



"""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


snake_case_    					=	logging.get_logger(__name__)

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

snake_case_    					=	{
    """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"""
        ),
    },
}
snake_case_    					=	{
    """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"""
        ),
    },
}
snake_case_    					=	{
    """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"""
        ),
    },
}

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


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







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							VOCAB_FILES_NAMES
						__UpperCamelCase		    =							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase		    =							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase		    =							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
						__UpperCamelCase		    =							DPRContextEncoderTokenizer







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							VOCAB_FILES_NAMES
						__UpperCamelCase		    =							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase		    =							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase		    =							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
						__UpperCamelCase		    =							DPRQuestionEncoderTokenizer


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

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


snake_case_    					=	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(SCREAMING_SNAKE_CASE_    )
class 				A_      :



						"""simple docstring"""






						def __call__( self			:Optional[int]       ,  lowercase_			:int       ,  lowercase_			:Optional[str] = None       ,  lowercase_			:Optional[str] = None       ,  lowercase_			:Union[bool, str] = False       ,  lowercase_			:Union[bool, str] = False       ,  lowercase_			:Optional[int] = None       ,  lowercase_			:Optional[Union[str, TensorType]] = None       ,  lowercase_			:Optional[bool] = None       ,  **lowercase_			:Optional[int]       ,  )    ->      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:
																		UpperCAmelCase					=			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_       ,  )
												UpperCAmelCase					=			titles if not isinstance(lowercase_       ,  lowercase_ ) else [titles]
												UpperCAmelCase					=			texts if not isinstance(lowercase_       ,  lowercase_ ) else [texts]
												UpperCAmelCase					=			len(lowercase_ )
												UpperCAmelCase					=			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."""
												UpperCAmelCase					=			super().__call__(lowercase_       ,  lowercase_       ,  padding=lowercase_       ,  truncation=lowercase_ )['input_ids']
												UpperCAmelCase					=			super().__call__(lowercase_       ,  add_special_tokens=lowercase_       ,  padding=lowercase_       ,  truncation=lowercase_ )['input_ids']
												UpperCAmelCase					=			{
												    '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:
																		UpperCAmelCase					=			[]
																		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					=			attention_mask
												return self.pad(lowercase_       ,  padding=lowercase_       ,  max_length=lowercase_       ,  return_tensors=lowercase_ )






						def      UpperCAmelCase__					( self			:List[str]       ,  lowercase_			:BatchEncoding       ,  lowercase_			:DPRReaderOutput       ,  lowercase_			:int = 16       ,  lowercase_			:int = 64       ,  lowercase_			:int = 4       ,  )    ->      List[DPRSpanPrediction]:
												UpperCAmelCase					=			reader_input['input_ids']
												UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase					=			reader_output[:3]
												UpperCAmelCase					=			len(lowercase_ )
												UpperCAmelCase					=			sorted(range(lowercase_ )       ,  reverse=lowercase_       ,  key=relevance_logits.__getitem__ )
												UpperCAmelCase					=			[]
												for doc_id in sorted_docs:
																		UpperCAmelCase					=			list(input_ids[doc_id] )
																		# assuming question & title information is at the beginning of the sequence
																		UpperCAmelCase					=			sequence_ids.index(self.sep_token_id       ,  2 ) + 1  # second sep id
																		if sequence_ids[-1] == self.pad_token_id:
																								UpperCAmelCase					=			sequence_ids.index(self.pad_token_id )
																		else:
																								UpperCAmelCase					=			len(lowercase_ )

																		UpperCAmelCase					=			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      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:List[int]       ,  lowercase_			:List[int]       ,  lowercase_			:int       ,  lowercase_			:int       ,  )    ->      List[DPRSpanPrediction]:
												UpperCAmelCase					=			[]
												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) )
												UpperCAmelCase					=			sorted(lowercase_       ,  key=lambda lowercase_ : x[1]       ,  reverse=lowercase_ )
												UpperCAmelCase					=			[]
												for (start_index, end_index), score in scores:
																		assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
																		UpperCAmelCase					=			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(SCREAMING_SNAKE_CASE_    )
class 				A_      (					SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							VOCAB_FILES_NAMES
						__UpperCamelCase		    =							READER_PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase		    =							READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase		    =							READER_PRETRAINED_INIT_CONFIGURATION
						__UpperCamelCase		    =							["""input_ids""", """attention_mask"""]
						__UpperCamelCase		    =							DPRReaderTokenizer










"""simple docstring"""


# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_    					=	{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_    					=	[
				    """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """CpmAntForCausalLM""",
				    """CpmAntModel""",
				    """CpmAntPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
				from .tokenization_cpmant import CpmAntTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_cpmant import (
								    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CpmAntForCausalLM,
								    CpmAntModel,
								    CpmAntPreTrainedModel,
								)


else:
				import sys

				snake_case_    					=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





