__=32_000		,     snake_case__=4_096		,     snake_case__=11_008		,     snake_case__=32		,     snake_case__=32		,     snake_case__=None		,     snake_case__="silu"		,     snake_case__=2_048		,     snake_case__=0.02		,     snake_case__=1e-6		,     snake_case__=True		,     snake_case__=0		,     snake_case__=1		,     snake_case__=2		,     snake_case__=1		,     snake_case__=False		,     snake_case__=None		,     **snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							vocab_size
         lowerCAmelCase     :     str								=							max_position_embeddings
         lowerCAmelCase     :     str								=							hidden_size
         lowerCAmelCase     :     Optional[int]								=							intermediate_size
         lowerCAmelCase     :     Any								=							num_hidden_layers
         lowerCAmelCase     :     List[str]								=							num_attention_heads

         # for backward compatibility
         if num_key_value_heads is None:
           lowerCAmelCase     :     Tuple								=							num_attention_heads

         lowerCAmelCase     :     Dict								=							num_key_value_heads
         lowerCAmelCase     :     Optional[Any]								=							hidden_act
         lowerCAmelCase     :     Optional[Any]								=							initializer_range
         lowerCAmelCase     :     Any								=							rms_norm_eps
         lowerCAmelCase     :     List[Any]								=							pretraining_tp
         lowerCAmelCase     :     int								=							use_cache
         lowerCAmelCase     :     List[str]								=							rope_scaling
         self._rope_scaling_validation()

         super().__init__(
             pad_token_id=snake_case__		,     bos_token_id=snake_case__		,     eos_token_id=snake_case__		,     tie_word_embeddings=snake_case__		,     **snake_case__		,     )







       def        lowercase__							(						self						):






         """simple docstring"""





         if self.rope_scaling is None:
           return

         if not isinstance(self.rope_scaling		,     snake_case__						) or len(self.rope_scaling						) != 2:
           raise ValueError(
               "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
               f"""got {self.rope_scaling}"""						)
         lowerCAmelCase     :     Optional[Any]								=							self.rope_scaling.get("type"		,     snake_case__						)
         lowerCAmelCase     :     int								=							self.rope_scaling.get("factor"		,     snake_case__						)
         if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
           raise ValueError(
               f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""						)
         if rope_scaling_factor is None or not isinstance(snake_case__		,     snake_case__						) or rope_scaling_factor <= 1.0:
           raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""						)





