
"""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


lowerCAmelCase__				=					logging.get_logger(__name__)

lowerCAmelCase__				=					{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__				=					{
    '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__				=					{
    '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__				=					{
    '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__				=					{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
lowerCAmelCase__				=					{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
lowerCAmelCase__				=					{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


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





class 			snake_case  (				__lowercase ):
							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 			snake_case  (				__lowercase ):
							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







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

lowerCAmelCase__				=					collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


lowerCAmelCase__				=					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(__lowercase )
class 			snake_case  :

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

									"""simple docstring"""







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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = 16							,							SCREAMING_SNAKE_CASE_ = 64							,							SCREAMING_SNAKE_CASE_ = 4							,							):

									"""simple docstring"""







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

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







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			[]
									for start_index, start_score in enumerate(SCREAMING_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)     )
									SCREAMING_SNAKE_CASE_    			=			sorted(SCREAMING_SNAKE_CASE_							,							key=lambda SCREAMING_SNAKE_CASE_     : x[1]							,							reverse=SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			[]
									for (start_index, end_index), score in scores:
											assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
											SCREAMING_SNAKE_CASE_    			=			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(SCREAMING_SNAKE_CASE_     ) == top_spans:
													break
									return chosen_span_intervals







@add_end_docstrings(__lowercase )
class 			snake_case  (				__lowercase							,      __lowercase ):
							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"""




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_pt_objects import *  # noqa F403
else:
						from .scheduling_consistency_models import CMStochasticIterativeScheduler
						from .scheduling_ddim import DDIMScheduler
						from .scheduling_ddim_inverse import DDIMInverseScheduler
						from .scheduling_ddim_parallel import DDIMParallelScheduler
						from .scheduling_ddpm import DDPMScheduler
						from .scheduling_ddpm_parallel import DDPMParallelScheduler
						from .scheduling_deis_multistep import DEISMultistepScheduler
						from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
						from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
						from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
						from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
						from .scheduling_euler_discrete import EulerDiscreteScheduler
						from .scheduling_heun_discrete import HeunDiscreteScheduler
						from .scheduling_ipndm import IPNDMScheduler
						from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
						from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
						from .scheduling_karras_ve import KarrasVeScheduler
						from .scheduling_pndm import PNDMScheduler
						from .scheduling_repaint import RePaintScheduler
						from .scheduling_sde_ve import ScoreSdeVeScheduler
						from .scheduling_sde_vp import ScoreSdeVpScheduler
						from .scheduling_unclip import UnCLIPScheduler
						from .scheduling_unipc_multistep import UniPCMultistepScheduler
						from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
						from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_flax_objects import *  # noqa F403
else:
						from .scheduling_ddim_flax import FlaxDDIMScheduler
						from .scheduling_ddpm_flax import FlaxDDPMScheduler
						from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
						from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
						from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
						from .scheduling_pndm_flax import FlaxPNDMScheduler
						from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
						from .scheduling_utils_flax import (
						    FlaxKarrasDiffusionSchedulers,
						    FlaxSchedulerMixin,
						    FlaxSchedulerOutput,
						    broadcast_to_shape_from_left,
						)


try:
						if not (is_torch_available() and is_scipy_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
						from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
						if not (is_torch_available() and is_torchsde_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
						from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler