

'''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   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:int      	=					VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE					:str      	=					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE					:int      	=					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
 __SCREAMING_SNAKE_CASE					:Dict      	=					DPRContextEncoderTokenizer






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:int      	=					VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE					:str      	=					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE					:Dict      	=					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE					:str      	=					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					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(__a     )
class   _SCREAMING_SNAKE_CASE   :



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



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

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



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   List[int]			,				a__		:   List[int]			,				a__		:   int			,				a__		:   int			,				):
      __magic_name__						       =    []
      for start_index, start_score in enumerate(a__      ):
           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)      )
      __magic_name__						       =    sorted(a__			,				key=lambda a__      : x[1]			,				reverse=a__      )
      __magic_name__						       =    []
      for (start_index, end_index), score in scores:
           assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
           __magic_name__						       =    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(a__      ) == top_spans:
                break
      return chosen_span_intervals






@add_end_docstrings(__a     )
class   _SCREAMING_SNAKE_CASE   (   __a		,__a     ):
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE					:str      	=					READER_PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE					:int      	=					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					READER_PRETRAINED_INIT_CONFIGURATION
 __SCREAMING_SNAKE_CASE					:Tuple      	=					["""input_ids""", """attention_mask"""]
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					DPRReaderTokenizer









'''simple docstring'''




import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Optional[Any]			,				a__		:   Any			,				a__		:   Tuple=sys.maxsize      ):
      __magic_name__						       =    '''bilinear'''
      __magic_name__						       =    max_size
      __magic_name__						       =    short_edge_length



 def __call__(   self		:   Tuple			,				a__		:   List[str]      ):
      __magic_name__						       =    []
      for img in imgs:
           __magic_name__       ,  __magic_name__						       =    img.shape[:2]
           # later: provide list and randomly choose index for resize
           __magic_name__						       =    np.random.randint(self.short_edge_length[0]			,				self.short_edge_length[1] + 1      )
           if size == 0:
                return img
           __magic_name__						       =    size * 1.0 / min(a__			,				a__      )
           if h < w:
                __magic_name__       ,  __magic_name__						       =    size, scale * w
           else:
                __magic_name__       ,  __magic_name__						       =    scale * h, size
           if max(a__			,				a__      ) > self.max_size:
                __magic_name__						       =    self.max_size * 1.0 / max(a__			,				a__      )
                __magic_name__						       =    newh * scale
                __magic_name__						       =    neww * scale
           __magic_name__						       =    int(neww + 0.5      )
           __magic_name__						       =    int(newh + 0.5      )

           if img.dtype == np.uinta:
                __magic_name__						       =    Image.fromarray(a__      )
                __magic_name__						       =    pil_image.resize((neww, newh)			,				PILImageResampling.BILINEAR      )
                __magic_name__						       =    np.asarray(a__      )
           else:
                __magic_name__						       =    img.permute(2			,				0			,				1      ).unsqueeze(0      )  # 3, 0, 1)  # hw(c) -> nchw
                __magic_name__						       =    nn.functional.interpolate(
                    a__			,				(newh, neww)			,				mode=self.interp_method			,				align_corners=a__      ).squeeze(0      )
           img_augs.append(a__      )

      return img_augs






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[Any]			,				a__		:   Tuple      ):
      __magic_name__						       =    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,				cfg.INPUT.MAX_SIZE_TEST      )
      __magic_name__						       =    cfg.INPUT.FORMAT
      __magic_name__						       =    cfg.SIZE_DIVISIBILITY
      __magic_name__						       =    cfg.PAD_VALUE
      __magic_name__						       =    cfg.INPUT.MAX_SIZE_TEST
      __magic_name__						       =    cfg.MODEL.DEVICE
      __magic_name__						       =    torch.tensor(cfg.MODEL.PIXEL_STD      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )			,				1			,				1      )
      __magic_name__						       =    torch.tensor(cfg.MODEL.PIXEL_MEAN      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )			,				1			,				1      )
      __magic_name__						       =    lambda a__      : (x - self.pixel_mean) / self.pixel_std



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   Dict      ):
      __magic_name__						       =    tuple(max(a__      ) for s in zip(*[img.shape for img in images]      )      )
      __magic_name__						       =    [im.shape[-2:] for im in images]
      __magic_name__						       =    [
          nn.functional.pad(
              a__			,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,				value=self.pad_value			,				)
          for size, im in zip(a__			,				a__      )
      ]

      return torch.stack(a__      ), torch.tensor(a__      )



 def __call__(   self		:   Dict			,				a__		:   Dict			,				a__		:   List[str]=False      ):
      with torch.no_grad():
           if not isinstance(a__			,				a__      ):
                __magic_name__						       =    [images]
           if single_image:
                assert len(a__      ) == 1
           for i in range(len(a__      )      ):
                if isinstance(images[i]			,				torch.Tensor      ):
                     images.insert(a__			,				images.pop(a__      ).to(self.device      ).float()      )
                elif not isinstance(images[i]			,				torch.Tensor      ):
                     images.insert(
                         a__			,				torch.as_tensor(img_tensorize(images.pop(a__      )			,				input_format=self.input_format      )      )
                         .to(self.device      )
                         .float()			,				)
            # resize smallest edge
           __magic_name__						       =    torch.tensor([im.shape[:2] for im in images]      )
           __magic_name__						       =    self.aug(a__      )
           # transpose images and convert to torch tensors
           # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
           # now normalize before pad to avoid useless arithmetic
           __magic_name__						       =    [self.normalizer(a__      ) for x in images]
           # now pad them to do the following operations
           __magic_name__       ,  __magic_name__						       =    self.pad(a__      )
           # Normalize

           if self.size_divisibility > 0:
                raise NotImplementedError()
           # pad
           __magic_name__						       =    torch.true_divide(a__			,				a__      )
           if single_image:
                return images[0], sizes[0], scales_yx[0]
           else:
                return images, sizes, scales_yx




def 						UpperCamelCase	(			a		,						a			)				->     List[Any]:






     '''simple docstring'''
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes




def 						UpperCamelCase	(			a		,						a			)				->     Any:






     '''simple docstring'''
     assert torch.isfinite(a			).all(), "Box tensor contains infinite or NaN!"
     __magic_name__       ,  __magic_name__						       =    box_size
     tensor[:, 0].clamp_(min=0		,						max=a			)
     tensor[:, 1].clamp_(min=0		,						max=a			)
     tensor[:, 2].clamp_(min=0		,						max=a			)
     tensor[:, 3].clamp_(min=0		,						max=a			)






