




'''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


UpperCamelCase_    	=				logging.get_logger(__name__)

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

UpperCamelCase_    	=				{
    "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"
        ),
    },
}
UpperCamelCase_    	=				{
    "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"
        ),
    },
}
UpperCamelCase_    	=				{
    "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"
        ),
    },
}

UpperCamelCase_    	=				{
    "facebook/dpr-ctx_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-ctx_encoder-multiset-base": 5_1_2,
}
UpperCamelCase_    	=				{
    "facebook/dpr-question_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-question_encoder-multiset-base": 5_1_2,
}
UpperCamelCase_    	=				{
    "facebook/dpr-reader-single-nq-base": 5_1_2,
    "facebook/dpr-reader-multiset-base": 5_1_2,
}


UpperCamelCase_    	=				{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
UpperCamelCase_    	=				{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
UpperCamelCase_    	=				{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				int        =						VOCAB_FILES_NAMES
       A						:				Tuple        =						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       A						:				List[str]        =						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A						:				Dict        =						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
       A						:				Union[str, Any]        =						DPRContextEncoderTokenizer






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				Optional[int]        =						VOCAB_FILES_NAMES
       A						:				str        =						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       A						:				List[Any]        =						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A						:				List[str]        =						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
       A						:				List[Any]        =						DPRQuestionEncoderTokenizer

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

UpperCamelCase_    	=				collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


UpperCamelCase_    	=				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(SCREAMING_SNAKE_CASE )
class 							_a				:





       '''simple docstring'''






       def __call__(      self,							A,							A = None,							A = None,							A = False,							A = False,							A = None,							A = None,							A = None,							**A,							):







             '''simple docstring'''







             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:
                   SCREAMING_SNAKE_CASE   :      int    					=						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,							)
             SCREAMING_SNAKE_CASE   :      Dict    					=						titles if not isinstance(A,							A  ) else [titles]
             SCREAMING_SNAKE_CASE   :      int    					=						texts if not isinstance(A,							A  ) else [texts]
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						len(A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						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."
             SCREAMING_SNAKE_CASE   :      str    					=						super().__call__(A,							A,							padding=A,							truncation=A  )['input_ids']
             SCREAMING_SNAKE_CASE   :      int    					=						super().__call__(A,							add_special_tokens=A,							padding=A,							truncation=A  )['input_ids']
             SCREAMING_SNAKE_CASE   :      Optional[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(A,							A  )
                 ]
             }
             if return_attention_mask is not False:
                   SCREAMING_SNAKE_CASE   :      List[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]  )
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						attention_mask
             return self.pad(A,							padding=A,							max_length=A,							return_tensors=A  )







       def        UpperCamelCase_							(      self,							A,							A,							A = 16,							A = 64,							A = 4,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						reader_input['input_ids']
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						reader_output[:3]
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						len(A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						sorted(range(A  ),							reverse=A,							key=relevance_logits.__getitem__  )
             SCREAMING_SNAKE_CASE   :      List[DPRReaderOutput]    					=						[]
             for doc_id in sorted_docs:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						list(input_ids[doc_id]  )
                   # assuming question & title information is at the beginning of the sequence
                   SCREAMING_SNAKE_CASE   :      Dict    					=						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   :      Union[str, Any]    					=						len(A  )

                   SCREAMING_SNAKE_CASE   :      Union[str, 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=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        UpperCamelCase_							(      self,							A,							A,							A,							A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						[]
             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)  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						sorted(A,							key=lambda A  : x[1],							reverse=A  )
             SCREAMING_SNAKE_CASE   :      str    					=						[]
             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(A  ) == top_spans:
                         break
             return chosen_span_intervals






@add_end_docstrings(SCREAMING_SNAKE_CASE )
class 							_a				(			SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				int        =						VOCAB_FILES_NAMES
       A						:				List[Any]        =						READER_PRETRAINED_VOCAB_FILES_MAP
       A						:				Optional[Any]        =						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A						:				Optional[int]        =						READER_PRETRAINED_INIT_CONFIGURATION
       A						:				Dict        =						['''input_ids''', '''attention_mask''']
       A						:				str        =						DPRReaderTokenizer








