





'''simple docstring'''






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


A :			str							   =      logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class       lowerCamelCase       (    __UpperCAmelCase	):
					_SCREAMING_SNAKE_CASE				 =     42







class       lowerCamelCase       (    __UpperCAmelCase  ,				__UpperCAmelCase	):
					_SCREAMING_SNAKE_CASE				 =     1


					@register_to_config
					def __init__(       self						:	int							,  __snake_case						:	int = 40							,  __snake_case						:	float = 0.002							,  __snake_case						:	float = 80.0							,  __snake_case						:	float = 0.5							,  __snake_case						:	float = 1.0							,  __snake_case						:	float = 7.0							,  __snake_case						:	bool = True							,  ):





											'''simple docstring'''





											_snake_case: List[Any]	       =		sigma_max

											_snake_case: str	       =		np.linspace(0							,  1							,  __snake_case							)
											_snake_case: Tuple	       =		self._convert_to_karras(__snake_case							)
											_snake_case: Optional[int]	       =		self.sigma_to_t(__snake_case							)

											# setable values
											_snake_case: Optional[Any]	       =		None
											_snake_case: Optional[int]	       =		torch.from_numpy(__snake_case							)
											_snake_case: Tuple	       =		torch.from_numpy(__snake_case							)
											_snake_case: str	       =		False
											_snake_case: List[str]	       =		False


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	Optional[Any]							,  __snake_case						:	Optional[int]=None							):





											'''simple docstring'''





											if schedule_timesteps is None:
																	_snake_case: Optional[Any]	       =		self.timesteps

											_snake_case: str	       =		(schedule_timesteps == timestep).nonzero()
											return indices.item()


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	torch.FloatTensor							,  __snake_case						:	Union[float, torch.FloatTensor]							):





											'''simple docstring'''





											if isinstance(__snake_case							,  torch.Tensor							):
																	_snake_case: Dict	       =		timestep.to(self.timesteps.device							)
											_snake_case: int	       =		self.index_for_timestep(__snake_case							)
											_snake_case: List[Any]	       =		self.sigmas[step_idx]

											_snake_case: Dict	       =		sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

											_snake_case: Optional[int]	       =		True
											return sample


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	Union[float, np.ndarray]							):





											'''simple docstring'''





											if not isinstance(__snake_case							,  np.ndarray							):
																	_snake_case: Union[str, Any]	       =		np.array(__snake_case							,  dtype=np.floataa							)

											_snake_case: List[str]	       =		10_00 * 0.25 * np.log(sigmas + 1e-44							)

											return timesteps


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	Optional[int] = None							,  __snake_case						:	Union[str, torch.device] = None							,  __snake_case						:	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(__snake_case							)							):
																							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}.'''							)

																	_snake_case: Any	       =		np.array(__snake_case							,  dtype=np.intaa							)
																	_snake_case: Dict	       =		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.'''							)

																	_snake_case: Optional[Any]	       =		num_inference_steps

																	_snake_case: Optional[int]	       =		self.config.num_train_timesteps // self.num_inference_steps
																	_snake_case: Optional[Any]	       =		(np.arange(0							,  __snake_case							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
																	_snake_case: Tuple	       =		False

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

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

											if str(__snake_case							).startswith('mps'							):
																	# mps does not support float64
																	_snake_case: int	       =		torch.from_numpy(__snake_case							).to(__snake_case							,  dtype=torch.floataa							)
											else:
																	_snake_case: Optional[int]	       =		torch.from_numpy(__snake_case							).to(device=__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: float	       =		self.config.sigma_min
											_snake_case: float	       =		self.config.sigma_max

											_snake_case: str	       =		self.config.rho
											_snake_case: Tuple	       =		sigma_min ** (1 / rho)
											_snake_case: Optional[Any]	       =		sigma_max ** (1 / rho)
											_snake_case: Dict	       =		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	Dict							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.config.sigma_data

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


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		self.config.sigma_min
											_snake_case: List[Any]	       =		self.config.sigma_data

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


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	torch.FloatTensor							,  __snake_case						:	Union[float, torch.FloatTensor]							,  __snake_case						:	torch.FloatTensor							,  __snake_case						:	Optional[torch.Generator] = None							,  __snake_case						:	bool = True							,  ):





											'''simple docstring'''





											if (
											    isinstance(__snake_case							,  __snake_case							)
											    or isinstance(__snake_case							,  torch.IntTensor							)
											    or isinstance(__snake_case							,  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(__snake_case							,  torch.Tensor							):
																	_snake_case: Optional[int]	       =		timestep.to(self.timesteps.device							)

