


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

import numpy as np
import torch

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




@dataclass
class  UpperCAmelCase       (							__snake_case     ):
			lowercase						       =			42
			lowercase						       =			42
			lowercase						       =			None




class  UpperCAmelCase       (							__snake_case   ,			__snake_case     ):
			lowercase						       =			2




			@register_to_config
			def __init__(    self				:		Union[str, Any]							,							__magic_name__				:		float = 0.02							,							__magic_name__				:		float = 1_0_0							,							__magic_name__				:		float = 1.007							,							__magic_name__				:		float = 8_0							,							__magic_name__				:		float = 0.05							,							__magic_name__				:		float = 5_0							,							):







						"""simple docstring"""







						UpperCamelCase								=       sigma_max

						# setable values
						UpperCamelCase								=       None
						UpperCamelCase								=       None
						UpperCamelCase								=       None  # sigma(t_i)




			def 	lowerCamelCase_       (    self				:		Optional[int]							,							__magic_name__				:		torch.FloatTensor							,							__magic_name__				:		Optional[int] = None    ):







						"""simple docstring"""







						return sample




			def 	lowerCamelCase_       (    self				:		Dict							,							__magic_name__				:		int							,							__magic_name__				:		Union[str, torch.device] = None    ):







						"""simple docstring"""







						UpperCamelCase								=       num_inference_steps
						UpperCamelCase								=       np.arange(0							,							self.num_inference_steps    )[::-1].copy()
						UpperCamelCase								=       torch.from_numpy(SCREAMING_SNAKE_CASE__    ).to(SCREAMING_SNAKE_CASE__    )
						UpperCamelCase								=       [
						    (
						        self.config.sigma_max**2
						        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
						    )
						    for i in self.timesteps
						]
						UpperCamelCase								=       torch.tensor(SCREAMING_SNAKE_CASE__							,							dtype=torch.floataa							,							device=SCREAMING_SNAKE_CASE__    )




			def 	lowerCamelCase_       (    self				:		Tuple							,							__magic_name__				:		torch.FloatTensor							,							__magic_name__				:		float							,							__magic_name__				:		Optional[torch.Generator] = None    ):







						"""simple docstring"""







						if self.config.s_min <= sigma <= self.config.s_max:
									UpperCamelCase								=       min(self.config.s_churn / self.num_inference_steps							,							2**0.5 - 1    )
						else:
									UpperCamelCase								=       0

						# sample eps ~ N(0, S_noise^2 * I)
						UpperCamelCase								=       self.config.s_noise * randn_tensor(sample.shape							,							generator=SCREAMING_SNAKE_CASE__    ).to(sample.device    )
						UpperCamelCase								=       sigma + gamma * sigma
						UpperCamelCase								=       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

						return sample_hat, sigma_hat




			def 	lowerCamelCase_       (    self				:		Optional[Any]							,							__magic_name__				:		torch.FloatTensor							,							__magic_name__				:		float							,							__magic_name__				:		float							,							__magic_name__				:		torch.FloatTensor							,							__magic_name__				:		bool = True							,							):







						"""simple docstring"""







						UpperCamelCase								=       sample_hat + sigma_hat * model_output
						UpperCamelCase								=       (sample_hat - pred_original_sample) / sigma_hat
						UpperCamelCase								=       sample_hat + (sigma_prev - sigma_hat) * derivative

						if not return_dict:
									return (sample_prev, derivative)

						return KarrasVeOutput(
						    prev_sample=SCREAMING_SNAKE_CASE__							,							derivative=SCREAMING_SNAKE_CASE__							,							pred_original_sample=SCREAMING_SNAKE_CASE__    )




			def 	lowerCamelCase_       (    self				:		Dict							,							__magic_name__				:		torch.FloatTensor							,							__magic_name__				:		float							,							__magic_name__				:		float							,							__magic_name__				:		torch.FloatTensor							,							__magic_name__				:		torch.FloatTensor							,							__magic_name__				:		torch.FloatTensor							,							__magic_name__				:		bool = True							,							):







						"""simple docstring"""







						UpperCamelCase								=       sample_prev + sigma_prev * model_output
						UpperCamelCase								=       (sample_prev - pred_original_sample) / sigma_prev
						UpperCamelCase								=       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

						if not return_dict:
									return (sample_prev, derivative)

						return KarrasVeOutput(
						    prev_sample=SCREAMING_SNAKE_CASE__							,							derivative=SCREAMING_SNAKE_CASE__							,							pred_original_sample=SCREAMING_SNAKE_CASE__    )





			def 	lowerCamelCase_       (    self				:		Dict							,							__magic_name__				:		Tuple							,							__magic_name__				:		Union[str, Any]							,							__magic_name__				:		Any    ):







						"""simple docstring"""







						raise NotImplementedError()






from collections import OrderedDict
from typing import Mapping

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


_snake_case      = logging.get_logger(__name__)

_snake_case      = {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Dict       						="roberta"

 def __init__(						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Tuple=5_02_65					, SCREAMING_SNAKE_CASE__			:			List[str]=7_68					, SCREAMING_SNAKE_CASE__			:			List[str]=12					, SCREAMING_SNAKE_CASE__			:			Any=12					, SCREAMING_SNAKE_CASE__			:			Tuple=30_72					, SCREAMING_SNAKE_CASE__			:			str="gelu"					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=0.1					, SCREAMING_SNAKE_CASE__			:			int=0.1					, SCREAMING_SNAKE_CASE__			:			int=5_12					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=2					, SCREAMING_SNAKE_CASE__			:			Dict=0.02					, SCREAMING_SNAKE_CASE__			:			List[str]=1e-12					, SCREAMING_SNAKE_CASE__			:			Any=1					, SCREAMING_SNAKE_CASE__			:			Optional[Any]=0					, SCREAMING_SNAKE_CASE__			:			str=2					, SCREAMING_SNAKE_CASE__			:			Any="absolute"					, SCREAMING_SNAKE_CASE__			:			Any=True					, SCREAMING_SNAKE_CASE__			:			List[Any]=None					, **SCREAMING_SNAKE_CASE__			:			Optional[int]					, ):






      """simple docstring"""



      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__					, bos_token_id=SCREAMING_SNAKE_CASE__					, eos_token_id=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					vocab_size
      UpperCamelCase						    =					hidden_size
      UpperCamelCase						    =					num_hidden_layers
      UpperCamelCase						    =					num_attention_heads
      UpperCamelCase						    =					hidden_act
      UpperCamelCase						    =					intermediate_size
      UpperCamelCase						    =					hidden_dropout_prob
      UpperCamelCase						    =					attention_probs_dropout_prob
      UpperCamelCase						    =					max_position_embeddings
      UpperCamelCase						    =					type_vocab_size
      UpperCamelCase						    =					initializer_range
      UpperCamelCase						    =					layer_norm_eps
      UpperCamelCase						    =					position_embedding_type
      UpperCamelCase						    =					use_cache
      UpperCamelCase						    =					classifier_dropout




class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""

 @property
 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      if self.task == "multiple-choice":
           UpperCamelCase						    =					{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
           UpperCamelCase						    =					{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]   )


