




"""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		,    UpperCamelCase_    ) if self.prefix_hidden_dim is not None else nn.Identity()
										)

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

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






										"""simple docstring"""

										snake_case								=			self.transformer.transformer.wte(UpperCamelCase_    )
										snake_case								=			self.encode_prefix(UpperCamelCase_    )
										snake_case								=			self.decode_prefix(UpperCamelCase_    )
										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=UpperCamelCase_		,    labels=UpperCamelCase_		,    attention_mask=UpperCamelCase_    )
										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(UpperCamelCase_		,    self.prefix_length		,    dtype=torch.intaa		,    device=UpperCamelCase_    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return self.encode_prefix(UpperCamelCase_    )

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






										"""simple docstring"""

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

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

										for i in range(UpperCamelCase_    ):
																snake_case								=			self.transformer(inputs_embeds=UpperCamelCase_    )
																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								=			logits.topk(UpperCamelCase_		,    -1    )
																						snake_case								=			generated.expand(UpperCamelCase_		,    *generated.shape[1:]    )
																						snake_case								=			next_tokens.permute(1		,    0    ), scores.squeeze(0    )
																						if tokens is None:
																												snake_case								=			next_tokens
																						else:
																												snake_case								=			tokens.expand(UpperCamelCase_		,    *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								=			scores_sum_average.view(-1    ).topk(UpperCamelCase_		,    -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(UpperCamelCase_    ).squeeze()
																if is_stopped.all():
																						break

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



"""simple docstring"""
import math





def    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				bool:





						"""simple docstring"""






						return math.sqrt(_UpperCamelCase					) * math.sqrt(_UpperCamelCase					) == num





def    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				bool:





						"""simple docstring"""






						snake_case								=			0
						snake_case								=			n
						while left <= right:
												snake_case								=			(left + right) // 2
												if mid**2 == n:
																		return True
												elif mid**2 > n:
																		snake_case								=			mid - 1
												else:
																		snake_case								=			mid + 1
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()


