
"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


_lowercase											=			logging.get_logger(__name__)

_lowercase											=			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowercase											=			{
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''allenai/longformer-base-4096''': (
            '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'''
        ),
    },
}

_lowercase											=			{
    '''allenai/longformer-base-4096''': 40_96,
    '''allenai/longformer-large-4096''': 40_96,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 40_96,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 40_96,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 40_96,
}







class 			lowerCAmelCase_			( _lowercase      ):



      '''simple docstring'''


      _lowerCamelCase:      List[str] =      VOCAB_FILES_NAMES
      _lowerCamelCase:      Tuple =      PRETRAINED_VOCAB_FILES_MAP
      _lowerCamelCase:      Dict =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowerCamelCase:      Any =      ['''input_ids''', '''attention_mask''']
      _lowerCamelCase:      Optional[Any] =      LongformerTokenizer


      def __init__(	self       : List[Any]					,A_       : Optional[int]=None					,A_       : Optional[int]=None					,A_       : List[str]=None					,A_       : Dict="replace"					,A_       : Dict="<s>"					,A_       : Any="</s>"					,A_       : Optional[Any]="</s>"					,A_       : int="<s>"					,A_       : Any="<unk>"					,A_       : Optional[int]="<pad>"					,A_       : Optional[int]="<mask>"					,A_       : int=False					,A_       : Tuple=True					,**A_       : str					,)       ->				int:
       super().__init__(
           A_					,A_					,tokenizer_file=A_					,errors=A_					,bos_token=A_					,eos_token=A_					,sep_token=A_					,cls_token=A_					,unk_token=A_					,pad_token=A_					,mask_token=A_					,add_prefix_space=A_					,trim_offsets=A_					,**A_					,)

       A  				=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
       if pre_tok_state.get('add_prefix_space'					,A_ ) != add_prefix_space:
        A  				=				getattr(A_					,pre_tok_state.pop('type' ) )
        A  				=				add_prefix_space
        A  				=				pre_tok_class(**A_ )

       A  				=				add_prefix_space

       A  				=				'post_processor'
       A  				=				getattr(self.backend_tokenizer					,A_					,A_ )
       if tokenizer_component_instance:
        A  				=				json.loads(tokenizer_component_instance.__getstate__() )

        # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
        if "sep" in state:
         A  				=				tuple(state['sep'] )
        if "cls" in state:
         A  				=				tuple(state['cls'] )

        A  				=				False

        if state.get('add_prefix_space'					,A_ ) != add_prefix_space:
         A  				=				add_prefix_space
         A  				=				True

        if state.get('trim_offsets'					,A_ ) != trim_offsets:
         A  				=				trim_offsets
         A  				=				True

        if changes_to_apply:
         A  				=				getattr(A_					,state.pop('type' ) )
         A  				=				component_class(**A_ )
         setattr(self.backend_tokenizer					,A_					,A_ )


      @property
      def   _SCREAMING_SNAKE_CASE (	self       : Any )       ->				str:
       if self._mask_token is None:
        if self.verbose:
         logger.error('Using mask_token, but it is not set yet.' )
        return None
       return str(self._mask_token )


      @mask_token.setter
      def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : Tuple )       ->				Union[str, Any]:
       A  				=				AddedToken(A_					,lstrip=A_					,rstrip=A_ ) if isinstance(A_					,A_ ) else value
       A  				=				value


      def   _SCREAMING_SNAKE_CASE (	self       : int					,*A_       : Dict					,**A_       : str )       ->				BatchEncoding:
       A  				=				kwargs.get('is_split_into_words'					,A_ )
       assert self.add_prefix_space or not is_split_into_words, (
           F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
           "to use it with pretokenized inputs."
       )

       return super()._batch_encode_plus(*A_					,**A_ )


      def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,*A_       : List[Any]					,**A_       : Tuple )       ->				BatchEncoding:
       A  				=				kwargs.get('is_split_into_words'					,A_ )

       assert self.add_prefix_space or not is_split_into_words, (
           F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
           "to use it with pretokenized inputs."
       )

       return super()._encode_plus(*A_					,**A_ )


      def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,A_       : str					,A_       : Optional[str] = None )       ->				Tuple[str]:
       A  				=				self._tokenizer.model.save(A_					,name=A_ )
       return tuple(A_ )


      def   _SCREAMING_SNAKE_CASE (	self       : Dict					,A_       : int					,A_       : List[str]=None )       ->				int:
       A  				=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
        return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




      def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : List[int]					,A_       : Optional[List[int]] = None )       ->				List[int]:
       A  				=				[self.sep_token_id]
       A  				=				[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]

"""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


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







@dataclass
class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      torch.FloatTensor







class 			lowerCAmelCase_			( _lowercase						,   _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      Dict =      1


