

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					(		UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase			):
						_lowercase        =     [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]


						@register_to_config
						def __init__(    self  ,   A_  ,   A_  ,   A_ = None  ,   A_ = 50257  ,   A_ = 1024  ,   A_ = 768  ,   A_ = 12  ,   A_ = 12  ,   A_ = None  ,   A_ = "gelu_new"  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 1e-5  ,   A_ = 0.02  ,   A_ = True  ,   A_ = True  ,   A_ = False  ,   A_ = False  ,   ):







										'''simple docstring'''


										super().__init__()

										_UpperCAmelCase    :						Tuple							=      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    :						Dict							=      prefix_inner_dim
										_UpperCAmelCase    :						List[str]							=      prefix_hidden_dim

										_UpperCAmelCase    :						int							=      (
										    nn.Linear(self.prefix_inner_dim  ,   self.prefix_hidden_dim		)
										    if self.prefix_hidden_dim is not None
										    else nn.Identity()
										)
										_UpperCAmelCase    :						str							=      (
										    nn.Linear(self.prefix_hidden_dim  ,   A_		) if self.prefix_hidden_dim is not None else nn.Identity()
										)

										_UpperCAmelCase    :						Optional[int]							=      GPTaConfig(
										    vocab_size=A_  ,   n_positions=A_  ,   n_embd=A_  ,   n_layer=A_  ,   n_head=A_  ,   n_inner=A_  ,   activation_function=A_  ,   resid_pdrop=A_  ,   embd_pdrop=A_  ,   attn_pdrop=A_  ,   layer_norm_epsilon=A_  ,   initializer_range=A_  ,   scale_attn_weights=A_  ,   use_cache=A_  ,   scale_attn_by_inverse_layer_idx=A_  ,   reorder_and_upcast_attn=A_  ,   )
										_UpperCAmelCase    :						Tuple							=      GPTaLMHeadModel(A_		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_ = None  ,   A_ = None  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.transformer.transformer.wte(A_		)
										_UpperCAmelCase    :						Dict							=      self.encode_prefix(A_		)
										_UpperCAmelCase    :						Dict							=      self.decode_prefix(A_		)
										_UpperCAmelCase    :						Tuple							=      torch.cat((prefix_embeds, embedding_text)  ,   dim=1		)

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


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										return self.encode_prefix(A_		)


						@torch.no_grad()
						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      torch.split(A_  ,   1  ,   dim=0		)
										_UpperCAmelCase    :						int							=      []
										_UpperCAmelCase    :						List[str]							=      []
										for feature in features:
														_UpperCAmelCase    :						Any							=      self.decode_prefix(feature.to(A_		)		)  # back to the clip feature
														# Only support beam search for now
														_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[Any]							=      self.generate_beam(
														    input_embeds=A_  ,   device=A_  ,   eos_token_id=A_		)
														generated_tokens.append(output_tokens[0]		)
														generated_seq_lengths.append(seq_lengths[0]		)
										_UpperCAmelCase    :						Union[str, Any]							=      torch.stack(A_		)
										_UpperCAmelCase    :						Tuple							=      torch.stack(A_		)
										return generated_tokens, generated_seq_lengths





						@torch.no_grad()
						def     _UpperCAmelCase			(    self  ,   A_=None  ,   A_=None  ,   A_=None  ,   A_ = 5  ,   A_ = 67  ,   A_ = 1.0  ,   A_ = None  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      eos_token_id
										_UpperCAmelCase    :						Optional[Any]							=      None
										_UpperCAmelCase    :						List[str]							=      None
										_UpperCAmelCase    :						Dict							=      torch.ones(A_  ,   device=A_  ,   dtype=torch.int		)
										_UpperCAmelCase    :						Tuple							=      torch.zeros(A_  ,   device=A_  ,   dtype=torch.bool		)

										if input_embeds is not None:
														_UpperCAmelCase    :						List[str]							=      input_embeds
										else:
														_UpperCAmelCase    :						List[Any]							=      self.transformer.transformer.wte(A_		)

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

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

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

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





from __future__ import annotations

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      order

										# a_{0} ... a_{k}
										_UpperCAmelCase    :						Tuple							=      [1.0] + [0.0] * order
										# b_{0} ... b_{k}
										_UpperCAmelCase    :						int							=      [1.0] + [0.0] * order

										# x[n-1] ... x[n-k]
										_UpperCAmelCase    :						Optional[Any]							=      [0.0] * self.order
										# y[n-1] ... y[n-k]
										_UpperCAmelCase    :						Dict							=      [0.0] * self.order


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if len(A_		) < self.order:
														_UpperCAmelCase    :						List[str]							=      [1.0, *a_coeffs]

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						List[Any]							=      (
														    f'Expected a_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						int							=      (
														    f'Expected b_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      a_coeffs
										_UpperCAmelCase    :						Union[str, Any]							=      b_coeffs





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      0.0

										# Start at index 1 and do index 0 at the end.
										for i in range(1  ,   self.order + 1		):
														result += (
														    self.b_coeffs[i] * self.input_history[i - 1]
														    - self.a_coeffs[i] * self.output_history[i - 1]
														)

										_UpperCAmelCase    :						Dict							=      (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

										_UpperCAmelCase    :						Optional[Any]							=      self.input_history[:-1]
										_UpperCAmelCase    :						Optional[int]							=      self.output_history[:-1]

										_UpperCAmelCase    :						Optional[Any]							=      sample
										_UpperCAmelCase    :						str							=      result

										return result


