






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      :       Dict    =						logging.get_logger(__name__)

lowerCamelCase      :       List[str]    =						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

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


lowerCamelCase      :       List[Any]    =						{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCamelCase      :       List[Any]    =						{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCamelCase      :       Dict    =						{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}



class 				_UpperCamelCase      (__a      ):
      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
      snake_case_	=				DPRContextEncoderTokenizer



class 				_UpperCamelCase      (__a      ):
      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
      snake_case_	=				DPRQuestionEncoderTokenizer

lowerCamelCase      :       Any    =						collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

lowerCamelCase      :       Optional[int]    =						collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


lowerCamelCase      :       Tuple    =						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 				_UpperCamelCase      :


      def __call__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = False       ,    __UpperCamelCase = False       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None       ,    **__UpperCamelCase       ,    )->						BatchEncoding:
           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:
                __lowerCAmelCase             =       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       ,    )
           __lowerCAmelCase             =       titles if not isinstance(_UpperCamelCase       ,    _UpperCamelCase						) else [titles]
           __lowerCAmelCase             =       texts if not isinstance(_UpperCamelCase       ,    _UpperCamelCase						) else [texts]
           __lowerCAmelCase             =       len(_UpperCamelCase						)
           __lowerCAmelCase             =       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."""
           __lowerCAmelCase             =       super().__call__(_UpperCamelCase       ,    _UpperCamelCase       ,    padding=_UpperCamelCase       ,    truncation=_UpperCamelCase						)["input_ids"]
           __lowerCAmelCase             =       super().__call__(_UpperCamelCase       ,    add_special_tokens=_UpperCamelCase       ,    padding=_UpperCamelCase       ,    truncation=_UpperCamelCase						)["input_ids"]
           __lowerCAmelCase             =       {
               "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:
                __lowerCAmelCase             =       []
                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             =       attention_mask
           return self.pad(_UpperCamelCase       ,    padding=_UpperCamelCase       ,    max_length=_UpperCamelCase       ,    return_tensors=_UpperCamelCase						)


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

                __lowerCAmelCase             =       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 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    )->						List[DPRSpanPrediction]:
           __lowerCAmelCase             =       []
           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)						)
           __lowerCAmelCase             =       sorted(_UpperCamelCase       ,    key=lambda __UpperCamelCase						: x[1]       ,    reverse=_UpperCamelCase						)
           __lowerCAmelCase             =       []
           for (start_index, end_index), score in scores:
                assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
                __lowerCAmelCase             =       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(__a      )
class 				_UpperCamelCase      (__a				,		__a      ):
      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"""]
      snake_case_	=				DPRReaderTokenizer








from __future__ import annotations

_A  =	[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]






def   lowerCamelCase__							(							__lowerCAmelCase    :    list[list[int]]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    list[int]       ,				__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    list[list[int]]       ,				):




			"""simple docstring"""


			lowerCAmelCase_         =      [
			    [0 for col in range(len(grid[0]							)							)] for row in range(len(__lowerCAmelCase							)							)
			]  # the reference grid
			lowerCAmelCase_         =      1
			lowerCAmelCase_         =      [
			    [0 for col in range(len(grid[0]							)							)] for row in range(len(__lowerCAmelCase							)							)
			]  # the action grid

			lowerCAmelCase_         =      init[0]
			lowerCAmelCase_         =      init[1]
			lowerCAmelCase_         =      0
			lowerCAmelCase_         =      g + heuristic[x][y]  # cost from starting cell to destination cell
			lowerCAmelCase_         =      [[f, g, x, y]]

			lowerCAmelCase_         =      False  # flag that is set when search is complete
			lowerCAmelCase_         =      False  # flag set if we can't find expand

			while not found and not resign:
						if len(__lowerCAmelCase							) == 0:
									raise ValueError("Algorithm is unable to find solution"							)
						else:  # to choose the least costliest action so as to move closer to the goal
									cell.sort()
									cell.reverse()
									lowerCAmelCase_         =      cell.pop()
									lowerCAmelCase_         =      next_cell[2]
									lowerCAmelCase_         =      next_cell[3]
									lowerCAmelCase_         =      next_cell[1]

									if x == goal[0] and y == goal[1]:
												lowerCAmelCase_         =      True
									else:
												for i in range(len(__lowerCAmelCase							)							):  # to try out different valid actions
															lowerCAmelCase_         =      x + DIRECTIONS[i][0]
															lowerCAmelCase_         =      y + DIRECTIONS[i][1]
															if xa >= 0 and xa < len(__lowerCAmelCase							) and ya >= 0 and ya < len(grid[0]							):
																		if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																					lowerCAmelCase_         =      g + cost
																					lowerCAmelCase_         =      ga + heuristic[xa][ya]
																					cell.append([fa, ga, xa, ya]							)
																					lowerCAmelCase_         =      1
																					lowerCAmelCase_         =      i
			lowerCAmelCase_         =      []
			lowerCAmelCase_         =      goal[0]
			lowerCAmelCase_         =      goal[1]
			invpath.append([x, y]							)  # we get the reverse path from here
			while x != init[0] or y != init[1]:
						lowerCAmelCase_         =      x - DIRECTIONS[action[x][y]][0]
						lowerCAmelCase_         =      y - DIRECTIONS[action[x][y]][1]
						lowerCAmelCase_         =      xa
						lowerCAmelCase_         =      ya
						invpath.append([x, y]							)

			lowerCAmelCase_         =      []
			for i in range(len(__lowerCAmelCase							)							):
						path.append(invpath[len(__lowerCAmelCase							) - 1 - i]							)
			return path, action


if __name__ == "__main__":
			_A  =	[
			    [0, 1, 0, 0, 0, 0],
			    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
			    [0, 1, 0, 0, 0, 0],
			    [0, 1, 0, 0, 1, 0],
			    [0, 0, 0, 0, 1, 0],
			]

			_A  =	[0, 0]
			# all coordinates are given in format [y,x]
			_A  =	[len(grid) - 1, len(grid[0]) - 1]
			_A  =	1

			# the cost map which pushes the path closer to the goal
			_A  =	[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
			for i in range(len(grid)):
						for j in range(len(grid[0])):
									_A  =	abs(i - goal[0]) + abs(j - goal[1])
									if grid[i][j] == 1:
												# added extra penalty in the heuristic map
												_A  =	99

			_A, _A  =	search(grid, init, goal, cost, heuristic)

			print("ACTION MAP")
			for i in range(len(action)):
						print(action[i])

			for i in range(len(path)):
						print(path[i])



