



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


__A   			=logging.get_logger(__name__)

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

__A   			={
    "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"
        ),
    },
}
__A   			={
    "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"
        ),
    },
}
__A   			={
    "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"
        ),
    },
}

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


__A   			={
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
__A   			={
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
__A   			={
    "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
__A   			=collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

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


__A   			=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 		UpperCAmelCase__			:




			'''simple docstring'''






			def __call__(   self							: List[Any]       ,       a_							: str       ,       a_							: Optional[str] = None       ,       a_							: Optional[str] = None       ,       a_							: Union[bool, str] = False       ,       a_							: Union[bool, str] = False       ,       a_							: Optional[int] = None       ,       a_							: Optional[Union[str, TensorType]] = None       ,       a_							: Optional[bool] = None       ,       **a_							: int       ,       ):
					'''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:
							__UpperCAmelCase    :     Dict													=	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_       ,       )
					__UpperCAmelCase    :     Any													=	titles if not isinstance(a_       ,       a_ ) else [titles]
					__UpperCAmelCase    :     Union[str, Any]													=	texts if not isinstance(a_       ,       a_ ) else [texts]
					__UpperCAmelCase    :     Optional[int]													=	len(a_ )
					__UpperCAmelCase    :     int													=	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.'
					__UpperCAmelCase    :     Any													=	super().__call__(a_       ,       a_       ,       padding=a_       ,       truncation=a_ )['''input_ids''']
					__UpperCAmelCase    :     Tuple													=	super().__call__(a_       ,       add_special_tokens=a_       ,       padding=a_       ,       truncation=a_ )['''input_ids''']
					__UpperCAmelCase    :     Union[str, 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(a_       ,       a_ )
					    ]
					}
					if return_attention_mask is not False:
							__UpperCAmelCase    :     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] )
							__UpperCAmelCase    :     Union[str, Any]													=	attention_mask
					return self.pad(a_       ,       padding=a_       ,       max_length=a_       ,       return_tensors=a_ )




			def  snake_case__     (   self							: Dict       ,       a_							: BatchEncoding       ,       a_							: DPRReaderOutput       ,       a_							: int = 16       ,       a_							: int = 64       ,       a_							: int = 4       ,       ):
					'''simple docstring'''





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

							__UpperCAmelCase    :     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  snake_case__     (   self							: Union[str, Any]       ,       a_							: List[int]       ,       a_							: List[int]       ,       a_							: int       ,       a_							: int       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	[]
					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) )
					__UpperCAmelCase    :     int													=	sorted(a_       ,       key=lambda a_ : x[1]       ,       reverse=a_ )
					__UpperCAmelCase    :     Optional[int]													=	[]
					for (start_index, end_index), score in scores:
							assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
							__UpperCAmelCase    :     Tuple													=	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(__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










import numpy as np

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	(0, 0)
					__UpperCAmelCase    :     List[str]													=	None
					__UpperCAmelCase    :     int													=	0
					__UpperCAmelCase    :     Union[str, Any]													=	0
					__UpperCAmelCase    :     List[Any]													=	0




			def __eq__(   self							: Optional[int]       ,       a_							: List[Any] ):
					'''simple docstring'''





					return self.position == cell.position




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					print(self.position )

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: str       ,       a_							: Dict=(5, 5) ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	np.zeros(a_ )
					__UpperCAmelCase    :     List[Any]													=	world_size[0]
					__UpperCAmelCase    :     List[str]													=	world_size[1]




			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					print(self.w )




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	[
					    (-1, -1),
					    (-1, 0),
					    (-1, 1),
					    (0, -1),
					    (0, 1),
					    (1, -1),
					    (1, 0),
					    (1, 1),
					]
					__UpperCAmelCase    :     str													=	cell.position[0]
					__UpperCAmelCase    :     List[Any]													=	cell.position[1]
					__UpperCAmelCase    :     str													=	[]
					for n in neughbour_cord:
							__UpperCAmelCase    :     Optional[Any]													=	current_x + n[0]
							__UpperCAmelCase    :     Any													=	current_y + n[1]
							if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
									__UpperCAmelCase    :     int													=	Cell()
									__UpperCAmelCase    :     List[str]													=	(x, y)
									__UpperCAmelCase    :     List[Any]													=	cell
									neighbours.append(a_ )
					return neighbours

def        a						(      _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	[]
		__UpperCAmelCase    :     Optional[Any]													=	[]
		_open.append(_UpperCAmelCase						)

		while _open:
				__UpperCAmelCase    :     str													=	np.argmin([n.f for n in _open]						)
				__UpperCAmelCase    :     Optional[Any]													=	_open[min_f]
				_closed.append(_open.pop(_UpperCAmelCase						)						)
				if current == goal:
						break
				for n in world.get_neigbours(_UpperCAmelCase						):
						for c in _closed:
								if c == n:
										continue
						__UpperCAmelCase    :     Optional[Any]													=	current.g + 1
						__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	n.position
						__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	goal.position
						__UpperCAmelCase    :     Union[str, Any]													=	(ya - ya) ** 2 + (xa - xa) ** 2
						__UpperCAmelCase    :     Optional[Any]													=	n.h + n.g

						for c in _open:
								if c == n and c.f < n.f:
										continue
						_open.append(_UpperCAmelCase						)
		__UpperCAmelCase    :     str													=	[]
		while current.parent is not None:
				path.append(current.position						)
				__UpperCAmelCase    :     int													=	current.parent
		path.append(current.position						)
		return path[::-1]


if __name__ == "__main__":
				__A   			=Gridworld()
				# Start position and goal
				__A   			=Cell()
				__A   			=(0, 0)
				__A   			=Cell()
				__A   			=(4, 4)
				print(f'''path from {start.position} to {goal.position}''')
				__A   			=astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								__A   			=1
				print(world.w)





