





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 		SCREAMING_SNAKE_CASE_							(    __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase   ):
				"""simple docstring"""
				__snake_case   :       str	 =		[r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]




				@register_to_config
				def __init__(     self  :int						,    __lowercase  :int						,    __lowercase  :int						,    __lowercase  :Optional[int] = None						,    __lowercase  :int = 5_0257						,    __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						,    ):
											super().__init__()

											__lowerCamelCase      :      List[Any]         =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.'			)

											__lowerCamelCase      :      Optional[int]         =prefix_inner_dim
											__lowerCamelCase      :      Tuple         =prefix_hidden_dim

											__lowerCamelCase      :      Dict         =(
											    nn.Linear(self.prefix_inner_dim						,    self.prefix_hidden_dim			)
											    if self.prefix_hidden_dim is not None
											    else nn.Identity()
											)
											__lowerCamelCase      :      str         =(
											    nn.Linear(self.prefix_hidden_dim						,    lowerCAmelCase_			) if self.prefix_hidden_dim is not None else nn.Identity()
											)

											__lowerCamelCase      :      List[Any]         =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_						,    )
											__lowerCamelCase      :      int         =GPTaLMHeadModel(lowerCAmelCase_			)




				def 					__lowercase  (     self  :int						,    __lowercase  :torch.Tensor						,    __lowercase  :torch.Tensor						,    __lowercase  :Optional[torch.Tensor] = None						,    __lowercase  :Optional[torch.Tensor] = None						,    ):
											__lowerCamelCase      :      str         =self.transformer.transformer.wte(lowerCAmelCase_			)
											__lowerCamelCase      :      Optional[Any]         =self.encode_prefix(lowerCAmelCase_			)
											__lowerCamelCase      :      Union[str, Any]         =self.decode_prefix(lowerCAmelCase_			)
											__lowerCamelCase      :      List[str]         =torch.cat((prefix_embeds, embedding_text)						,    dim=1			)

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




				def 					__lowercase  (     self  :List[str]						,    __lowercase  :List[Any]			):
											return self.encode_prefix(lowerCAmelCase_			)




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




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

											if input_embeds is not None:
																		__lowerCamelCase      :      Tuple         =input_embeds
											else:
																		__lowerCamelCase      :      List[str]         =self.transformer.transformer.wte(lowerCAmelCase_			)

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

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

																		__lowerCamelCase      :      Tuple         =self.transformer.transformer.wte(next_tokens.squeeze()			).view(generated.shape[0]						,    1						,    -1			)
																		__lowerCamelCase      :      List[Any]         =torch.cat((generated, next_token_embed)						,    dim=1			)
																		__lowerCamelCase      :      int         =is_stopped + next_tokens.eq(lowerCAmelCase_			).squeeze()
																		if is_stopped.all():
																									break

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











"""simple docstring"""





def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							list    ,						SCREAMING_SNAKE_CASE       :							list    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[Any]         =len(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Union[str, Any]         =[[0] * n for i in range(SCREAMING_SNAKE_CASE   )]
							for i in range(SCREAMING_SNAKE_CASE   ):
														__lowerCamelCase      :      Union[str, Any]         =y_points[i]

							for i in range(2    ,						SCREAMING_SNAKE_CASE   ):
														for j in range(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      List[Any]         =(
																					    (xa - x_points[j - i + 1]) * q[j][i - 1]
																					    - (xa - x_points[j]) * q[j - 1][i - 1]
																					) / (x_points[j] - x_points[j - i + 1])

							return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()




