




'''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 			SCREAMING_SNAKE_CASE_			(							__A    :			List[str]	,					__A    :			Union[str, Any]=0.9_9_9	,					__A    :			Union[str, Any]="cosine"	,					)			->			Optional[int]:
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__A    :			Union[str, Any]  ):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2  ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(__A    :			Optional[Any]  ):
         return math.exp(t * -1_2.0  )

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

   _SCREAMING_SNAKE_CASE	=				[]
   for i in range(__A  ):
      _SCREAMING_SNAKE_CASE	=				i / num_diffusion_timesteps
      _SCREAMING_SNAKE_CASE	=				(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 		lowercase_   (			A					,  A       ):


   """simple docstring"""

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



   @register_to_config
   def __init__(   self :     Dict  ,				__lowerCamelCase :     int = 1_0_0_0  ,				__lowerCamelCase :     float = 0.0_0_0_8_5  ,				__lowerCamelCase :     float = 0.0_1_2  ,				__lowerCamelCase :     str = "linear"  ,				__lowerCamelCase :     Optional[Union[np.ndarray, List[float]]] = None  ,				__lowerCamelCase :     str = "epsilon"  ,				__lowerCamelCase :     str = "linspace"  ,				__lowerCamelCase :     int = 0  ,				):

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

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

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



   def 							lowerCAmelCase_      (   self :     str  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     int=None   ):

      """simple docstring"""
      if schedule_timesteps is None:
         _SCREAMING_SNAKE_CASE	=				self.timesteps

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

      return indices[pos].item()



   @property
   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      # 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 							lowerCAmelCase_      (   self :     Any  ,				__lowerCamelCase :     torch.FloatTensor  ,				__lowerCamelCase :     Union[float, torch.FloatTensor]  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.index_for_timestep(__lowerCamelCase   )

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

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



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				num_inference_steps

      _SCREAMING_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":
         _SCREAMING_SNAKE_CASE	=				np.linspace(0  ,				num_train_timesteps - 1  ,				__lowerCamelCase  ,				dtype=__lowerCamelCase   )[::-1].copy()
      elif self.config.timestep_spacing == "leading":
         _SCREAMING_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
         _SCREAMING_SNAKE_CASE	=				(np.arange(0  ,				__lowerCamelCase   ) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase   )
         timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
         _SCREAMING_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
         _SCREAMING_SNAKE_CASE	=				(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'."""   )

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

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

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

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

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

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

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

      _SCREAMING_SNAKE_CASE	=				None

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



   def 							lowerCAmelCase_      (   self :     Any  ,				__lowerCamelCase :     Dict   ):

      """simple docstring"""
      # get log sigma
      _SCREAMING_SNAKE_CASE	=				sigma.log()

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

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

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

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

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



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

      """simple docstring"""
      return self.sample is None



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     Union[torch.FloatTensor, np.ndarray]  ,				__lowerCamelCase :     Union[float, torch.FloatTensor]  ,				__lowerCamelCase :     Union[torch.FloatTensor, np.ndarray]  ,				__lowerCamelCase :     bool = True  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.index_for_timestep(__lowerCamelCase   )

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

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

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

         # 3. delta timestep
         _SCREAMING_SNAKE_CASE	=				sigma_next - sigma_hat

         _SCREAMING_SNAKE_CASE	=				self.sample
         _SCREAMING_SNAKE_CASE	=				None

      _SCREAMING_SNAKE_CASE	=				sample + derivative * dt

      if not return_dict:
         return (prev_sample,)

      return SchedulerOutput(prev_sample=__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     torch.FloatTensor  ,				__lowerCamelCase :     torch.FloatTensor  ,				__lowerCamelCase :     torch.FloatTensor  ,				):

