


"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


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

snake_case__						:   List[Any]			 = {
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}



class 						snake_case_(				a__	):
     __UpperCamelCase     =    '''gpt_neo'''
     __UpperCamelCase     =    ['''past_key_values''']
     __UpperCamelCase     =    {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






     def __init__(		self     :    str    , UpperCamelCase_     :    Union[str, Any]=5_0_2_5_7    , UpperCamelCase_     :    List[Any]=2_0_4_8    , UpperCamelCase_     :    Optional[int]=2_0_4_8    , UpperCamelCase_     :    List[str]=2_4    , UpperCamelCase_     :    Dict=[[["global", "local"], 1_2]]    , UpperCamelCase_     :    List[Any]=1_6    , UpperCamelCase_     :    Tuple=None    , UpperCamelCase_     :    Tuple=2_5_6    , UpperCamelCase_     :    Tuple="gelu_new"    , UpperCamelCase_     :    List[str]=0.0    , UpperCamelCase_     :    int=0.0    , UpperCamelCase_     :    List[Any]=0.0    , UpperCamelCase_     :    Dict=0.1    , UpperCamelCase_     :    Union[str, Any]=1E-5    , UpperCamelCase_     :    Dict=0.02    , UpperCamelCase_     :    List[str]=True    , UpperCamelCase_     :    str=5_0_2_5_6    , UpperCamelCase_     :    Tuple=5_0_2_5_6    , **UpperCamelCase_     :    Optional[int]    , ):
           lowerCAmelCase    :		Optional[Any]				=      vocab_size
           lowerCAmelCase    :		str				=      max_position_embeddings
           lowerCAmelCase    :		Optional[Any]				=      hidden_size
           lowerCAmelCase    :		Any				=      num_layers
           lowerCAmelCase    :		Union[str, Any]				=      num_heads
           lowerCAmelCase    :		int				=      intermediate_size
           lowerCAmelCase    :		Optional[Any]				=      window_size
           lowerCAmelCase    :		str				=      activation_function
           lowerCAmelCase    :		str				=      resid_dropout
           lowerCAmelCase    :		Dict				=      embed_dropout
           lowerCAmelCase    :		List[str]				=      attention_dropout
           lowerCAmelCase    :		List[str]				=      classifier_dropout
           lowerCAmelCase    :		str				=      layer_norm_epsilon
           lowerCAmelCase    :		Optional[Any]				=      initializer_range
           lowerCAmelCase    :		List[Any]				=      use_cache

           lowerCAmelCase    :		Optional[Any]				=      bos_token_id
           lowerCAmelCase    :		int				=      eos_token_id

           lowerCAmelCase    :		List[str]				=      attention_types
           lowerCAmelCase    :		Optional[int]				=      self.expand_attention_types_params(UpperCamelCase_ )

           if len(self.attention_layers ) != self.num_layers:
                 raise ValueError(
                     '''Configuration for convolutional module is incorrect. '''
                     '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
                     F'''but is `len(config.attention_layers) = {len(self.attention_layers )}`, '''
                     F'''`config.num_layers = {self.num_layers}`. '''
                     '''`config.attention_layers` is prepared using `config.attention_types`. '''
                     '''Please verify the value of `config.attention_types` argument.''' )

           super().__init__(bos_token_id=UpperCamelCase_    , eos_token_id=UpperCamelCase_    , **UpperCamelCase_ )






     @staticmethod
     def    lowerCamelCase__   (		UpperCamelCase_     :    Optional[Any] ):
           lowerCAmelCase    :		Any				=      []
           for item in attention_types:
                 for _ in range(item[1] ):
                       attentions.extend(item[0] )
           return attentions



def    _snake_case  (  _snake_case :	Any       ,      _snake_case :	Optional[Any]       ,      _snake_case :	Optional[int]       ,      _snake_case :	Optional[int]	):
      import torch

      lowerCAmelCase    :		Any				=      input.size()
      lowerCAmelCase    :		List[Any]				=      len(_snake_case	)
      lowerCAmelCase    :		Union[str, Any]				=      shape[dimension]

      lowerCAmelCase    :		List[Any]				=      torch.arange(0       ,      _snake_case       ,      _snake_case	)
      lowerCAmelCase    :		int				=      torch.div(sizedim - size       ,      _snake_case       ,      rounding_mode='''floor'''	) + 1
      lowerCAmelCase    :		List[Any]				=      torch.arange(_snake_case	) + low_indices[:min_length][:, None]

