



from __future__ import annotations



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase = None  ,  __lowerCAmelCase = None  ,  __lowerCAmelCase = False  ,  )   ->					tuple[int, float, str]:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	cipher_alphabet or [chr(lowercase_      ) for i in range(97  ,  123      )]

				# If the argument is None or the user provided an empty dictionary
				if not frequencies_dict:
								# Frequencies of letters in the english language (how much they show up)
								snake_case__	:      Dict     		=	{
								    '''a''': 0.08_497,
								    '''b''': 0.01_492,
								    '''c''': 0.02_202,
								    '''d''': 0.04_253,
								    '''e''': 0.11_162,
								    '''f''': 0.02_228,
								    '''g''': 0.02_015,
								    '''h''': 0.06_094,
								    '''i''': 0.07_546,
								    '''j''': 0.00_153,
								    '''k''': 0.01_292,
								    '''l''': 0.04_025,
								    '''m''': 0.02_406,
								    '''n''': 0.06_749,
								    '''o''': 0.07_507,
								    '''p''': 0.01_929,
								    '''q''': 0.00_095,
								    '''r''': 0.07_587,
								    '''s''': 0.06_327,
								    '''t''': 0.09_356,
								    '''u''': 0.02_758,
								    '''v''': 0.00_978,
								    '''w''': 0.02_560,
								    '''x''': 0.00_150,
								    '''y''': 0.01_994,
								    '''z''': 0.00_077,
								}
				else:
								# Custom frequencies dictionary
								snake_case__	:      int     		=	frequencies_dict

				if not case_sensitive:
								snake_case__	:      Dict     		=	ciphertext.lower()

				# Chi squared statistic values
				snake_case__	:      List[Any]     		=	{}

				# cycle through all of the shifts
				for shift in range(len(lowercase_      )      ):
								snake_case__	:      Union[str, Any]     		=	''''''

								# decrypt the message with the shift
								for letter in ciphertext:
												try:
																# Try to index the letter in the alphabet
																snake_case__	:      List[str]     		=	(alphabet_letters.index(letter.lower()      ) - shift) % len(
																    lowercase_      )
																decrypted_with_shift += (
																    alphabet_letters[new_key].upper()
																    if case_sensitive and letter.isupper()
																    else alphabet_letters[new_key]
																)
												except ValueError:
																# Append the character if it isn't in the alphabet
																decrypted_with_shift += letter

								snake_case__	:      Any     		=	0.0

								# Loop through each letter in the decoded message with the shift
								for letter in decrypted_with_shift:
												if case_sensitive:
																snake_case__	:      Union[str, Any]     		=	letter.lower()
																if letter in frequencies:
																				# Get the amount of times the letter occurs in the message
																				snake_case__	:      List[str]     		=	decrypted_with_shift.lower().count(lowercase_      )

																				# Get the excepcted amount of times the letter should appear based
																				# on letter frequencies
																				snake_case__	:      int     		=	frequencies[letter] * occurrences

																				# Complete the chi squared statistic formula
																				snake_case__	:      int     		=	((occurrences - expected) ** 2) / expected

																				# Add the margin of error to the total chi squared statistic
																				chi_squared_statistic += chi_letter_value
												else:
																if letter.lower() in frequencies:
																				# Get the amount of times the letter occurs in the message
																				snake_case__	:      List[str]     		=	decrypted_with_shift.count(lowercase_      )

																				# Get the excepcted amount of times the letter should appear based
																				# on letter frequencies
																				snake_case__	:      Dict     		=	frequencies[letter] * occurrences

																				# Complete the chi squared statistic formula
																				snake_case__	:      Optional[int]     		=	((occurrences - expected) ** 2) / expected

																				# Add the margin of error to the total chi squared statistic
																				chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
								snake_case__	:      int     		=	(
								    chi_squared_statistic,
								    decrypted_with_shift,
								)

				# Get the most likely cipher by finding the cipher with the smallest chi squared
				# statistic
				def chi_squared_statistic_values_sorting_key(__lowerCAmelCase      ) -> tuple[float, str]:
								return chi_squared_statistic_values[key]

				snake_case__	:      Dict     		=	min(
				    lowercase_  ,  key=lowercase_  ,  )

				# Get all the data from the most likely cipher (key, decoded message)
				(
				    (
				    snake_case__							
				)		, (
				    snake_case__							
				)		, 							
				)	:      Union[str, Any]     		=	chi_squared_statistic_values[most_likely_cipher]

				# Return the data on the most likely shift
				return (
				    most_likely_cipher,
				    most_likely_cipher_chi_squared_value,
				    decoded_most_likely_cipher,
				)







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	:		Union[str, Any]							       =   logging.get_logger(__name__)

_lowerCamelCase	:		Tuple							       =   {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCamelCase	:		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"""
        ),
    },
}
_lowerCamelCase	:		str							       =   {
    """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	:		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"""
        ),
    },
}

_lowerCamelCase	:		Any							       =   {
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
_lowerCamelCase	:		List[str]							       =   {
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
_lowerCamelCase	:		Tuple							       =   {
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


_lowerCamelCase	:		Optional[Any]							       =   {
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowerCamelCase	:		Optional[int]							       =   {
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowerCamelCase	:		Optional[Any]							       =   {
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    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 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






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

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


_lowerCamelCase	:		Dict							       =   r"""
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer's default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    """


@add_start_docstrings(UpperCAmelCase__     )
class 				UpperCamelCase_    :

    '''simple docstring'''






    def __call__(			self      :	Optional[int]					, UpperCAmelCase__      :	Tuple					, UpperCAmelCase__      :	Optional[str] = None					, UpperCAmelCase__      :	Optional[str] = None					, UpperCAmelCase__      :	Union[bool, str] = False					, UpperCAmelCase__      :	Union[bool, str] = False					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	Optional[Union[str, TensorType]] = None					, UpperCAmelCase__      :	Optional[bool] = None					, **UpperCAmelCase__      :	Optional[int]					, )						->BatchEncoding:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	BatchEncoding					, UpperCAmelCase__      :	DPRReaderOutput					, UpperCAmelCase__      :	int = 16					, UpperCAmelCase__      :	int = 64					, UpperCAmelCase__      :	int = 4					, )						->List[DPRSpanPrediction]:





           '''simple docstring'''

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

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





    def        SCREAMING_SNAKE_CASE							(			self      :	Any					, UpperCAmelCase__      :	List[int]					, UpperCAmelCase__      :	List[int]					, UpperCAmelCase__      :	int					, UpperCAmelCase__      :	int					, )						->List[DPRSpanPrediction]:





           '''simple docstring'''

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


@add_end_docstrings(UpperCAmelCase__     )
class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__     ):

    '''simple docstring'''






    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


