




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def 						lowerCamelCase__    (		_A     ,	_A=0.9_99     ,	_A="cosine"     ,	):







	'''simple docstring'''





	if alpha_transform_type == "cosine":

		def alpha_bar_fn(_A							):
			return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2							) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(_A							):
			return math.exp(t * -12.0							)

	else:
		raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"							)

	snake_case_		    =     []
	for i in range(lowerCAmelCase__							):
		snake_case_		    =     i / num_diffusion_timesteps
		snake_case_		    =     (i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(lowerCAmelCase__							) / alpha_bar_fn(lowerCAmelCase__							)     ,	lowerCAmelCase__							)							)
	return torch.tensor(lowerCAmelCase__     ,	dtype=torch.floataa							)






class 		UpperCAmelCase   (				__lowercase       ,   __lowercase			):







						'''simple docstring'''





						lowerCAmelCase_     		=   [e.name for e in KarrasDiffusionSchedulers]
						lowerCAmelCase_     		=   2






						@register_to_config
						def __init__(						self							:     List[str]       ,	__lowercase							:     int = 10_00       ,	__lowercase							:     float = 0.0_0085       ,	__lowercase							:     float = 0.012       ,	__lowercase							:     str = "linear"       ,	__lowercase							:     Optional[Union[np.ndarray, List[float]]] = None       ,	__lowercase							:     str = "epsilon"       ,	__lowercase							:     str = "linspace"       ,	__lowercase							:     int = 0       ,	):






							"""simple docstring"""






							if trained_betas is not None:
								snake_case_		    =     torch.tensor(snake_case_       ,	dtype=torch.floataa						)
							elif beta_schedule == "linear":
								snake_case_		    =     torch.linspace(snake_case_       ,	snake_case_       ,	snake_case_       ,	dtype=torch.floataa						)
							elif beta_schedule == "scaled_linear":
								# this schedule is very specific to the latent diffusion model.
								snake_case_		    =     (
								    torch.linspace(beta_start**0.5       ,	beta_end**0.5       ,	snake_case_       ,	dtype=torch.floataa						) ** 2
								)
							elif beta_schedule == "squaredcos_cap_v2":
								# Glide cosine schedule
								snake_case_		    =     betas_for_alpha_bar(snake_case_						)
							else:
								raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"						)

							snake_case_		    =     1.0 - self.betas
							snake_case_		    =     torch.cumprod(self.alphas       ,	dim=0						)

							#  set all values
							self.set_timesteps(snake_case_       ,	snake_case_       ,	snake_case_						)






						def        snake_case__						(						self							:     Optional[Any]       ,	__lowercase							:     Optional[Any]       ,	__lowercase							:     Dict=None						):






							"""simple docstring"""






							if schedule_timesteps is None:
								snake_case_		    =     self.timesteps

							snake_case_		    =     (schedule_timesteps == timestep).nonzero()

							# The sigma index that is taken for the **very** first `step`
							# is always the second index (or the last index if there is only 1)
							# This way we can ensure we don't accidentally skip a sigma in
							# case we start in the middle of the denoising schedule (e.g. for image-to-image)
							if len(self._index_counter						) == 0:
								snake_case_		    =     1 if len(snake_case_						) > 1 else 0
							else:
								snake_case_		    =     timestep.cpu().item() if torch.is_tensor(snake_case_						) else timestep
								snake_case_		    =     self._index_counter[timestep_int]

							return indices[pos].item()






						@property
						def        snake_case__						(						self							:     List[Any]						):






							"""simple docstring"""






							if self.config.timestep_spacing in ["linspace", "trailing"]:
								return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5






						def        snake_case__						(						self							:     List[Any]       ,	__lowercase							:     torch.FloatTensor       ,	__lowercase							:     Union[float, torch.FloatTensor]       ,	):






							"""simple docstring"""






							snake_case_		    =     self.index_for_timestep(snake_case_						)

							if self.state_in_first_order:
								snake_case_		    =     self.sigmas[step_index]
							else:
								snake_case_		    =     self.sigmas_interpol[step_index]

							snake_case_		    =     sample / ((sigma**2 + 1) ** 0.5)
							return sample






						def        snake_case__						(						self							:     Any       ,	__lowercase							:     int       ,	__lowercase							:     Union[str, torch.device] = None       ,	__lowercase							:     Optional[int] = None       ,	):






