
"""simple docstring"""





import math
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 SchedulerMixin, SchedulerOutput


class A_	(lowercase__				,lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Tuple       =		1







  @register_to_config
  def __init__(		self				,   lowercase_ = 1000				,   lowercase_ = None  ):

     """simple docstring"""
     self.set_timesteps(__lowerCAmelCase  )

     # standard deviation of the initial noise distribution
     UpperCAmelCase_					:					List[str]			     =    1.0

     # For now we only support F-PNDM, i.e. the runge-kutta method
     # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
     # mainly at formula (9), (12), (13) and the Algorithm 2.
     UpperCAmelCase_					:					Union[str, Any]			     =    4

     # running values
     UpperCAmelCase_					:					Optional[Any]			     =    []







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    num_inference_steps
     UpperCAmelCase_					:					Any			     =    torch.linspace(1				,   0				,   num_inference_steps + 1  )[:-1]
     UpperCAmelCase_					:					int			     =    torch.cat([steps, torch.tensor([0.0]  )]  )

     if self.config.trained_betas is not None:
        UpperCAmelCase_					:					Any			     =    torch.tensor(self.config.trained_betas				,   dtype=torch.floataa  )
     else:
        UpperCAmelCase_					:					Optional[Any]			     =    torch.sin(steps * math.pi / 2  ) ** 2

     UpperCAmelCase_					:					Optional[int]			     =    (1.0 - self.betas**2) ** 0.5

     UpperCAmelCase_					:					str			     =    (torch.atana(self.betas				,   self.alphas  ) / math.pi * 2)[:-1]
     UpperCAmelCase_					:					str			     =    timesteps.to(__lowerCAmelCase  )

     UpperCAmelCase_					:					Dict			     =    []







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_ = True				,   ):

     """simple docstring"""



     if self.num_inference_steps is None:
        raise ValueError(
            "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"  )

     UpperCAmelCase_					:					Tuple			     =    (self.timesteps == timestep).nonzero().item()
     UpperCAmelCase_					:					List[str]			     =    timestep_index + 1

     UpperCAmelCase_					:					Optional[Any]			     =    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
     self.ets.append(__lowerCAmelCase  )

     if len(self.ets  ) == 1:
        UpperCAmelCase_					:					Optional[int]			     =    self.ets[-1]
     elif len(self.ets  ) == 2:
        UpperCAmelCase_					:					int			     =    (3 * self.ets[-1] - self.ets[-2]) / 2
     elif len(self.ets  ) == 3:
        UpperCAmelCase_					:					List[str]			     =    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
     else:
        UpperCAmelCase_					:					Tuple			     =    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

     UpperCAmelCase_					:					Optional[Any]			     =    self._get_prev_sample(__lowerCAmelCase				,   __lowerCAmelCase				,   __lowerCAmelCase				,   __lowerCAmelCase  )

     if not return_dict:
        return (prev_sample,)

     return SchedulerOutput(prev_sample=__lowerCAmelCase  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   *lowercase_				,   **lowercase_  ):

     """simple docstring"""
     return sample







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    self.alphas[timestep_index]
     UpperCAmelCase_					:					Tuple			     =    self.betas[timestep_index]

     UpperCAmelCase_					:					List[Any]			     =    self.alphas[prev_timestep_index]
     UpperCAmelCase_					:					str			     =    self.betas[prev_timestep_index]

     UpperCAmelCase_					:					str			     =    (sample - sigma * ets) / max(__lowerCAmelCase				,   1E-8  )
     UpperCAmelCase_					:					int			     =    next_alpha * pred + ets * next_sigma

     return prev_sample







  def __len__(		self  ):

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



"""simple docstring"""






from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
       from ..models.auto import AutoTokenizer





class  _A						:

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            raise NotImplementedError()







      def 				A__       (						self						):







            """simple docstring"""



            raise NotImplementedError()






class  _A						(       lowerCAmelCase	):

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase = False		,       **__lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	tokenizer
            lowercase														=	skip_prompt
            lowercase														=	decode_kwargs

            # variables used in the streaming process
            lowercase														=	[]
            lowercase														=	0
            lowercase														=	True

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if len(value.shape						) > 1 and value.shape[0] > 1:
                  raise ValueError("""TextStreamer only supports batch size 1"""						)
            elif len(value.shape						) > 1:
                  lowercase														=	value[0]

            if self.skip_prompt and self.next_tokens_are_prompt:
                  lowercase														=	False
                  return

            # Add the new token to the cache and decodes the entire thing.
            self.token_cache.extend(value.tolist()						)
            lowercase														=	self.tokenizer.decode(self.token_cache		,       **self.decode_kwargs						)

            # After the symbol for a new line, we flush the cache.
            if text.endswith("""\n"""						):
                  lowercase														=	text[self.print_len :]
                  lowercase														=	[]
                  lowercase														=	0
            # If the last token is a CJK character, we print the characters.
            elif len(__lowerCAmelCase						) > 0 and self._is_chinese_char(ord(text[-1]						)						):
                  lowercase														=	text[self.print_len :]
                  self.print_len += len(__lowerCAmelCase						)
            # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
            # which may change with the subsequent token -- there are probably smarter ways to do this!)
            else:
                  lowercase														=	text[self.print_len : text.rfind(""" """						) + 1]
                  self.print_len += len(__lowerCAmelCase						)

            self.on_finalized_text(__lowerCAmelCase						)

      def 				A__       (						self						):







            """simple docstring"""



            if len(self.token_cache						) > 0:
                  lowercase														=	self.tokenizer.decode(self.token_cache		,       **self.decode_kwargs						)
                  lowercase														=	text[self.print_len :]
                  lowercase														=	[]
                  lowercase														=	0
            else:
                  lowercase														=	""""""

            lowercase														=	True
            self.on_finalized_text(__lowerCAmelCase		,       stream_end=__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = False						):







            """simple docstring"""



            print(__lowerCAmelCase		,       flush=__lowerCAmelCase		,       end="""""" if not stream_end else None						)







      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if (
                (cp >= 0X4_e00 and cp <= 0X9_fff)
                or (cp >= 0X3_400 and cp <= 0X4_dbf)  #
                or (cp >= 0X20_000 and cp <= 0X2a_6df)  #
                or (cp >= 0X2a_700 and cp <= 0X2b_73f)  #
                or (cp >= 0X2b_740 and cp <= 0X2b_81f)  #
                or (cp >= 0X2b_820 and cp <= 0X2c_eaf)  #
                or (cp >= 0Xf_900 and cp <= 0Xf_aff)
                or (cp >= 0X2f_800 and cp <= 0X2f_a1f)  #
            ):  #
                  return True

            return False






class  _A						(       lowerCAmelCase	):

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase = False		,       __lowerCAmelCase = None		,       **__lowerCAmelCase						):







            """simple docstring"""



            super().__init__(__lowerCAmelCase		,       __lowerCAmelCase		,       **__lowerCAmelCase						)
            lowercase														=	Queue()
            lowercase														=	None
            lowercase														=	timeout

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = False						):







            """simple docstring"""



            self.text_queue.put(__lowerCAmelCase		,       timeout=self.timeout						)
            if stream_end:
                  self.text_queue.put(self.stop_signal		,       timeout=self.timeout						)

      def __iter__(						self						):







            """simple docstring"""



            return self







      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.text_queue.get(timeout=self.timeout						)
            if value == self.stop_signal:
                  raise StopIteration()
            else:
                  return value



