






"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase       =				logging.get_logger(__name__)

_lowerCAmelCase       =				{
    "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
    "junnyu/roformer_chinese_char_small": (
        "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"
    ),
    "junnyu/roformer_chinese_char_base": (
        "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"
    ),
    "junnyu/roformer_small_discriminator": (
        "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"
    ),
    "junnyu/roformer_small_generator": (
        "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Optional[Any] 			=					"""roformer"""





	def __init__(self  ,  lowerCAmelCase_=50000  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=768  ,  lowerCAmelCase_=12  ,  lowerCAmelCase_=12  ,  lowerCAmelCase_=3072  ,  lowerCAmelCase_="gelu"  ,  lowerCAmelCase_=0.1  ,  lowerCAmelCase_=0.1  ,  lowerCAmelCase_=1536  ,  lowerCAmelCase_=2  ,  lowerCAmelCase_=0.02  ,  lowerCAmelCase_=1e-1_2  ,  lowerCAmelCase_=0  ,  lowerCAmelCase_=False  ,  lowerCAmelCase_=True  ,  **lowerCAmelCase_  ,  ):
		super().__init__(pad_token_id=lowerCAmelCase_  ,  **lowerCAmelCase_				)

		A_		:    Dict						     = vocab_size
		A_		:    str						     = hidden_size if embedding_size is None else embedding_size
		A_		:    Tuple						     = hidden_size
		A_		:    str						     = num_hidden_layers
		A_		:    List[str]						     = num_attention_heads
		A_		:    List[Any]						     = hidden_act
		A_		:    List[Any]						     = intermediate_size
		A_		:    Optional[int]						     = hidden_dropout_prob
		A_		:    Union[str, Any]						     = attention_probs_dropout_prob
		A_		:    int						     = max_position_embeddings
		A_		:    Any						     = type_vocab_size
		A_		:    int						     = initializer_range
		A_		:    int						     = layer_norm_eps
		A_		:    Dict						     = rotary_value
		A_		:    Tuple						     = use_cache






class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	@property
	def  lowerCamelCase(self				):
		if self.task == "multiple-choice":
			A_		:    str						     = {0: """batch""", 1: """choice""", 2: """sequence"""}
		else:
			A_		:    int						     = {0: """batch""", 1: """sequence"""}
		A_		:    Any						     = {0: """batch""", 1: """sequence"""}
		return OrderedDict(
		    [
		        ("""input_ids""", dynamic_axis),
		        ("""attention_mask""", dynamic_axis),
		        ("""token_type_ids""", dynamic_axis),
		    ]				)









"""simple docstring"""
import numpy as np






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ = 1E-12 , snake_case__ = 100 , ):
	assert np.shape(snake_case__       )[0] == np.shape(snake_case__       )[1]
	# Ensure proper dimensionality.
	assert np.shape(snake_case__       )[0] == np.shape(snake_case__       )[0]
	# Ensure inputs are either both complex or both real
	assert np.iscomplexobj(snake_case__       ) == np.iscomplexobj(snake_case__       )
	A_		:    Union[str, Any]						     = np.iscomplexobj(snake_case__       )
	if is_complex:
		# Ensure complex input_matrix is Hermitian
		assert np.array_equal(snake_case__ , input_matrix.conj().T       )

	# Set convergence to False. Will define convergence when we exceed max_iterations
	# or when we have small changes from one iteration to next.

	A_		:    List[str]						     = False
	A_		:    int						     = 0
	A_		:    List[Any]						     = 0
	A_		:    Tuple						     = 1E12

	while not convergence:
		# Multiple matrix by the vector.
		A_		:    Dict						     = np.dot(snake_case__ , snake_case__       )
		# Normalize the resulting output vector.
		A_		:    str						     = w / np.linalg.norm(snake_case__       )
		# Find rayleigh quotient
		# (faster than usual b/c we know vector is normalized already)
		A_		:    str						     = vector.conj().T if is_complex else vector.T
		A_		:    Optional[Any]						     = np.dot(snake_case__ , np.dot(snake_case__ , snake_case__       )       )

		# Check convergence.
		A_		:    Any						     = np.abs(lambda_ - lambda_previous       ) / lambda_
		iterations += 1

		if error <= error_tol or iterations >= max_iterations:
			A_		:    Optional[Any]						     = True

		A_		:    Union[str, Any]						     = lambda_

	if is_complex:
		A_		:    str						     = np.real(lambda_       )

	return lambda_, vector






def       __UpperCamelCase						(		):
	A_		:    Optional[int]						     = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]       )
	A_		:    Tuple						     = np.array([41, 4, 20]       )
	A_		:    Tuple						     = real_input_matrix.astype(np.complexaaa       )
	A_		:    List[Any]						     = np.triu(1J * complex_input_matrix , 1       )
	complex_input_matrix += imag_matrix
	complex_input_matrix += -1 * imag_matrix.T
	A_		:    Any						     = np.array([41, 4, 20]       ).astype(np.complexaaa       )

	for problem_type in ["real", "complex"]:
		if problem_type == "real":
			A_		:    Optional[int]						     = real_input_matrix
			A_		:    List[str]						     = real_vector
		elif problem_type == "complex":
			A_		:    List[Any]						     = complex_input_matrix
			A_		:    str						     = complex_vector

		# Our implementation.
		A_     ,  A_		:    Dict						     = power_iteration(snake_case__ , snake_case__       )

		# Numpy implementation.

		# Get eigenvalues and eigenvectors using built-in numpy
		# eigh (eigh used for symmetric or hermetian matrices).
		A_     ,  A_		:    Optional[Any]						     = np.linalg.eigh(snake_case__       )
		# Last eigenvalue is the maximum one.
		A_		:    List[Any]						     = eigen_values[-1]
		# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
		A_		:    Optional[Any]						     = eigen_vectors[:, -1]

		# Check our implementation and numpy gives close answers.
		assert np.abs(eigen_value - eigen_value_max       ) <= 1E-6
		# Take absolute values element wise of each eigenvector.
		# as they are only unique to a minus sign.
		assert np.linalg.norm(np.abs(snake_case__       ) - np.abs(snake_case__       )       ) <= 1E-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()

