import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase


@require_tokenizers
class 						a__							(   unittest.TestCase		):
			A__				:   int								=			EsmTokenizer



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[Any]:
								super().setUp()
								__a							=   tempfile.mkdtemp()
								# fmt: off
								__a							=   ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
								# fmt: on
								__a							=   os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES['vocab_file']    )
								with open(self.vocab_file      ,						'w'      ,						encoding='utf-8'    ) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    List[PreTrainedTokenizerBase]:
								return [self.get_tokenizer(**UpperCAmelCase    )]



			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    PreTrainedTokenizer:
								return self.tokenizer_class.from_pretrained(self.tmpdirname      ,						**UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								__a							=   self.tokenizer_class(self.vocab_file    )

								__a							=   tokenizer.tokenize('LAGVS'    )
								self.assertListEqual(UpperCAmelCase      ,						['L', 'A', 'G', 'V', 'S']    )
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase    )      ,						[4, 5, 6, 7, 8]    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								__a							=   self.tokenizer_class(self.vocab_file    )

								__a							=   'LAGVS'
								self.assertListEqual(tokenizer.encode(UpperCAmelCase    )      ,						[0, 4, 5, 6, 7, 8, 2]    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    int:
								__a							=   self.tokenizer_class(self.vocab_file    )

								__a							=   ['LAGVS', 'WCB']
								__a							=   tokenizer(UpperCAmelCase      ,						padding=UpperCAmelCase    )['input_ids']

								self.assertListEqual(UpperCAmelCase      ,						[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2]]    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								__a							=   self.tokenizer_class(self.vocab_file    )

								__a							=   ['LAGVS', 'WCB']
								__a							=   tokenizer(UpperCAmelCase      ,						padding=UpperCAmelCase    )['input_ids']

								self.assertListEqual(UpperCAmelCase      ,						[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2, 1, 1]]    )







			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[int]:
								__a							=   self.get_tokenizers(fast=UpperCAmelCase    )
								for tokenizer in tokenizers:
													with self.subTest(f'''{tokenizer.__class__.__name__}'''    ):
																		__a							=   '<unk>'
																		__a							=   '<mask>'

																		__a							=   tokenizer.tokenize(UpperCAmelCase    )
																		__a							=   tokenizer.tokenize(UpperCAmelCase    )

																		self.assertEqual(len(UpperCAmelCase    )      ,						1    )
																		self.assertEqual(len(UpperCAmelCase    )      ,						1    )
																		self.assertEqual(token_a[0]      ,						UpperCAmelCase    )
																		self.assertEqual(token_a[0]      ,						UpperCAmelCase    )





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()




