






import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				     =			{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__				     =			{
    '''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'''
        ),
    },
}
SCREAMING_SNAKE_CASE__				     =			{
    '''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'''
        ),
    },
}
SCREAMING_SNAKE_CASE__				     =			{
    '''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'''
        ),
    },
}

SCREAMING_SNAKE_CASE__				     =			{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
SCREAMING_SNAKE_CASE__				     =			{
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
SCREAMING_SNAKE_CASE__				     =			{
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


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






class 			_UpperCamelCase(       __lowerCamelCase		):
		__SCREAMING_SNAKE_CASE  :       Dict							=							VOCAB_FILES_NAMES
		__SCREAMING_SNAKE_CASE  :       List[Any]							=							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		__SCREAMING_SNAKE_CASE  :       Tuple							=							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__SCREAMING_SNAKE_CASE  :       Optional[int]							=							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class 			_UpperCamelCase(       __lowerCamelCase		):
		__SCREAMING_SNAKE_CASE  :       Any							=							VOCAB_FILES_NAMES
		__SCREAMING_SNAKE_CASE  :       Union[str, Any]							=							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
		__SCREAMING_SNAKE_CASE  :       List[Any]							=							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__SCREAMING_SNAKE_CASE  :       List[Any]							=							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION




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

SCREAMING_SNAKE_CASE__				     =			collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


SCREAMING_SNAKE_CASE__				     =			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)

    Returns:
        `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(__lowerCamelCase		)
class 			_UpperCamelCase:

		def __call__(					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[str] = None    ,       SCREAMING_SNAKE_CASE__		:    Optional[str] = None    ,       SCREAMING_SNAKE_CASE__		:    Union[bool, str] = False    ,       SCREAMING_SNAKE_CASE__		:    Union[bool, str] = False    ,       SCREAMING_SNAKE_CASE__		:    Optional[int] = None    ,       SCREAMING_SNAKE_CASE__		:    Optional[Union[str, TensorType]] = None    ,       SCREAMING_SNAKE_CASE__		:    Optional[bool] = None    ,       **SCREAMING_SNAKE_CASE__		:    Tuple    ,       ):



					'''simple docstring'''



					if titles is None and texts is None:
								return super().__call__(
								    SCREAMING_SNAKE_CASE__    ,       padding=SCREAMING_SNAKE_CASE__    ,       truncation=SCREAMING_SNAKE_CASE__    ,       max_length=SCREAMING_SNAKE_CASE__    ,       return_tensors=SCREAMING_SNAKE_CASE__    ,       return_attention_mask=SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    ,       )
					elif titles is None or texts is None:
								__a						:  Dict									=     titles if texts is None else texts
								return super().__call__(
								    SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       padding=SCREAMING_SNAKE_CASE__    ,       truncation=SCREAMING_SNAKE_CASE__    ,       max_length=SCREAMING_SNAKE_CASE__    ,       return_tensors=SCREAMING_SNAKE_CASE__    ,       return_attention_mask=SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    ,       )
					__a						:  Optional[Any]									=     titles if not isinstance(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ) else [titles]
					__a						:  List[Any]									=     texts if not isinstance(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ) else [texts]
					__a						:  Dict									=     len(SCREAMING_SNAKE_CASE__    )
					__a						:  Optional[Any]									=     questions if not isinstance(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ) else [questions] * n_passages
					if len(SCREAMING_SNAKE_CASE__    ) != len(SCREAMING_SNAKE_CASE__    ):
								raise ValueError(
								    f'''There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE__    )} titles and {len(SCREAMING_SNAKE_CASE__    )} texts.'''    )
					__a						:  Union[str, Any]									=     super().__call__(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       padding=SCREAMING_SNAKE_CASE__    ,       truncation=SCREAMING_SNAKE_CASE__    )['input_ids']
					__a						:  List[str]									=     super().__call__(SCREAMING_SNAKE_CASE__    ,       add_special_tokens=SCREAMING_SNAKE_CASE__    ,       padding=SCREAMING_SNAKE_CASE__    ,       truncation=SCREAMING_SNAKE_CASE__    )['input_ids']
					__a						:  str									=     {
					    '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(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )
					    ]
					}
					if return_attention_mask is not False:
								__a						:  Optional[int]									=     []
								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						:  str									=     attention_mask
					return self.pad(SCREAMING_SNAKE_CASE__    ,       padding=SCREAMING_SNAKE_CASE__    ,       max_length=SCREAMING_SNAKE_CASE__    ,       return_tensors=SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    BatchEncoding    ,       SCREAMING_SNAKE_CASE__		:    DPRReaderOutput    ,       SCREAMING_SNAKE_CASE__		:    int = 1_6    ,       SCREAMING_SNAKE_CASE__		:    int = 6_4    ,       SCREAMING_SNAKE_CASE__		:    int = 4    ,       ):



