



'''simple docstring'''




from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
							from ..models.auto import AutoTokenizer

class     UpperCamelCase_				:





	def 				_lowercase(						self	,	A					)    ->    Optional[int]:
								raise NotImplementedError()






	def 				_lowercase(						self					)    ->    Any:
								raise NotImplementedError()




class     UpperCamelCase_				(							__magic_name__    ):





	def __init__(						self	,	A	,	A = False	,	**A					)    ->    List[Any]:
								UpperCAmelCase  :	Any							  =	tokenizer
								UpperCAmelCase  :	List[str]							  =	skip_prompt
								UpperCAmelCase  :	int							  =	decode_kwargs

								# variables used in the streaming process
								UpperCAmelCase  :	int							  =	[]
								UpperCAmelCase  :	Any							  =	0
								UpperCAmelCase  :	Dict							  =	True





	def 				_lowercase(						self	,	A					)    ->    Optional[Any]:
								if len(value.shape					) > 1 and value.shape[0] > 1:
															raise ValueError("""TextStreamer only supports batch size 1"""					)
								elif len(value.shape					) > 1:
															UpperCAmelCase  :	int							  =	value[0]

								if self.skip_prompt and self.next_tokens_are_prompt:
															UpperCAmelCase  :	Optional[int]							  =	False
															return

								# Add the new token to the cache and decodes the entire thing.
								self.token_cache.extend(value.tolist()					)
								UpperCAmelCase  :	Optional[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"""					):
															UpperCAmelCase  :	List[str]							  =	text[self.print_len :]
															UpperCAmelCase  :	Any							  =	[]
															UpperCAmelCase  :	List[Any]							  =	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]					)					):
															UpperCAmelCase  :	Tuple							  =	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:
															UpperCAmelCase  :	Dict							  =	text[self.print_len : text.rfind(""" """					) + 1]
															self.print_len += len(A					)

								self.on_finalized_text(A					)





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

								UpperCAmelCase  :	Dict							  =	True
								self.on_finalized_text(A	,	stream_end=A					)





	def 				_lowercase(						self	,	A	,	A = False					)    ->    Dict:
								print(A	,	flush=A	,	end="""""" if not stream_end else None					)






	def 				_lowercase(						self	,	A					)    ->    Tuple:
								# 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 >= 0X4e00 and cp <= 0X9fff)
								    or (cp >= 0X3400 and cp <= 0X4dbf)  #
								    or (cp >= 0X20000 and cp <= 0X2a6df)  #
								    or (cp >= 0X2a700 and cp <= 0X2b73f)  #
								    or (cp >= 0X2b740 and cp <= 0X2b81f)  #
								    or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
								    or (cp >= 0Xf900 and cp <= 0Xfaff)
								    or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
								):  #
															return True

								return False




class     UpperCamelCase_				(							__magic_name__    ):





	def __init__(						self	,	A	,	A = False	,	A = None	,	**A					)    ->    Dict:
								super().__init__(A	,	A	,	**A					)
								UpperCAmelCase  :	Optional[int]							  =	Queue()
								UpperCAmelCase  :	Dict							  =	None
								UpperCAmelCase  :	List[str]							  =	timeout





	def 				_lowercase(						self	,	A	,	A = False					)    ->    Union[str, Any]:
								self.text_queue.put(A	,	timeout=self.timeout					)
								if stream_end:
															self.text_queue.put(self.stop_signal	,	timeout=self.timeout					)





	def __iter__(						self					)    ->    List[str]:
								return self






	def 				_lowercase(						self					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Optional[int]							  =	self.text_queue.get(timeout=self.timeout					)
								if value == self.stop_signal:
															raise StopIteration()
								else:
															return value






'''simple docstring'''




import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


a     :    Tuple      						=					False

class     UpperCamelCase_				(							unittest.TestCase    ):
	pass




@slow
@require_torch_gpu
class     UpperCamelCase_				(							unittest.TestCase    ):





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Any							  =	VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""					)
								pipe.to(A					)
								pipe.set_progress_bar_config(disable=A					)

								UpperCAmelCase  :	List[str]							  =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""					)
								UpperCAmelCase  :	List[Any]							  =	torch.manual_seed(0					)
								UpperCAmelCase  :	List[str]							  =	pipe(
								    image=A	,	generator=A	,	guidance_scale=7.5	,	num_inference_steps=50	,	output_type="""numpy"""	,	).images

								UpperCAmelCase  :	Tuple							  =	image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase  :	List[str]							  =	np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]					)

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