      lowerCAmelCase    :		Optional[Any]				=      [slice(_snake_case	)] * rank
      lowerCAmelCase    :		Optional[int]				=      indices
      lowerCAmelCase    :		Dict				=      input[s]

      lowerCAmelCase    :		str				=      list(range(0       ,      rank + 1	)	)
      perm.append(perm.pop(dimension + 1	)	)

      return sliced.permute(_snake_case	)


def    _snake_case  (  _snake_case :	Optional[Any]       ,      _snake_case :	Optional[int]	):
      import torch

      lowerCAmelCase    :		Optional[Any]				=      torch.arange(1       ,      _snake_case	)
      lowerCAmelCase    :		List[Any]				=      torch.remainder(_snake_case       ,      _snake_case	)
      lowerCAmelCase    :		Optional[int]				=      remainders == 0
      lowerCAmelCase    :		Tuple				=      candidates[divisor_indices]
      lowerCAmelCase    :		Dict				=      torch.max(_snake_case	)
      return largest_divisor, torch.div(_snake_case       ,      _snake_case       ,      rounding_mode='''floor'''	)






class 						snake_case_(				a__	):






     @property
     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase    :		Dict				=      OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
           if self.use_past:
                 self.fill_with_past_key_values_(UpperCamelCase_    , direction='''inputs''' )
                 lowerCAmelCase    :		str				=      {0: '''batch''', 1: '''past_sequence + sequence'''}
           else:
                 lowerCAmelCase    :		List[str]				=      {0: '''batch''', 1: '''sequence'''}

           return common_inputs






     @property
     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           return self._config.num_heads






     def    lowerCamelCase__   (		self     :    Optional[int]    , UpperCamelCase_     :    PreTrainedTokenizer    , UpperCamelCase_     :    int = -1    , UpperCamelCase_     :    int = -1    , UpperCamelCase_     :    bool = False    , UpperCamelCase_     :    Optional[TensorType] = None    , ):
           lowerCAmelCase    :		int				=      super(UpperCamelCase_    , self ).generate_dummy_inputs(
               UpperCamelCase_    , batch_size=UpperCamelCase_    , seq_length=UpperCamelCase_    , is_pair=UpperCamelCase_    , framework=UpperCamelCase_ )

           # We need to order the input in the way they appears in the forward()
           lowerCAmelCase    :		Dict				=      OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )

           # Need to add the past_keys
           if self.use_past:
                 if not is_torch_available():
                       raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
                 else:
                       import torch

                       lowerCAmelCase,					lowerCAmelCase    :		int				=      common_inputs['''input_ids'''].shape
                       # Not using the same length for past_key_values
                       lowerCAmelCase    :		List[Any]				=      seqlen + 2
                       lowerCAmelCase    :		Optional[int]				=      (
                           batch,
                           self.num_attention_heads,
                           past_key_values_length,
                           self._config.hidden_size // self.num_attention_heads,
                       )
                       lowerCAmelCase    :		int				=      [
                           (torch.zeros(UpperCamelCase_ ), torch.zeros(UpperCamelCase_ )) for _ in range(self.num_layers )
                       ]

           lowerCAmelCase    :		str				=      common_inputs['''attention_mask''']
           if self.use_past:
                 lowerCAmelCase    :		List[Any]				=      ordered_inputs['''attention_mask'''].dtype
                 lowerCAmelCase    :		Dict				=      torch.cat(
                     [ordered_inputs['''attention_mask'''], torch.ones(UpperCamelCase_    , UpperCamelCase_    , dtype=UpperCamelCase_ )]    , dim=1 )

           return ordered_inputs






     @property
     def    lowerCamelCase__   (		self     :    Tuple ):
           return 1_3






"""simple docstring"""





from __future__ import annotations

from decimal import Decimal

from numpy import array


def    _snake_case  (  _snake_case :	list[list[float]]	):
      lowerCAmelCase    :		str				=      Decimal

      # Check if the provided matrix has 2 rows and 2 columns
      # since this implementation only works for 2x2 matrices
      if len(_snake_case	) == 2 and len(matrix[0]	) == 2 and len(matrix[1]	) == 2:
            # Calculate the determinant of the matrix
            lowerCAmelCase    :		int				=      float(
                d(matrix[0][0]	) * d(matrix[1][1]	) - d(matrix[1][0]	) * d(matrix[0][1]	)	)
            if determinant == 0:
                  raise ValueError('''This matrix has no inverse.'''	)

