import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


__lowercase							 =					logging.getLogger(__name__)

__lowercase							 =					50  # max width of layer names
__lowercase							 =					70  # max width of quantizer names



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		parser.add_argument_group('''quant_trainer arguments'''	)
    group.add_argument('''--wprec'''  ,			type=SCREAMING_SNAKE_CASE  ,			default=8  ,			help='''weight precision'''	)
    group.add_argument('''--aprec'''  ,			type=SCREAMING_SNAKE_CASE  ,			default=8  ,			help='''activation precision'''	)
    group.add_argument('''--quant-per-tensor'''  ,			action='''store_true'''  ,			help='''per tensor weight scaling'''	)
    group.add_argument('''--quant-disable'''  ,			action='''store_true'''  ,			help='''disable all quantizers'''	)
    group.add_argument('''--quant-disable-embeddings'''  ,			action='''store_true'''  ,			help='''disable all embeddings quantizers'''	)
    group.add_argument('''--quant-disable-keyword'''  ,			type=SCREAMING_SNAKE_CASE  ,			nargs='''+'''  ,			help='''disable quantizers by keyword'''	)
    group.add_argument('''--quant-disable-layer-module'''  ,			type=SCREAMING_SNAKE_CASE  ,			help='''disable quantizers by keyword under layer.'''	)
    group.add_argument('''--quant-enable-layer-module'''  ,			type=SCREAMING_SNAKE_CASE  ,			help='''enable quantizers by keyword under layer'''	)
    group.add_argument('''--calibrator'''  ,			default='''max'''  ,			help='''which quantization range calibrator to use'''	)
    group.add_argument('''--percentile'''  ,			default=SCREAMING_SNAKE_CASE  ,			type=SCREAMING_SNAKE_CASE  ,			help='''percentile for PercentileCalibrator'''	)
    group.add_argument('''--fuse-qkv'''  ,			action='''store_true'''  ,			help='''use the same scale factor for qkv'''	)
    group.add_argument('''--clip-gelu'''  ,			metavar='''N'''  ,			type=SCREAMING_SNAKE_CASE  ,			help='''clip gelu output maximum value to N'''	)
    group.add_argument(
        '''--recalibrate-weights'''  ,			action='''store_true'''  ,			help=(
            '''recalibrate weight amaxes by taking the max of the weights.'''
            ''' amaxes will be computed with the current quantization granularity (axis).'''
        )  ,			)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    if args.calibrator == "max":
        __UpperCamelCase     :Union[str, Any]			  =		'''max'''
    elif args.calibrator == "percentile":
        if args.percentile is None:
            raise ValueError('''Specify --percentile when using percentile calibrator'''	)
        __UpperCamelCase     :str			  =		'''histogram'''
    elif args.calibrator == "mse":
        __UpperCamelCase     :Union[str, Any]			  =		'''histogram'''
    else:
        raise ValueError(f"""Invalid calibrator {args.calibrator}"""	)

    __UpperCamelCase     :List[str]			  =		QuantDescriptor(num_bits=args.aprec  ,			calib_method=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Tuple			  =		QuantDescriptor(num_bits=args.wprec  ,			axis=(None if args.quant_per_tensor else (0,))	)
    quant_nn.QuantLinear.set_default_quant_desc_input(SCREAMING_SNAKE_CASE	)
    quant_nn.QuantLinear.set_default_quant_desc_weight(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=False  ,			SCREAMING_SNAKE_CASE=False	):
    '''simple docstring'''







    logger.info('''Configuring Model for Quantization'''	)
    logger.info(f"""using quantization package {pytorch_quantization.__file__}"""	)

    if not calib:
        if args.quant_disable_embeddings:
            set_quantizer_by_name(SCREAMING_SNAKE_CASE  ,			['''embeddings''']  ,			which='''weight'''  ,			_disabled=SCREAMING_SNAKE_CASE	)

        if args.quant_disable:
            set_quantizer_by_name(SCREAMING_SNAKE_CASE  ,			['''''']  ,			_disabled=SCREAMING_SNAKE_CASE	)

        if args.quant_disable_keyword:
            set_quantizer_by_name(SCREAMING_SNAKE_CASE  ,			args.quant_disable_keyword  ,			_disabled=SCREAMING_SNAKE_CASE	)