'''simple docstring'''



from typing import Any






class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						data
             SCREAMING_SNAKE_CASE   :      Any    					=						None







       def __repr__(      self  ):







             '''simple docstring'''







             return F"Node({self.data})"






class 							_a				:





       '''simple docstring'''






       def __init__(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						None







       def __iter__(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.head
             while node:
                   yield node.data
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						node.next







       def __len__(      self  ):







             '''simple docstring'''







             return sum(1 for _ in self  )







       def __repr__(      self  ):







             '''simple docstring'''







             return "->".join([str(A  ) for item in self]  )







       def __getitem__(      self,							A  ):







             '''simple docstring'''







             if not 0 <= index < len(self  ):
                   raise ValueError('list index out of range.'  )
             for i, node in enumerate(self  ):
                   if i == index:
                         return node
             return None







       def __setitem__(      self,							A,							A  ):







             '''simple docstring'''







             if not 0 <= index < len(self  ):
                   raise ValueError('list index out of range.'  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.head
             for _ in range(A  ):
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						current.next
             SCREAMING_SNAKE_CASE   :      Any    					=						data







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             self.insert_nth(len(self  ),							A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             self.insert_nth(0,							A  )







       def        UpperCamelCase_							(      self,							A,							A  ):







             '''simple docstring'''







             if not 0 <= index <= len(self  ):
                   raise IndexError('list index out of range'  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						Node(A  )
             if self.head is None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						new_node
             elif index == 0:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.head  # link new_node to head
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						new_node
             else:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.head
                   for _ in range(index - 1  ):
                         SCREAMING_SNAKE_CASE   :      str    					=						temp.next
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						temp.next
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						new_node







       def        UpperCamelCase_							(      self  ):  # print every node data







             '''simple docstring'''







             print(self  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return self.delete_nth(0  )







       def        UpperCamelCase_							(      self  ):  # delete from tail







             '''simple docstring'''







             return self.delete_nth(len(self  ) - 1  )







       def        UpperCamelCase_							(      self,							A = 0  ):







             '''simple docstring'''







             if not 0 <= index <= len(self  ) - 1:  # test if index is valid
                   raise IndexError('List index out of range.'  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.head  # default first node
             if index == 0:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						self.head.next
             else:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.head
                   for _ in range(index - 1  ):
                         SCREAMING_SNAKE_CASE   :      Any    					=						temp.next
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						temp.next
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						temp.next.next
             return delete_node.data







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return self.head is None







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						None
             SCREAMING_SNAKE_CASE   :      Any    					=						self.head

             while current:
                   # Store the current node's next node.
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						current.next
                   # Make the current node's next point backwards
                   SCREAMING_SNAKE_CASE   :      int    					=						prev
                   # Make the previous node be the current node
                   SCREAMING_SNAKE_CASE   :      int    					=						current
                   # Make the current node the next node (to progress iteration)
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						next_node
             # Return prev in order to put the head at the end
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						prev




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						LinkedList()
      assert linked_list.is_empty() is True
      assert str(__UpperCamelCase    ) == ""

      try:
            linked_list.delete_head()
            raise AssertionError  # This should not happen.
      except IndexError:
            assert True  # This should happen.

      try:
            linked_list.delete_tail()
            raise AssertionError  # This should not happen.
      except IndexError:
            assert True  # This should happen.

