




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


__magic_name__     :     Optional[int]  							=					logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
class 						SCREAMING_SNAKE_CASE__		(_a ):
     lowercase_   :			torch.FloatTensor





class 						SCREAMING_SNAKE_CASE__		(_a						,				_a ):
     lowercase_   :			int   =   1
     @register_to_config
     def __init__(	self  :		List[str]					,	__lowerCamelCase  :		int = 40					,	__lowerCamelCase  :		float = 0.002					,	__lowerCamelCase  :		float = 80.0					,	__lowerCamelCase  :		float = 0.5					,	__lowerCamelCase  :		float = 1.0					,	__lowerCamelCase  :		float = 7.0					,	__lowerCamelCase  :		bool = True					,	):



       """simple docstring"""
       lowerCAmelCase__								=			sigma_max

       lowerCAmelCase__								=			np.linspace(0					,	1					,	__lowerCamelCase	)
       lowerCAmelCase__								=			self._convert_to_karras(__lowerCamelCase	)
       lowerCAmelCase__								=			self.sigma_to_t(__lowerCamelCase	)

       # setable values
       lowerCAmelCase__								=			None
       lowerCAmelCase__								=			torch.from_numpy(__lowerCamelCase	)
       lowerCAmelCase__								=			torch.from_numpy(__lowerCamelCase	)
       lowerCAmelCase__								=			False
       lowerCAmelCase__								=			False
     def 					A__			(	self  :		Any					,	__lowerCamelCase  :		Optional[Any]					,	__lowerCamelCase  :		Optional[Any]=None	):



       """simple docstring"""
       if schedule_timesteps is None:
         lowerCAmelCase__								=			self.timesteps

       lowerCAmelCase__								=			(schedule_timesteps == timestep).nonzero()
       return indices.item()
     def 					A__			(	self  :		Tuple					,	__lowerCamelCase  :		torch.FloatTensor					,	__lowerCamelCase  :		Union[float, torch.FloatTensor]	):



       """simple docstring"""
       # Get sigma corresponding to timestep
       if isinstance(__lowerCamelCase					,	torch.Tensor	):
         lowerCAmelCase__								=			timestep.to(self.timesteps.device	)
       lowerCAmelCase__								=			self.index_for_timestep(__lowerCamelCase	)
       lowerCAmelCase__								=			self.sigmas[step_idx]

       lowerCAmelCase__								=			sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

       lowerCAmelCase__								=			True
       return sample
     def 					A__			(	self  :		Union[str, Any]					,	__lowerCamelCase  :		Union[float, np.ndarray]	):



       """simple docstring"""
       if not isinstance(__lowerCamelCase					,	np.ndarray	):
         lowerCAmelCase__								=			np.array(__lowerCamelCase					,	dtype=np.floataa	)

       lowerCAmelCase__								=			10_00 * 0.25 * np.log(sigmas + 1e-44	)

       return timesteps
     def 					A__			(	self  :		Tuple					,	__lowerCamelCase  :		Optional[int] = None					,	__lowerCamelCase  :		Union[str, torch.device] = None					,	__lowerCamelCase  :		Optional[List[int]] = None					,	):



       """simple docstring"""
       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}."""	)

         lowerCAmelCase__								=			np.array(__lowerCamelCase					,	dtype=np.intaa	)
         lowerCAmelCase__								=			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."""	)

         lowerCAmelCase__								=			num_inference_steps

         lowerCAmelCase__								=			self.config.num_train_timesteps // self.num_inference_steps
         lowerCAmelCase__								=			(np.arange(0					,	__lowerCamelCase	) * step_ratio).round()[::-1].copy().astype(np.intaa	)
         lowerCAmelCase__								=			False

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

       lowerCAmelCase__								=			np.concatenate([sigmas, [self.sigma_min]]	).astype(np.floataa	)
       lowerCAmelCase__								=			torch.from_numpy(__lowerCamelCase	).to(device=__lowerCamelCase	)

       if str(__lowerCamelCase	).startswith('''mps'''	):
         # mps does not support float64
         lowerCAmelCase__								=			torch.from_numpy(__lowerCamelCase	).to(__lowerCamelCase					,	dtype=torch.floataa	)
       else:
         lowerCAmelCase__								=			torch.from_numpy(__lowerCamelCase	).to(device=__lowerCamelCase	)
     def 					A__			(	self  :		int					,	__lowerCamelCase  :		int	):



       """simple docstring"""
       lowerCAmelCase__								=			self.config.sigma_min
       lowerCAmelCase__								=			self.config.sigma_max

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



       """simple docstring"""
       lowerCAmelCase__								=			self.config.sigma_data

       lowerCAmelCase__								=			sigma_data**2 / (sigma**2 + sigma_data**2)
       lowerCAmelCase__								=			sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out
     def 					A__			(	self  :		Tuple					,	__lowerCamelCase  :		Dict	):



       """simple docstring"""
       lowerCAmelCase__								=			self.config.sigma_min
       lowerCAmelCase__								=			self.config.sigma_data

       lowerCAmelCase__								=			sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
       lowerCAmelCase__								=			(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out
     def 					A__			(	self  :		Union[str, Any]					,	__lowerCamelCase  :		torch.FloatTensor					,	__lowerCamelCase  :		Union[float, torch.FloatTensor]					,	__lowerCamelCase  :		torch.FloatTensor					,	__lowerCamelCase  :		Optional[torch.Generator] = None					,	__lowerCamelCase  :		bool = True					,	):



