





'''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 import BertTokenizer


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""": 5_12,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_12,
}
lowerCAmelCase									=   {
    """facebook/dpr-question_encoder-single-nq-base""": 5_12,
    """facebook/dpr-question_encoder-multiset-base""": 5_12,
}
lowerCAmelCase									=   {
    """facebook/dpr-reader-single-nq-base""": 5_12,
    """facebook/dpr-reader-multiset-base""": 5_12,
}


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							):
	snake_case_   = VOCAB_FILES_NAMES
	snake_case_   = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	snake_case_   = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_   = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class 		lowerCamelCase	(   __UpperCAmelCase							):
	snake_case_   = VOCAB_FILES_NAMES
	snake_case_   = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	snake_case_   = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_   = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






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    ```\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(__UpperCAmelCase							)
class 		lowerCamelCase	:



	def __call__( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = False		,   a_ = False		,   a_ = None		,   a_ = None		,   a_ = None		,   **a_		,   ):
			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:
					lowerCAmelCase  :     Optional[int]       						= 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_		,   )
			lowerCAmelCase  :     List[str]       						= titles if not isinstance(lowerCAmelCase_		,   lowerCAmelCase_						) else [titles]
			lowerCAmelCase  :     List[Any]       						= texts if not isinstance(lowerCAmelCase_		,   lowerCAmelCase_						) else [texts]
			lowerCAmelCase  :     str       						= len(lowerCAmelCase_						)
			lowerCAmelCase  :     int       						= 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.'''						)
			lowerCAmelCase  :     Union[str, Any]       						= super().__call__(lowerCAmelCase_		,   lowerCAmelCase_		,   padding=lowerCAmelCase_		,   truncation=lowerCAmelCase_						)['input_ids']
			lowerCAmelCase  :     Optional[int]       						= super().__call__(lowerCAmelCase_		,   add_special_tokens=lowerCAmelCase_		,   padding=lowerCAmelCase_		,   truncation=lowerCAmelCase_						)['input_ids']
			lowerCAmelCase  :     Dict       						= {
			    '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:
					lowerCAmelCase  :     str       						= []
					for input_ids in encoded_inputs["input_ids"]:
							attention_mask.append([int(input_id != self.pad_token_id						) for input_id in input_ids]						)
					lowerCAmelCase  :     Optional[Any]       						= attention_mask
			return self.pad(lowerCAmelCase_		,   padding=lowerCAmelCase_		,   max_length=lowerCAmelCase_		,   return_tensors=lowerCAmelCase_						)



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

					lowerCAmelCase  :     Any       						= 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 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_		,   ):
			lowerCAmelCase  :     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)						)
			lowerCAmelCase  :     str       						= sorted(lowerCAmelCase_		,   key=lambda a_						: x[1]		,   reverse=lowerCAmelCase_						)
			lowerCAmelCase  :     List[Any]       						= []
			for (start_index, end_index), score in scores:
					if start_index > end_index:
							raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]'''						)
					lowerCAmelCase  :     Optional[int]       						= 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(__UpperCAmelCase							)
class 		lowerCamelCase	(   __UpperCAmelCase   , __UpperCAmelCase							):
	snake_case_   = VOCAB_FILES_NAMES
	snake_case_   = READER_PRETRAINED_VOCAB_FILES_MAP
	snake_case_   = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_   = READER_PRETRAINED_INIT_CONFIGURATION
	snake_case_   = ['input_ids', 'attention_mask']











"""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}.')

