

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_	:			Optional[Any]  	=					logging.get_logger(__name__)

A_	:			List[Any]  	=					{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A_	:			Optional[Any]  	=					{
    '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_	:			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'
        ),
    },
}
A_	:			str  	=					{
    '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_	:			List[Any]  	=					{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
A_	:			int  	=					{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
A_	:			Optional[int]  	=					{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


A_	:			Tuple  	=					{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
A_	:			List[str]  	=					{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
A_	:			int  	=					{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}



class    _a						(__snake_case    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	VOCAB_FILES_NAMES
					UpperCAmelCase__:       List[Any]  							=	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:       Any  							=	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:       int  							=	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__:       Union[str, Any]  							=	DPRContextEncoderTokenizer



class    _a						(__snake_case    ):


					'''simple docstring'''






					UpperCAmelCase__:       Union[str, Any]  							=	VOCAB_FILES_NAMES
					UpperCAmelCase__:       List[str]  							=	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:       Optional[Any]  							=	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:       Optional[int]  							=	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__:       Tuple  							=	DPRQuestionEncoderTokenizer



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

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


A_	:			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    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\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    Return:\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(__snake_case    )
class    _a						:


					'''simple docstring'''






					def __call__(			self   ,	A__   ,	A__ = None   ,	A__ = None   ,	A__ = False   ,	A__ = False   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	**A__   ,	):


						if titles is None and texts is None:
							return super().__call__(
							    UpperCamelCase__   ,	padding=UpperCamelCase__   ,	truncation=UpperCamelCase__   ,	max_length=UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	return_attention_mask=UpperCamelCase__   ,	**UpperCamelCase__   ,	)
						elif titles is None or texts is None:
							A__							:   Tuple					       =		titles if texts is None else texts
							return super().__call__(
							    UpperCamelCase__   ,	UpperCamelCase__   ,	padding=UpperCamelCase__   ,	truncation=UpperCamelCase__   ,	max_length=UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	return_attention_mask=UpperCamelCase__   ,	**UpperCamelCase__   ,	)
						A__							:   Dict					       =		titles if not isinstance(UpperCamelCase__   ,	UpperCamelCase__      ) else [titles]
						A__							:   Optional[Any]					       =		texts if not isinstance(UpperCamelCase__   ,	UpperCamelCase__      ) else [texts]
						A__							:   Union[str, Any]					       =		len(UpperCamelCase__      )
						A__							:   List[str]					       =		questions if not isinstance(UpperCamelCase__   ,	UpperCamelCase__      ) else [questions] * n_passages
						assert len(UpperCamelCase__      ) == len(
						    UpperCamelCase__      ), F"""There should be as many titles than texts but got {len(UpperCamelCase__      )} titles and {len(UpperCamelCase__      )} texts."""
						A__							:   Tuple					       =		super().__call__(UpperCamelCase__   ,	UpperCamelCase__   ,	padding=UpperCamelCase__   ,	truncation=UpperCamelCase__      )["input_ids"]
						A__							:   Union[str, Any]					       =		super().__call__(UpperCamelCase__   ,	add_special_tokens=UpperCamelCase__   ,	padding=UpperCamelCase__   ,	truncation=UpperCamelCase__      )["input_ids"]
						A__							:   Optional[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(UpperCamelCase__   ,	UpperCamelCase__      )
						    ]
						}
						if return_attention_mask is not False:
							A__							:   Tuple					       =		[]
							for input_ids in encoded_inputs["input_ids"]:
								attention_mask.append([int(input_id != self.pad_token_id      ) for input_id in input_ids]      )
							A__							:   List[Any]					       =		attention_mask
						return self.pad(UpperCamelCase__   ,	padding=UpperCamelCase__   ,	max_length=UpperCamelCase__   ,	return_tensors=UpperCamelCase__      )






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

							A__							:   Tuple					       =		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=UpperCamelCase__   ,	top_spans=UpperCamelCase__   ,	)
							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=UpperCamelCase__   ,	start_index=UpperCamelCase__   ,	end_index=UpperCamelCase__   ,	text=self.decode(sequence_ids[start_index : end_index + 1]      )   ,	)      )
							if len(UpperCamelCase__      ) >= num_spans:
								break
						return nbest_spans_predictions[:num_spans]






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	):
						A__							:   Tuple					       =		[]
						for start_index, start_score in enumerate(UpperCamelCase__      ):
							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)      )
						A__							:   Tuple					       =		sorted(UpperCamelCase__   ,	key=lambda A__      : x[1]   ,	reverse=UpperCamelCase__      )
						A__							:   Tuple					       =		[]
						for (start_index, end_index), score in scores:
							assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
							A__							:   Any					       =		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(UpperCamelCase__      ) == top_spans:
								break
						return chosen_span_intervals



@add_end_docstrings(__snake_case    )
class    _a						(__snake_case		,       __snake_case    ):


					'''simple docstring'''






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






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


A_	:			str  	=      logging.get_logger(__name__)

A_	:			Any  	=      {
    'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
    'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Dict  							=	'''falcon'''
					UpperCAmelCase__:       Any  							=	['''past_key_values''']






					def __init__(			self   ,	A__=6_5024   ,	A__=4544   ,	A__=32   ,	A__=71   ,	A__=1e-5   ,	A__=0.0_2   ,	A__=True   ,	A__=0.0   ,	A__=0.0   ,	A__=None   ,	A__=False   ,	A__=False   ,	A__=True   ,	A__=True   ,	A__=False   ,	A__=11   ,	A__=11   ,	**A__   ,	):
						A__							:   Dict					       =		vocab_size
						# Backward compatibility with n_embed kwarg
						A__							:   Union[str, Any]					       =		kwargs.pop("""n_embed"""   ,	A__      )
						A__							:   Optional[Any]					       =		hidden_size if n_embed is None else n_embed
						A__							:   Any					       =		num_hidden_layers
						A__							:   Any					       =		num_attention_heads
						A__							:   Optional[Any]					       =		layer_norm_epsilon
						A__							:   Tuple					       =		initializer_range
						A__							:   Tuple					       =		use_cache
						A__							:   str					       =		hidden_dropout
						A__							:   List[str]					       =		attention_dropout

						A__							:   List[Any]					       =		bos_token_id
						A__							:   Optional[Any]					       =		eos_token_id
						A__							:   Optional[Any]					       =		num_attention_heads if num_kv_heads is None else num_kv_heads
						A__							:   List[str]					       =		alibi
						A__							:   Tuple					       =		new_decoder_architecture
						A__							:   List[str]					       =		multi_query  # Ignored when new_decoder_architecture is True
						A__							:   List[Any]					       =		parallel_attn
						A__							:   int					       =		bias

						super().__init__(bos_token_id=A__   ,	eos_token_id=A__   ,	**A__      )






					@property
					def   __A      (			self      ):
						return self.hidden_size // self.num_attention_heads






					@property
					def   __A      (			self      ):
						return not self.alibi



