

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


__UpperCAmelCase						=					logging.get_logger(__name__)

__UpperCAmelCase						=					{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__UpperCAmelCase						=					{
    """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"""
        ),
    },
}
__UpperCAmelCase						=					{
    """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"""
        ),
    },
}
__UpperCAmelCase						=					{
    """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"""
        ),
    },
}

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


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



class SCREAMING_SNAKE_CASE      (    __A					):
  """simple docstring"""






  lowerCamelCase       :     str		 =VOCAB_FILES_NAMES
  lowerCamelCase       :     Union[str, Any]		 =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase       :     Optional[int]		 =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase       :     str		 =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class SCREAMING_SNAKE_CASE      (    __A					):
  """simple docstring"""






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






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

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


__UpperCAmelCase						=					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(__A					)
class SCREAMING_SNAKE_CASE      :
  """simple docstring"""






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






     """simple docstring"""







     if titles is None and texts is None:
        return super().__call__(
            lowerCAmelCase			,				padding=lowerCAmelCase			,				truncation=lowerCAmelCase			,				max_length=lowerCAmelCase			,				return_tensors=lowerCAmelCase			,				return_attention_mask=lowerCAmelCase			,				**lowerCAmelCase			,				)
     elif titles is None or texts is None:
        __lowerCAmelCase    :     Tuple          =		titles if texts is None else texts
        return super().__call__(
            lowerCAmelCase			,				lowerCAmelCase			,				padding=lowerCAmelCase			,				truncation=lowerCAmelCase			,				max_length=lowerCAmelCase			,				return_tensors=lowerCAmelCase			,				return_attention_mask=lowerCAmelCase			,				**lowerCAmelCase			,				)
     __lowerCAmelCase    :     Dict          =		titles if not isinstance(lowerCAmelCase			,				lowerCAmelCase       ) else [titles]
     __lowerCAmelCase    :     Optional[int]          =		texts if not isinstance(lowerCAmelCase			,				lowerCAmelCase       ) else [texts]
     __lowerCAmelCase    :     str          =		len(lowerCAmelCase       )
     __lowerCAmelCase    :     Optional[Any]          =		questions if not isinstance(lowerCAmelCase			,				lowerCAmelCase       ) else [questions] * n_passages
     if len(lowerCAmelCase       ) != len(lowerCAmelCase       ):
        raise ValueError(
            f'''There should be as many titles than texts but got {len(lowerCAmelCase       )} titles and {len(lowerCAmelCase       )} texts.'''       )
     __lowerCAmelCase    :     Tuple          =		super().__call__(lowerCAmelCase			,				lowerCAmelCase			,				padding=lowerCAmelCase			,				truncation=lowerCAmelCase       )["input_ids"]
     __lowerCAmelCase    :     Optional[int]          =		super().__call__(lowerCAmelCase			,				add_special_tokens=lowerCAmelCase			,				padding=lowerCAmelCase			,				truncation=lowerCAmelCase       )["input_ids"]
     __lowerCAmelCase    :     int          =		{
         "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(lowerCAmelCase			,				lowerCAmelCase       )
         ]
     }
     if return_attention_mask is not False:
        __lowerCAmelCase    :     Optional[Any]          =		[]
        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    :     Dict          =		attention_mask
     return self.pad(lowerCAmelCase			,				padding=lowerCAmelCase			,				max_length=lowerCAmelCase			,				return_tensors=lowerCAmelCase       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       List[str]			,				lowerCAmelCase					:       BatchEncoding			,				lowerCAmelCase					:       DPRReaderOutput			,				lowerCAmelCase					:       int = 16			,				lowerCAmelCase					:       int = 64			,				lowerCAmelCase					:       int = 4			,				)				->      List[DPRSpanPrediction]:






     """simple docstring"""







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

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







  def 			SCREAMING_SNAKE_CASE       (     self					:       str			,				lowerCAmelCase					:       List[int]			,				lowerCAmelCase					:       List[int]			,				lowerCAmelCase					:       int			,				lowerCAmelCase					:       int			,				)				->      List[DPRSpanPrediction]:






     """simple docstring"""







     __lowerCAmelCase    :     Tuple          =		[]
     for start_index, start_score in enumerate(lowerCAmelCase       ):
        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    :     Tuple          =		sorted(lowerCAmelCase			,				key=lambda lowerCAmelCase       : x[1]			,				reverse=lowerCAmelCase       )
     __lowerCAmelCase    :     str          =		[]
     for (start_index, end_index), score in scores:
        if start_index > end_index:
           raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''       )
        __lowerCAmelCase    :     int          =		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(lowerCAmelCase       ) == top_spans:
           break
     return chosen_span_intervals






@add_end_docstrings(__A					)
class SCREAMING_SNAKE_CASE      (    __A  ,				__A					):
  """simple docstring"""






  lowerCamelCase       :     Any		 =VOCAB_FILES_NAMES
  lowerCamelCase       :     int		 =READER_PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase       :     Optional[int]		 =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase       :     Union[str, Any]		 =READER_PRETRAINED_INIT_CONFIGURATION
  lowerCamelCase       :     Union[str, Any]		 =["input_ids", "attention_mask"]





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_      =     logging.get_logger(__name__)

a_      =     {
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='roformer'
			def __init__(      self				:			Dict   ,   a				:			Any=5_0000   ,   a				:			List[Any]=None   ,   a				:			str=768   ,   a				:			str=12   ,   a				:			Tuple=12   ,   a				:			Optional[Any]=3072   ,   a				:			List[str]="gelu"   ,   a				:			List[Any]=0.1   ,   a				:			Union[str, Any]=0.1   ,   a				:			Tuple=1536   ,   a				:			List[str]=2   ,   a				:			Tuple=0.02   ,   a				:			Any=1e-12   ,   a				:			Optional[int]=0   ,   a				:			Union[str, Any]=False   ,   a				:			int=True   ,   **a				:			str   ,   )				->							int:

				"""simple docstring"""

				super().__init__(pad_token_id=a   ,   **a						)

				SCREAMING_SNAKE_CASE :   str					  =							vocab_size
				SCREAMING_SNAKE_CASE :   int					  =							hidden_size if embedding_size is None else embedding_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   int					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_act
				SCREAMING_SNAKE_CASE :   int					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   int					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   Any					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   List[str]					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   List[str]					  =							rotary_value
				SCREAMING_SNAKE_CASE :   int					  =							use_cache






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				if self.task == "multiple-choice":
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{0: "batch", 1: "choice", 2: "sequence"}
				else:
					SCREAMING_SNAKE_CASE :   str					  =							{0: "batch", 1: "sequence"}
				SCREAMING_SNAKE_CASE :   List[Any]					  =							{0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]						)