


"""simple docstring"""







from typing import Any

import numpy as np






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						assert np.shape(_UpperCamelCase			)[0] == np.shape(_UpperCamelCase			)[1]

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

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

						# Check sign of all eigenvalues.
						# np.all returns a value of type np.bool_
						return bool(np.all(eigen_values > 0			)			)






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     np.random.randn(_UpperCamelCase						,      _UpperCamelCase			)
						UpperCAmelCase__												=     np.dot(_UpperCamelCase						,      random_matrix.T			)
						assert _is_matrix_spd(_UpperCamelCase			)
						return spd_matrix






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase = 1000						,      _UpperCamelCase = 1E-8						,      ):





						"""simple docstring"""

						assert np.shape(_UpperCamelCase			)[0] == np.shape(_UpperCamelCase			)[1]
						assert np.shape(_UpperCamelCase			)[0] == np.shape(_UpperCamelCase			)[0]
						assert _is_matrix_spd(_UpperCamelCase			)

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

						# Set initial errors in solution guess and residual.
						UpperCAmelCase__												=     1E9
						UpperCAmelCase__												=     1E9
						UpperCAmelCase__												=     1E9

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

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

												# The main algorithm.

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

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

												# Update variables.
												UpperCAmelCase__												=     np.copy(_UpperCamelCase			)
												UpperCAmelCase__												=     np.copy(_UpperCamelCase			)
												UpperCAmelCase__												=     np.copy(_UpperCamelCase			)

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

						return x






def        a__			(							):





						"""simple docstring"""

						UpperCAmelCase__												=     3
						UpperCAmelCase__												=     _create_spd_matrix(_UpperCamelCase			)
						UpperCAmelCase__												=     np.random.randn(_UpperCamelCase						,      1			)
						UpperCAmelCase__												=     np.dot(_UpperCamelCase						,      _UpperCamelCase			)

						# Numpy solution.
						UpperCAmelCase__												=     np.linalg.solve(_UpperCamelCase						,      _UpperCamelCase			)

						# Our implementation.
						UpperCAmelCase__												=     conjugate_gradient(_UpperCamelCase						,      _UpperCamelCase			)

						# 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()



"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


__a      			= logging.get_logger(__name__)

__a      			= {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__a      			= {
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

__a      			= {
    "microsoft/deberta-base": 5_12,
    "microsoft/deberta-large": 5_12,
    "microsoft/deberta-xlarge": 5_12,
    "microsoft/deberta-base-mnli": 5_12,
    "microsoft/deberta-large-mnli": 5_12,
    "microsoft/deberta-xlarge-mnli": 5_12,
}

__a      			= {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}
class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE              =   PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE              =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE              =   ["""input_ids""", """attention_mask""", """token_type_ids"""]
							SCREAMING_SNAKE_CASE              =   DebertaTokenizer






							def __init__(    self			:  Dict		,			lowerCamelCase_			:  Dict=None		,			lowerCamelCase_			:  Union[str, Any]=None		,			lowerCamelCase_			:  Tuple=None		,			lowerCamelCase_			:  Dict="replace"		,			lowerCamelCase_			:  List[Any]="[CLS]"		,			lowerCamelCase_			:  Optional[Any]="[SEP]"		,			lowerCamelCase_			:  List[Any]="[SEP]"		,			lowerCamelCase_			:  str="[CLS]"		,			lowerCamelCase_			:  List[str]="[UNK]"		,			lowerCamelCase_			:  Any="[PAD]"		,			lowerCamelCase_			:  Tuple="[MASK]"		,			lowerCamelCase_			:  List[str]=False		,			**lowerCamelCase_			:  List[str]		,			):
													super().__init__(
													    lowerCamelCase_		,			lowerCamelCase_		,			tokenizer_file=lowerCamelCase_		,			errors=lowerCamelCase_		,			bos_token=lowerCamelCase_		,			eos_token=lowerCamelCase_		,			unk_token=lowerCamelCase_		,			sep_token=lowerCamelCase_		,			cls_token=lowerCamelCase_		,			pad_token=lowerCamelCase_		,			mask_token=lowerCamelCase_		,			add_prefix_space=lowerCamelCase_		,			**lowerCamelCase_		,			)
													UpperCAmelCase__												=     kwargs.pop('''add_bos_token'''		,			lowerCamelCase_					)

													UpperCAmelCase__												=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
													if pre_tok_state.get('''add_prefix_space'''		,			lowerCamelCase_					) != add_prefix_space:
																			UpperCAmelCase__												=     getattr(lowerCamelCase_		,			pre_tok_state.pop('''type'''					)					)
																			UpperCAmelCase__												=     add_prefix_space
																			UpperCAmelCase__												=     pre_tok_class(**lowerCamelCase_					)

													UpperCAmelCase__												=     add_prefix_space






							@property
							def        lowerCAmelCase  (    self			:  int					):
													if self._mask_token is None:
																			if self.verbose:
																									logger.error('''Using mask_token, but it is not set yet.'''					)
																			return None
													return str(self._mask_token					)






							@mask_token.setter
							def        lowerCAmelCase  (    self			:  Union[str, Any]		,			lowerCamelCase_			:  Union[str, Any]					):
													UpperCAmelCase__												=     AddedToken(lowerCamelCase_		,			lstrip=lowerCamelCase_		,			rstrip=lowerCamelCase_					) if isinstance(lowerCamelCase_		,			lowerCamelCase_					) else value
													UpperCAmelCase__												=     value






							def        lowerCAmelCase  (    self			:  int		,			lowerCamelCase_			:  List[int]		,			lowerCamelCase_			:  Optional[List[int]] = None					):
													if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													UpperCAmelCase__												=     [self.cls_token_id]
													UpperCAmelCase__												=     [self.sep_token_id]
													return cls + token_ids_a + sep + token_ids_a + sep






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			lowerCamelCase_			:  List[int]		,			lowerCamelCase_			:  Optional[List[int]] = None					):
													UpperCAmelCase__												=     [self.sep_token_id]
													UpperCAmelCase__												=     [self.cls_token_id]

													if token_ids_a is None:
																			return len(cls + token_ids_a + sep					) * [0]
													return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






							def        lowerCAmelCase  (    self			:  Tuple		,			*lowerCamelCase_			:  Optional[int]		,			**lowerCamelCase_			:  Union[str, Any]					):
													UpperCAmelCase__												=     kwargs.get('''is_split_into_words'''		,			lowerCamelCase_					)
													assert self.add_prefix_space or not is_split_into_words, (
													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._batch_encode_plus(*lowerCamelCase_		,			**lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Tuple		,			*lowerCamelCase_			:  str		,			**lowerCamelCase_			:  Any					):
													UpperCAmelCase__												=     kwargs.get('''is_split_into_words'''		,			lowerCamelCase_					)

													assert self.add_prefix_space or not is_split_into_words, (
													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._encode_plus(*lowerCamelCase_		,			**lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Dict		,			lowerCamelCase_			:  str		,			lowerCamelCase_			:  Optional[str] = None					):
													UpperCAmelCase__												=     self._tokenizer.model.save(lowerCamelCase_		,			name=lowerCamelCase_					)
													return tuple(lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			lowerCamelCase_			:  "Conversation"					):
													UpperCAmelCase__												=     []
													for is_user, text in conversation.iter_texts():
																			input_ids.extend(self.encode(lowerCamelCase_		,			add_special_tokens=lowerCamelCase_					) + [self.eos_token_id]					)

													if len(lowerCamelCase_					) > self.model_max_length:
																			UpperCAmelCase__												=     input_ids[-self.model_max_length :]
													return input_ids