





'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}


class 					a_     (				snake_case__       ):

    '''simple docstring'''
    UpperCamelCase     = '''switch_transformers'''
    UpperCamelCase     = ['''past_key_values''']
    UpperCamelCase     = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}


    def __init__(		self ,		A=3_2128 ,		A=768 ,		A=64 ,		A=2048 ,		A=64 ,		A=12 ,		A=3 ,		A=12 ,		A=3 ,		A=12 ,		A=8 ,		A=False ,		A=0.01 ,		A="float32" ,		A=False ,		A=32 ,		A=128 ,		A=0.1 ,		A=1e-6 ,		A=0.001 ,		A=0.001 ,		A=1.0 ,		A="relu" ,		A=True ,		A=False ,		A=True ,		A=0 ,		A=1 ,		**A ,		)		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			d_model
        _SCREAMING_SNAKE_CASE									=			d_kv
        _SCREAMING_SNAKE_CASE									=			d_ff

        _SCREAMING_SNAKE_CASE									=			num_sparse_encoder_layers

        _SCREAMING_SNAKE_CASE									=			num_layers
        _SCREAMING_SNAKE_CASE									=			(
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        _SCREAMING_SNAKE_CASE									=			num_sparse_decoder_layers

        # This tells us, each how many encoder layer we'll have to set a sparse layer.
        if self.num_sparse_encoder_layers > 0:
            _SCREAMING_SNAKE_CASE									=			self.num_layers // self.num_sparse_encoder_layers
        else:
            _SCREAMING_SNAKE_CASE									=			self.num_layers  # HACK: this will create 0 sparse layers

        # This tells us, each how many encoder layer we'll have to set a sparse layer.
        if self.num_sparse_decoder_layers > 0:
            _SCREAMING_SNAKE_CASE									=			self.num_decoder_layers // self.num_sparse_decoder_layers
        else:
            _SCREAMING_SNAKE_CASE									=			self.num_decoder_layers  # HACK: this will create 0 sparse layers

        _SCREAMING_SNAKE_CASE									=			num_heads
        _SCREAMING_SNAKE_CASE									=			num_experts
        _SCREAMING_SNAKE_CASE									=			expert_capacity
        _SCREAMING_SNAKE_CASE									=			router_bias
        _SCREAMING_SNAKE_CASE									=			router_jitter_noise
        if router_dtype not in ["float32", "float16", "bfloat16"]:
            raise ValueError(f'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'       )
        _SCREAMING_SNAKE_CASE									=			router_dtype

        _SCREAMING_SNAKE_CASE									=			router_ignore_padding_tokens
        _SCREAMING_SNAKE_CASE									=			relative_attention_num_buckets
        _SCREAMING_SNAKE_CASE									=			relative_attention_max_distance

        _SCREAMING_SNAKE_CASE									=			dropout_rate
        _SCREAMING_SNAKE_CASE									=			layer_norm_epsilon
        _SCREAMING_SNAKE_CASE									=			initializer_factor
        _SCREAMING_SNAKE_CASE									=			feed_forward_proj
        _SCREAMING_SNAKE_CASE									=			use_cache
        _SCREAMING_SNAKE_CASE									=			add_router_probs

        _SCREAMING_SNAKE_CASE									=			router_z_loss_coef
        _SCREAMING_SNAKE_CASE									=			router_aux_loss_coef

        _SCREAMING_SNAKE_CASE									=			self.feed_forward_proj.split("""-"""       )
        _SCREAMING_SNAKE_CASE									=			act_info[-1]
        _SCREAMING_SNAKE_CASE									=			act_info[0] == 'gated'

        if len(_A       ) > 1 and act_info[0] != "gated" or len(_A       ) > 2:
            raise ValueError(
                f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                """\'gated-gelu\' or \'relu\'"""       )

