





"""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


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





@dataclass
class 			snake_case		(   __UpperCAmelCase   ):
      lowerCamelCase__						= 42







class 			snake_case		(   __UpperCAmelCase ,     __UpperCAmelCase   ):
      lowerCamelCase__						= 1

      @register_to_config
      def __init__(			self   :Optional[Any]						,							_lowerCamelCase   :List[str] = 4_0						,							_lowerCamelCase   :Optional[Any] = 0.0_0_2						,							_lowerCamelCase   :Dict = 8_0.0						,							_lowerCamelCase   :Union[str, Any] = 0.5						,							_lowerCamelCase   :str = 1.0						,							_lowerCamelCase   :Tuple = 7.0						,							_lowerCamelCase   :Optional[int] = True						,							):
       # standard deviation of the initial noise distribution
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       sigma_max

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

       # setable values
       __SCREAMING_SNAKE_CASE :			Any												=       None
       __SCREAMING_SNAKE_CASE :			Tuple												=       torch.from_numpy(__UpperCAmelCase						)
       __SCREAMING_SNAKE_CASE :			Dict												=       torch.from_numpy(__UpperCAmelCase						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       False
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       False

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						,							_lowerCamelCase   :Any						,							_lowerCamelCase   :str=None						):
       if schedule_timesteps is None:
        __SCREAMING_SNAKE_CASE :			str												=       self.timesteps

       __SCREAMING_SNAKE_CASE :			Any												=       (schedule_timesteps == timestep).nonzero()
       return indices.item()

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :Dict						):
       # Get sigma corresponding to timestep
       if isinstance(__UpperCAmelCase						,							torch.Tensor						):
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       timestep.to(self.timesteps.device						)
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.index_for_timestep(__UpperCAmelCase						)
       __SCREAMING_SNAKE_CASE :			Dict												=       self.sigmas[step_idx]

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

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						,							_lowerCamelCase   :List[str]						):
       if not isinstance(__UpperCAmelCase						,							np.ndarray						):
        __SCREAMING_SNAKE_CASE :			Tuple												=       np.array(__UpperCAmelCase						,							dtype=np.floataa						)

       __SCREAMING_SNAKE_CASE :			Dict												=       1_0_0_0 * 0.2_5 * np.log(sigmas + 1e-44						)

       return timesteps

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						,							_lowerCamelCase   :Tuple = None						,							_lowerCamelCase   :Optional[int] = None						,							_lowerCamelCase   :List[Any] = 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(__UpperCAmelCase						)						):
         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}.'''						)

        __SCREAMING_SNAKE_CASE :			Dict												=       np.array(__UpperCAmelCase						,							dtype=np.intaa						)
        __SCREAMING_SNAKE_CASE :			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.'''						)

        __SCREAMING_SNAKE_CASE :			Any												=       num_inference_steps

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

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

       __SCREAMING_SNAKE_CASE :			Any												=       np.concatenate([sigmas, [self.sigma_min]]						).astype(np.floataa						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       torch.from_numpy(__UpperCAmelCase						).to(device=__UpperCAmelCase						)

       if str(__UpperCAmelCase						).startswith('''mps'''						):
        # mps does not support float64
        __SCREAMING_SNAKE_CASE :			List[str]												=       torch.from_numpy(__UpperCAmelCase						).to(__UpperCAmelCase						,							dtype=torch.floataa						)
       else:
        __SCREAMING_SNAKE_CASE :			Tuple												=       torch.from_numpy(__UpperCAmelCase						).to(device=__UpperCAmelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						,							_lowerCamelCase   :Dict						):
       __SCREAMING_SNAKE_CASE :			List[str]												=       self.config.sigma_min
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.config.sigma_max

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						,							_lowerCamelCase   :str						):
       __SCREAMING_SNAKE_CASE :			int												=       self.config.sigma_data

       __SCREAMING_SNAKE_CASE :			Dict												=       sigma_data**2 / (sigma**2 + sigma_data**2)
       __SCREAMING_SNAKE_CASE :			Any												=       sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out

