




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       {
    "bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						"gpt_bigcode"
     _a							 =						["past_key_values"]
     _a							 =						{
         "hidden_size": "n_embd",
         "max_position_embeddings": "n_positions",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }

     def __init__(  self		,     _a=5_0257		,     _a=1024		,     _a=768		,     _a=12		,     _a=12		,     _a=None		,     _a="gelu_pytorch_tanh"		,     _a=0.1		,     _a=0.1		,     _a=0.1		,     _a=1e-5		,     _a=0.02		,     _a=True		,     _a=True		,     _a=5_0256		,     _a=5_0256		,     _a=True		,     _a=True		,     _a=True		,     **_a		,     )						->							Union[str, Any]:
           _A							:     List[str]			=						vocab_size
           _A							:     List[Any]			=						n_positions
           _A							:     Union[str, Any]			=						n_embd
           _A							:     Any			=						n_layer
           _A							:     Tuple			=						n_head
           _A							:     List[str]			=						n_inner
           _A							:     Optional[int]			=						activation_function
           _A							:     Optional[int]			=						resid_pdrop
           _A							:     List[str]			=						embd_pdrop
           _A							:     int			=						attn_pdrop
           _A							:     str			=						layer_norm_epsilon
           _A							:     int			=						initializer_range
           _A							:     List[str]			=						scale_attn_weights
           _A							:     Optional[int]			=						use_cache
           _A							:     Any			=						attention_softmax_in_fpaa
           _A							:     Dict			=						scale_attention_softmax_in_fpaa
           _A							:     Any			=						multi_query

           _A							:     Any			=						bos_token_id
           _A							:     Union[str, Any]			=						eos_token_id

           super().__init__(bos_token_id=_a		,     eos_token_id=_a		,     **_a				)







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class 						lowercase							(   UpperCamelCase__,UpperCamelCase__		):
     _a							 =						1

     @register_to_config
     def __init__(  self		,     _a=2000		,     _a=0.1		,     _a=20		,     _a=1e-3				)						->							List[Any]:
           _A							:     Dict			=						None
           _A							:     List[Any]			=						None
           _A							:     Dict			=						None

     def 					a__	(  self		,     _a		,     _a = None				)						->							Union[str, Any]:
           _A							:     Union[str, Any]			=						torch.linspace(1		,     self.config.sampling_eps		,     _a		,     device=_a				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a=None				)						->							Dict:
           if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""				)

           # TODO(Patrick) better comments + non-PyTorch
           # postprocess model score
           _A							:     Any			=						(
               -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
           )
           _A							:     List[Any]			=						torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff				)				)
           _A							:     List[str]			=						std.flatten()
           while len(std.shape				) < len(score.shape				):
                 _A							:     List[Any]			=						std.unsqueeze(-1				)
           _A							:     int			=						-score / std

           # compute
           _A							:     Tuple			=						-1.0 / len(self.timesteps				)

           _A							:     str			=						self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
           _A							:     List[str]			=						beta_t.flatten()
           while len(beta_t.shape				) < len(x.shape				):
                 _A							:     Union[str, Any]			=						beta_t.unsqueeze(-1				)
           _A							:     Tuple			=						-0.5 * beta_t * x

           _A							:     Tuple			=						torch.sqrt(_a				)
           _A							:     Dict			=						drift - diffusion**2 * score
           _A							:     Dict			=						x + drift * dt

           # add noise
           _A							:     Any			=						randn_tensor(x.shape		,     layout=x.layout		,     generator=_a		,     device=x.device		,     dtype=x.dtype				)
           _A							:     str			=						x_mean + diffusion * math.sqrt(-dt				) * noise

           return x, x_mean



     def __len__(  self				)						->							Optional[Any]:
           return self.config.num_train_timesteps