      for i in range(10    ):
            assert len(__UpperCamelCase    ) == i
            linked_list.insert_nth(__UpperCamelCase      ,i + 1    )
      assert str(__UpperCamelCase    ) == "->".join(str(__UpperCamelCase    ) for i in range(1      ,11    )    )

      linked_list.insert_head(0    )
      linked_list.insert_tail(11    )
      assert str(__UpperCamelCase    ) == "->".join(str(__UpperCamelCase    ) for i in range(0      ,12    )    )

      assert linked_list.delete_head() == 0
      assert linked_list.delete_nth(9    ) == 10
      assert linked_list.delete_tail() == 11
      assert len(__UpperCamelCase    ) == 9
      assert str(__UpperCamelCase    ) == "->".join(str(__UpperCamelCase    ) for i in range(1      ,10    )    )

      assert all(linked_list[i] == i + 1 for i in range(0      ,9    )    ) is True

      for i in range(0      ,9    ):
            SCREAMING_SNAKE_CASE   :      Any    					=						-i
      assert all(linked_list[i] == -i for i in range(0      ,9    )    ) is True

      linked_list.reverse()
      assert str(__UpperCamelCase    ) == "->".join(str(__UpperCamelCase    ) for i in range(-8      ,1    )    )




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[str]    					=						[
          -9,
          1_00,
          Node(77_34_51_12    ),
          'dlrow olleH',
          7,
          55_55,
          0,
          -1_9_2.5_5_5_5_5,
          'Hello, world!',
          7_7.9,
          Node(10    ),
          None,
          None,
          1_2.2_0,
      ]
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						LinkedList()

      for i in test_input:
            linked_list.insert_tail(__UpperCamelCase    )

      # Check if it's empty or not
      assert linked_list.is_empty() is False
      assert (
          str(__UpperCamelCase    ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
          "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
      )

      # Delete the head
      SCREAMING_SNAKE_CASE   :      str    					=						linked_list.delete_head()
      assert result == -9
      assert (
          str(__UpperCamelCase    ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
          "Hello, world!->77.9->Node(10)->None->None->12.2"
      )

      # Delete the tail
      SCREAMING_SNAKE_CASE   :      Dict    					=						linked_list.delete_tail()
      assert result == 1_2.2
      assert (
          str(__UpperCamelCase    ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
          "Hello, world!->77.9->Node(10)->None->None"
      )

      # Delete a node in specific location in linked list
      SCREAMING_SNAKE_CASE   :      str    					=						linked_list.delete_nth(10    )
      assert result is None
      assert (
          str(__UpperCamelCase    ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
          "Hello, world!->77.9->Node(10)->None"
      )

      # Add a Node instance to its head
      linked_list.insert_head(Node('Hello again, world!'    )    )
      assert (
          str(__UpperCamelCase    )
          == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
          "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
      )

      # Add None to its tail
      linked_list.insert_tail(__UpperCamelCase    )
      assert (
          str(__UpperCamelCase    )
          == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
          "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
      )

      # Reverse the linked list
      linked_list.reverse()
      assert (
          str(__UpperCamelCase    )
          == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
          "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
      )




def   lowercase__(			):

      """simple docstring"""






      from doctest import testmod

      testmod()

      SCREAMING_SNAKE_CASE   :      Dict    					=						LinkedList()
      linked_list.insert_head(input('Inserting 1st at head '    ).strip()    )
      linked_list.insert_head(input('Inserting 2nd at head '    ).strip()    )
      print('\nPrint list:'    )
      linked_list.print_list()
      linked_list.insert_tail(input('\nInserting 1st at tail '    ).strip()    )
      linked_list.insert_tail(input('Inserting 2nd at tail '    ).strip()    )
      print('\nPrint list:'    )
      linked_list.print_list()
      print('\nDelete head'    )
      linked_list.delete_head()
      print('Delete tail'    )
      linked_list.delete_tail()
      print('\nPrint list:'    )
      linked_list.print_list()
      print('\nReverse linked list'    )
      linked_list.reverse()
      print('\nPrint list:'    )
      linked_list.print_list()
      print('\nString representation of linked list:'    )
      print(__UpperCamelCase    )
      print('\nReading/changing Node data using indexing:'    )
      print(f"Element at Position 1: {linked_list[1]}"    )
      SCREAMING_SNAKE_CASE   :      str    					=						input('Enter New Value: '    ).strip()
      print('New list:'    )
      print(__UpperCamelCase    )
      print(f"length of linked_list is : {len(__UpperCamelCase    )}"    )


if __name__ == "__main__":
      main()


