


import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

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







@dataclass
class  _lowercase   (   UpperCamelCase_						):
      _a							:						List[Any]										=							42
      _a							:						Dict										=							42







class  _lowercase   (   UpperCamelCase_						,				UpperCamelCase_						):
      _a							:						List[Any]										=							1
      @register_to_config
      def __init__(     self				,			a = 2_0_0_0				,			a = 0.15				,			a = 0.01				,			a = 1_3_4_8.0				,			a = 1e-5				,			a = 1				,			):
       snake_case__  :    List[Any]							       =sigma_max

       # setable values
       snake_case__  :    Optional[int]							       =None

       self.set_sigmas(__a				,			__a				,			__a				,			__a   )
      def 						lowercase__					(     self				,			a				,			a = None   ):
       return sample
      def 						lowercase__					(     self				,			a				,			a = None				,			a = None   ):
       snake_case__  :    Optional[Any]							       =sampling_eps if sampling_eps is not None else self.config.sampling_eps

       snake_case__  :    Optional[int]							       =torch.linspace(1				,			__a				,			__a				,			device=__a   )
      def 						lowercase__					(     self				,			a				,			a = None				,			a = None				,			a = None   ):
       snake_case__  :    List[Any]							       =sigma_min if sigma_min is not None else self.config.sigma_min
       snake_case__  :    List[Any]							       =sigma_max if sigma_max is not None else self.config.sigma_max
       snake_case__  :    Optional[Any]							       =sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
        self.set_timesteps(__a				,			__a   )

       snake_case__  :    List[str]							       =sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       snake_case__  :    str							       =torch.exp(torch.linspace(math.log(__a   )				,			math.log(__a   )				,			__a   )   )
       snake_case__  :    Dict							       =torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]   )
      def 						lowercase__					(     self				,			a				,			a   ):
       return torch.where(
           timesteps == 0				,			torch.zeros_like(t.to(timesteps.device   )   )				,			self.discrete_sigmas[timesteps - 1].to(timesteps.device   )				,			)
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a = None				,			a = True				,			):






       if self.timesteps is None:
        raise ValueError(
            """`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"""   )

       snake_case__  :    List[str]							       =timestep * torch.ones(
           sample.shape[0]				,			device=sample.device   )  # torch.repeat_interleave(timestep, sample.shape[0])
       snake_case__  :    Optional[int]							       =(timestep * (len(self.timesteps   ) - 1)).long()

       # mps requires indices to be in the same device, so we use cpu as is the default with cuda
       snake_case__  :    str							       =timesteps.to(self.discrete_sigmas.device   )

       snake_case__  :    Tuple							       =self.discrete_sigmas[timesteps].to(sample.device   )
       snake_case__  :    List[Any]							       =self.get_adjacent_sigma(__a				,			__a   ).to(sample.device   )
       snake_case__  :    int							       =torch.zeros_like(__a   )
       snake_case__  :    int							       =(sigma**2 - adjacent_sigma**2) ** 0.5

       # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
       # also equation 47 shows the analog from SDE models to ancestral sampling methods
       snake_case__  :    Optional[Any]							       =diffusion.flatten()
       while len(diffusion.shape   ) < len(sample.shape   ):
        snake_case__  :    List[Any]							       =diffusion.unsqueeze(-1   )
       snake_case__  :    List[Any]							       =drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       snake_case__  :    Tuple							       =randn_tensor(
           sample.shape				,			layout=sample.layout				,			generator=__a				,			device=sample.device				,			dtype=sample.dtype   )
       snake_case__  :    Any							       =sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       snake_case__  :    Optional[int]							       =prev_sample_mean + diffusion * noise  # add impact of diffusion field g

       if not return_dict:
        return (prev_sample, prev_sample_mean)

       return SdeVeOutput(prev_sample=__a				,			prev_sample_mean=__a   )
      def 						lowercase__					(     self				,			a				,			a				,			a = None				,			a = True				,			):






       if self.timesteps is None:
        raise ValueError(
            """`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"""   )

       # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
       # sample noise for correction
       snake_case__  :    Optional[int]							       =randn_tensor(sample.shape				,			layout=sample.layout				,			generator=__a   ).to(sample.device   )

       # compute step size from the model_output, the noise, and the snr
       snake_case__  :    Tuple							       =torch.norm(model_output.reshape(model_output.shape[0]				,			-1   )				,			dim=-1   ).mean()
       snake_case__  :    Union[str, Any]							       =torch.norm(noise.reshape(noise.shape[0]				,			-1   )				,			dim=-1   ).mean()
       snake_case__  :    Tuple							       =(self.config.snr * noise_norm / grad_norm) ** 2 * 2
       snake_case__  :    List[str]							       =step_size * torch.ones(sample.shape[0]   ).to(sample.device   )
       # self.repeat_scalar(step_size, sample.shape[0])

