



"""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       _a  ( SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     [R"""h\.\d+\.attn\.bias""", R"""h\.\d+\.attn\.masked_bias"""]



			@register_to_config
			def __init__(			self							:				str   ,	_lowercase							:				int   ,	_lowercase							:				int   ,	_lowercase							:				Optional[int] = None   ,	_lowercase							:				int = 50257   ,	_lowercase							:				int = 1024   ,	_lowercase							:				int = 768   ,	_lowercase							:				int = 12   ,	_lowercase							:				int = 12   ,	_lowercase							:				Optional[int] = None   ,	_lowercase							:				str = "gelu_new"   ,	_lowercase							:				float = 0.1   ,	_lowercase							:				float = 0.1   ,	_lowercase							:				float = 0.1   ,	_lowercase							:				float = 1E-5   ,	_lowercase							:				float = 0.02   ,	_lowercase							:				bool = True   ,	_lowercase							:				bool = True   ,	_lowercase							:				bool = False   ,	_lowercase							:				bool = False   ,	)							->					Any:
									super().__init__()

									snake_case   :			str						    = 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   :			List[str]						    = prefix_inner_dim
									snake_case   :			Union[str, Any]						    = prefix_hidden_dim

									snake_case   :			Optional[Any]						    = (
									    nn.Linear(self.prefix_inner_dim   ,	self.prefix_hidden_dim		)
									    if self.prefix_hidden_dim is not None
									    else nn.Identity()
									)
									snake_case   :			int						    = (
									    nn.Linear(self.prefix_hidden_dim   ,	_lowercase		) if self.prefix_hidden_dim is not None else nn.Identity()
									)

									snake_case   :			List[str]						    = GPTaConfig(
									    vocab_size=_lowercase   ,	n_positions=_lowercase   ,	n_embd=_lowercase   ,	n_layer=_lowercase   ,	n_head=_lowercase   ,	n_inner=_lowercase   ,	activation_function=_lowercase   ,	resid_pdrop=_lowercase   ,	embd_pdrop=_lowercase   ,	attn_pdrop=_lowercase   ,	layer_norm_epsilon=_lowercase   ,	initializer_range=_lowercase   ,	scale_attn_weights=_lowercase   ,	use_cache=_lowercase   ,	scale_attn_by_inverse_layer_idx=_lowercase   ,	reorder_and_upcast_attn=_lowercase   ,	)
									snake_case   :			Optional[int]						    = GPTaLMHeadModel(_lowercase		)



			def 				__lowercase					(			self							:				Optional[int]   ,	_lowercase							:				torch.Tensor   ,	_lowercase							:				torch.Tensor   ,	_lowercase							:				Optional[torch.Tensor] = None   ,	_lowercase							:				Optional[torch.Tensor] = None   ,	)							->					Dict:
									snake_case   :			Union[str, Any]						    = self.transformer.transformer.wte(_lowercase		)
									snake_case   :			Any						    = self.encode_prefix(_lowercase		)
									snake_case   :			Union[str, Any]						    = self.decode_prefix(_lowercase		)
									snake_case   :			Union[str, Any]						    = torch.cat((prefix_embeds, embedding_text)   ,	dim=1		)

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



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				int   ,	_lowercase							:				torch.device		)							->					torch.Tensor:
									return torch.zeros(_lowercase   ,	self.prefix_length   ,	dtype=torch.intaa   ,	device=_lowercase		)



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				Any		)							->					Dict:
									return self.encode_prefix(_lowercase		)



			@torch.no_grad()
			def 				__lowercase					(			self							:				Dict   ,	_lowercase							:				Dict   ,	_lowercase							:				List[str]   ,	_lowercase							:				Union[str, Any]		)							->					Union[str, Any]:
									snake_case   :			Optional[int]						    = torch.split(_lowercase   ,	1   ,	dim=0		)
									snake_case   :			Dict						    = []
									snake_case   :			Optional[Any]						    = []
									for feature in features:
															snake_case   :			Any						    = self.decode_prefix(feature.to(_lowercase		)		)  # back to the clip feature
															# Only support beam search for now
															snake_case    ,			snake_case   :			Optional[int]						    = self.generate_beam(
															    input_embeds=_lowercase   ,	device=_lowercase   ,	eos_token_id=_lowercase		)
															generated_tokens.append(output_tokens[0]		)
															generated_seq_lengths.append(seq_lengths[0]		)
									snake_case   :			Dict						    = torch.stack(_lowercase		)
									snake_case   :			Optional[Any]						    = torch.stack(_lowercase		)
									return generated_tokens, generated_seq_lengths



			@torch.no_grad()
			def 				__lowercase					(			self							:				int   ,	_lowercase							:				List[Any]=None   ,	_lowercase							:				List[str]=None   ,	_lowercase							:				Optional[int]=None   ,	_lowercase							:				int = 5   ,	_lowercase							:				int = 67   ,	_lowercase							:				float = 1.0   ,	_lowercase							:				Optional[int] = None   ,	)							->					int:
									snake_case   :			Tuple						    = eos_token_id
									snake_case   :			Optional[int]						    = None
									snake_case   :			Dict						    = None
									snake_case   :			Union[str, Any]						    = torch.ones(_lowercase   ,	device=_lowercase   ,	dtype=torch.int		)
									snake_case   :			List[Any]						    = torch.zeros(_lowercase   ,	device=_lowercase   ,	dtype=torch.bool		)

									if input_embeds is not None:
															snake_case   :			Optional[int]						    = input_embeds
									else:
															snake_case   :			Union[str, Any]						    = self.transformer.transformer.wte(_lowercase		)

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

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

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

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








"""simple docstring"""







import requests

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			List[str]						    = {"Content-Type": "application/json"}
						snake_case   :			int						    = requests.post(lowerCamelCase_     ,		json={"text": message_body}     ,		headers=lowerCamelCase_ )
						if response.status_code != 2_0_0:
												snake_case   :			str						    = (
												    "Request to slack returned an error "
												    f'''{response.status_code}, the response is:\n{response.text}'''
												)
												raise ValueError(lowerCamelCase_ )


if __name__ == "__main__":
			# Set the slack url to the one provided by Slack when you create the webhook at
			# https://my.slack.com/services/new/incoming-webhook/
			send_slack_message('<YOUR MESSAGE BODY>', '<SLACK CHANNEL URL>')



