


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

import numpy as np
import torch

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


snake_case       :					Any        =  logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 42







class 	_snake_case						(   _snake_case		,					_snake_case							):
 SCREAMING_SNAKE_CASE__   = 1




 @register_to_config
 def __init__(	self      ,						_lowerCamelCase = 40      ,						_lowerCamelCase = 0.002      ,						_lowerCamelCase = 80.0      ,						_lowerCamelCase = 0.5      ,						_lowerCamelCase = 1.0      ,						_lowerCamelCase = 7.0      ,						_lowerCamelCase = True      ,						):
       # standard deviation of the initial noise distribution
       a		:Union[str, Any]											= sigma_max

       a		:Any											= np.linspace(0      ,						1      ,						_lowerCamelCase					)
       a		:List[Any]											= self._convert_to_karras(_lowerCamelCase					)
       a		:List[Any]											= self.sigma_to_t(_lowerCamelCase					)

       # setable values
       a		:Tuple											= None
       a		:Tuple											= torch.from_numpy(_lowerCamelCase					)
       a		:int											= torch.from_numpy(_lowerCamelCase					)
       a		:Any											= False
       a		:Tuple											= False




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase=None					):
       if schedule_timesteps is None:
             a		:Optional[Any]											= self.timesteps

       a		:Dict											= (schedule_timesteps == timestep).nonzero()
       return indices.item()




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       # Get sigma corresponding to timestep
       if isinstance(_lowerCamelCase      ,						torch.Tensor					):
             a		:List[str]											= timestep.to(self.timesteps.device					)
       a		:List[str]											= self.index_for_timestep(_lowerCamelCase					)
       a		:Dict											= self.sigmas[step_idx]

       a		:List[str]											= sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

       a		:Union[str, Any]											= True
       return sample




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       if not isinstance(_lowerCamelCase      ,						np.ndarray					):
             a		:str											= np.array(_lowerCamelCase      ,						dtype=np.floataa					)

       a		:str											= 1000 * 0.25 * np.log(sigmas + 1e-44					)

       return timesteps




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						):
       if num_inference_steps is None and timesteps is None:
             raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''					)

       if num_inference_steps is not None and timesteps is not None:
             raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''					)

       # Follow DDPMScheduler custom timesteps logic
       if timesteps is not None:
             for i in range(1      ,						len(_lowerCamelCase					)					):
                   if timesteps[i] >= timesteps[i - 1]:
                         raise ValueError('''`timesteps` must be in descending order.'''					)

             if timesteps[0] >= self.config.num_train_timesteps:
                   raise ValueError(
                       F'''`timesteps` must start before `self.config.train_timesteps`:'''
                       F''' {self.config.num_train_timesteps}.'''					)

             a		:Optional[int]											= np.array(_lowerCamelCase      ,						dtype=np.intaa					)
             a		:List[str]											= True
       else:
             if num_inference_steps > self.config.num_train_timesteps:
                   raise ValueError(
                       F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
                       F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
                       F''' maximal {self.config.num_train_timesteps} timesteps.'''					)

             a		:List[Any]											= num_inference_steps

             a		:str											= self.config.num_train_timesteps // self.num_inference_steps
             a		:List[Any]											= (np.arange(0      ,						_lowerCamelCase					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
             a		:str											= False

       # Map timesteps to Karras sigmas directly for multistep sampling
       # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
       a		:List[Any]											= self.config.num_train_timesteps
       a		:int											= timesteps[::-1].copy()
       a		:Optional[Any]											= ramp / (num_train_timesteps - 1)
       a		:str											= self._convert_to_karras(_lowerCamelCase					)
       a		:Optional[int]											= self.sigma_to_t(_lowerCamelCase					)

       a		:Optional[Any]											= np.concatenate([sigmas, [self.sigma_min]]					).astype(np.floataa					)
       a		:Optional[Any]											= torch.from_numpy(_lowerCamelCase					).to(device=_lowerCamelCase					)

       if str(_lowerCamelCase					).startswith('''mps'''					):
             # mps does not support float64
             a		:int											= torch.from_numpy(_lowerCamelCase					).to(_lowerCamelCase      ,						dtype=torch.floataa					)
       else:
             a		:Union[str, Any]											= torch.from_numpy(_lowerCamelCase					).to(device=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       a		:float											= self.config.sigma_min
       a		:float											= self.config.sigma_max

       a		:List[str]											= self.config.rho
       a		:Union[str, Any]											= sigma_min ** (1 / rho)
       a		:int											= sigma_max ** (1 / rho)
       a		:List[str]											= (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       a		:int											= self.config.sigma_data

       a		:Optional[Any]											= sigma_data**2 / (sigma**2 + sigma_data**2)
       a		:str											= sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       a		:int											= self.config.sigma_min
       a		:Any											= self.config.sigma_data

       a		:Any											= sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
       a		:Optional[int]											= (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						_lowerCamelCase = True      ,						):

       if (
           isinstance(_lowerCamelCase      ,						_lowerCamelCase					)
           or isinstance(_lowerCamelCase      ,						torch.IntTensor					)
           or isinstance(_lowerCamelCase      ,						torch.LongTensor					)
       ):
             raise ValueError(
                 (
                     '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                     F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
                     ''' one of the `scheduler.timesteps` as a timestep.'''
                 )      ,						)

       if not self.is_scale_input_called:
             logger.warning(
                 '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                 '''See `StableDiffusionPipeline` for a usage example.'''					)

       if isinstance(_lowerCamelCase      ,						torch.Tensor					):
             a		:List[str]											= timestep.to(self.timesteps.device					)

       a		:Optional[Any]											= self.config.sigma_min
       a		:Dict											= self.config.sigma_max

       a		:int											= self.index_for_timestep(_lowerCamelCase					)

