






"""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 _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					List[Any] 					=				VOCAB_FILES_NAMES
						SCREAMING_SNAKE_CASE_:					Any 					=				CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						SCREAMING_SNAKE_CASE_:					List[str] 					=				CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						SCREAMING_SNAKE_CASE_:					str 					=				CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
						SCREAMING_SNAKE_CASE_:					Optional[int] 					=				DPRContextEncoderTokenizer







class _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Dict 					=				VOCAB_FILES_NAMES
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						SCREAMING_SNAKE_CASE_:					List[Any] 					=				QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						SCREAMING_SNAKE_CASE_:					Dict 					=				QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
						SCREAMING_SNAKE_CASE_:					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(__UpperCAmelCase       )
class _lowerCAmelCase						:







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = False   ,				lowerCAmelCase_ = False   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				**lowerCAmelCase_   ,				)		->							BatchEncoding:
										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:
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    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_   ,				)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    titles if not isinstance(lowerCAmelCase_   ,				lowerCAmelCase_							) else [titles]
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    texts if not isinstance(lowerCAmelCase_   ,				lowerCAmelCase_							) else [texts]
										_SCREAMING_SNAKE_CASE   :		int       		=    len(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		str       		=    questions if not isinstance(lowerCAmelCase_   ,				lowerCAmelCase_							) else [questions] * n_passages
										assert len(lowerCAmelCase_							) == len(
										    lowerCAmelCase_							), F"""There should be as many titles than texts but got {len(lowerCAmelCase_							)} titles and {len(lowerCAmelCase_							)} texts."""
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    super().__call__(lowerCAmelCase_   ,				lowerCAmelCase_   ,				padding=lowerCAmelCase_   ,				truncation=lowerCAmelCase_							)['input_ids']
										_SCREAMING_SNAKE_CASE   :		Dict       		=    super().__call__(lowerCAmelCase_   ,				add_special_tokens=lowerCAmelCase_   ,				padding=lowerCAmelCase_   ,				truncation=lowerCAmelCase_							)['input_ids']
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    {
										    '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:
														_SCREAMING_SNAKE_CASE   :		int       		=    []
														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   :		str       		=    attention_mask
										return self.pad(lowerCAmelCase_   ,				padding=lowerCAmelCase_   ,				max_length=lowerCAmelCase_   ,				return_tensors=lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = 1_6   ,				lowerCAmelCase_ = 6_4   ,				lowerCAmelCase_ = 4   ,				)		->							List[DPRSpanPrediction]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    reader_input['input_ids']
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    reader_output[:3]
										_SCREAMING_SNAKE_CASE   :		Any       		=    len(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    sorted(range(lowerCAmelCase_							)   ,				reverse=lowerCAmelCase_   ,				key=relevance_logits.__getitem__							)
										_SCREAMING_SNAKE_CASE   :		List[DPRReaderOutput]       		=    []
										for doc_id in sorted_docs:
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    list(input_ids[doc_id]							)
														# assuming question & title information is at the beginning of the sequence
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    sequence_ids.index(self.sep_token_id   ,				2							) + 1  # second sep id
														if sequence_ids[-1] == self.pad_token_id:
																		_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    sequence_ids.index(self.pad_token_id							)
														else:
																		_SCREAMING_SNAKE_CASE   :		int       		=    len(lowerCAmelCase_							)

														_SCREAMING_SNAKE_CASE   :		Dict       		=    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 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				)		->							List[DPRSpanPrediction]:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    []
										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)							)
										_SCREAMING_SNAKE_CASE   :		str       		=    sorted(lowerCAmelCase_   ,				key=lambda lowerCAmelCase_							: x[1]   ,				reverse=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    []
										for (start_index, end_index), score in scores:
														assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
														_SCREAMING_SNAKE_CASE   :		Dict       		=    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(lowerCAmelCase_							) == top_spans:
																		break
										return chosen_span_intervals







@add_end_docstrings(__UpperCAmelCase       )
class _lowerCAmelCase						(    __UpperCAmelCase   ,  __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				VOCAB_FILES_NAMES
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				READER_PRETRAINED_VOCAB_FILES_MAP
						SCREAMING_SNAKE_CASE_:					Dict 					=				READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						SCREAMING_SNAKE_CASE_:					int 					=				READER_PRETRAINED_INIT_CONFIGURATION
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				['input_ids', 'attention_mask']
						SCREAMING_SNAKE_CASE_:					int 					=				DPRReaderTokenizer









"""simple docstring"""
from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
			lowerCAmelCase__    						=			input('Enter image url: ').strip()
			print(F'Downloading image from {url} ...')
			lowerCAmelCase__    						=			BeautifulSoup(requests.get(url).content, 'html.parser')
			# The image URL is in the content field of the first meta tag with property og:image
			lowerCAmelCase__    						=			soup.find('meta', {'property': 'og:image'})['content']
			lowerCAmelCase__    						=			requests.get(image_url).content
			lowerCAmelCase__    						=			F'{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'
			with open(file_name, 'wb') as fp:
						fp.write(image_data)
			print(F'Done. Image saved to disk as {file_name}.')