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
            _SCREAMING_SNAKE_CASE									=			'gelu_new'

        super().__init__(
            pad_token_id=_A ,		eos_token_id=_A ,		is_encoder_decoder=_A ,		**_A ,		)










from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A = None     ):
									if components is None:
														__A	:						int       =				[]
									__A	:						Tuple       =				list(_A     )



				def __len__(			self     ):
									return len(self.__components     )



				def __str__(			self     ):
									return "(" + ",".join(map(_A     ,					self.__components     )     ) + ")"



				def __add__(			self     ,					_A     ):
									__A	:						Optional[int]       =				len(self     )
									if size == len(_A     ):
														__A	:						Any       =				[self.__components[i] + other.component(_A     ) for i in range(_A     )]
														return Vector(_A     )
									else:
														raise Exception('must have the same size'     )



				def __sub__(			self     ,					_A     ):
									__A	:						Tuple       =				len(self     )
									if size == len(_A     ):
														__A	:						Union[str, Any]       =				[self.__components[i] - other.component(_A     ) for i in range(_A     )]
														return Vector(_A     )
									else:  # error case
														raise Exception('must have the same size'     )



				@overload
				def __mul__(			self     ,					_A     ):
									...



				@overload
				def __mul__(			self     ,					_A     ):
									...



				def __mul__(			self     ,					_A     ):
									if isinstance(_A     ,					(float, int)     ):
														__A	:						str       =				[c * other for c in self.__components]
														return Vector(_A     )
									elif isinstance(_A     ,					_A     ) and len(self     ) == len(_A     ):
														__A	:						Union[str, Any]       =				len(self     )
														__A	:						Dict       =				[self.__components[i] * other.component(_A     ) for i in range(_A     )]
														return sum(_A     )
									else:  # error case
														raise Exception('invalid operand!'     )



				def 					UpperCAmelCase_		(			self     ):
									return Vector(self.__components     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									if isinstance(_A     ,					_A     ) and -len(self.__components     ) <= i < len(self.__components     ):
														return self.__components[i]
									else:
														raise Exception('index out of range'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									assert -len(self.__components     ) <= pos < len(self.__components     )
									__A	:						Optional[int]       =				value



				def 					UpperCAmelCase_		(			self     ):
									if len(self.__components     ) == 0:
														raise Exception('Vector is empty'     )
									__A	:						Optional[Any]       =				[c**2 for c in self.__components]
									return math.sqrt(sum(_A     )     )





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = False     ):
									__A	:						Optional[Any]       =				self * other
									__A	:						Optional[Any]       =				self.euclidean_length() * other.euclidean_length()
									if deg:
														return math.degrees(math.acos(num / den     )     )
									else:
														return math.acos(num / den     )







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Vector:
					assert isinstance(a					,						a  )
					return Vector([0] * dimension  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Vector:
					assert isinstance(a					,						a  ) and (isinstance(a					,						a  ))
					__A	:						Optional[Any]       =				[0] * dimension
					__A	:						Tuple       =				1
					return Vector(a  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Vector:
					assert (
					    isinstance(a					,						a  )
					    and isinstance(a					,						a  )
					    and (isinstance(a					,						(int, float)  ))
					)
					return x * scalar + y







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Vector:
					random.seed(a  )
					__A	:						str       =				[random.randint(a					,						a  ) for _ in range(a  )]
					return Vector(a  )





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						Optional[Any]       =				matrix
									__A	:						Dict       =				w
									__A	:						Optional[int]       =				h



				def __str__(			self     ):
									__A	:						Tuple       =				''
									for i in range(self.__height     ):
														ans += "|"
														for j in range(self.__width     ):
																			if j < self.__width - 1:
																								ans += str(self.__matrix[i][j]     ) + ","
																			else:
																								ans += str(self.__matrix[i][j]     ) + "|\n"
									return ans



