


"""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 import BertTokenizer


_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 lowerCamelCase		(__UpperCAmelCase      ):






					'''simple docstring'''


					a   	=		VOCAB_FILES_NAMES
					a   	=		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					a   	=		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a   	=		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class lowerCamelCase		(__UpperCAmelCase      ):






					'''simple docstring'''


					a   	=		VOCAB_FILES_NAMES
					a   	=		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
					a   	=		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a   	=		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



_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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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 lowerCamelCase		:






					'''simple docstring'''


					def __call__(						self			:				Any   , _snake_case			:				str   , _snake_case			:				Optional[str] = None   , _snake_case			:				Optional[str] = None   , _snake_case			:				Union[bool, str] = False   , _snake_case			:				Union[bool, str] = False   , _snake_case			:				Optional[int] = None   , _snake_case			:				Optional[Union[str, TensorType]] = None   , _snake_case			:				Optional[bool] = None   , **_snake_case			:				List[str]   , )							->       Dict:







									if titles is None and texts is None:
													return super().__call__(
													    lowerCAmelCase_   , padding=lowerCAmelCase_   , truncation=lowerCAmelCase_   , max_length=lowerCAmelCase_   , return_tensors=lowerCAmelCase_   , return_attention_mask=lowerCAmelCase_   , **lowerCAmelCase_   , )
									elif titles is None or texts is None:
													SCREAMING_SNAKE_CASE__		  =				titles if texts is None else texts
													return super().__call__(
													    lowerCAmelCase_   , lowerCAmelCase_   , padding=lowerCAmelCase_   , truncation=lowerCAmelCase_   , max_length=lowerCAmelCase_   , return_tensors=lowerCAmelCase_   , return_attention_mask=lowerCAmelCase_   , **lowerCAmelCase_   , )
									SCREAMING_SNAKE_CASE__		  =				titles if not isinstance(lowerCAmelCase_   , lowerCAmelCase_		) else [titles]
									SCREAMING_SNAKE_CASE__		  =				texts if not isinstance(lowerCAmelCase_   , lowerCAmelCase_		) else [texts]
									SCREAMING_SNAKE_CASE__		  =				len(lowerCAmelCase_		)
									SCREAMING_SNAKE_CASE__		  =				questions if not isinstance(lowerCAmelCase_   , lowerCAmelCase_		) else [questions] * n_passages
									if len(lowerCAmelCase_		) != len(lowerCAmelCase_		):
													raise ValueError(
													    F"""There should be as many titles than texts but got {len(lowerCAmelCase_		)} titles and {len(lowerCAmelCase_		)} texts."""		)
									SCREAMING_SNAKE_CASE__		  =				super().__call__(lowerCAmelCase_   , lowerCAmelCase_   , padding=lowerCAmelCase_   , truncation=lowerCAmelCase_		)["input_ids"]
									SCREAMING_SNAKE_CASE__		  =				super().__call__(lowerCAmelCase_   , add_special_tokens=lowerCAmelCase_   , padding=lowerCAmelCase_   , truncation=lowerCAmelCase_		)["input_ids"]
									SCREAMING_SNAKE_CASE__		  =				{
									    "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(lowerCAmelCase_   , lowerCAmelCase_		)
									    ]
									}
									if return_attention_mask is not False:
													SCREAMING_SNAKE_CASE__		  =				[]
													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__		  =				attention_mask
									return self.pad(lowerCAmelCase_   , padding=lowerCAmelCase_   , max_length=lowerCAmelCase_   , return_tensors=lowerCAmelCase_		)



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

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



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				List[int]   , _snake_case			:				List[int]   , _snake_case			:				int   , _snake_case			:				int   , )							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				[]
									for start_index, start_score in enumerate(lowerCAmelCase_		):
													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__		  =				sorted(lowerCAmelCase_   , key=lambda _snake_case		: x[1]   , reverse=lowerCAmelCase_		)
									SCREAMING_SNAKE_CASE__		  =				[]
									for (start_index, end_index), score in scores:
													if start_index > end_index:
																	raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""		)
													SCREAMING_SNAKE_CASE__		  =				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(lowerCAmelCase_		) == top_spans:
																	break
									return chosen_span_intervals



@add_end_docstrings(__UpperCAmelCase      )
class lowerCamelCase		(__UpperCAmelCase    ,    __UpperCAmelCase      ):






					'''simple docstring'''


					a   	=		VOCAB_FILES_NAMES
					a   	=		READER_PRETRAINED_VOCAB_FILES_MAP
					a   	=		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a   	=		READER_PRETRAINED_INIT_CONFIGURATION
					a   	=		["input_ids", "attention_mask"]





"""simple docstring"""






# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[str]:
				return 1 / (1 + np.exp(-z       ))




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Dict:
				return (-y * np.log(__UpperCAmelCase       ) - (1 - y) * np.log(1 - h       )).mean()




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )
				return np.sum(y * scores - np.log(1 + np.exp(__UpperCAmelCase       )       )       )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=70_000       )							->   Optional[Any]:
				SCREAMING_SNAKE_CASE__		  =				np.zeros(x.shape[1]       )

				for iterations in range(__UpperCAmelCase       ):
								SCREAMING_SNAKE_CASE__		  =				np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				sigmoid_function(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				np.dot(x.T  ,					h - y       ) / y.size
								SCREAMING_SNAKE_CASE__		  =				theta - alpha * gradient  # updating the weights
								SCREAMING_SNAKE_CASE__		  =				np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				sigmoid_function(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				cost_function(__UpperCAmelCase  ,					__UpperCAmelCase       )
								if iterations % 100 == 0:
												print(F"""loss: {j} \t"""       )  # printing the loss after every 100 iterations
				return theta


# In[68]:

if __name__ == "__main__":
						_A   					=    datasets.load_iris()
						_A   					=    iris.data[:, :2]
						_A   					=    (iris.target != 0) * 1

						_A   					=    0.1
						_A   					=    logistic_reg(alpha, x, y, max_iterations=7_0_0_0_0)
						print('theta: ', theta)  # printing the theta i.e our weights vector




						def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Union[str, Any]:
										return sigmoid_function(
										    np.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )       )  # predicting the value of probability from the logistic regression algorithm

						plt.figure(figsize=(1_0, 6))
						plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
						plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
						((_A)					,			(_A))   					=    (x[:, 0].min(), x[:, 0].max())
						((_A)					,			(_A))   					=    (x[:, 1].min(), x[:, 1].max())
						((_A)					,			(_A))   					=    np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
						_A   					=    np.c_[xxa.ravel(), xxa.ravel()]
						_A   					=    predict_prob(grid).reshape(xxa.shape)
						plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

						plt.legend()

						plt.show()