        if args.quant_disable_layer_module:
            set_quantizer_by_name(SCREAMING_SNAKE_CASE  ,			[R'''layer.\d+.''' + args.quant_disable_layer_module]  ,			_disabled=SCREAMING_SNAKE_CASE	)

        if args.quant_enable_layer_module:
            set_quantizer_by_name(SCREAMING_SNAKE_CASE  ,			[R'''layer.\d+.''' + args.quant_enable_layer_module]  ,			_disabled=SCREAMING_SNAKE_CASE	)

        if args.recalibrate_weights:
            recalibrate_weights(SCREAMING_SNAKE_CASE	)

        if args.fuse_qkv:
            fuse_qkv(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    if args.clip_gelu:
        clip_gelu(SCREAMING_SNAKE_CASE  ,			args.clip_gelu	)

    # if args.local_rank in [-1, 0] and not calib:
    print_quant_summary(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    logger.info('''Enabling Calibration'''	)
    for name, module in model.named_modules():
        if name.endswith('''_quantizer'''	):
            if module._calibrator is not None:
                module.disable_quant()
                module.enable_calib()
            else:
                module.disable()
            logger.info(f"""{name:80}: {module}"""	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    logger.info('''Loading calibrated amax'''	)
    for name, module in model.named_modules():
        if name.endswith('''_quantizer'''	):
            if module._calibrator is not None:
                if isinstance(module._calibrator  ,			calib.MaxCalibrator	):
                    module.load_calib_amax()
                else:
                    module.load_calib_amax('''percentile'''  ,			percentile=args.percentile	)
                module.enable_quant()
                module.disable_calib()
            else:
                module.enable()
    model.cuda()
    print_quant_summary(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    def fusea(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
        for mod in [qq, qk, qv]:
            if not hasattr(SCREAMING_SNAKE_CASE  ,			'''_amax'''	):
                print('''          WARNING: NO AMAX BUFFER'''	)
                return
        __UpperCamelCase     :Tuple			  =		qq._amax.detach().item()
        __UpperCamelCase     :List[str]			  =		qk._amax.detach().item()
        __UpperCamelCase     :Dict			  =		qv._amax.detach().item()

        __UpperCamelCase     :Any			  =		max(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        qq._amax.fill_(SCREAMING_SNAKE_CASE	)
        qk._amax.fill_(SCREAMING_SNAKE_CASE	)
        qv._amax.fill_(SCREAMING_SNAKE_CASE	)
        logger.info(f"""          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}"""	)

    for name, mod in model.named_modules():
        if name.endswith('''.attention.self'''	):
            logger.info(f"""FUSE_QKV: {name:{name_width}}"""	)
            fusea(mod.matmul_q_input_quantizer  ,			mod.matmul_k_input_quantizer  ,			mod.matmul_v_input_quantizer	)
            if args.quant_per_tensor:
                fusea(mod.query._weight_quantizer  ,			mod.key._weight_quantizer  ,			mod.value._weight_quantizer	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    for name, mod in model.named_modules():
        if name.endswith('''.output.dense'''	) and not name.endswith('''attention.output.dense'''	):
            __UpperCamelCase     :List[str]			  =		mod._input_quantizer._amax.data.detach().item()
            mod._input_quantizer._amax.data.detach().clamp_(max=SCREAMING_SNAKE_CASE	)
            __UpperCamelCase     :Optional[Any]			  =		mod._input_quantizer._amax.data.detach().item()
            logger.info(f"""CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}"""	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    for name, mod in model.named_modules():
        if hasattr(SCREAMING_SNAKE_CASE  ,			'''_weight_quantizer'''	) and mod._weight_quantizer.axis is not None:
            __UpperCamelCase     :Dict			  =		mod.weight.shape[0]
            __UpperCamelCase     :Tuple			  =		mod._weight_quantizer._amax.detach()
            __UpperCamelCase     :str			  =		torch.ones(SCREAMING_SNAKE_CASE  ,			dtype=amax.dtype  ,			device=amax.device	) * amax
            print(f"""expanding {name} {amax} -> {mod._weight_quantizer._amax}"""	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    for name, mod in model.named_modules():
        if hasattr(SCREAMING_SNAKE_CASE  ,			'''_weight_quantizer'''	):
            if not hasattr(mod.weight_quantizer  ,			'''_amax'''	):
                print('''RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER'''	)
                continue