							"""simple docstring"""






							snake_case_		    =     num_inference_steps

							snake_case_		    =     num_train_timesteps or self.config.num_train_timesteps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
								snake_case_		    =     np.linspace(0       ,	num_train_timesteps - 1       ,	snake_case_       ,	dtype=snake_case_						)[::-1].copy()
							elif self.config.timestep_spacing == "leading":
								snake_case_		    =     num_train_timesteps // self.num_inference_steps
								# creates integer timesteps by multiplying by ratio
								# casting to int to avoid issues when num_inference_step is power of 3
								snake_case_		    =     (np.arange(0       ,	snake_case_						) * step_ratio).round()[::-1].copy().astype(snake_case_						)
								timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
								snake_case_		    =     num_train_timesteps / self.num_inference_steps
								# creates integer timesteps by multiplying by ratio
								# casting to int to avoid issues when num_inference_step is power of 3
								snake_case_		    =     (np.arange(snake_case_       ,	0       ,	-step_ratio						)).round().copy().astype(snake_case_						)
								timesteps -= 1
							else:
								raise ValueError(
								    f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."						)

							snake_case_		    =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
							snake_case_		    =     torch.from_numpy(np.log(snake_case_						)						).to(snake_case_						)

							snake_case_		    =     np.interp(snake_case_       ,	np.arange(0       ,	len(snake_case_						)						)       ,	snake_case_						)
							snake_case_		    =     np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
							snake_case_		    =     torch.from_numpy(snake_case_						).to(device=snake_case_						)

							# interpolate sigmas
							snake_case_		    =     sigmas.log().lerp(sigmas.roll(1						).log()       ,	0.5						).exp()

							snake_case_		    =     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2						), sigmas[-1:]]						)
							snake_case_		    =     torch.cat(
							    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2						), sigmas_interpol[-1:]]						)

							if str(snake_case_						).startswith("mps"						):
								# mps does not support float64
								snake_case_		    =     torch.from_numpy(snake_case_						).to(snake_case_       ,	dtype=torch.floataa						)
							else:
								snake_case_		    =     torch.from_numpy(snake_case_						).to(snake_case_						)

							# interpolate timesteps
							snake_case_		    =     self.sigma_to_t(snake_case_						).to(snake_case_       ,	dtype=timesteps.dtype						)
							snake_case_		    =     torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])       ,	dim=-1						).flatten()

							snake_case_		    =     torch.cat([timesteps[:1], interleaved_timesteps]						)

							snake_case_		    =     None

							# for exp beta schedules, such as the one for `pipeline_shap_e.py`
							# we need an index counter
							snake_case_		    =     defaultdict(snake_case_						)






						def        snake_case__						(						self							:     Dict       ,	__lowercase							:     Optional[int]						):






							"""simple docstring"""






							snake_case_		    =     sigma.log()

							# get distribution
							snake_case_		    =     log_sigma - self.log_sigmas[:, None]

							# get sigmas range
							snake_case_		    =     dists.ge(0						).cumsum(dim=0						).argmax(dim=0						).clamp(max=self.log_sigmas.shape[0] - 2						)
							snake_case_		    =     low_idx + 1

							snake_case_		    =     self.log_sigmas[low_idx]
							snake_case_		    =     self.log_sigmas[high_idx]

							# interpolate sigmas
							snake_case_		    =     (low - log_sigma) / (low - high)
							snake_case_		    =     w.clamp(0       ,	1						)

							# transform interpolation to time range
							snake_case_		    =     (1 - w) * low_idx + w * high_idx
							snake_case_		    =     t.view(sigma.shape						)
							return t






						@property
						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							return self.sample is None






						def        snake_case__						(						self							:     Optional[int]       ,	__lowercase							:     Union[torch.FloatTensor, np.ndarray]       ,	__lowercase							:     Union[float, torch.FloatTensor]       ,	__lowercase							:     Union[torch.FloatTensor, np.ndarray]       ,	__lowercase							:     bool = True       ,	):






							"""simple docstring"""






							snake_case_		    =     self.index_for_timestep(snake_case_						)

							# advance index counter by 1
							snake_case_		    =     timestep.cpu().item() if torch.is_tensor(snake_case_						) else timestep
							self._index_counter[timestep_int] += 1

							if self.state_in_first_order:
								snake_case_		    =     self.sigmas[step_index]
								snake_case_		    =     self.sigmas_interpol[step_index + 1]
								snake_case_		    =     self.sigmas[step_index + 1]
							else:
								# 2nd order / KDPM2's method
								snake_case_		    =     self.sigmas[step_index - 1]
								snake_case_		    =     self.sigmas_interpol[step_index]
								snake_case_		    =     self.sigmas[step_index]

							# currently only gamma=0 is supported. This usually works best anyways.
							# We can support gamma in the future but then need to scale the timestep before
							# passing it to the model which requires a change in API
							snake_case_		    =     0
							snake_case_		    =     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
								snake_case_		    =     sigma_hat if self.state_in_first_order else sigma_interpol
								snake_case_		    =     sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
								snake_case_		    =     sigma_hat if self.state_in_first_order else sigma_interpol
								snake_case_		    =     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
								    sample / (sigma_input**2 + 1)
								)
							elif self.config.prediction_type == "sample":
								raise NotImplementedError("prediction_type not implemented yet: sample"						)
							else:
								raise ValueError(
								    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"						)

							if self.state_in_first_order:
								# 2. Convert to an ODE derivative for 1st order
								snake_case_		    =     (sample - pred_original_sample) / sigma_hat
								# 3. delta timestep
								snake_case_		    =     sigma_interpol - sigma_hat