					'''simple docstring'''



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

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



		def    __lowerCAmelCase  (					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    List[int]    ,       SCREAMING_SNAKE_CASE__		:    List[int]    ,       SCREAMING_SNAKE_CASE__		:    int    ,       SCREAMING_SNAKE_CASE__		:    int    ,       ):



					'''simple docstring'''



					__a						:  str									=     []
					for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE__    ):
								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						:  Dict									=     sorted(SCREAMING_SNAKE_CASE__    ,       key=lambda SCREAMING_SNAKE_CASE__    : x[1]    ,       reverse=SCREAMING_SNAKE_CASE__    )
					__a						:  int									=     []
					for (start_index, end_index), score in scores:
								if start_index > end_index:
											raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''    )
								__a						:  Tuple									=     end_index - start_index + 1
								if length > max_answer_length:
											raise ValueError(f'''Span is too long: {length} > {max_answer_length}'''    )
								if any(
								    start_index <= prev_start_index <= prev_end_index <= end_index
								    or prev_start_index <= start_index <= end_index <= prev_end_index
								    for (prev_start_index, prev_end_index) in chosen_span_intervals    ):
											continue
								chosen_span_intervals.append((start_index, end_index)    )

								if len(SCREAMING_SNAKE_CASE__    ) == top_spans:
											break
					return chosen_span_intervals






@add_end_docstrings(__lowerCamelCase		)
class 			_UpperCamelCase(       __lowerCamelCase		,				__lowerCamelCase		):
		__SCREAMING_SNAKE_CASE  :       Any							=							VOCAB_FILES_NAMES
		__SCREAMING_SNAKE_CASE  :       Optional[Any]							=							READER_PRETRAINED_VOCAB_FILES_MAP
		__SCREAMING_SNAKE_CASE  :       Union[str, Any]							=							READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__SCREAMING_SNAKE_CASE  :       Dict							=							READER_PRETRAINED_INIT_CONFIGURATION
		__SCREAMING_SNAKE_CASE  :       int							=							['''input_ids''', '''attention_mask''']






"""simple docstring"""




from __future__ import annotations

from statistics import mean







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = [0] * no_of_processes
		lowerCAmelCase__   = [0] * no_of_processes

		# Initialize remaining_time to waiting_time.

		for i in range(lowerCamelCase__  ):
				lowerCAmelCase__   = burst_time[i]
		lowerCAmelCase__   = []

		lowerCAmelCase__   = 0
		lowerCAmelCase__   = 0

		# When processes are not completed,
		# A process whose arrival time has passed \
		# and has remaining execution time is put into the ready_process.
		# The shortest process in the ready_process, target_process is executed.

		while completed != no_of_processes:
				lowerCAmelCase__   = []
				lowerCAmelCase__   = -1

				for i in range(lowerCamelCase__  ):
						if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
								ready_process.append(lowerCamelCase__  )

				if len(lowerCamelCase__  ) > 0:
						lowerCAmelCase__   = ready_process[0]
						for i in ready_process:
								if remaining_time[i] < remaining_time[target_process]:
										lowerCAmelCase__   = i
						total_time += burst_time[target_process]
						completed += 1
						lowerCAmelCase__   = 0
						lowerCAmelCase__   = (
						    total_time - arrival_time[target_process] - burst_time[target_process]
						)
				else:
						total_time += 1

		return waiting_time







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = [0] * no_of_processes
		for i in range(lowerCamelCase__  ):
				lowerCAmelCase__   = burst_time[i] + waiting_time[i]
		return turn_around_time


if __name__ == "__main__":
			print("[TEST CASE 01]")

			__lowerCAmelCase     :				str    				=    4
			__lowerCAmelCase     :				Dict    				=    [2, 5, 3, 7]
			__lowerCAmelCase     :				List[str]    				=    [0, 0, 0, 0]
			__lowerCAmelCase     :				Union[str, Any]    				=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
			__lowerCAmelCase     :				Union[str, Any]    				=    calculate_turnaroundtime(
			    burst_time, no_of_processes, waiting_time
			)

			# Printing the Result
			print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
			for i, process_id in enumerate(list(range(1, 5))):
						print(
						    F"{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"
						    F"{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"
						)
			print(F"\nAverage waiting time = {mean(waiting_time):.5f}")
			print(F"Average turnaround time = {mean(turn_around_time):.5f}")