       # compute corrected sample: model_output term and noise term
       snake_case__  :    Dict							       =step_size.flatten()
       while len(step_size.shape   ) < len(sample.shape   ):
        snake_case__  :    Any							       =step_size.unsqueeze(-1   )
       snake_case__  :    Optional[int]							       =sample + step_size * model_output
       snake_case__  :    Any							       =prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
        return (prev_sample,)

       return SchedulerOutput(prev_sample=__a   )
      def 						lowercase__					(     self				,			a				,			a				,			a				,			):
       snake_case__  :    Any							       =timesteps.to(original_samples.device   )
       snake_case__  :    int							       =self.discrete_sigmas.to(original_samples.device   )[timesteps]
       snake_case__  :    str							       =(
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(__a   ) * sigmas[:, None, None, None]
       )
       snake_case__  :    Union[str, Any]							       =noise + original_samples
       return noisy_samples



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













from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)

__lowerCamelCase	:		Union[str, Any]										=   {
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}







class  _lowercase   (   _A						):
      _a							:						int										=							'xlm'
      _a							:						List[Any]										=							{
          'hidden_size': 'emb_dim',
          'num_attention_heads': 'n_heads',
          'num_hidden_layers': 'n_layers',
          'n_words': 'vocab_size',  # For backward compatibility
      }
      def __init__(     self				,			a=3_0_1_4_5				,			a=2_0_4_8				,			a=1_2				,			a=1_6				,			a=0.1				,			a=0.1				,			a=True				,			a=False				,			a=False				,			a=False				,			a=1				,			a=True				,			a=5_1_2				,			a=2_0_4_8**-0.5				,			a=1e-12				,			a=0.02				,			a=0				,			a=1				,			a=2				,			a=3				,			a=5				,			a=True				,			a="first"				,			a=True				,			a=None				,			a=True				,			a=0.1				,			a=5				,			a=5				,			a=0				,			a=0				,			a=2				,			a=0				,			**a				,			):
       snake_case__  :    str							       =vocab_size
       snake_case__  :    List[Any]							       =emb_dim
       snake_case__  :    List[str]							       =n_layers
       snake_case__  :    Any							       =n_heads
       snake_case__  :    Any							       =dropout
       snake_case__  :    Optional[int]							       =attention_dropout
       snake_case__  :    Dict							       =gelu_activation
       snake_case__  :    Tuple							       =sinusoidal_embeddings
       snake_case__  :    Optional[int]							       =causal
       snake_case__  :    Optional[int]							       =asm
       snake_case__  :    Optional[int]							       =n_langs
       snake_case__  :    Union[str, Any]							       =use_lang_emb
       snake_case__  :    Optional[Any]							       =layer_norm_eps
       snake_case__  :    Union[str, Any]							       =bos_index
       snake_case__  :    List[Any]							       =eos_index
       snake_case__  :    Optional[Any]							       =pad_index
       snake_case__  :    Union[str, Any]							       =unk_index
       snake_case__  :    Dict							       =mask_index
       snake_case__  :    str							       =is_encoder
       snake_case__  :    Optional[Any]							       =max_position_embeddings
       snake_case__  :    Any							       =embed_init_std
       snake_case__  :    Optional[Any]							       =init_std
       snake_case__  :    str							       =summary_type
       snake_case__  :    List[Any]							       =summary_use_proj
       snake_case__  :    str							       =summary_activation
       snake_case__  :    Optional[Any]							       =summary_proj_to_labels
       snake_case__  :    Optional[int]							       =summary_first_dropout
       snake_case__  :    Optional[Any]							       =start_n_top
       snake_case__  :    List[Any]							       =end_n_top
       snake_case__  :    Union[str, Any]							       =mask_token_id
       snake_case__  :    Any							       =lang_id

       if "n_words" in kwargs:
        snake_case__  :    int							       =kwargs["""n_words"""]

       super().__init__(pad_token_id=a				,			bos_token_id=a				,			**a   )







class  _lowercase   (   _A						):
      @property
      def 						lowercase__					(     self   ):
       if self.task == "multiple-choice":
        snake_case__  :    Any							       ={0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
        snake_case__  :    Union[str, Any]							       ={0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
               ("""token_type_ids""", dynamic_axis),
           ]   )





