"""simple docstring"""






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class  _A						(       lowerCAmelCase	):
      snake_case__			:   torch.FloatTensor
      snake_case__			:   torch.FloatTensor
      snake_case__			:   Optional[torch.FloatTensor]							  =	None






class  _A						(       lowerCAmelCase  ,    lowerCAmelCase	):
      snake_case__			:   Union[str, Any]							  =	2

      @register_to_config
      def __init__(						self		,       __lowerCAmelCase = 0.0_2		,       __lowerCAmelCase = 100		,       __lowerCAmelCase = 1.0_0_7		,       __lowerCAmelCase = 80		,       __lowerCAmelCase = 0.0_5		,       __lowerCAmelCase = 50		,       ):







            """simple docstring"""



            lowercase														=	sigma_max

            # setable values
            lowercase														=	None
            lowercase														=	None
            lowercase														=	None  # sigma(t_i)

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



            return sample

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



            lowercase														=	num_inference_steps
            lowercase														=	np.arange(0		,       self.num_inference_steps						)[::-1].copy()
            lowercase														=	torch.from_numpy(__lowerCAmelCase						).to(__lowerCAmelCase						)
            lowercase														=	[
                (
                    self.config.sigma_max**2
                    * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
                )
                for i in self.timesteps
            ]
            lowercase														=	torch.tensor(__lowerCAmelCase		,       dtype=torch.floataa		,       device=__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



            if self.config.s_min <= sigma <= self.config.s_max:
                  lowercase														=	min(self.config.s_churn / self.num_inference_steps		,       2**0.5 - 1						)
            else:
                  lowercase														=	0

            # sample eps ~ N(0, S_noise^2 * I)
            lowercase														=	self.config.s_noise * randn_tensor(sample.shape		,       generator=__lowerCAmelCase						).to(sample.device						)
            lowercase														=	sigma + gamma * sigma
            lowercase														=	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

            return sample_hat, sigma_hat

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







            """simple docstring"""



            lowercase														=	sample_hat + sigma_hat * model_output
            lowercase														=	(sample_hat - pred_original_sample) / sigma_hat
            lowercase														=	sample_hat + (sigma_prev - sigma_hat) * derivative

            if not return_dict:
                  return (sample_prev, derivative)

            return KarrasVeOutput(
                prev_sample=__lowerCAmelCase		,       derivative=__lowerCAmelCase		,       pred_original_sample=__lowerCAmelCase						)

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







            """simple docstring"""



            lowercase														=	sample_prev + sigma_prev * model_output
            lowercase														=	(sample_prev - pred_original_sample) / sigma_prev
            lowercase														=	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

            if not return_dict:
                  return (sample_prev, derivative)

            return KarrasVeOutput(
                prev_sample=__lowerCAmelCase		,       derivative=__lowerCAmelCase		,       pred_original_sample=__lowerCAmelCase						)







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







            """simple docstring"""



            raise NotImplementedError()




"""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



