
'''simple docstring'''
from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class   snake_case__					(			__SCREAMING_SNAKE_CASE				):







		"""simple docstring"""

		lowerCamelCase           =	DPRContextEncoderTokenizer
		lowerCamelCase           =	DPRContextEncoderTokenizerFast
		lowerCamelCase           =	True







@require_tokenizers
class   snake_case__					(			__SCREAMING_SNAKE_CASE				):







		"""simple docstring"""

		lowerCamelCase           =	DPRQuestionEncoderTokenizer
		lowerCamelCase           =	DPRQuestionEncoderTokenizerFast
		lowerCamelCase           =	True







@require_tokenizers
class   snake_case__					(			__SCREAMING_SNAKE_CASE				):







		"""simple docstring"""

		lowerCamelCase           =	DPRReaderTokenizer
		lowerCamelCase           =	DPRReaderTokenizerFast
		lowerCamelCase           =	True



		@slow
		def 			lowerCAmelCase							(			self							: str			)	-> Union[str, Any]:






				"""simple docstring"""







				snake_case     :					Union[str, Any]     =	self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

				snake_case     :					List[Any]     =	tokenizer.encode('''question sequence'''     ,  add_special_tokens=UpperCamelCase__			)
				snake_case     :					List[str]     =	tokenizer.encode('''title sequence'''     ,  add_special_tokens=UpperCamelCase__			)
				snake_case     :					Optional[int]     =	tokenizer.encode('''text sequence ''' * 4     ,  add_special_tokens=UpperCamelCase__			)
				snake_case     :					Union[str, Any]     =	[[101] + text_a + [102] + text_a + [102] + text_a]
				snake_case     :					Union[str, Any]     =	BatchEncoding({'''input_ids''': input_ids}			)

				snake_case     :					Optional[Any]     =	[[0] * len(input_ids[0]			)]
				snake_case     :					Optional[int]     =	[[0] * len(input_ids[0]			)]
				snake_case     :					List[str]     =	[0]
				snake_case     :					Union[str, Any]     =	DPRReaderOutput(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__			)

				snake_case ,snake_case     :					Union[str, Any]     =	8, 9
				snake_case     :					Dict     =	10
				snake_case     :					List[str]     =	10
				snake_case     :					List[str]     =	tokenizer.decode_best_spans(UpperCamelCase__     ,  UpperCamelCase__			)
				self.assertEqual(predicted_spans[0].start_index     ,  UpperCamelCase__			)
				self.assertEqual(predicted_spans[0].end_index     ,  UpperCamelCase__			)
				self.assertEqual(predicted_spans[0].doc_id     ,  0			)



		@slow
		def 			lowerCAmelCase							(			self							: List[str]			)	-> str:






				"""simple docstring"""







				snake_case     :					List[str]     =	self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

				snake_case     :					Tuple     =	tokenizer.encode('''question sequence'''     ,  add_special_tokens=UpperCamelCase__			)
				snake_case     :					Optional[Any]     =	tokenizer.encode('''title sequence'''     ,  add_special_tokens=UpperCamelCase__			)
				snake_case     :					Any     =	tokenizer.encode('''text sequence'''     ,  add_special_tokens=UpperCamelCase__			)
				snake_case     :					Any     =	[101] + text_a + [102] + text_a + [102] + text_a
				snake_case     :					Optional[Any]     =	tokenizer(questions=['''question sequence''']     ,  titles=['''title sequence''']     ,  texts=['''text sequence''']			)
				self.assertIn('''input_ids'''     ,  UpperCamelCase__			)
				self.assertIn('''attention_mask'''     ,  UpperCamelCase__			)
				self.assertListEqual(encoded_input['''input_ids'''][0]     ,  UpperCamelCase__			)




'''simple docstring'''
import numpy as np




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Optional[Any]:


		'''simple docstring'''


		snake_case ,snake_case     :					str     =	a.shape
		snake_case     :					Union[str, Any]     =	min(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
		snake_case     :					List[Any]     =	np.eye(SCREAMING_SNAKE_CASE__ )
		snake_case     :					Union[str, Any]     =	a.copy()

		for k in range(t - 1 ):
				# select a column of modified matrix A':
				snake_case     :					Union[str, Any]     =	r[k:, [k]]
				# construct first basis vector
				snake_case     :					str     =	np.zeros_like(SCREAMING_SNAKE_CASE__ )
				snake_case     :					List[str]     =	1.0
				# determine scaling factor
				snake_case     :					int     =	np.linalg.norm(SCREAMING_SNAKE_CASE__ )
				# construct vector v for Householder reflection
				snake_case     :					Optional[int]     =	x + np.sign(x[0] ) * alpha * ea
				v /= np.linalg.norm(SCREAMING_SNAKE_CASE__ )

				# construct the Householder matrix
				snake_case     :					Tuple     =	np.eye(m - k ) - 2.0 * v @ v.T
				# pad with ones and zeros as necessary
				snake_case     :					int     =	np.block([[np.eye(SCREAMING_SNAKE_CASE__ ), np.zeros((k, m - k) )], [np.zeros((m - k, k) ), q_k]] )

				snake_case     :					int     =	q @ q_k.T
				snake_case     :					Dict     =	q_k @ r

		return q, r


if __name__ == "__main__":
						import doctest

						doctest.testmod()