						@register_to_config
						def __init__(	self       : List[Any]					,A_       : int = 40					,A_       : float = 0.0_02					,A_       : float = 80.0					,A_       : float = 0.5					,A_       : float = 1.0					,A_       : float = 7.0					,A_       : bool = True					,)       ->				List[str]:
							# standard deviation of the initial noise distribution
							A  				=				sigma_max

							A  				=				np.linspace(0					,1					,A_ )
							A  				=				self._convert_to_karras(A_ )
							A  				=				self.sigma_to_t(A_ )

							# setable values
							A  				=				None
							A  				=				torch.from_numpy(A_ )
							A  				=				torch.from_numpy(A_ )
							A  				=				False
							A  				=				False


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : List[Any]					,A_       : List[Any]=None )       ->				List[Any]:
							if schedule_timesteps is None:
								A  				=				self.timesteps

							A  				=				(schedule_timesteps == timestep).nonzero()
							return indices.item()


						def   _SCREAMING_SNAKE_CASE (	self       : List[str]					,A_       : torch.FloatTensor					,A_       : Union[float, torch.FloatTensor] )       ->				torch.FloatTensor:
							# Get sigma corresponding to timestep
							if isinstance(A_					,torch.Tensor ):
								A  				=				timestep.to(self.timesteps.device )
							A  				=				self.index_for_timestep(A_ )
							A  				=				self.sigmas[step_idx]

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

							A  				=				True
							return sample


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : Union[float, np.ndarray] )       ->				Tuple:
							if not isinstance(A_					,np.ndarray ):
								A  				=				np.array(A_					,dtype=np.floataa )

							A  				=				1000 * 0.25 * np.log(sigmas + 1e-44 )

							return timesteps


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : Optional[int] = None					,A_       : Union[str, torch.device] = None					,A_       : Optional[List[int]] = None					,)       ->				Optional[Any]:
							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(A_ ) ):
									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  				=				np.array(A_					,dtype=np.intaa )
								A  				=				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  				=				num_inference_steps

								A  				=				self.config.num_train_timesteps // self.num_inference_steps
								A  				=				(np.arange(0					,A_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
								A  				=				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  				=				self.config.num_train_timesteps
							A  				=				timesteps[::-1].copy()
							A  				=				ramp / (num_train_timesteps - 1)
							A  				=				self._convert_to_karras(A_ )
							A  				=				self.sigma_to_t(A_ )

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

							if str(A_ ).startswith('mps' ):
								# mps does not support float64
								A  				=				torch.from_numpy(A_ ).to(A_					,dtype=torch.floataa )
							else:
								A  				=				torch.from_numpy(A_ ).to(device=A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : Optional[Any] )       ->				Any:
							A  				=				self.config.sigma_min
							A  				=				self.config.sigma_max

							A  				=				self.config.rho
							A  				=				sigma_min ** (1 / rho)
							A  				=				sigma_max ** (1 / rho)
							A  				=				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
							return sigmas


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : Dict )       ->				Any:
							A  				=				self.config.sigma_data

							A  				=				sigma_data**2 / (sigma**2 + sigma_data**2)
							A  				=				sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : Optional[Any] )       ->				Dict:
							A  				=				self.config.sigma_min
							A  				=				self.config.sigma_data

							A  				=				sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
							A  				=				(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
							return c_skip, c_out


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : torch.FloatTensor					,A_       : Union[float, torch.FloatTensor]					,A_       : torch.FloatTensor					,A_       : Optional[torch.Generator] = None					,A_       : bool = True					,)       ->				Union[CMStochasticIterativeSchedulerOutput, Tuple]:

							if (
							    isinstance(A_					,A_ )
							    or isinstance(A_					,torch.IntTensor )
							    or isinstance(A_					,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(A_					,torch.Tensor ):
								A  				=				timestep.to(self.timesteps.device )

							A  				=				self.config.sigma_min
							A  				=				self.config.sigma_max

							A  				=				self.index_for_timestep(A_ )

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

							# Get scalings for boundary conditions
							A				, A  				=				self.get_scalings_for_boundary_condition(A_ )

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

							A  				=				sigma_next.clamp(min=A_					,max=A_ )

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

							if not return_dict:
								return (prev_sample,)

							return CMStochasticIterativeSchedulerOutput(prev_sample=A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : torch.FloatTensor					,A_       : torch.FloatTensor					,A_       : torch.FloatTensor					,)       ->				torch.FloatTensor:
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							A  				=				self.sigmas.to(device=original_samples.device					,dtype=original_samples.dtype )
							if original_samples.device.type == "mps" and torch.is_floating_point(A_ ):
								# mps does not support float64
								A  				=				self.timesteps.to(original_samples.device					,dtype=torch.floataa )
								A  				=				timesteps.to(original_samples.device					,dtype=torch.floataa )
							else:
								A  				=				self.timesteps.to(original_samples.device )
								A  				=				timesteps.to(original_samples.device )

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

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

							A  				=				original_samples + noise * sigma
							return noisy_samples




						def __len__(	self       : List[str] )       ->				List[Any]:
							return self.config.num_train_timesteps