       # sigma_next corresponds to next_t in original implementation
       a		:List[str]											= self.sigmas[step_index]
       if step_index + 1 < self.config.num_train_timesteps:
             a		:Tuple											= self.sigmas[step_index + 1]
       else:
             # Set sigma_next to sigma_min
             a		:Tuple											= self.sigmas[-1]

       # Get scalings for boundary conditions
       a		:Tuple											= self.get_scalings_for_boundary_condition(_lowerCamelCase					)

       # 1. Denoise model output using boundary conditions
       a		:Tuple											= c_out * model_output + c_skip * sample
       if self.config.clip_denoised:
             a		:List[str]											= denoised.clamp(-1      ,						1					)

       # 2. Sample z ~ N(0, s_noise^2 * I)
       # Noise is not used for onestep sampling.
       if len(self.timesteps					) > 1:
             a		:int											= randn_tensor(
                 model_output.shape      ,						dtype=model_output.dtype      ,						device=model_output.device      ,						generator=_lowerCamelCase					)
       else:
             a		:Union[str, Any]											= torch.zeros_like(_lowerCamelCase					)
       a		:Tuple											= noise * self.config.s_noise

       a		:str											= sigma_next.clamp(min=_lowerCamelCase      ,						max=_lowerCamelCase					)

       # 3. Return noisy sample
       # tau = sigma_hat, eps = sigma_min
       a		:List[Any]											= denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

       if not return_dict:
             return (prev_sample,)

       return CMStochasticIterativeSchedulerOutput(prev_sample=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       a		:Optional[int]											= self.sigmas.to(device=original_samples.device      ,						dtype=original_samples.dtype					)
       if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCamelCase					):
             # mps does not support float64
             a		:int											= self.timesteps.to(original_samples.device      ,						dtype=torch.floataa					)
             a		:int											= timesteps.to(original_samples.device      ,						dtype=torch.floataa					)
       else:
             a		:Dict											= self.timesteps.to(original_samples.device					)
             a		:Optional[Any]											= timesteps.to(original_samples.device					)

       a		:str											= [(schedule_timesteps == t).nonzero().item() for t in timesteps]

       a		:List[Any]											= sigmas[step_indices].flatten()
       while len(sigma.shape					) < len(original_samples.shape					):
             a		:Optional[int]											= sigma.unsqueeze(-1					)

       a		:Union[str, Any]											= original_samples + noise * sigma
       return noisy_samples




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









from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'MarkupLMFeatureExtractor'
 SCREAMING_SNAKE_CASE__   = ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
 SCREAMING_SNAKE_CASE__   = True




 def __call__(	self      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase = True      ,						_lowerCamelCase = False      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = 0      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = True      ,						_lowerCamelCase = None      ,						**_lowerCamelCase      ,						):
       # first, create nodes and xpaths
       if self.parse_html:
             if html_strings is None:
                   raise ValueError('''Make sure to pass HTML strings in case `parse_html` is set to `True`'''					)

             if nodes is not None or xpaths is not None or node_labels is not None:
                   raise ValueError(
                       '''Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'''					)

             a		:int											= self.feature_extractor(_lowerCamelCase					)
             a		:Tuple											= features['''nodes''']
             a		:Dict											= features['''xpaths''']
       else:
             if html_strings is not None:
                   raise ValueError('''You have passed HTML strings but `parse_html` is set to `False`.'''					)
             if nodes is None or xpaths is None:
                   raise ValueError('''Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'''					)

        # # second, apply the tokenizer
       if questions is not None and self.parse_html:
             if isinstance(_lowerCamelCase      ,						_lowerCamelCase					):
                   a		:Tuple											= [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

       a		:Optional[int]											= self.tokenizer(
           text=questions if questions is not None else nodes      ,						text_pair=nodes if questions is not None else None      ,						xpaths=_lowerCamelCase      ,						node_labels=_lowerCamelCase      ,						add_special_tokens=_lowerCamelCase      ,						padding=_lowerCamelCase      ,						truncation=_lowerCamelCase      ,						max_length=_lowerCamelCase      ,						stride=_lowerCamelCase      ,						pad_to_multiple_of=_lowerCamelCase      ,						return_token_type_ids=_lowerCamelCase      ,						return_attention_mask=_lowerCamelCase      ,						return_overflowing_tokens=_lowerCamelCase      ,						return_special_tokens_mask=_lowerCamelCase      ,						return_offsets_mapping=_lowerCamelCase      ,						return_length=_lowerCamelCase      ,						verbose=_lowerCamelCase      ,						return_tensors=_lowerCamelCase      ,						**_lowerCamelCase      ,						)

       return encoded_inputs




 def  SCREAMING_SNAKE_CASE__       (	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       return self.tokenizer.batch_decode(*_lowerCamelCase      ,						**_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       return self.tokenizer.decode(*_lowerCamelCase      ,						**_lowerCamelCase					)




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Any											= self.tokenizer.model_input_names
       return tokenizer_input_names





