
"""simple docstring"""






from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
				from ..models.auto import AutoTokenizer




class 			_SCREAMING_SNAKE_CASE					:






			def  __lowerCAmelCase (					self       ,   __A       )			->     int:
						raise NotImplementedError()






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						raise NotImplementedError()




class 			_SCREAMING_SNAKE_CASE					(			A__							):






			def __init__(					self       ,   __A       ,   __A = False       ,   **__A       )			->     int:
						lowerCAmelCase_    :List[str]			     =  tokenizer
						lowerCAmelCase_    :Dict			     =  skip_prompt
						lowerCAmelCase_    :Union[str, Any]			     =  decode_kwargs

						# variables used in the streaming process
						lowerCAmelCase_    :Any			     =  []
						lowerCAmelCase_    :List[Any]			     =  0
						lowerCAmelCase_    :List[Any]			     =  True






			def  __lowerCAmelCase (					self       ,   __A       )			->     str:
						if len(value.shape       ) > 1 and value.shape[0] > 1:
									raise ValueError("""TextStreamer only supports batch size 1"""       )
						elif len(value.shape       ) > 1:
									lowerCAmelCase_    :Tuple			     =  value[0]

						if self.skip_prompt and self.next_tokens_are_prompt:
									lowerCAmelCase_    :int			     =  False
									return

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

						# After the symbol for a new line, we flush the cache.
						if text.endswith("""\n"""       ):
									lowerCAmelCase_    :List[str]			     =  text[self.print_len :]
									lowerCAmelCase_    :Tuple			     =  []
									lowerCAmelCase_    :Tuple			     =  0
						# If the last token is a CJK character, we print the characters.
						elif len(__A       ) > 0 and self._is_chinese_char(ord(text[-1]       )       ):
									lowerCAmelCase_    :Union[str, Any]			     =  text[self.print_len :]
									self.print_len += len(__A       )
						# 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:
									lowerCAmelCase_    :Tuple			     =  text[self.print_len : text.rfind(""" """       ) + 1]
									self.print_len += len(__A       )

						self.on_finalized_text(__A       )






			def  __lowerCAmelCase (					self       )			->     Dict:
						# Flush the cache, if it exists
						if len(self.token_cache       ) > 0:
									lowerCAmelCase_    :Optional[int]			     =  self.tokenizer.decode(self.token_cache       ,   **self.decode_kwargs       )
									lowerCAmelCase_    :List[str]			     =  text[self.print_len :]
									lowerCAmelCase_    :Optional[int]			     =  []
									lowerCAmelCase_    :int			     =  0
						else:
									lowerCAmelCase_    :Tuple			     =  """"""

						lowerCAmelCase_    :int			     =  True
						self.on_finalized_text(__A       ,   stream_end=__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A = False       )			->     Optional[Any]:
						print(__A       ,   flush=__A       ,   end="""""" if not stream_end else None       )






			def  __lowerCAmelCase (					self       ,   __A       )			->     Dict:
						# This defines a "chinese character" as anything in the CJK Unicode block:
						#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
						#
						# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
						# despite its name. The modern Korean Hangul alphabet is a different block,
						# as is Japanese Hiragana and Katakana. Those alphabets are used to write
						# space-separated words, so they are not treated specially and handled
						# like the all of the other languages.
						if (
						    (cp >= 0x4E_00 and cp <= 0x9F_FF)
						    or (cp >= 0x34_00 and cp <= 0x4D_BF)  #
						    or (cp >= 0x2_00_00 and cp <= 0x2_A6_DF)  #
						    or (cp >= 0x2_A7_00 and cp <= 0x2_B7_3F)  #
						    or (cp >= 0x2_B7_40 and cp <= 0x2_B8_1F)  #
						    or (cp >= 0x2_B8_20 and cp <= 0x2_CE_AF)  #
						    or (cp >= 0xF9_00 and cp <= 0xFA_FF)
						    or (cp >= 0x2_F8_00 and cp <= 0x2_FA_1F)  #
						):  #
									return True

						return False




class 			_SCREAMING_SNAKE_CASE					(			A__							):






			def __init__(					self       ,   __A       ,   __A = False       ,   __A = None       ,   **__A       )			->     Dict:
						super().__init__(__A       ,   __A       ,   **__A       )
						lowerCAmelCase_    :Union[str, Any]			     =  Queue()
						lowerCAmelCase_    :Any			     =  None
						lowerCAmelCase_    :List[Any]			     =  timeout






			def  __lowerCAmelCase (					self       ,   __A       ,   __A = False       )			->     List[str]:
						self.text_queue.put(__A       ,   timeout=self.timeout       )
						if stream_end:
									self.text_queue.put(self.stop_signal       ,   timeout=self.timeout       )






			def __iter__(					self       )			->     Optional[Any]:
						return self






			def  __lowerCAmelCase (					self       )			->     Tuple:
						lowerCAmelCase_    :int			     =  self.text_queue.get(timeout=self.timeout       )
						if value == self.stop_signal:
									raise StopIteration()
						else:
									return value













import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Optional[int]:




           '''simple docstring'''


           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						UNetaDModel(
               block_out_channels=(32, 64)						,layers_per_block=2						,sample_size=32						,in_channels=3						,out_channels=3						,down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,)
           return model






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.dummy_uncond_unet
           SCREAMING_SNAKE_CASE		=						KarrasVeScheduler()

           SCREAMING_SNAKE_CASE		=						KarrasVePipeline(unet=lowerCamelCase__						,scheduler=lowerCamelCase__       )
           pipe.to(lowerCamelCase__       )
           pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=2						,generator=lowerCamelCase__						,output_type="""numpy"""       ).images

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=2						,generator=lowerCamelCase__						,output_type="""numpy"""						,return_dict=lowerCamelCase__       )[0]

           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE		=						image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2

@slow
@require_torch
class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""google/ncsnpp-celebahq-256"""
           SCREAMING_SNAKE_CASE		=						UNetaDModel.from_pretrained(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						KarrasVeScheduler()

           SCREAMING_SNAKE_CASE		=						KarrasVePipeline(unet=lowerCamelCase__						,scheduler=lowerCamelCase__       )
           pipe.to(lowerCamelCase__       )
           pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=20						,generator=lowerCamelCase__						,output_type="""numpy"""       ).images

           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]
           assert image.shape == (1, 256, 256, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2


