




"""simple docstring"""





import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 					__lowercase				(	__snake_case  ,      unittest.TestCase		):


				'''simple docstring'''


				_lowerCAmelCase			=HerbertTokenizer
				_lowerCAmelCase			=HerbertTokenizerFast
				_lowerCAmelCase			=True

				def 					_lowerCamelCase    (      self      :   Any					):
										super().setUp()

										# Use a simpler test file without japanese/chinese characters
										with open(F'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''					,encoding='''utf-8'''					) as f_data:
																snake_case__				:    Optional[int]								=    f_data.read().replace('''\n\n'''					,'''\n'''					).strip()

										snake_case__				:    List[str]								=    [
										    '''<s>''',
										    '''</s>''',
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''w</w>''',
										    '''r</w>''',
										    '''t</w>''',
										    '''lo''',
										    '''low''',
										    '''er</w>''',
										    '''low</w>''',
										    '''lowest</w>''',
										    '''newer</w>''',
										    '''wider</w>''',
										    ''',</w>''',
										    '''<unk>''',
										]
										snake_case__				:    Optional[int]								=    dict(zip(SCREAMING_SNAKE_CASE_					,range(len(SCREAMING_SNAKE_CASE_					)					)					)					)
										snake_case__				:    Optional[int]								=    ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

										snake_case__				:    str								=    os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['''vocab_file''']					)
										snake_case__				:    List[str]								=    os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['''merges_file''']					)
										with open(self.vocab_file					,'''w'''					) as fp:
																fp.write(json.dumps(SCREAMING_SNAKE_CASE_					)					)
										with open(self.merges_file					,'''w'''					) as fp:
																fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_					)					)

				def 					_lowerCamelCase    (      self      :   Union[str, Any]					,SCREAMING_SNAKE_CASE_      :   Tuple					):
										snake_case__				:    List[Any]								=    '''lower newer'''
										snake_case__				:    Optional[Any]								=    '''lower newer'''
										return input_text, output_text

				def 					_lowerCamelCase    (      self      :   Tuple					):
										snake_case__				:    Union[str, Any]								=    self.tokenizer_class(vocab_file=self.vocab_file					,merges_file=self.merges_file					)

										snake_case__				:    str								=    '''lower'''
										snake_case__				:    str								=    ['''low''', '''er</w>''']
										snake_case__				:    List[Any]								=    tokenizer.tokenize(SCREAMING_SNAKE_CASE_					)
										self.assertListEqual(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					)

										snake_case__				:    List[Any]								=    tokens + ['''<unk>''']
										snake_case__				:    List[str]								=    [1_6, 1_7, 2_3]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_					)					,SCREAMING_SNAKE_CASE_					)

				def 					_lowerCamelCase    (      self      :   Dict					):
										if not self.test_rust_tokenizer:
																return

										snake_case__				:    Tuple								=    self.get_tokenizer()
										snake_case__				:    Tuple								=    self.get_rust_tokenizer()

										snake_case__				:    List[Any]								=    '''lower,newer'''

										snake_case__				:    Tuple								=    tokenizer.tokenize(SCREAMING_SNAKE_CASE_					)
										snake_case__				:    int								=    rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_					)
										self.assertListEqual(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					)

										snake_case__				:    Any								=    tokenizer.encode(SCREAMING_SNAKE_CASE_					,add_special_tokens=SCREAMING_SNAKE_CASE_					)
										snake_case__				:    Union[str, Any]								=    rust_tokenizer.encode(SCREAMING_SNAKE_CASE_					,add_special_tokens=SCREAMING_SNAKE_CASE_					)
										self.assertListEqual(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					)

										snake_case__				:    Tuple								=    self.get_rust_tokenizer()
										snake_case__				:    Tuple								=    tokenizer.encode(SCREAMING_SNAKE_CASE_					)
										snake_case__				:    Optional[Any]								=    rust_tokenizer.encode(SCREAMING_SNAKE_CASE_					)
										self.assertListEqual(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					)