            # Creates a copy of the matrix with swapped positions of the elements
            lowerCAmelCase    :		Optional[int]				=      [[0.0, 0.0], [0.0, 0.0]]
            lowerCAmelCase,					lowerCAmelCase    :		List[Any]				=      matrix[1][1], matrix[0][0]
            lowerCAmelCase,					lowerCAmelCase    :		Union[str, Any]				=      -matrix[1][0], -matrix[0][1]

            # Calculate the inverse of the matrix
            return [
                [(float(d(_snake_case	)	) / determinant) or 0.0 for n in row] for row in swapped_matrix
            ]
      elif (
          len(_snake_case	) == 3
          and len(matrix[0]	) == 3
          and len(matrix[1]	) == 3
          and len(matrix[2]	) == 3
      ):
            # Calculate the determinant of the matrix using Sarrus rule
            lowerCAmelCase    :		int				=      float(
                (
                    (d(matrix[0][0]	) * d(matrix[1][1]	) * d(matrix[2][2]	))
                    + (d(matrix[0][1]	) * d(matrix[1][2]	) * d(matrix[2][0]	))
                    + (d(matrix[0][2]	) * d(matrix[1][0]	) * d(matrix[2][1]	))
                )
                - (
                    (d(matrix[0][2]	) * d(matrix[1][1]	) * d(matrix[2][0]	))
                    + (d(matrix[0][1]	) * d(matrix[1][0]	) * d(matrix[2][2]	))
                    + (d(matrix[0][0]	) * d(matrix[1][2]	) * d(matrix[2][1]	))
                )	)
            if determinant == 0:
                  raise ValueError('''This matrix has no inverse.'''	)

            # Creating cofactor matrix
            lowerCAmelCase    :		Dict				=      [
                [d(0.0	), d(0.0	), d(0.0	)],
                [d(0.0	), d(0.0	), d(0.0	)],
                [d(0.0	), d(0.0	), d(0.0	)],
            ]
            lowerCAmelCase    :		List[str]				=      (d(matrix[1][1]	) * d(matrix[2][2]	)) - (
                d(matrix[1][2]	) * d(matrix[2][1]	)
            )
            lowerCAmelCase    :		Dict				=      -(
                (d(matrix[1][0]	) * d(matrix[2][2]	)) - (d(matrix[1][2]	) * d(matrix[2][0]	))
            )
            lowerCAmelCase    :		str				=      (d(matrix[1][0]	) * d(matrix[2][1]	)) - (
                d(matrix[1][1]	) * d(matrix[2][0]	)
            )
            lowerCAmelCase    :		Any				=      -(
                (d(matrix[0][1]	) * d(matrix[2][2]	)) - (d(matrix[0][2]	) * d(matrix[2][1]	))
            )
            lowerCAmelCase    :		Any				=      (d(matrix[0][0]	) * d(matrix[2][2]	)) - (
                d(matrix[0][2]	) * d(matrix[2][0]	)
            )
            lowerCAmelCase    :		Optional[int]				=      -(
                (d(matrix[0][0]	) * d(matrix[2][1]	)) - (d(matrix[0][1]	) * d(matrix[2][0]	))
            )
            lowerCAmelCase    :		Optional[int]				=      (d(matrix[0][1]	) * d(matrix[1][2]	)) - (
                d(matrix[0][2]	) * d(matrix[1][1]	)
            )
            lowerCAmelCase    :		Dict				=      -(
                (d(matrix[0][0]	) * d(matrix[1][2]	)) - (d(matrix[0][2]	) * d(matrix[1][0]	))
            )
            lowerCAmelCase    :		List[Any]				=      (d(matrix[0][0]	) * d(matrix[1][1]	)) - (
                d(matrix[0][1]	) * d(matrix[1][0]	)
            )

            # Transpose the cofactor matrix (Adjoint matrix)
            lowerCAmelCase    :		str				=      array(_snake_case	)
            for i in range(3	):
                  for j in range(3	):
                        lowerCAmelCase    :		Optional[Any]				=      cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
            lowerCAmelCase    :		Tuple				=      array(_snake_case	)
            for i in range(3	):
                  for j in range(3	):
                        inverse_matrix[i][j] /= d(_snake_case	)

        # Calculate the inverse of the matrix
            return [[float(d(_snake_case	)	) or 0.0 for n in row] for row in inverse_matrix]
      raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''	)


