"""simple docstring"""
from __future__ import annotations



class 					lowerCAmelCase_      :



    """simple docstring"""

    def __init__(			self						,      lowerCAmelCase      ):






          """simple docstring"""

          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(lowerCAmelCase      ) != 0:
                snake_case        =     len(rows[0]      )
                if cols == 0:
                      raise error
                for row in rows:
                      if len(lowerCAmelCase      ) != cols:
                            raise error
                      for value in row:
                            if not isinstance(lowerCAmelCase						,      (int, float)      ):
                                  raise error
                snake_case        =     rows
          else:
                snake_case        =     []

    def 						snake_case	(			self      ):






          """simple docstring"""

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

    @property
    def 						snake_case	(			self      ):






          """simple docstring"""

          return len(self.rows      )

    @property
    def 						snake_case	(			self      ):






          """simple docstring"""

          return len(self.rows[0]      )

    @property
    def 						snake_case	(			self      ):






          """simple docstring"""

          return (self.num_rows, self.num_columns)

    @property
    def 						snake_case	(			self      ):






          """simple docstring"""

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

    def 						snake_case	(			self      ):






          """simple docstring"""

          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(lowerCAmelCase      )

    def 						snake_case	(			self      ):






          """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      ):






          """simple docstring"""

          return bool(self.determinant()      )

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






          """simple docstring"""

          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(lowerCAmelCase      ).determinant()

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






          """simple docstring"""

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

    def 						snake_case	(			self      ):






          """simple docstring"""

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

    def 						snake_case	(			self      ):






          """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      ):






          """simple docstring"""

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

    def 						snake_case	(			self      ):






          """simple docstring"""

          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      ):






          """simple docstring"""

          return str(self.rows      )

    def __str__(			self      ):






          """simple docstring"""

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

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None      ):






          """simple docstring"""

          snake_case        =     TypeError('Row must be a list containing all ints and/or floats'      )
          if not isinstance(lowerCAmelCase						,      lowerCAmelCase      ):
                raise type_error
          for value in row:
                if not isinstance(lowerCAmelCase						,      (int, float)      ):
                      raise type_error
          if len(lowerCAmelCase      ) != 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(lowerCAmelCase      )
          else:
                snake_case        =     self.rows[0:position] + [row] + self.rows[position:]

    def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None      ):






          """simple docstring"""

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

    def __eq__(			self						,      lowerCAmelCase      ):






          """simple docstring"""

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

    def __ne__(			self						,      lowerCAmelCase      ):






          """simple docstring"""

          return not self == other

    def __neg__(			self      ):






          """simple docstring"""

          return self * -1

    def __add__(			self						,      lowerCAmelCase      ):






          """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						,      lowerCAmelCase      ):






          """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						,      lowerCAmelCase      ):






          """simple docstring"""

          if isinstance(lowerCAmelCase						,      (int, float)      ):
                return Matrix(
                    [[int(element * other      ) for element in row] for row in self.rows]      )
          elif isinstance(lowerCAmelCase						,      lowerCAmelCase      ):
                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(lowerCAmelCase						,      lowerCAmelCase      ) 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						,      lowerCAmelCase      ):






          """simple docstring"""

          if not isinstance(lowerCAmelCase						,      lowerCAmelCase      ):
                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'      )
          snake_case        =     self
          for _ in range(other - 1      ):
                result *= self
          return result

    @classmethod
    def 						snake_case	(			cls						,      lowerCAmelCase						,      lowerCAmelCase      ):






          """simple docstring"""

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


if __name__ == "__main__":
     import doctest

     doctest.testmod()



"""simple docstring"""
from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      int     					=  [r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]

				@register_to_config
				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = 5_02_57						,      lowerCAmelCase = 10_24						,      lowerCAmelCase = 7_68						,      lowerCAmelCase = 12						,      lowerCAmelCase = 12						,      lowerCAmelCase = None						,      lowerCAmelCase = "gelu_new"						,      lowerCAmelCase = 0.1						,      lowerCAmelCase = 0.1						,      lowerCAmelCase = 0.1						,      lowerCAmelCase = 1E-5						,      lowerCAmelCase = 0.02						,      lowerCAmelCase = True						,      lowerCAmelCase = True						,      lowerCAmelCase = False						,      lowerCAmelCase = False						,      ):






										"""simple docstring"""

										super().__init__()

										snake_case        =     prefix_length

										if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
																raise ValueError(
																    F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
																    F""" `n_embd`: {n_embd} are not equal."""      )

										snake_case        =     prefix_inner_dim
										snake_case        =     prefix_hidden_dim

										snake_case        =     (
										    nn.Linear(self.prefix_inner_dim						,      self.prefix_hidden_dim      )
										    if self.prefix_hidden_dim is not None
										    else nn.Identity()
										)
										snake_case        =     (
										    nn.Linear(self.prefix_hidden_dim						,      lowerCAmelCase      ) if self.prefix_hidden_dim is not None else nn.Identity()
										)