            # determine which axes to reduce across
            # e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
            __UpperCamelCase     :Tuple			  =		set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis	)
            __UpperCamelCase     :List[str]			  =		set(range(len(mod.weight.size()	)	)	) - axis_set
            __UpperCamelCase     :Tuple			  =		pytorch_quantization.utils.reduce_amax(mod.weight  ,			axis=SCREAMING_SNAKE_CASE  ,			keepdims=SCREAMING_SNAKE_CASE	).detach()
            logger.info(f"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"""	)
            __UpperCamelCase     :List[Any]			  =		amax



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=25  ,			SCREAMING_SNAKE_CASE=180  ,			SCREAMING_SNAKE_CASE=None	):
    '''simple docstring'''







    if ignore is None:
        __UpperCamelCase     :Optional[int]			  =		[]
    elif not isinstance(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
        __UpperCamelCase     :Optional[Any]			  =		[ignore]

    __UpperCamelCase     :str			  =		0
    for name, mod in model.named_modules():
        if not hasattr(SCREAMING_SNAKE_CASE  ,			'''weight'''	):
            continue
        __UpperCamelCase     :Any			  =		max(SCREAMING_SNAKE_CASE  ,			len(SCREAMING_SNAKE_CASE	)	)

    for name, mod in model.named_modules():
        __UpperCamelCase     :Any			  =		getattr(SCREAMING_SNAKE_CASE  ,			'''_input_quantizer'''  ,			SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :Union[str, Any]			  =		getattr(SCREAMING_SNAKE_CASE  ,			'''_weight_quantizer'''  ,			SCREAMING_SNAKE_CASE	)
        if not hasattr(SCREAMING_SNAKE_CASE  ,			'''weight'''	):
            continue
        if type(SCREAMING_SNAKE_CASE	) in ignore:
            continue
        if [True for s in ignore if type(SCREAMING_SNAKE_CASE	) is str and s in name]:
            continue
        __UpperCamelCase     :Optional[int]			  =		f"""Act:{input_q.extra_repr()}"""
        __UpperCamelCase     :List[str]			  =		f"""Wgt:{weight_q.extra_repr()}"""
        __UpperCamelCase     :int			  =		f"""{name:{name_width}} {act_str} {wgt_str}"""
        if len(SCREAMING_SNAKE_CASE	) <= line_width:
            logger.info(SCREAMING_SNAKE_CASE	)
        else:
            logger.info(f"""{name:{name_width}} {act_str}"""	)
            logger.info(f"""{'  ':{name_width}} {wgt_str}"""	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :str			  =		0
    for name, mod in model.named_modules():
        if isinstance(SCREAMING_SNAKE_CASE  ,			pytorch_quantization.nn.TensorQuantizer	):
            print(f"""{name:80} {mod}"""	)
            count += 1
    print(f"""{count} TensorQuantizers found in model"""	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Any			  =		getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    if quantizer_mod is not None:
        assert hasattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        setattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    else:
        logger.warning(f"""{name} has no {quantizer}"""	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE="both"  ,			**SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		f"""Warning: changing {which} quantizers of {name:{qname_width}}"""
    for k, v in kwargs.items():
        s += f""" {k}={v}"""
        if which in ["input", "both"]:
            set_quantizer(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			'''_input_quantizer'''  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        if which in ["weight", "both"]:
            set_quantizer(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			'''_weight_quantizer'''  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    logger.info(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			**SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    for name, mod in model.named_modules():
        if hasattr(SCREAMING_SNAKE_CASE  ,			'''_input_quantizer'''	) or hasattr(SCREAMING_SNAKE_CASE  ,			'''_weight_quantizer'''	):
            for n in names:
                if re.search(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
                    set_quantizers(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			**SCREAMING_SNAKE_CASE	)
        elif name.endswith('''_quantizer'''	):
            for n in names:
                if re.search(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
                    __UpperCamelCase     :str			  =		f"""Warning: changing {name:{name_width}}"""
                    for k, v in kwargs.items():
                        s += f""" {k}={v}"""
                        setattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
                    logger.info(SCREAMING_SNAKE_CASE	)