       """simple docstring"""

       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	):
         lowerCAmelCase__								=			timestep.to(self.timesteps.device	)

       lowerCAmelCase__								=			self.config.sigma_min
       lowerCAmelCase__								=			self.config.sigma_max

       lowerCAmelCase__								=			self.index_for_timestep(__lowerCamelCase	)

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

       # Get scalings for boundary conditions
       lowerCAmelCase__							,			lowerCAmelCase__								=			self.get_scalings_for_boundary_condition(__lowerCamelCase	)

       # 1. Denoise model output using boundary conditions
       lowerCAmelCase__								=			c_out * model_output + c_skip * sample
       if self.config.clip_denoised:
         lowerCAmelCase__								=			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:
         lowerCAmelCase__								=			randn_tensor(
             model_output.shape					,	dtype=model_output.dtype					,	device=model_output.device					,	generator=__lowerCamelCase	)
       else:
         lowerCAmelCase__								=			torch.zeros_like(__lowerCamelCase	)
       lowerCAmelCase__								=			noise * self.config.s_noise

       lowerCAmelCase__								=			sigma_next.clamp(min=__lowerCamelCase					,	max=__lowerCamelCase	)

       # 3. Return noisy sample
       # tau = sigma_hat, eps = sigma_min
       lowerCAmelCase__								=			denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

       if not return_dict:
         return (prev_sample,)

       return CMStochasticIterativeSchedulerOutput(prev_sample=__lowerCamelCase	)
     def 					A__			(	self  :		Optional[int]					,	__lowerCamelCase  :		torch.FloatTensor					,	__lowerCamelCase  :		torch.FloatTensor					,	__lowerCamelCase  :		torch.FloatTensor					,	):



       """simple docstring"""
       lowerCAmelCase__								=			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
         lowerCAmelCase__								=			self.timesteps.to(original_samples.device					,	dtype=torch.floataa	)
         lowerCAmelCase__								=			timesteps.to(original_samples.device					,	dtype=torch.floataa	)
       else:
         lowerCAmelCase__								=			self.timesteps.to(original_samples.device	)
         lowerCAmelCase__								=			timesteps.to(original_samples.device	)

       lowerCAmelCase__								=			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

       lowerCAmelCase__								=			sigmas[step_indices].flatten()
       while len(sigma.shape	) < len(original_samples.shape	):
         lowerCAmelCase__								=			sigma.unsqueeze(-1	)

       lowerCAmelCase__								=			original_samples + noise * sigma
       return noisy_samples



     def __len__(	self  :		List[str]	):



       """simple docstring"""
       return self.config.num_train_timesteps







import os

from ...utils import logging
from .utils import DataProcessor, InputExample


__magic_name__     :     str  							=					logging.get_logger(__name__)





class 						SCREAMING_SNAKE_CASE__		(_a ):
     def __init__(	self  :		Any					,	__lowerCamelCase  :		Any					,	__lowerCamelCase  :		Optional[int]=None	):



       """simple docstring"""
       lowerCAmelCase__								=			language
       lowerCAmelCase__								=			train_language
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		List[str]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.language if self.train_language is None else self.train_language
       lowerCAmelCase__								=			self._read_tsv(os.path.join(__lowerCamelCase					,	F"""XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"""	)	)
       lowerCAmelCase__								=			[]
       for i, line in enumerate(__lowerCamelCase	):
         if i == 0:
           continue
         lowerCAmelCase__								=			F"""train-{i}"""
         lowerCAmelCase__								=			line[0]
         lowerCAmelCase__								=			line[1]
         lowerCAmelCase__								=			'''contradiction''' if line[2] == '''contradictory''' else line[2]
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_a} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_b} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training label {label} is not a string"""	)
         examples.append(InputExample(guid=__lowerCamelCase					,	text_a=__lowerCamelCase					,	text_b=__lowerCamelCase					,	label=__lowerCamelCase	)	)
       return examples
     def 					A__			(	self  :		Dict					,	__lowerCamelCase  :		Union[str, Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			self._read_tsv(os.path.join(__lowerCamelCase					,	'''XNLI-1.0/xnli.test.tsv'''	)	)
       lowerCAmelCase__								=			[]
       for i, line in enumerate(__lowerCamelCase	):
         if i == 0:
           continue
         lowerCAmelCase__								=			line[0]
         if language != self.language:
           continue
         lowerCAmelCase__								=			F"""test-{i}"""
         lowerCAmelCase__								=			line[6]
         lowerCAmelCase__								=			line[7]
         lowerCAmelCase__								=			line[1]
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_a} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training input {text_b} is not a string"""	)
         if not isinstance(__lowerCamelCase					,	__lowerCamelCase	):
           raise ValueError(F"""Training label {label} is not a string"""	)
         examples.append(InputExample(guid=__lowerCamelCase					,	text_a=__lowerCamelCase					,	text_b=__lowerCamelCase					,	label=__lowerCamelCase	)	)
       return examples



     def 					A__			(	self  :		List[str]	):



       """simple docstring"""
       return ["contradiction", "entailment", "neutral"]



__magic_name__     :     Tuple  							=					{
    """xnli""": XnliProcessor,
}

__magic_name__     :     List[Any]  							=					{
    """xnli""": """classification""",
}

__magic_name__     :     str  							=					{
    """xnli""": 3,
}

