

'''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


_lowerCamelCase      					=     logging.get_logger(__name__)

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

_lowerCamelCase      					=     {
    '''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      					=     {
    '''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      					=     {
    '''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      					=     {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
_lowerCamelCase      					=     {
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
_lowerCamelCase      					=     {
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


_lowerCamelCase      					=     {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
_lowerCamelCase      					=     {
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
_lowerCamelCase      					=     {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class 					_snake_case		(_lowercase):
			__A  :	Union[str, Any]        =VOCAB_FILES_NAMES
			__A  :	str        =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			__A  :	Optional[Any]        =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__A  :	Any        =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION




class 					_snake_case		(_lowercase):
			__A  :	Dict        =VOCAB_FILES_NAMES
			__A  :	List[str]        =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			__A  :	Union[str, Any]        =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__A  :	str        =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION




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

_lowerCamelCase      					=     collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_lowerCamelCase      					=     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)

    Returns:
        `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(_lowercase)
class 					_snake_case		:






			def __call__(  self			,_snake_case			,_snake_case = None			,_snake_case = None			,_snake_case = False			,_snake_case = False			,_snake_case = None			,_snake_case = None			,_snake_case = None			,**_snake_case			,):
				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:
					UpperCAmelCase_     :      List[str]						=  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			,)
				UpperCAmelCase_     :      Union[str, Any]						=  titles if not isinstance(_snake_case			,_snake_case     ) else [titles]
				UpperCAmelCase_     :      str						=  texts if not isinstance(_snake_case			,_snake_case     ) else [texts]
				UpperCAmelCase_     :      List[str]						=  len(_snake_case     )
				UpperCAmelCase_     :      Any						=  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.'''     )
				UpperCAmelCase_     :      Union[str, Any]						=  super().__call__(_snake_case			,_snake_case			,padding=_snake_case			,truncation=_snake_case     )["input_ids"]
				UpperCAmelCase_     :      Tuple						=  super().__call__(_snake_case			,add_special_tokens=_snake_case			,padding=_snake_case			,truncation=_snake_case     )["input_ids"]
				UpperCAmelCase_     :      Dict						=  {
				    "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:
					UpperCAmelCase_     :      int						=  []
					for input_ids in encoded_inputs["input_ids"]:
						attention_mask.append([int(input_id != self.pad_token_id     ) for input_id in input_ids]     )
					UpperCAmelCase_     :      Union[str, Any]						=  attention_mask
				return self.pad(_snake_case			,padding=_snake_case			,max_length=_snake_case			,return_tensors=_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case = 16			,_snake_case = 64			,_snake_case = 4			,):
				UpperCAmelCase_     :      Any						=  reader_input["input_ids"]
				UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[int]						=  reader_output[:3]
				UpperCAmelCase_     :      Union[str, Any]						=  len(_snake_case     )
				UpperCAmelCase_     :      Optional[Any]						=  sorted(range(_snake_case     )			,reverse=_snake_case			,key=relevance_logits.__getitem__     )
				UpperCAmelCase_     :      int						=  []
				for doc_id in sorted_docs:
					UpperCAmelCase_     :      Union[str, Any]						=  list(input_ids[doc_id]     )
					# assuming question & title information is at the beginning of the sequence
					UpperCAmelCase_     :      str						=  sequence_ids.index(self.sep_token_id			,2     ) + 1  # second sep id
					if sequence_ids[-1] == self.pad_token_id:
						UpperCAmelCase_     :      Tuple						=  sequence_ids.index(self.pad_token_id     )
					else:
						UpperCAmelCase_     :      Union[str, Any]						=  len(_snake_case     )

					UpperCAmelCase_     :      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=_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 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,):
				UpperCAmelCase_     :      Dict						=  []
				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)     )
				UpperCAmelCase_     :      str						=  sorted(_snake_case			,key=lambda _snake_case     : x[1]			,reverse=_snake_case     )
				UpperCAmelCase_     :      Optional[Any]						=  []
				for (start_index, end_index), score in scores:
					if start_index > end_index:
						raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''     )
					UpperCAmelCase_     :      Tuple						=  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(_lowercase)
class 					_snake_case		(_lowercase			,			_lowercase):
			__A  :	Dict        =VOCAB_FILES_NAMES
			__A  :	Union[str, Any]        =READER_PRETRAINED_VOCAB_FILES_MAP
			__A  :	List[Any]        =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__A  :	Any        =READER_PRETRAINED_INIT_CONFIGURATION
			__A  :	Dict        =["input_ids", "attention_mask"]






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_       :    List[str]						      =			{'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    Tuple						      =			[
		    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SEWForCTC''',
		    '''SEWForSequenceClassification''',
		    '''SEWModel''',
		    '''SEWPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew import (
				    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWForCTC,
				    SEWForSequenceClassification,
				    SEWModel,
				    SEWPreTrainedModel,
				)


else:
		import sys

		UpperCamelCase_       :    List[str]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