										snake_case        =     GPTaConfig(
										    vocab_size=lowerCAmelCase						,      n_positions=lowerCAmelCase						,      n_embd=lowerCAmelCase						,      n_layer=lowerCAmelCase						,      n_head=lowerCAmelCase						,      n_inner=lowerCAmelCase						,      activation_function=lowerCAmelCase						,      resid_pdrop=lowerCAmelCase						,      embd_pdrop=lowerCAmelCase						,      attn_pdrop=lowerCAmelCase						,      layer_norm_epsilon=lowerCAmelCase						,      initializer_range=lowerCAmelCase						,      scale_attn_weights=lowerCAmelCase						,      use_cache=lowerCAmelCase						,      scale_attn_by_inverse_layer_idx=lowerCAmelCase						,      reorder_and_upcast_attn=lowerCAmelCase						,      )
										snake_case        =     GPTaLMHeadModel(lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										snake_case        =     self.transformer.transformer.wte(lowerCAmelCase      )
										snake_case        =     self.encode_prefix(lowerCAmelCase      )
										snake_case        =     self.decode_prefix(lowerCAmelCase      )
										snake_case        =     torch.cat((prefix_embeds, embedding_text)						,      dim=1      )

										if labels is not None:
																snake_case        =     self.get_dummy_token(input_ids.shape[0]						,      input_ids.device      )
																snake_case        =     torch.cat((dummy_token, input_ids)						,      dim=1      )
										snake_case        =     self.transformer(inputs_embeds=lowerCAmelCase						,      labels=lowerCAmelCase						,      attention_mask=lowerCAmelCase      )
										if self.prefix_hidden_dim is not None:
																return out, hidden
										else:
																return out

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										return torch.zeros(lowerCAmelCase						,      self.prefix_length						,      dtype=torch.intaa						,      device=lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										return self.encode_prefix(lowerCAmelCase      )

				@torch.no_grad()
				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     torch.split(lowerCAmelCase						,      1						,      dim=0      )
										snake_case        =     []
										snake_case        =     []
										for feature in features:
																snake_case        =     self.decode_prefix(feature.to(lowerCAmelCase      )      )  # back to the clip feature
																# Only support beam search for now
																snake_case   ,snake_case        =     self.generate_beam(
																    input_embeds=lowerCAmelCase						,      device=lowerCAmelCase						,      eos_token_id=lowerCAmelCase      )
																generated_tokens.append(output_tokens[0]      )
																generated_seq_lengths.append(seq_lengths[0]      )
										snake_case        =     torch.stack(lowerCAmelCase      )
										snake_case        =     torch.stack(lowerCAmelCase      )
										return generated_tokens, generated_seq_lengths

				@torch.no_grad()
				def 						snake_case	(			self						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase = 5						,      lowerCAmelCase = 67						,      lowerCAmelCase = 1.0						,      lowerCAmelCase = None						,      ):






										"""simple docstring"""

										snake_case        =     eos_token_id
										snake_case        =     None
										snake_case        =     None
										snake_case        =     torch.ones(lowerCAmelCase						,      device=lowerCAmelCase						,      dtype=torch.int      )
										snake_case        =     torch.zeros(lowerCAmelCase						,      device=lowerCAmelCase						,      dtype=torch.bool      )

										if input_embeds is not None:
																snake_case        =     input_embeds
										else:
																snake_case        =     self.transformer.transformer.wte(lowerCAmelCase      )

										for i in range(lowerCAmelCase      ):
																snake_case        =     self.transformer(inputs_embeds=lowerCAmelCase      )
																snake_case        =     outputs.logits
																snake_case        =     logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
																snake_case        =     logits.softmax(-1      ).log()

																if scores is None:
																						snake_case   ,snake_case        =     logits.topk(lowerCAmelCase						,      -1      )
																						snake_case        =     generated.expand(lowerCAmelCase						,      *generated.shape[1:]      )
																						snake_case   ,snake_case        =     next_tokens.permute(1						,      0      ), scores.squeeze(0      )
																						if tokens is None:
																												snake_case        =     next_tokens
																						else:
																												snake_case        =     tokens.expand(lowerCAmelCase						,      *tokens.shape[1:]      )
																												snake_case        =     torch.cat((tokens, next_tokens)						,      dim=1      )
																else:
																						snake_case        =     -float(np.inf      )
																						snake_case        =     0
																						snake_case        =     scores[:, None] + logits
																						seq_lengths[~is_stopped] += 1
																						snake_case        =     scores_sum / seq_lengths[:, None]
																						snake_case   ,snake_case        =     scores_sum_average.view(-1      ).topk(lowerCAmelCase						,      -1      )
																						snake_case        =     next_tokens // scores_sum.shape[1]
																						snake_case        =     seq_lengths[next_tokens_source]
																						snake_case        =     next_tokens % scores_sum.shape[1]
																						snake_case        =     next_tokens.unsqueeze(1      )
																						snake_case        =     tokens[next_tokens_source]
																						snake_case        =     torch.cat((tokens, next_tokens)						,      dim=1      )
																						snake_case        =     generated[next_tokens_source]
																						snake_case        =     scores_sum_average * seq_lengths
																						snake_case        =     is_stopped[next_tokens_source]

																snake_case        =     self.transformer.transformer.wte(next_tokens.squeeze()      ).view(generated.shape[0]						,      1						,      -1      )
																snake_case        =     torch.cat((generated, next_token_embed)						,      dim=1      )
																snake_case        =     is_stopped + next_tokens.eq(lowerCAmelCase      ).squeeze()
																if is_stopped.all():
																						break

										snake_case        =     scores / seq_lengths
										snake_case        =     scores.argsort(descending=lowerCAmelCase      )
										# tokens tensors are already padded to max_seq_length
										snake_case        =     [tokens[i] for i in order]
										snake_case        =     torch.stack(lowerCAmelCase						,      dim=0      )
										snake_case        =     torch.tensor([seq_lengths[i] for i in order]						,      dtype=seq_lengths.dtype      )
										return output_texts, seq_lengths


