"""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


_a					:					List[Any]=			logging.get_logger(__name__)

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

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


_a					:					Union[str, Any]=			{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_a					:					str=			{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_a					:					List[str]=			{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				Optional[int]   =		VOCAB_FILES_NAMES
				UpperCAmelCase    :				List[str]   =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase    :				Optional[int]   =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase    :				int   =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
				UpperCAmelCase    :				List[Any]   =		DPRContextEncoderTokenizer







class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				List[str]   =		VOCAB_FILES_NAMES
				UpperCAmelCase    :				int   =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase    :				List[Any]   =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase    :				str   =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
				UpperCAmelCase    :				Optional[int]   =		DPRQuestionEncoderTokenizer







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

_a					:					int=			collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


_a					:					int=			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(lowercase						)
class 		UpperCamelCase      :



				def __call__(self    :				int     ,  _A    :				Any     ,  _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    :				Tuple     ,  )   ->     BatchEncoding:
											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:
																		__snake_case      :							List[Any]						=			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     ,  )
											__snake_case      :							Optional[int]						=			titles if not isinstance(_A     ,  _A) else [titles]
											__snake_case      :							Optional[Any]						=			texts if not isinstance(_A     ,  _A) else [texts]
											__snake_case      :							List[str]						=			len(_A)
											__snake_case      :							Union[str, Any]						=			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."
											__snake_case      :							Any						=			super().__call__(_A     ,  _A     ,  padding=_A     ,  truncation=_A)['input_ids']
											__snake_case      :							Optional[int]						=			super().__call__(_A     ,  add_special_tokens=_A     ,  padding=_A     ,  truncation=_A)['input_ids']
											__snake_case      :							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:
																		__snake_case      :							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])
																		__snake_case      :							Union[str, Any]						=			attention_mask
											return self.pad(_A     ,  padding=_A     ,  max_length=_A     ,  return_tensors=_A)



				def  _lowercase					(self    :				Optional[Any]     ,  _A    :				BatchEncoding     ,  _A    :				DPRReaderOutput     ,  _A    :				int = 16     ,  _A    :				int = 64     ,  _A    :				int = 4     ,  )   ->     List[DPRSpanPrediction]:
											__snake_case      :							List[Any]						=			reader_input['input_ids']
											__snake_case      :							Optional[Any]						=			reader_output[:3]
											__snake_case      :							Optional[int]						=			len(_A)
											__snake_case      :							Any						=			sorted(range(_A)     ,  reverse=_A     ,  key=relevance_logits.__getitem__)
											__snake_case      :							List[DPRReaderOutput]						=			[]
											for doc_id in sorted_docs:
																		__snake_case      :							Optional[int]						=			list(input_ids[doc_id])
																		# assuming question & title information is at the beginning of the sequence
																		__snake_case      :							Any						=			sequence_ids.index(self.sep_token_id     ,  2) + 1  # second sep id
																		if sequence_ids[-1] == self.pad_token_id:
																									__snake_case      :							str						=			sequence_ids.index(self.pad_token_id)
																		else:
																									__snake_case      :							Any						=			len(_A)

																		__snake_case      :							List[str]						=			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  _lowercase					(self    :				Optional[int]     ,  _A    :				List[int]     ,  _A    :				List[int]     ,  _A    :				int     ,  _A    :				int     ,  )   ->     List[DPRSpanPrediction]:
											__snake_case      :							List[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))
											__snake_case      :							str						=			sorted(_A     ,  key=lambda _A: x[1]     ,  reverse=_A)
											__snake_case      :							Any						=			[]
											for (start_index, end_index), score in scores:
																		assert start_index <= end_index, f"Wrong span indices: [{start_index}:{end_index}]"
																		__snake_case      :							Any						=			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(lowercase						)
class 		UpperCamelCase      (   lowercase				,     lowercase						):
				UpperCAmelCase    :				str   =		VOCAB_FILES_NAMES
				UpperCAmelCase    :				str   =		READER_PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase    :				Dict   =		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase    :				List[Any]   =		READER_PRETRAINED_INIT_CONFIGURATION
				UpperCAmelCase    :				Dict   =		["""input_ids""", """attention_mask"""]
				UpperCAmelCase    :				List[str]   =		DPRReaderTokenizer






"""simple docstring"""







import numpy

# List of input, output pairs
_a					:					Optional[int]=			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_a					:					int=			(((515, 22, 13), 555), ((61, 35, 49), 150))
_a					:					List[Any]=			[2, 4, 1, 5]
_a					:					Optional[int]=			len(train_data)
_a					:					str=			0.0_0_9
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    List[str]="train"  )							->      int:

	'''simple docstring'''





	return calculate_hypothesis_value(UpperCAmelCase_      ,							UpperCAmelCase_  ) - output(
	    UpperCAmelCase_      ,							UpperCAmelCase_  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Tuple  )							->      Dict:

	'''simple docstring'''





	__snake_case      :							int						=			0
	for i in range(len(UpperCAmelCase_  ) - 1  ):
		hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
	hyp_val += parameter_vector[0]
	return hyp_val
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    List[Any]  )							->      List[Any]:

	'''simple docstring'''





	if data_set == "train":
		return train_data[example_no][1]
	elif data_set == "test":
		return test_data[example_no][1]
	return None
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    Optional[int]  )							->      Any:

	'''simple docstring'''





	if data_set == "train":
		return _hypothesis_value(train_data[example_no][0]  )
	elif data_set == "test":
		return _hypothesis_value(test_data[example_no][0]  )
	return None
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Union[str, Any]      ,							UpperCAmelCase_   :    Any=m  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			0
	for i in range(UpperCAmelCase_  ):
		if index == -1:
			summation_value += _error(UpperCAmelCase_  )
		else:
			summation_value += _error(UpperCAmelCase_  ) * train_data[i][0][index]
	return summation_value
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      str:

	'''simple docstring'''





	__snake_case      :							Dict						=			summation_of_cost_derivative(UpperCAmelCase_      ,							UpperCAmelCase_  ) / m
	return cost_derivative_value
def   __UpperCAmelCase    (   )							->      List[str]:

	'''simple docstring'''





	global parameter_vector
	# Tune these values to set a tolerance value for predicted output
	__snake_case      :							Dict						=			0.000_002
	__snake_case      :							Optional[int]						=			0
	__snake_case      :							str						=			0
	while True:
		j += 1
		__snake_case      :							Optional[int]						=			[0, 0, 0, 0]
		for i in range(0      ,							len(UpperCAmelCase_  )  ):
			__snake_case      :							Union[str, Any]						=			get_cost_derivative(i - 1  )
			__snake_case      :							Any						=			(
			    parameter_vector[i] - LEARNING_RATE * cost_derivative
			)
		if numpy.allclose(
		    UpperCAmelCase_      ,							UpperCAmelCase_      ,							atol=UpperCAmelCase_      ,							rtol=UpperCAmelCase_      ,							):
			break
		__snake_case      :							Optional[Any]						=			temp_parameter_vector
	print(('Number of iterations:', j)  )
def   __UpperCAmelCase    (   )							->      int:

	'''simple docstring'''





	for i in range(len(UpperCAmelCase_  )  ):
		print(('Actual output value:', output(UpperCAmelCase_      ,							'test'  ))  )
		print(('Hypothesis output:', calculate_hypothesis_value(UpperCAmelCase_      ,							'test'  ))  )


if __name__ == "__main__":
	run_gradient_descent()
	print("\nTesting gradient descent for a linear hypothesis function.\n")
	test_gradient_descent()





