






'''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   :					List[Any]   							=					logging.get_logger(__name__)

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

__lowerCamelCase   :					str   							=					{
    "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   :					List[str]   							=					{
    "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   :					List[Any]   							=					{
    "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   :					Dict   							=					{
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
__lowerCamelCase   :					Tuple   							=					{
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
__lowerCamelCase   :					int   							=					{
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


__lowerCamelCase   :					Any   							=					{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
__lowerCamelCase   :					List[str]   							=					{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
__lowerCamelCase   :					Any   							=					{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}







class    A_	(_UpperCAmelCase							):
							"""simple docstring"""




							a__   		=	VOCAB_FILES_NAMES
							a__   		=	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							a__   		=	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__   		=	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class    A_	(_UpperCAmelCase							):
							"""simple docstring"""




							a__   		=	VOCAB_FILES_NAMES
							a__   		=	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
							a__   		=	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__   		=	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase   :					Dict   							=					collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

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


__lowerCamelCase   :					Union[str, 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(_UpperCAmelCase							)
class    A_	:
							"""simple docstring"""




							def __call__(		self			:List[Any]       ,							lowerCAmelCase__			:List[Any]       ,							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__			:int       ,							)				->  Dict:


										'''simple docstring'''




										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:
													snake_case_				:					Tuple   =       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_       ,							)
										snake_case_				:					Optional[int]   =       titles if not isinstance(lowercase_       ,							lowercase_				) else [titles]
										snake_case_				:					List[str]   =       texts if not isinstance(lowercase_       ,							lowercase_				) else [texts]
										snake_case_				:					str   =       len(lowercase_				)
										snake_case_				:					int   =       questions if not isinstance(lowercase_       ,							lowercase_				) else [questions] * n_passages
										if len(lowercase_				) != len(lowercase_				):
													raise ValueError(
													    F'''There should be as many titles than texts but got {len(lowercase_				)} titles and {len(lowercase_				)} texts.'''				)
										snake_case_				:					Tuple   =       super().__call__(lowercase_       ,							lowercase_       ,							padding=lowercase_       ,							truncation=lowercase_				)["""input_ids"""]
										snake_case_				:					int   =       super().__call__(lowercase_       ,							add_special_tokens=lowercase_       ,							padding=lowercase_       ,							truncation=lowercase_				)["""input_ids"""]
										snake_case_				:					str   =       {
										    """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:
													snake_case_				:					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]				)
													snake_case_				:					Any   =       attention_mask
										return self.pad(lowercase_       ,							padding=lowercase_       ,							max_length=lowercase_       ,							return_tensors=lowercase_				)

							def 							_A	(		self			:int       ,							lowerCAmelCase__			:BatchEncoding       ,							lowerCAmelCase__			:DPRReaderOutput       ,							lowerCAmelCase__			:int = 16       ,							lowerCAmelCase__			:int = 64       ,							lowerCAmelCase__			:int = 4       ,							)				->  Optional[Any]:


										'''simple docstring'''




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

													snake_case_				:					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=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 							_A	(		self			:List[str]       ,							lowerCAmelCase__			:List[int]       ,							lowerCAmelCase__			:List[int]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:int       ,							)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					int   =       []
										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)				)
										snake_case_				:					Dict   =       sorted(lowercase_       ,							key=lambda lowerCAmelCase__				: x[1]       ,							reverse=lowercase_				)
										snake_case_				:					Any   =       []
										for (start_index, end_index), score in scores:
													if start_index > end_index:
																raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]'''				)
													snake_case_				:					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(lowercase_				) == top_spans:
																break
										return chosen_span_intervals







@add_end_docstrings(_UpperCAmelCase							)
class    A_	(_UpperCAmelCase						,					_UpperCAmelCase							):
							"""simple docstring"""




							a__   		=	VOCAB_FILES_NAMES
							a__   		=	READER_PRETRAINED_VOCAB_FILES_MAP
							a__   		=	READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__   		=	READER_PRETRAINED_INIT_CONFIGURATION
							a__   		=	['''input_ids''', '''attention_mask''']












'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__lowerCamelCase   :					Dict   							=					{
    '''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
    '''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					int   							=					[
		    '''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTNeoXJapaneseForCausalLM''',
		    '''GPTNeoXJapaneseLayer''',
		    '''GPTNeoXJapaneseModel''',
		    '''GPTNeoXJapanesePreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
		from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_neox_japanese import (
				    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTNeoXJapaneseForCausalLM,
				    GPTNeoXJapaneseLayer,
				    GPTNeoXJapaneseModel,
				    GPTNeoXJapanesePreTrainedModel,
				)


else:
		import sys

		__lowerCamelCase   :					Optional[int]   							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




