"""simple docstring"""






from __future__ import annotations
class        snake_case :




					"""simple docstring"""

					def __init__(       self     :			Dict					,lowerCamelCase__     :			list[list[int]]    ):
									UpperCAmelCase__    =							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:
													UpperCAmelCase__    =							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
													UpperCAmelCase__    =							rows
									else:
													UpperCAmelCase__    =							[]







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									return [[row[i] for row in self.rows] for i in range(len(self.rows[0]    )    )]







					@property
					def 	__lowerCAmelCase		(       self     :			str    ):
									return len(self.rows    )







					@property
					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									return len(self.rows[0]    )







					@property
					def 	__lowerCAmelCase		(       self     :			Any    ):
									return (self.num_rows, self.num_columns)







					@property
					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									return self.order[0] == self.order[1]







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							[
									    [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 	__lowerCAmelCase		(       self     :			str    ):
									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 	__lowerCAmelCase		(       self     :			List[str]    ):
									return bool(self.determinant()    )







					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			int					,lowerCamelCase__     :			int    ):
									UpperCAmelCase__    =							[
									    [
									        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 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			int    ):
									if (row + column) % 2 == 0:
													return self.get_minor(lowerCamelCase__					,lowerCamelCase__    )
									return -1 * self.get_minor(lowerCamelCase__					,lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									return Matrix(
									    [
									        [self.get_minor(lowerCamelCase__					,lowerCamelCase__    ) for column in range(self.num_columns    )]
									        for row in range(self.num_rows    )
									    ]    )







					def 	__lowerCAmelCase		(       self     :			int    ):
									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 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									UpperCAmelCase__    =							[
									    [self.cofactors().rows[column][row] for column in range(self.num_columns    )]
									    for row in range(self.num_rows    )
									]
									return Matrix(lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									UpperCAmelCase__    =							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     :			Optional[Any]    ):
									return str(self.rows    )







					def __str__(       self     :			List[str]    ):
									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 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			list[int]					,lowerCamelCase__     :			int | None = None    ):
									UpperCAmelCase__    =							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:
													UpperCAmelCase__    =							self.rows[0:position] + [row] + self.rows[position:]







					def 	__lowerCAmelCase		(       self     :			Dict					,lowerCamelCase__     :			list[int]					,lowerCamelCase__     :			int | None = None    ):
									UpperCAmelCase__    =							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:
													UpperCAmelCase__    =							[self.rows[i] + [column[i]] for i in range(self.num_rows    )]
									else:
													UpperCAmelCase__    =							[
													    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
													    for i in range(self.num_rows    )
													]







					def __eq__(       self     :			List[Any]					,lowerCamelCase__     :			object    ):
									if not isinstance(lowerCamelCase__					,lowerCamelCase__    ):
													return NotImplemented
									return self.rows == other.rows







					def __ne__(       self     :			Any					,lowerCamelCase__     :			object    ):
									return not self == other







					def __neg__(       self     :			Dict    ):
									return self * -1







					def __add__(       self     :			str					,lowerCamelCase__     :			Matrix    ):
									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     :			List[str]					,lowerCamelCase__     :			Matrix    ):
									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     :			List[str]					,lowerCamelCase__     :			Matrix | int | float    ):
									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     :			Optional[int]					,lowerCamelCase__     :			int    ):
									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'    )
									UpperCAmelCase__    =							self
									for _ in range(other - 1    ):
													result *= self
									return result







					@classmethod
					def 	__lowerCAmelCase		(       cls     :			Union[str, Any]					,lowerCamelCase__     :			list[int]					,lowerCamelCase__     :			list[int]    ):
									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        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase   ,					__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]







					@register_to_config
					def __init__(       self     :			str					,lowerCamelCase__     :			int					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			int = 50_257					,lowerCamelCase__     :			int = 1_024					,lowerCamelCase__     :			int = 768					,lowerCamelCase__     :			int = 12					,lowerCamelCase__     :			int = 12					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			str = "gelu_new"					,lowerCamelCase__     :			float = 0.1					,lowerCamelCase__     :			float = 0.1					,lowerCamelCase__     :			float = 0.1					,lowerCamelCase__     :			float = 1e-5					,lowerCamelCase__     :			float = 0.0_2					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = False					,lowerCamelCase__     :			bool = False					,):
									super().__init__()

									UpperCAmelCase__    =							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.'''    )

									UpperCAmelCase__    =							prefix_inner_dim
									UpperCAmelCase__    =							prefix_hidden_dim

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

									UpperCAmelCase__    =							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__					,)
									UpperCAmelCase__    =							GPTaLMHeadModel(lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			torch.Tensor					,lowerCamelCase__     :			torch.Tensor					,lowerCamelCase__     :			Optional[torch.Tensor] = None					,lowerCamelCase__     :			Optional[torch.Tensor] = None					,):
									UpperCAmelCase__    =							self.transformer.transformer.wte(lowerCamelCase__    )
									UpperCAmelCase__    =							self.encode_prefix(lowerCamelCase__    )
									UpperCAmelCase__    =							self.decode_prefix(lowerCamelCase__    )
									UpperCAmelCase__    =							torch.cat((prefix_embeds, embedding_text)					,dim=1    )

									if labels is not None:
													UpperCAmelCase__    =							self.get_dummy_token(input_ids.shape[0]					,input_ids.device    )
													UpperCAmelCase__    =							torch.cat((dummy_token, input_ids)					,dim=1    )
									UpperCAmelCase__    =							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 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			torch.device    ):
									return torch.zeros(lowerCamelCase__					,self.prefix_length					,dtype=torch.intaa					,device=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			List[str]    ):
									return self.encode_prefix(lowerCamelCase__    )







					@torch.no_grad()
					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Optional[int]    ):
									UpperCAmelCase__    =							torch.split(lowerCamelCase__					,1					,dim=0    )
									UpperCAmelCase__    =							[]
									UpperCAmelCase__    =							[]
									for feature in features:
													UpperCAmelCase__    =							self.decode_prefix(feature.to(lowerCamelCase__    )    )  # back to the clip feature
													# Only support beam search for now
													UpperCAmelCase__   ,			UpperCAmelCase__    =							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]    )
									UpperCAmelCase__    =							torch.stack(lowerCamelCase__    )
									UpperCAmelCase__    =							torch.stack(lowerCamelCase__    )
									return generated_tokens, generated_seq_lengths







					@torch.no_grad()
					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			str=None					,lowerCamelCase__     :			List[Any]=None					,lowerCamelCase__     :			Tuple=None					,lowerCamelCase__     :			int = 5					,lowerCamelCase__     :			int = 67					,lowerCamelCase__     :			float = 1.0					,lowerCamelCase__     :			Optional[int] = None					,):
									UpperCAmelCase__    =							eos_token_id
									UpperCAmelCase__    =							None
									UpperCAmelCase__    =							None
									UpperCAmelCase__    =							torch.ones(lowerCamelCase__					,device=lowerCamelCase__					,dtype=torch.int    )
									UpperCAmelCase__    =							torch.zeros(lowerCamelCase__					,device=lowerCamelCase__					,dtype=torch.bool    )

									if input_embeds is not None:
													UpperCAmelCase__    =							input_embeds
									else:
													UpperCAmelCase__    =							self.transformer.transformer.wte(lowerCamelCase__    )

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

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

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

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