				@slow
				def 					_lowerCamelCase    (      self      :   Any					):
										snake_case__				:    int								=    self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased'''					)

										snake_case__				:    Tuple								=    tokenizer.encode('''konstruowanie sekwencji'''					,add_special_tokens=SCREAMING_SNAKE_CASE_					)
										snake_case__				:    Union[str, Any]								=    tokenizer.encode('''konstruowanie wielu sekwencji'''					,add_special_tokens=SCREAMING_SNAKE_CASE_					)

										snake_case__				:    Dict								=    tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_					)
										snake_case__				:    Dict								=    tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					)

										assert encoded_sentence == [0] + text + [2]
										assert encoded_pair == [0] + text + [2] + text_a + [2]



from typing import Any

import numpy as np
def 							lowerCAmelCase(	__lowerCamelCase						):
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[1]

					# If matrix not symmetric, exit right away.
					if np.allclose(__lowerCamelCase      ,				matrix.T						) is False:
										return False

					# Get eigenvalues and eignevectors for a symmetric matrix.
					__a					,      __a							=   np.linalg.eigh(__lowerCamelCase						)

					# Check sign of all eigenvalues.
					# np.all returns a value of type np.bool_
					return bool(np.all(eigen_values > 0						)						)
def 							lowerCAmelCase(	__lowerCamelCase						):
					__a							=   np.random.randn(__lowerCamelCase      ,				__lowerCamelCase						)
					__a							=   np.dot(__lowerCamelCase      ,				random_matrix.T						)
					assert _is_matrix_spd(__lowerCamelCase						)
					return spd_matrix
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase = 1000      ,				__lowerCamelCase = 1e-8      ,				):
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[1]
					assert np.shape(__lowerCamelCase						)[0] == np.shape(__lowerCamelCase						)[0]
					assert _is_matrix_spd(__lowerCamelCase						)

					# Initialize solution guess, residual, search direction.
					__a							=   np.zeros((np.shape(__lowerCamelCase						)[0], 1)						)
					__a							=   np.copy(__lowerCamelCase						)
					__a							=   np.copy(__lowerCamelCase						)

					# Set initial errors in solution guess and residual.
					__a							=   1e9
					__a							=   1e9
					__a							=   1e9

					# Set iteration counter to threshold number of iterations.
					__a							=   0

					while error > tol:
										# Save this value so we only calculate the matrix-vector product once.
										__a							=   np.dot(__lowerCamelCase      ,				__lowerCamelCase						)

										# The main algorithm.

										# Update search direction magnitude.
										__a							=   np.dot(ra.T      ,				__lowerCamelCase						) / np.dot(pa.T      ,				__lowerCamelCase						)
										# Update solution guess.
										__a							=   xa + alpha * pa
										# Calculate new residual.
										__a							=   ra - alpha * w
										# Calculate new Krylov subspace scale.
										__a							=   np.dot(r.T      ,				__lowerCamelCase						) / np.dot(ra.T      ,				__lowerCamelCase						)
										# Calculate new A conjuage search direction.
										__a							=   r + beta * pa

										# Calculate errors.
										__a							=   np.linalg.norm(r - ra						)
										__a							=   np.linalg.norm(x - xa						)
										__a							=   np.maximum(__lowerCamelCase      ,				__lowerCamelCase						)

										# Update variables.
										__a							=   np.copy(__lowerCamelCase						)
										__a							=   np.copy(__lowerCamelCase						)
										__a							=   np.copy(__lowerCamelCase						)

										# Update number of iterations.
										iterations += 1
										if iterations > max_iterations:
															break

					return x
def 							lowerCAmelCase(	):
					__a							=   3
					__a							=   _create_spd_matrix(__lowerCamelCase						)
					__a							=   np.random.randn(__lowerCamelCase      ,				1						)
					__a							=   np.dot(__lowerCamelCase      ,				__lowerCamelCase						)

					# Numpy solution.
					__a							=   np.linalg.solve(__lowerCamelCase      ,				__lowerCamelCase						)

					# Our implementation.
					__a							=   conjugate_gradient(__lowerCamelCase      ,				__lowerCamelCase						)

					# Ensure both solutions are close to x_true (and therefore one another).
					assert np.linalg.norm(x_numpy - x_true						) <= 1e-6
					assert np.linalg.norm(x_conjugate_gradient - x_true						) <= 1e-6


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	test_conjugate_gradient()




