





# 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      lowerCamelCase(						__snake_case			, __snake_case	):
     '''simple docstring'''


     __magic_name__   =			1




     @register_to_config
     def __init__(    self				,     snake_case_=2000				,     snake_case_=0.1				,     snake_case_=20				,     snake_case_=1E-3							):
         _A     =							None
         _A     =							None
         _A     =							None




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None							):
         _A     =							torch.linspace(1				,     self.config.sampling_eps				,     snake_case_				,     device=snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_=None							):
         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     =							(
             -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
         )
         _A     =							torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
         _A     =							std.flatten()
         while len(std.shape							) < len(score.shape							):
             _A     =							std.unsqueeze(-1							)
         _A     =							-score / std

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

         _A     =							self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
         _A     =							beta_t.flatten()
         while len(beta_t.shape							) < len(x.shape							):
             _A     =							beta_t.unsqueeze(-1							)
         _A     =							-0.5 * beta_t * x

         _A     =							torch.sqrt(snake_case_							)
         _A     =							drift - diffusion**2 * score
         _A     =							x + drift * dt

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

         return x, x_mean




     def __len__(    self							):
         return self.config.num_train_timesteps







"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A     :			str  						=			logging.get_logger(__name__)

_A     :			Tuple  						=			{
    """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
    """google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
    """google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class 						a__		( a_		):
   __lowerCAmelCase				=							"""big_bird"""
   def __init__(     self      ,       _a=50_358      ,       _a=768      ,       _a=12      ,       _a=12      ,       _a=3_072      ,       _a="gelu_new"      ,       _a=0.1      ,       _a=0.1      ,       _a=4_096      ,       _a=2      ,       _a=0.0_2      ,       _a=1E-12      ,       _a=True      ,       _a=0      ,       _a=1      ,       _a=2      ,       _a=66      ,       _a="block_sparse"      ,       _a=True      ,       _a=False      ,       _a=64      ,       _a=3      ,       _a=None      ,       **_a      ,       ):
          super().__init__(
              pad_token_id=_a      ,       bos_token_id=_a      ,       eos_token_id=_a      ,       sep_token_id=_a      ,       **_a      ,       )

          lowercase       :							List[Any]									=				vocab_size
          lowercase       :							Optional[Any]									=				max_position_embeddings
          lowercase       :							str									=				hidden_size
          lowercase       :							int									=				num_hidden_layers
          lowercase       :							Optional[Any]									=				num_attention_heads
          lowercase       :							List[Any]									=				intermediate_size
          lowercase       :							int									=				hidden_act
          lowercase       :							str									=				hidden_dropout_prob
          lowercase       :							List[Any]									=				attention_probs_dropout_prob
          lowercase       :							Tuple									=				initializer_range
          lowercase       :							Optional[int]									=				type_vocab_size
          lowercase       :							str									=				layer_norm_eps
          lowercase       :							Tuple									=				use_cache

          lowercase       :							Any									=				rescale_embeddings
          lowercase       :							List[str]									=				attention_type
          lowercase       :							int									=				use_bias
          lowercase       :							Dict									=				block_size
          lowercase       :							List[str]									=				num_random_blocks
          lowercase       :							int									=				classifier_dropout


class 						a__		( a_		):
   @property
   def      __magic_name__     (     self							):
          if self.task == "multiple-choice":
                 lowercase       :							Union[str, Any]									=				{0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase       :							Dict									=				{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]							)


