





from __future__ import annotations




class 	UpperCamelCase_							:


							def __init__(				self     :Tuple				,					__A     :list[list[int]]		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   TypeError(
									    """Matrices must be formed from a list of zero or more lists containing at """
									    """least one and the same number of values, each of which must be of type """
									    """int or float."""		)
									if len(__A		) != 0:
											SCREAMING_SNAKE_CASE__								=   len(rows[0]		)
											if cols == 0:
													raise error
											for row in rows:
													if len(__A		) != cols:
															raise error
													for value in row:
															if not isinstance(__A				,					(int, float)		):
																	raise error
											SCREAMING_SNAKE_CASE__								=   rows
									else:
											SCREAMING_SNAKE_CASE__								=   []


							def   _snake_case	(				self     :int		) -> list[list[int]]:



									"""simple docstring"""



									return [[row[i] for row in self.rows] for i in range(len(self.rows[0]		)		)]


							@property
							def   _snake_case	(				self     :List[Any]		) -> int:



									"""simple docstring"""



									return len(self.rows		)


							@property
							def   _snake_case	(				self     :Optional[int]		) -> int:



									"""simple docstring"""



									return len(self.rows[0]		)


							@property
							def   _snake_case	(				self     :Dict		) -> tuple[int, int]:



									"""simple docstring"""



									return (self.num_rows, self.num_columns)


							@property
							def   _snake_case	(				self     :Any		) -> bool:



									"""simple docstring"""



									return self.order[0] == self.order[1]


							def   _snake_case	(				self     :int		) -> Matrix:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    [0 if column_num != row_num else 1 for column_num in range(self.num_rows		)]
									    for row_num in range(self.num_rows		)
									]
									return Matrix(__A		)


							def   _snake_case	(				self     :List[Any]		) -> int:



									"""simple docstring"""



									if not self.is_square:
											return 0
									if self.order == (0, 0):
											return 1
									if self.order == (1, 1):
											return int(self.rows[0][0]		)
									if self.order == (2, 2):
											return int(
											    (self.rows[0][0] * self.rows[1][1])
											    - (self.rows[0][1] * self.rows[1][0])		)
									else:
											return sum(
											    self.rows[0][column] * self.cofactors().rows[0][column]
											    for column in range(self.num_columns		)		)


							def   _snake_case	(				self     :Any		) -> bool:



									"""simple docstring"""



									return bool(self.determinant()		)


							def   _snake_case	(				self     :str				,					__A     :int				,					__A     :int		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    [
									        self.rows[other_row][other_column]
									        for other_column in range(self.num_columns		)
									        if other_column != column
									    ]
									    for other_row in range(self.num_rows		)
									    if other_row != row
									]
									return Matrix(__A		).determinant()


							def   _snake_case	(				self     :List[str]				,					__A     :int				,					__A     :int		) -> int:



									"""simple docstring"""



									if (row + column) % 2 == 0:
											return self.get_minor(__A				,					__A		)
									return -1 * self.get_minor(__A				,					__A		)


							def   _snake_case	(				self     :Dict		) -> Matrix:



									"""simple docstring"""



									return Matrix(
									    [
									        [self.get_minor(__A				,					__A		) for column in range(self.num_columns		)]
									        for row in range(self.num_rows		)
									    ]		)


							def   _snake_case	(				self     :List[Any]		) -> Matrix:



									"""simple docstring"""



									return Matrix(
									    [
									        [
									            self.minors().rows[row][column]
									            if (row + column) % 2 == 0
									            else self.minors().rows[row][column] * -1
									            for column in range(self.minors().num_columns		)
									        ]
									        for row in range(self.minors().num_rows		)
									    ]		)


							def   _snake_case	(				self     :Tuple		) -> Matrix:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    [self.cofactors().rows[column][row] for column in range(self.num_columns		)]
									    for row in range(self.num_rows		)
									]
									return Matrix(__A		)


							def   _snake_case	(				self     :List[str]		) -> Matrix:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.determinant()
									if not determinant:
											raise TypeError("""Only matrices with a non-zero determinant have an inverse"""		)
									return self.adjugate() * (1 / determinant)


							def __repr__(				self     :Any		) -> str:



									"""simple docstring"""



									return str(self.rows		)


							def __str__(				self     :Optional[Any]		) -> str:



									"""simple docstring"""



									if self.num_rows == 0:
											return "[]"
									if self.num_rows == 1:
											return "[[" + ". ".join(str(self.rows[0]		)		) + "]]"
									return (
									    "["
									    + "\n ".join(
									        [
									            """[""" + """. """.join([str(__A		) for value in row]		) + """.]"""
									            for row in self.rows
									        ]		)
									    + "]"
									)


							def   _snake_case	(				self     :Optional[Any]				,					__A     :list[int]				,					__A     :int | None = None		) -> None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   TypeError("""Row must be a list containing all ints and/or floats"""		)
									if not isinstance(__A				,					__A		):
											raise type_error
									for value in row:
											if not isinstance(__A				,					(int, float)		):
													raise type_error
									if len(__A		) != self.num_columns:
											raise ValueError(
											    """Row must be equal in length to the other rows in the matrix"""		)
									if position is None:
											self.rows.append(__A		)
									else:
											SCREAMING_SNAKE_CASE__								=   self.rows[0:position] + [row] + self.rows[position:]


							def   _snake_case	(				self     :List[str]				,					__A     :list[int]				,					__A     :int | None = None		) -> None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   TypeError(
									    """Column must be a list containing all ints and/or floats"""		)
									if not isinstance(__A				,					__A		):
											raise type_error
									for value in column:
											if not isinstance(__A				,					(int, float)		):
													raise type_error
									if len(__A		) != self.num_rows:
											raise ValueError(
											    """Column must be equal in length to the other columns in the matrix"""		)
									if position is None:
											SCREAMING_SNAKE_CASE__								=   [self.rows[i] + [column[i]] for i in range(self.num_rows		)]
									else:
											SCREAMING_SNAKE_CASE__								=   [
											    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
											    for i in range(self.num_rows		)
											]


							def __eq__(				self     :Optional[int]				,					__A     :object		) -> bool:



									"""simple docstring"""



									if not isinstance(__A				,					__A		):
											return NotImplemented
									return self.rows == other.rows


							def __ne__(				self     :Dict				,					__A     :object		) -> bool:



									"""simple docstring"""



									return not self == other


							def __neg__(				self     :Optional[int]		) -> Matrix:



									"""simple docstring"""



									return self * -1


							def __add__(				self     :Dict				,					__A     :Matrix		) -> Matrix:



									"""simple docstring"""



									if self.order != other.order:
											raise ValueError("""Addition requires matrices of the same order"""		)
									return Matrix(
									    [
									        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns		)]
									        for i in range(self.num_rows		)
									    ]		)


							def __sub__(				self     :str				,					__A     :Matrix		) -> Matrix:



									"""simple docstring"""



									if self.order != other.order:
											raise ValueError("""Subtraction requires matrices of the same order"""		)
									return Matrix(
									    [
									        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns		)]
									        for i in range(self.num_rows		)
									    ]		)


							def __mul__(				self     :Optional[int]				,					__A     :Matrix | int | float		) -> Matrix:



									"""simple docstring"""



									if isinstance(__A				,					(int, float)		):
											return Matrix(
											    [[int(element * other		) for element in row] for row in self.rows]		)
									elif isinstance(__A				,					__A		):
											if self.num_columns != other.num_rows:
													raise ValueError(
													    """The number of columns in the first matrix must """
													    """be equal to the number of rows in the second"""		)
											return Matrix(
											    [
											        [Matrix.dot_product(__A				,					__A		) for column in other.columns()]
											        for row in self.rows
											    ]		)
									else:
											raise TypeError(
											    """A Matrix can only be multiplied by an int, float, or another matrix"""		)


							def __pow__(				self     :str				,					__A     :int		) -> Matrix:



									"""simple docstring"""



									if not isinstance(__A				,					__A		):
											raise TypeError("""A Matrix can only be raised to the power of an int"""		)
									if not self.is_square:
											raise ValueError("""Only square matrices can be raised to a power"""		)
									if other == 0:
											return self.identity()
									if other < 0:
											if self.is_invertable():
													return self.inverse() ** (-other)
											raise ValueError(
											    """Only invertable matrices can be raised to a negative power"""		)
									SCREAMING_SNAKE_CASE__								=   self
									for _ in range(other - 1		):
											result *= self
									return result


							@classmethod
							def   _snake_case	(				cls     :str				,					__A     :list[int]				,					__A     :list[int]		) -> int:



									"""simple docstring"""



									return sum(row[i] * column[i] for i in range(len(__A		)		)		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()






from typing import Mapping

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


_lowerCAmelCase				:   Optional[int]						       =						logging.get_logger(__name__)

_lowerCAmelCase				:   List[str]						       =						{
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''umt5'''
     __UpperCamelCase            =  ['''past_key_values''']



     def __init__(				self							:int      ,   snake_case							:Optional[Any]=250_112      ,   snake_case							:Optional[int]=512      ,   snake_case							:Any=64      ,   snake_case							:Union[str, Any]=1_024      ,   snake_case							:Tuple=8      ,   snake_case							:Optional[int]=None      ,   snake_case							:Union[str, Any]=6      ,   snake_case							:List[Any]=32      ,   snake_case							:Dict=128      ,   snake_case							:List[str]=0.1      ,   snake_case							:List[Any]=1e-6      ,   snake_case							:Dict=1.0      ,   snake_case							:Union[str, Any]="gated-gelu"      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:Any=True      ,   snake_case							:List[str]="T5Tokenizer"      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:Union[str, Any]=0      ,   snake_case							:List[Any]=1      ,   snake_case							:List[Any]=0      ,   **snake_case							:Any      ,   ):





        '''simple docstring'''




        super().__init__(
            is_encoder_decoder=snake_case      ,   tokenizer_class=snake_case      ,   tie_word_embeddings=snake_case      ,   pad_token_id=snake_case      ,   eos_token_id=snake_case      ,   decoder_start_token_id=snake_case      ,   **snake_case      ,   )
        A_       :					Union[str, Any]        =      vocab_size
        A_       :					Tuple        =      d_model
        A_       :					List[str]        =      d_kv
        A_       :					Union[str, Any]        =      d_ff
        A_       :					Any        =      num_layers
        A_       :					Optional[Any]        =      (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A_       :					List[Any]        =      num_heads
        A_       :					List[str]        =      relative_attention_num_buckets
        A_       :					Dict        =      relative_attention_max_distance
        A_       :					Optional[Any]        =      dropout_rate
        A_       :					Any        =      layer_norm_epsilon
        A_       :					List[Any]        =      initializer_factor
        A_       :					Any        =      feed_forward_proj
        A_       :					Optional[Any]        =      use_cache

        A_       :					int        =      self.feed_forward_proj.split("-"	)
        A_       :					Any        =      act_info[-1]
        A_       :					Tuple        =      act_info[0] == "gated"

        if len(snake_case	) > 1 and act_info[0] != "gated" or len(snake_case	) > 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'"	)

        if feed_forward_proj == "gated-gelu":
           A_       :					Optional[Any]        =      "gelu_new"



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        return self.d_model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        return self.num_heads





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        return self.num_layers




class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					List[Any]        =      {
            "input_ids": {0: "batch", 1: "encoder_sequence"},
            "attention_mask": {0: "batch", 1: "encoder_sequence"},
        }
        if self.use_past:
           A_       :					Any        =      "past_encoder_sequence + sequence"
           A_       :					Union[str, Any]        =      {0: "batch"}
           A_       :					Optional[int]        =      {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
           A_       :					Dict        =      {0: "batch", 1: "decoder_sequence"}
           A_       :					str        =      {0: "batch", 1: "decoder_sequence"}

        if self.use_past:
           self.fill_with_past_key_values_(snake_case      ,   direction="inputs"	)

        return common_inputs



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        return 13





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        return 5e-4