      """simple docstring"""
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      _SCREAMING_SNAKE_CASE	=				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
         _SCREAMING_SNAKE_CASE	=				self.timesteps.to(original_samples.device  ,				dtype=torch.floataa   )
         _SCREAMING_SNAKE_CASE	=				timesteps.to(original_samples.device  ,				dtype=torch.floataa   )
      else:
         _SCREAMING_SNAKE_CASE	=				self.timesteps.to(original_samples.device   )
         _SCREAMING_SNAKE_CASE	=				timesteps.to(original_samples.device   )

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

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

      _SCREAMING_SNAKE_CASE	=				original_samples + noise * sigma
      return noisy_samples



   def __len__(   self :     Optional[int]   ):

      """simple docstring"""
      return self.config.num_train_timesteps







'''simple docstring'''



import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCamelCase_			=						logging.get_logger(__name__)


lowerCamelCase_			=						{name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			Tuple	,					__A    :			List[str]	,					__A    :			List[str]  )			->			List[Any]:
   if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
      raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()  )}."""  )

   if tokenizer_name is None:
      _SCREAMING_SNAKE_CASE	=				TOKENIZER_CLASSES
   else:
      _SCREAMING_SNAKE_CASE	=				{tokenizer_name: getattr(__A	,					tokenizer_name + "Fast"  )}

   logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""  )

   for tokenizer_name in tokenizer_names:
      _SCREAMING_SNAKE_CASE	=				TOKENIZER_CLASSES[tokenizer_name]

      _SCREAMING_SNAKE_CASE	=				True
      if checkpoint_name is None:
         _SCREAMING_SNAKE_CASE	=				list(tokenizer_class.max_model_input_sizes.keys()  )
      else:
         _SCREAMING_SNAKE_CASE	=				[checkpoint_name]

      logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""  )

      for checkpoint in checkpoint_names:
         logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""  )

         # Load tokenizer
         _SCREAMING_SNAKE_CASE	=				tokenizer_class.from_pretrained(__A	,					force_download=__A  )

         # Save fast tokenizer
         logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""  )

         # For organization names we create sub-directories
         if "/" in checkpoint:
            _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				checkpoint.split("/"  )
            _SCREAMING_SNAKE_CASE	=				os.path.join(__A	,					__A  )
         elif add_prefix:
            _SCREAMING_SNAKE_CASE	=				checkpoint
            _SCREAMING_SNAKE_CASE	=				dump_path
         else:
            _SCREAMING_SNAKE_CASE	=				None
            _SCREAMING_SNAKE_CASE	=				dump_path

         logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""  )

         if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()  )[0]:
            _SCREAMING_SNAKE_CASE	=				list(tokenizer.pretrained_vocab_files_map.values()  )[0][checkpoint]
            _SCREAMING_SNAKE_CASE	=				file_path.split(__A  )[-1][0]
            if next_char == "/":
               _SCREAMING_SNAKE_CASE	=				os.path.join(__A	,					__A  )
               _SCREAMING_SNAKE_CASE	=				None

            logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""  )

         _SCREAMING_SNAKE_CASE	=				tokenizer.save_pretrained(
             __A	,					legacy_format=__A	,					filename_prefix=__A  )
         logger.info(f"""=> File names {file_names}"""  )

         for file_name in file_names:
            if not file_name.endswith("tokenizer.json"  ):
               os.remove(__A  )
               logger.info(f"""=> removing {file_name}"""  )


if __name__ == "__main__":
 lowerCamelCase_			=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--dump_path', default=None, type=str, required=True, help='Path to output generated fast tokenizer files.'
 )
 parser.add_argument(
     '--tokenizer_name',
     default=None,
     type=str,
     help=(
         f'''Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '''
         'download and convert all the checkpoints from AWS.'
     ),
 )
 parser.add_argument(
     '--checkpoint_name',
     default=None,
     type=str,
     help='Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.',
 )
 parser.add_argument(
     '--force_download',
     action='store_true',
     help='Re-download checkpoints.',
 )
 lowerCamelCase_			=						parser.parse_args()

 convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)