      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						,							_lowerCamelCase   :str						):
       __SCREAMING_SNAKE_CASE :			Any												=       self.config.sigma_min
       __SCREAMING_SNAKE_CASE :			int												=       self.config.sigma_data

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

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

       if (
           isinstance(__UpperCAmelCase						,							__UpperCAmelCase						)
           or isinstance(__UpperCAmelCase						,							torch.IntTensor						)
           or isinstance(__UpperCAmelCase						,							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(__UpperCAmelCase						,							torch.Tensor						):
        __SCREAMING_SNAKE_CASE :			Any												=       timestep.to(self.timesteps.device						)

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.config.sigma_min
       __SCREAMING_SNAKE_CASE :			int												=       self.config.sigma_max

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       self.index_for_timestep(__UpperCAmelCase						)

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

       # Get scalings for boundary conditions
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Any												=       self.get_scalings_for_boundary_condition(__UpperCAmelCase						)

       # 1. Denoise model output using boundary conditions
       __SCREAMING_SNAKE_CASE :			int												=       c_out * model_output + c_skip * sample
       if self.config.clip_denoised:
        __SCREAMING_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:
        __SCREAMING_SNAKE_CASE :			int												=       randn_tensor(
            model_output.shape						,							dtype=model_output.dtype						,							device=model_output.device						,							generator=__UpperCAmelCase						)
       else:
        __SCREAMING_SNAKE_CASE :			List[Any]												=       torch.zeros_like(__UpperCAmelCase						)
       __SCREAMING_SNAKE_CASE :			str												=       noise * self.config.s_noise

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       sigma_next.clamp(min=__UpperCAmelCase						,							max=__UpperCAmelCase						)

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

       if not return_dict:
        return (prev_sample,)

       return CMStochasticIterativeSchedulerOutput(prev_sample=__UpperCAmelCase						)

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

       __SCREAMING_SNAKE_CASE :			Any												=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

       __SCREAMING_SNAKE_CASE :			Dict												=       original_samples + noise * sigma
       return noisy_samples



      def __len__(			self   :Tuple						):
       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       						=       None


__A       						=       logging.get_logger(__name__)

__A       						=       {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__A       						=       {
    '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       						=       {
    '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__					(					__magic_name__				):





			'''simple docstring'''
			lowerCamelCase  					=		VOCAB_FILES_NAMES
			lowerCamelCase  					=		PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase  					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase  					=		['''input_ids''', '''attention_mask''']
			lowerCamelCase  					=		XLMRobertaTokenizer



			def __init__(							self     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase="<s>"     ,     __UpperCAmelCase="</s>"     ,     __UpperCAmelCase="</s>"     ,     __UpperCAmelCase="<s>"     ,     __UpperCAmelCase="<unk>"     ,     __UpperCAmelCase="<pad>"     ,     __UpperCAmelCase="<mask>"     ,     **__UpperCAmelCase     ,     ) ->     str:
				# Mask token behave like a normal word, i.e. include the space before it
				_lowerCAmelCase								=AddedToken(__UpperCAmelCase     ,     lstrip=__UpperCAmelCase     ,     rstrip=__UpperCAmelCase  ) if isinstance(__UpperCAmelCase     ,     __UpperCAmelCase  ) else mask_token

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

				_lowerCAmelCase								=vocab_file
				_lowerCAmelCase								=False if not self.vocab_file else True



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     List[int]:

				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				_lowerCAmelCase								=[self.cls_token_id]
				_lowerCAmelCase								=[self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     List[int]:
				_lowerCAmelCase								=[self.sep_token_id]
				_lowerCAmelCase								=[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 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     Tuple[str]:
				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(__UpperCAmelCase  ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''  )
					return
				_lowerCAmelCase								=os.path.join(
				    __UpperCAmelCase     ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

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

				return (out_vocab_file,)



