

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					:			Tuple  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Union[str, Any]  				=			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase					:			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"
        ),
    },
}
_lowerCAmelCase					:			Any  				=			{
    "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[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"
        ),
    },
}

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


_lowerCAmelCase					:			str  				=			{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_lowerCAmelCase					:			Optional[int]  				=			{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_lowerCAmelCase					:			int  				=			{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE							   =						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE							   =						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE							   =						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE							   =						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE							   =						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE							   =						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


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

_lowerCAmelCase					:			Optional[Any]  				=			collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


_lowerCAmelCase					:			Optional[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(lowerCAmelCase_      )
class 	__magic_name__    :





			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					,  )   -> BatchEncoding:




						'''simple docstring'''
						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:
									__a        =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					,  )
						__a        =titles if not isinstance(__snake_case					,  __snake_case					) else [titles]
						__a        =texts if not isinstance(__snake_case					,  __snake_case					) else [texts]
						__a        =len(__snake_case					)
						__a        =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.'					)
						__a        =super().__call__(__snake_case					,  __snake_case					,  padding=__snake_case					,  truncation=__snake_case					)['input_ids']
						__a        =super().__call__(__snake_case					,  add_special_tokens=__snake_case					,  padding=__snake_case					,  truncation=__snake_case					)['input_ids']
						__a        ={
						    '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:
									__a        =[]
									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        =attention_mask
						return self.pad(__snake_case					,  padding=__snake_case					,  max_length=__snake_case					,  return_tensors=__snake_case					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case = 16					,  __snake_case = 64					,  __snake_case = 4					,  )   -> List[DPRSpanPrediction]:




						'''simple docstring'''
						__a        =reader_input['input_ids']
						__a      ,    __a      ,    __a        =reader_output[:3]
						__a        =len(__snake_case					)
						__a        =sorted(range(__snake_case					)					,  reverse=__snake_case					,  key=relevance_logits.__getitem__					)
						__a        =[]
						for doc_id in sorted_docs:
									__a        =list(input_ids[doc_id]					)
									# assuming question & title information is at the beginning of the sequence
									__a        =sequence_ids.index(self.sep_token_id					,  2					) + 1  # second sep id
									if sequence_ids[-1] == self.pad_token_id:
												__a        =sequence_ids.index(self.pad_token_id					)
									else:
												__a        =len(__snake_case					)

									__a        =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 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  )   -> List[DPRSpanPrediction]:




						'''simple docstring'''
						__a        =[]
						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)					)
						__a        =sorted(__snake_case					,  key=lambda __snake_case					: x[1]					,  reverse=__snake_case					)
						__a        =[]
						for (start_index, end_index), score in scores:
									if start_index > end_index:
												raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]'					)
									__a        =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(lowerCAmelCase_      )
class 	__magic_name__    (			lowerCAmelCase_  ,    lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE							   =						READER_PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE							   =						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE							   =						READER_PRETRAINED_INIT_CONFIGURATION
			SCREAMING_SNAKE_CASE							   =						['input_ids', 'attention_mask']









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


_lowerCAmelCase					:			Tuple  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Union[str, Any]  				=			{"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'ctrl'
			SCREAMING_SNAKE_CASE							   =						['past_key_values']
			SCREAMING_SNAKE_CASE							   =						{
			    'max_position_embeddings': 'n_positions',
			    'hidden_size': 'n_embd',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}





			def __init__(		self					,  __snake_case=24_6534					,  __snake_case=256					,  __snake_case=1280					,  __snake_case=8192					,  __snake_case=48					,  __snake_case=16					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=1e-6					,  __snake_case=0.02					,  __snake_case=True					,  **__snake_case					,  )   -> Union[str, Any]:




						'''simple docstring'''
						__a        =vocab_size
						__a        =n_positions
						__a        =n_embd
						__a        =n_layer
						__a        =n_head
						__a        =dff
						__a        =resid_pdrop
						__a        =embd_pdrop
						__a        =layer_norm_epsilon
						__a        =initializer_range

						__a        =use_cache

						super().__init__(**__snake_case					)