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


SCREAMING_SNAKE_CASE       :      Any												= logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE       :      Union[str, 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"
        ),
    },
}
SCREAMING_SNAKE_CASE       :      Dict												= {
    "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"
        ),
    },
}
SCREAMING_SNAKE_CASE       :      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"
        ),
    },
}

SCREAMING_SNAKE_CASE       :      Dict												= {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE       :      Optional[Any]												= {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE       :      Dict												= {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


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






class _lowerCamelCase(						_a   ):
       lowercase_ :	Any     =			VOCAB_FILES_NAMES
       lowercase_ :	Optional[int]     =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowercase_ :	str     =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_ :	str     =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class _lowerCamelCase(						_a   ):
       lowercase_ :	Optional[int]     =			VOCAB_FILES_NAMES
       lowercase_ :	Any     =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowercase_ :	str     =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_ :	Union[str, Any]     =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


SCREAMING_SNAKE_CASE       :      Optional[int]												= collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

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


SCREAMING_SNAKE_CASE       :      str												= 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(_a   )
class _lowerCamelCase:
       def __call__(					self,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = False,      lowerCamelCase = False,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   BatchEncoding:




           """simple docstring"""

           if titles is None and texts is None:
               return super().__call__(
                   lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase,      max_length=lowerCamelCase,      return_tensors=lowerCamelCase,      return_attention_mask=lowerCamelCase,      **lowerCamelCase,      )
           elif titles is None or texts is None:
               _lowercase       :							Dict      		=		titles if texts is None else texts
               return super().__call__(
                   lowerCamelCase,      lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase,      max_length=lowerCamelCase,      return_tensors=lowerCamelCase,      return_attention_mask=lowerCamelCase,      **lowerCamelCase,      )
           _lowercase       :							Union[str, Any]      		=		titles if not isinstance(lowerCamelCase,      lowerCamelCase) else [titles]
           _lowercase       :							Tuple      		=		texts if not isinstance(lowerCamelCase,      lowerCamelCase) else [texts]
           _lowercase       :							Optional[Any]      		=		len(lowerCamelCase)
           _lowercase       :							Any      		=		questions if not isinstance(lowerCamelCase,      lowerCamelCase) else [questions] * n_passages
           if len(lowerCamelCase) != len(lowerCamelCase):
               raise ValueError(
                   F'''There should be as many titles than texts but got {len(lowerCamelCase)} titles and {len(lowerCamelCase)} texts.''')
           _lowercase       :							Any      		=		super().__call__(lowerCamelCase,      lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase)['input_ids']
           _lowercase       :							Tuple      		=		super().__call__(lowerCamelCase,      add_special_tokens=lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase)['input_ids']
           _lowercase       :							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(lowerCamelCase,      lowerCamelCase)
               ]
           }
           if return_attention_mask is not False:
               _lowercase       :							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])
               _lowercase       :							Union[str, Any]      		=		attention_mask
           return self.pad(lowerCamelCase,      padding=lowerCamelCase,      max_length=lowerCamelCase,      return_tensors=lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = 16,      lowerCamelCase = 64,      lowerCamelCase = 4,      )       ->   List[DPRSpanPrediction]:




           """simple docstring"""

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

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




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )       ->   List[DPRSpanPrediction]:




           """simple docstring"""

           _lowercase       :							str      		=		[]
           for start_index, start_score in enumerate(lowerCamelCase):
               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))
           _lowercase       :							Dict      		=		sorted(lowerCamelCase,      key=lambda lowerCamelCase: x[1],      reverse=lowerCamelCase)
           _lowercase       :							List[str]      		=		[]
           for (start_index, end_index), score in scores:
               if start_index > end_index:
                   raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]''')
               _lowercase       :							Dict      		=		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(lowerCamelCase) == top_spans:
                   break
           return chosen_span_intervals






@add_end_docstrings(_a   )
class _lowerCamelCase(						_a, _a   ):
       lowercase_ :	Union[str, Any]     =			VOCAB_FILES_NAMES
       lowercase_ :	Any     =			READER_PRETRAINED_VOCAB_FILES_MAP
       lowercase_ :	Dict     =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_ :	Optional[int]     =			READER_PRETRAINED_INIT_CONFIGURATION
       lowercase_ :	str     =			["""input_ids""", """attention_mask"""]