								# store for 2nd order step
								snake_case_		    =     sample
							else:
								# DPM-Solver-2
								# 2. Convert to an ODE derivative for 2nd order
								snake_case_		    =     (sample - pred_original_sample) / sigma_interpol

								# 3. delta timestep
								snake_case_		    =     sigma_next - sigma_hat

								snake_case_		    =     self.sample
								snake_case_		    =     None

							snake_case_		    =     sample + derivative * dt

							if not return_dict:
								return (prev_sample,)

							return SchedulerOutput(prev_sample=snake_case_						)






						def        snake_case__						(						self							:     List[str]       ,	__lowercase							:     torch.FloatTensor       ,	__lowercase							:     torch.FloatTensor       ,	__lowercase							:     torch.FloatTensor       ,	):






							"""simple docstring"""






							snake_case_		    =     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_		    =     self.timesteps.to(original_samples.device       ,	dtype=torch.floataa						)
								snake_case_		    =     timesteps.to(original_samples.device       ,	dtype=torch.floataa						)
							else:
								snake_case_		    =     self.timesteps.to(original_samples.device						)
								snake_case_		    =     timesteps.to(original_samples.device						)

							snake_case_		    =     [self.index_for_timestep(snake_case_       ,	snake_case_						) for t in timesteps]

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

							snake_case_		    =     original_samples + noise * sigma
							return noisy_samples






						def __len__(						self							:     int						):






							"""simple docstring"""






							return self.config.num_train_timesteps


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


lowercase__       =logging.get_logger(__name__)

lowercase__       ={'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__       ={
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

lowercase__       ={
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


lowercase__       ={
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE      :							List[Any]	       =      PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE      :							List[str]	       =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      RoFormerTokenizer
   def __init__(self :					List[str]					,				snake_case_ :					Optional[int]=None					,				snake_case_ :					str=None					,				snake_case_ :					Optional[Any]=True					,				snake_case_ :					str="[UNK]"					,				snake_case_ :					Dict="[SEP]"					,				snake_case_ :					Any="[PAD]"					,				snake_case_ :					str="[CLS]"					,				snake_case_ :					List[Any]="[MASK]"					,				snake_case_ :					Any=True					,				snake_case_ :					List[str]=None					,				**snake_case_ :					Optional[int]					,				):
    super().__init__(
        snake_case_					,				tokenizer_file=snake_case_					,				do_lower_case=snake_case_					,				unk_token=snake_case_					,				sep_token=snake_case_					,				pad_token=snake_case_					,				cls_token=snake_case_					,				mask_token=snake_case_					,				tokenize_chinese_chars=snake_case_					,				strip_accents=snake_case_					,				**snake_case_					,				)

    __a :  Optional[int]						=  json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
    if (
        pre_tok_state.get('''lowercase'''					,				snake_case_   ) != do_lower_case
        or pre_tok_state.get('''strip_accents'''					,				snake_case_   ) != strip_accents
    ):
     __a :  List[str]						=  getattr(snake_case_					,				pre_tok_state.pop('''type'''   )   )
     __a :  Optional[Any]						=  do_lower_case
     __a :  Optional[int]						=  strip_accents
     __a :  List[str]						=  pre_tok_class(**snake_case_   )

    __a :  Optional[Any]						=  do_lower_case
   def __getstate__(self :					Union[str, Any]   ):
    __a :  Any						=  self.__dict__.copy()
    __a :  Union[str, Any]						=  BertPreTokenizer()
    return state
   def __setstate__(self :					Tuple					,				snake_case_ :					Optional[Any]   ):
    __a :  Dict						=  d
    __a :  str						=  self.__dict__['''_tokenizer'''].get_vocab()
    __a :  Optional[Any]						=  PreTokenizer.custom(JiebaPreTokenizer(snake_case_   )   )
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					List[Any]					,				snake_case_ :					Optional[Any]=None   ):
    __a :  Optional[int]						=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

    return output
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					List[int]					,				snake_case_ :					Optional[List[int]] = None   ):
    __a :  int						=  [self.sep_token_id]
    __a :  List[Any]						=  [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   ) * [0] + len(token_ids_a + sep   ) * [1]
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					str					,				snake_case_ :					Optional[str] = None   ):
    __a :  Optional[Any]						=  self._tokenizer.model.save(snake_case_					,				name=snake_case_   )
    return tuple(snake_case_   )







   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					Dict					,				snake_case_ :					Tuple=None					,				snake_case_ :					Optional[Any]=None					,				snake_case_ :					Union[str, Any]=False					,				**snake_case_ :					Tuple					,				):
    __a :  List[str]						=  BertPreTokenizer()
    return super().save_pretrained(snake_case_					,				snake_case_					,				snake_case_					,				snake_case_					,				**snake_case_   )