				def __add__(			self     ,					_A     ):
									if self.__width == other.width() and self.__height == other.height():
														__A	:						Optional[Any]       =				[]
														for i in range(self.__height     ):
																			__A	:						Optional[Any]       =				[
																			    self.__matrix[i][j] + other.component(_A     ,					_A     )
																			    for j in range(self.__width     )
																			]
																			matrix.append(_A     )
														return Matrix(_A     ,					self.__width     ,					self.__height     )
									else:
														raise Exception('matrix must have the same dimension!'     )



				def __sub__(			self     ,					_A     ):
									if self.__width == other.width() and self.__height == other.height():
														__A	:						Tuple       =				[]
														for i in range(self.__height     ):
																			__A	:						str       =				[
																			    self.__matrix[i][j] - other.component(_A     ,					_A     )
																			    for j in range(self.__width     )
																			]
																			matrix.append(_A     )
														return Matrix(_A     ,					self.__width     ,					self.__height     )
									else:
														raise Exception('matrices must have the same dimension!'     )



				@overload
				def __mul__(			self     ,					_A     ):
									...



				@overload
				def __mul__(			self     ,					_A     ):
									...



				def __mul__(			self     ,					_A     ):
									if isinstance(_A     ,					_A     ):  # matrix-vector
														if len(_A     ) == self.__width:
																			__A	:						List[Any]       =				zero_vector(self.__height     )
																			for i in range(self.__height     ):
																								__A	:						List[str]       =				[
																								    self.__matrix[i][j] * other.component(_A     )
																								    for j in range(self.__width     )
																								]
																								ans.change_component(_A     ,					sum(_A     )     )
																			return ans
														else:
																			raise Exception(
																			    'vector must have the same size as the '
																			    'number of columns of the matrix!'     )
									elif isinstance(_A     ,					(int, float)     ):  # matrix-scalar
														__A	:						List[str]       =				[
														    [self.__matrix[i][j] * other for j in range(self.__width     )]
														    for i in range(self.__height     )
														]
														return Matrix(_A     ,					self.__width     ,					self.__height     )
									return None



				def 					UpperCAmelCase_		(			self     ):
									return self.__height



				def 					UpperCAmelCase_		(			self     ):
									return self.__width



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if 0 <= x < self.__height and 0 <= y < self.__width:
														return self.__matrix[x][y]
									else:
														raise Exception('change_component: indices out of bounds'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									if 0 <= x < self.__height and 0 <= y < self.__width:
														__A	:						int       =				value
									else:
														raise Exception('change_component: indices out of bounds'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									__A	:						List[str]       =				self.__matrix[:x] + self.__matrix[x + 1 :]
									for i in range(len(_A     )     ):
														__A	:						Optional[int]       =				minor[i][:y] + minor[i][y + 1 :]
									return Matrix(_A     ,					self.__width - 1     ,					self.__height - 1     ).determinant()



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									if 0 <= x < self.__height and 0 <= y < self.__width:
														return (-1) ** (x + y) * self.minor(_A     ,					_A     )
									else:
														raise Exception('Indices out of bounds'     )





				def 					UpperCAmelCase_		(			self     ):
									if self.__height != self.__width:
														raise Exception('Matrix is not square'     )
									if self.__height < 1:
														raise Exception('Matrix has no element'     )
									elif self.__height == 1:
														return self.__matrix[0][0]
									elif self.__height == 2:
														return (
														    self.__matrix[0][0] * self.__matrix[1][1]
														    - self.__matrix[0][1] * self.__matrix[1][0]
														)
									else:
														__A	:						List[str]       =				[
														    self.__matrix[0][y] * self.cofactor(0     ,					_A     ) for y in range(self.__width     )
														]
														return sum(_A     )







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Matrix:
					__A	:						list[list[float]]       =				[[0] * n for _ in range(a  )]
					return Matrix(a					,						a					,						a  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a  )      ->      Matrix:
					random.seed(a  )
					__A	:						list[list[float]]       =				[
					    [random.randint(a					,						a  ) for _ in range(a  )] for _ in range(a  )
					]
					return Matrix(a					,						a					,						a  )