											_snake_case: Optional[int]	       =		self.config.sigma_min
											_snake_case: List[str]	       =		self.config.sigma_max

											_snake_case: str	       =		self.index_for_timestep(__snake_case							)

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

											# Get scalings for boundary conditions
											_snake_case       ,    _snake_case: Dict	       =		self.get_scalings_for_boundary_condition(__snake_case							)

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

											_snake_case: List[Any]	       =		sigma_next.clamp(min=__snake_case							,  max=__snake_case							)

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

											if not return_dict:
																	return (prev_sample,)

											return CMStochasticIterativeSchedulerOutput(prev_sample=__snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	torch.FloatTensor							,  __snake_case						:	torch.FloatTensor							,  __snake_case						:	torch.FloatTensor							,  ):





											'''simple docstring'''





											_snake_case: Any	       =		self.sigmas.to(device=original_samples.device							,  dtype=original_samples.dtype							)
											if original_samples.device.type == "mps" and torch.is_floating_point(__snake_case							):
																	# mps does not support float64
																	_snake_case: Tuple	       =		self.timesteps.to(original_samples.device							,  dtype=torch.floataa							)
																	_snake_case: Optional[Any]	       =		timesteps.to(original_samples.device							,  dtype=torch.floataa							)
											else:
																	_snake_case: Optional[int]	       =		self.timesteps.to(original_samples.device							)
																	_snake_case: List[str]	       =		timesteps.to(original_samples.device							)

											_snake_case: Dict	       =		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

											_snake_case: Tuple	       =		original_samples + noise * sigma
											return noisy_samples




					def __len__(       self						:	str							):





											'''simple docstring'''





											return self.config.num_train_timesteps












'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
		A :			Optional[Any]							   =      None


A :			Tuple							   =      logging.get_logger(__name__)

A :			Optional[int]							   =      {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A :			Tuple							   =      {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

A :			int							   =      {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}







class       lowerCamelCase       (    __UpperCAmelCase	):
					_SCREAMING_SNAKE_CASE				 =     VOCAB_FILES_NAMES
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_VOCAB_FILES_MAP
					_SCREAMING_SNAKE_CASE				 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_SCREAMING_SNAKE_CASE				 =     ["input_ids", "attention_mask"]
					_SCREAMING_SNAKE_CASE				 =     XLMRobertaTokenizer


					def __init__(       self						:	Any							,  __snake_case						:	Tuple=None							,  __snake_case						:	List[Any]=None							,  __snake_case						:	List[str]="<s>"							,  __snake_case						:	int="</s>"							,  __snake_case						:	str="</s>"							,  __snake_case						:	Any="<s>"							,  __snake_case						:	int="<unk>"							,  __snake_case						:	Optional[Any]="<pad>"							,  __snake_case						:	Optional[int]="<mask>"							,  **__snake_case						:	List[str]							,  ):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		AddedToken(__snake_case							,  lstrip=__snake_case							,  rstrip=__snake_case							) if isinstance(__snake_case							,  __snake_case							) else mask_token

											super().__init__(
											    __snake_case							,  tokenizer_file=__snake_case							,  bos_token=__snake_case							,  eos_token=__snake_case							,  sep_token=__snake_case							,  cls_token=__snake_case							,  unk_token=__snake_case							,  pad_token=__snake_case							,  mask_token=__snake_case							,  **__snake_case							,  )

											_snake_case: int	       =		vocab_file
											_snake_case: List[Any]	       =		False if not self.vocab_file else True


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							,  __snake_case						:	List[int]							,  __snake_case						:	Optional[List[int]] = None							):





											'''simple docstring'''





											if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_snake_case: Union[str, Any]	       =		[self.cls_token_id]
											_snake_case: int	       =		[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	List[int]							,  __snake_case						:	Optional[List[int]] = None							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		[self.sep_token_id]
											_snake_case: List[str]	       =		[self.cls_token_id]

											if token_ids_a is None:
																	return len(cls + token_ids_a + sep							) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]




					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							,  __snake_case						:	str							,  __snake_case						:	Optional[str] = None							):





											'''simple docstring'''





											if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
																	    'tokenizer.'							)

											if not os.path.isdir(__snake_case							):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''							)
																	return
											_snake_case: Optional[int]	       =		os.path.join(
											    __snake_case							,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

											if os.path.abspath(self.vocab_file							) != os.path.abspath(__snake_case							):
																	copyfile(self.vocab_file							,  __snake_case							)

											return (out_vocab_file,)





