'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


_a   :	int 							=   logging.get_logger(__name__)

_a   :	Tuple 							=   {
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}






class 		__A       (__lowercase   ):

       '''simple docstring'''





       snake_case       :List[str]				    =					'data2vec-text'






       def __init__(      self				,     UpperCamelCase_=3_05_22				,     UpperCamelCase_=7_68				,     UpperCamelCase_=12				,     UpperCamelCase_=12				,     UpperCamelCase_=30_72				,     UpperCamelCase_="gelu"				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.1				,     UpperCamelCase_=5_12				,     UpperCamelCase_=2				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=1E-12				,     UpperCamelCase_=1				,     UpperCamelCase_=0				,     UpperCamelCase_=2				,     UpperCamelCase_="absolute"				,     UpperCamelCase_=True				,     UpperCamelCase_=None				,     **UpperCamelCase_				,     ):
              super().__init__(pad_token_id=__A				,     bos_token_id=__A				,     eos_token_id=__A				,     **__A      )

              __UpperCAmelCase  :				str       				=		vocab_size
              __UpperCAmelCase  :				Union[str, Any]       				=		hidden_size
              __UpperCAmelCase  :				str       				=		num_hidden_layers
              __UpperCAmelCase  :				str       				=		num_attention_heads
              __UpperCAmelCase  :				Optional[int]       				=		hidden_act
              __UpperCAmelCase  :				Tuple       				=		intermediate_size
              __UpperCAmelCase  :				Optional[Any]       				=		hidden_dropout_prob
              __UpperCAmelCase  :				Tuple       				=		attention_probs_dropout_prob
              __UpperCAmelCase  :				Tuple       				=		max_position_embeddings
              __UpperCAmelCase  :				Dict       				=		type_vocab_size
              __UpperCAmelCase  :				Tuple       				=		initializer_range
              __UpperCAmelCase  :				Any       				=		layer_norm_eps
              __UpperCAmelCase  :				Optional[Any]       				=		position_embedding_type
              __UpperCAmelCase  :				Dict       				=		use_cache
              __UpperCAmelCase  :				List[str]       				=		classifier_dropout






class 		__A       (__lowercase   ):

       '''simple docstring'''





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







'''simple docstring'''




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    )					->       list[float]:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Union[str, Any]       				=		coefficient_matrix.shape
       __UpperCAmelCase				, __UpperCAmelCase  :				Any       				=		constant_matrix.shape

       if rowsa != colsa:
              __UpperCAmelCase  :				str       				=		f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if colsa != 1:
              __UpperCAmelCase  :				Optional[Any]       				=		f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if rowsa != rowsa:
              __UpperCAmelCase  :				Optional[int]       				=		(
                  "Coefficient and constant matrices dimensions must be nxn and nx1 but "
                  f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if len(lowerCamelCase__						) != rowsa:
              __UpperCAmelCase  :				List[str]       				=		(
                  "Number of initial values must be equal to number of rows in coefficient "
                  f"""matrix but received {len(lowerCamelCase__						)} and {rowsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if iterations <= 0:
              raise ValueError("Iterations must be at least 1"						)

       __UpperCAmelCase  :				NDArray[floataa]       				=		np.concatenate(
           (coefficient_matrix, constant_matrix)							,    axis=1						)

       __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		table.shape

       strictly_diagonally_dominant(lowerCamelCase__						)

       # Iterates the whole matrix for given number of times
       for _ in range(lowerCamelCase__						):
              __UpperCAmelCase  :				int       				=		[]
              for row in range(lowerCamelCase__						):
                     __UpperCAmelCase  :				List[str]       				=		0
                     for col in range(lowerCamelCase__						):
                            if col == row:
                                   __UpperCAmelCase  :				int       				=		table[row][col]
                            elif col == cols - 1:
                                   __UpperCAmelCase  :				Any       				=		table[row][col]
                            else:
                                   temp += (-1) * table[row][col] * init_val[col]
                     __UpperCAmelCase  :				List[Any]       				=		(temp + val) / denom
                     new_val.append(lowerCamelCase__						)
              __UpperCAmelCase  :				str       				=		new_val

       return [float(lowerCamelCase__						) for i in new_val]

def _lowercase	(      lowerCamelCase__						)					->       bool:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		table.shape

       __UpperCAmelCase  :				str       				=		True

       for i in range(0							,    lowerCamelCase__						):
              __UpperCAmelCase  :				Union[str, Any]       				=		0
              for j in range(0							,    cols - 1						):
                     if i == j:
                            continue
                     else:
                            total += table[i][j]

              if table[i][i] <= total:
                     raise ValueError("Coefficient matrix is not strictly diagonally dominant"						)

       return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
   import doctest

   doctest.testmod()






