"""simple docstring"""



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" ,  ):
       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}"							)

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






class 				snake_case__     (   snake_case_,		snake_case_   ):
 _snake_case   :			Tuple           =		[e.name for e in KarrasDiffusionSchedulers]
 _snake_case   :			Tuple           =		2


 @register_to_config
 def __init__(				self  ,						lowerCamelCase = 1000  ,						lowerCamelCase = 0.0_0085  ,						lowerCamelCase = 0.012  ,						lowerCamelCase = "linear"  ,						lowerCamelCase = None  ,						lowerCamelCase = "epsilon"  ,						lowerCamelCase = "linspace"  ,						lowerCamelCase = 0  ,						):
        if trained_betas is not None:
               __a     				=  torch.tensor(lowerCamelCase  ,						dtype=torch.floataa		)
        elif beta_schedule == "linear":
               __a     				=  torch.linspace(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						dtype=torch.floataa		)
        elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               __a     				=  (
                   torch.linspace(beta_start**0.5  ,						beta_end**0.5  ,						lowerCamelCase  ,						dtype=torch.floataa		) ** 2
               )
        elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               __a     				=  betas_for_alpha_bar(lowerCamelCase		)
        else:
               raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"		)

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

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


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=None		):
        if schedule_timesteps is None:
               __a     				=  self.timesteps

        __a     				=  (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:
               __a     				=  1 if len(lowerCamelCase		) > 1 else 0
        else:
               __a     				=  timestep.cpu().item() if torch.is_tensor(lowerCamelCase		) else timestep
               __a     				=  self._index_counter[timestep_int]

        return indices[pos].item()


 @property
 def 		a__ (				self		):
        # standard deviation of the initial noise distribution
        if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

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


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						):
        __a     				=  self.index_for_timestep(lowerCamelCase		)

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

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


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						):
        __a     				=  num_inference_steps

        __a     				=  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":
               __a     				=  np.linspace(0  ,						num_train_timesteps - 1  ,						lowerCamelCase  ,						dtype=lowerCamelCase		)[::-1].copy()
        elif self.config.timestep_spacing == "leading":
               __a     				=  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
               __a     				=  (np.arange(0  ,						lowerCamelCase		) * step_ratio).round()[::-1].copy().astype(lowerCamelCase		)
               timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
               __a     				=  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
               __a     				=  (np.arange(lowerCamelCase  ,						0  ,						-step_ratio		)).round().copy().astype(lowerCamelCase		)
               timesteps -= 1
        else:
               raise ValueError(
                   F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."		)

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

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

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

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

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

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

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

        __a     				=  None

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


 def 		a__ (				self  ,						lowerCamelCase		):
        # get log sigma
        __a     				=  sigma.log()

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

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

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

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

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


 @property
 def 		a__ (				self		):
        return self.sample is None


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = True  ,						):
        __a     				=  self.index_for_timestep(lowerCamelCase		)

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

        if self.state_in_first_order:
               __a     				=  self.sigmas[step_index]
               __a     				=  self.sigmas_interpol[step_index + 1]
               __a     				=  self.sigmas[step_index + 1]
        else:
               # 2nd order / KDPM2's method
               __a     				=  self.sigmas[step_index - 1]
               __a     				=  self.sigmas_interpol[step_index]
               __a     				=  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
        __a     				=  0
        __a     				=  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":
               __a     				=  sigma_hat if self.state_in_first_order else sigma_interpol
               __a     				=  sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
               __a     				=  sigma_hat if self.state_in_first_order else sigma_interpol
               __a     				=  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
               __a     				=  (sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               __a     				=  sigma_interpol - sigma_hat

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

               # 3. delta timestep
               __a     				=  sigma_next - sigma_hat

               __a     				=  self.sample
               __a     				=  None

        __a     				=  sample + derivative * dt

        if not return_dict:
               return (prev_sample,)

        return SchedulerOutput(prev_sample=lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						):
        # 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(lowerCamelCase		):
               # 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     				=  [self.index_for_timestep(lowerCamelCase  ,						lowerCamelCase		) 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		):
        return self.config.num_train_timesteps

"""simple docstring"""



import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__:int			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[Any]			=	{"""vocab_file""": """vocab.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

SCREAMING_SNAKE_CASE__:Optional[Any]			=	{"""mgp-str""": 27}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Tuple           =		VOCAB_FILES_NAMES
 _snake_case   :			Tuple           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[Any]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase="[GO]"  ,						lowerCamelCase="[GO]"  ,						lowerCamelCase="[s]"  ,						lowerCamelCase="[GO]"  ,						**lowerCamelCase		):
        super().__init__(
            unk_token=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						**lowerCamelCase  ,						)

        with open(lowerCamelCase  ,						encoding="utf-8"		) as vocab_handle:
               __a     				=  json.load(lowerCamelCase		)
        __a     				=  {v: k for k, v in self.vocab.items()}


 @property
 def 		a__ (				self		):
        return len(self.vocab		)


 def 		a__ (				self		):
        return dict(self.vocab  ,						**self.added_tokens_encoder		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  []
        for s in text:
               char_tokens.extend(lowerCamelCase		)
        return char_tokens


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.vocab.get(lowerCamelCase  ,						self.vocab.get(self.unk_token		)		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.decoder.get(lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not os.path.isdir(lowerCamelCase		):
               logger.error("Vocabulary path ({}) should be a directory".format(lowerCamelCase		)		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        with open(lowerCamelCase  ,						"w"  ,						encoding="utf-8"		) as f:
               f.write(json.dumps(self.vocab  ,						indent=2  ,						sort_keys=lowerCamelCase  ,						ensure_ascii=lowerCamelCase		) + "\n"		)

        return (vocab_file,)
