






from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
						from ..models.auto import AutoTokenizer


class        __UpperCamelCase    :







	def  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							Any			)		-> str:
		"""simple docstring"""







		raise NotImplementedError()







	def  _a   (  self      :							Dict			)		-> int:
		"""simple docstring"""







		raise NotImplementedError()


class        __UpperCamelCase    (     _lowerCAmelCase   ):







	def __init__(  self      :							Dict       ,				_lowerCAmelCase      :							"AutoTokenizer"       ,				_lowerCAmelCase      :							bool = False       ,				**_lowerCAmelCase      :							Dict			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  tokenizer
		__lowercase             =  skip_prompt
		__lowercase             =  decode_kwargs

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







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							List[Any]			)		-> Optional[Any]:
		"""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      :							int			)		-> Optional[int]:
		"""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      :							int       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							bool = False			)		-> str:
		"""simple docstring"""







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







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							Tuple			)		-> int:
		"""simple docstring"""







		if (
		    (cp >= 0X4e00 and cp <= 0X9fff)
		    or (cp >= 0X3400 and cp <= 0X4dbf)  #
		    or (cp >= 0X2_0000 and cp <= 0X2_a6df)  #
		    or (cp >= 0X2_a700 and cp <= 0X2_b73f)  #
		    or (cp >= 0X2_b740 and cp <= 0X2_b81f)  #
		    or (cp >= 0X2_b820 and cp <= 0X2_ceaf)  #
		    or (cp >= 0Xf900 and cp <= 0Xfaff)
		    or (cp >= 0X2_f800 and cp <= 0X2_fa1f)  #
		):  #
			return True

		return False


class        __UpperCamelCase    (     _lowerCAmelCase   ):







	def __init__(  self      :							Optional[int]       ,				_lowerCAmelCase      :							"AutoTokenizer"       ,				_lowerCAmelCase      :							bool = False       ,				_lowerCAmelCase      :							Optional[float] = None       ,				**_lowerCAmelCase      :							List[Any]			)		-> List[str]:
		"""simple docstring"""







		super().__init__(_lowerCAmelCase       ,				_lowerCAmelCase       ,				**_lowerCAmelCase			)
		__lowercase             =  Queue()
		__lowercase             =  None
		__lowercase             =  timeout







	def  _a   (  self      :							Dict       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							bool = False			)		-> int:
		"""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      :							Any			)		-> Any:
		"""simple docstring"""







		return self







	def  _a   (  self      :							Any			)		-> Optional[int]:
		"""simple docstring"""







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









import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:str   =       (UnCLIPScheduler,)







	def  _a   (  self      :							Optional[int]       ,				**_lowerCAmelCase      :							Any			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  {
		    """num_train_timesteps""": 1000,
		    """variance_type""": """fixed_small_log""",
		    """clip_sample""": True,
		    """clip_sample_range""": 1.0,
		    """prediction_type""": """epsilon""",
		}

		config.update(**_lowerCAmelCase			)
		return config







	def  _a   (  self      :							Dict			)		-> List[Any]:
		"""simple docstring"""







		for timesteps in [1, 5, 100, 1000]:
			self.check_over_configs(num_train_timesteps=_lowerCAmelCase			)







	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		for variance in ["fixed_small_log", "learned_range"]:
			self.check_over_configs(variance_type=_lowerCAmelCase			)







	def  _a   (  self      :							Any			)		-> Any:
		"""simple docstring"""







		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=_lowerCAmelCase			)







	def  _a   (  self      :							Any			)		-> Optional[Any]:
		"""simple docstring"""







		for clip_sample_range in [1, 5, 10, 20]:
			self.check_over_configs(clip_sample_range=_lowerCAmelCase			)







	def  _a   (  self      :							Optional[int]			)		-> Tuple:
		"""simple docstring"""







		for prediction_type in ["epsilon", "sample"]:
			self.check_over_configs(prediction_type=_lowerCAmelCase			)







	def  _a   (  self      :							str			)		-> int:
		"""simple docstring"""







		for time_step in [0, 500, 999]:
			for prev_timestep in [None, 5, 100, 250, 500, 750]:
				if prev_timestep is not None and prev_timestep >= time_step:
					continue

				self.check_over_forward(time_step=_lowerCAmelCase       ,				prev_timestep=_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config(variance_type="""fixed_small_log"""			)
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0000e-10			)			) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0_549_625			)			) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9_994_987			)			) < 1e-5







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config(variance_type="""learned_range"""			)
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		__lowercase             =  0.5

		assert scheduler._get_variance(1       ,				predicted_variance=_lowerCAmelCase			) - -10.1_712_790 < 1e-5
		assert scheduler._get_variance(487       ,				predicted_variance=_lowerCAmelCase			) - -5.7_998_052 < 1e-5
		assert scheduler._get_variance(999       ,				predicted_variance=_lowerCAmelCase			) - -0.0_010_011 < 1e-5







	def  _a   (  self      :							Optional[Any]			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config()
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		__lowercase             =  scheduler.timesteps

		__lowercase             =  self.dummy_model()
		__lowercase             =  self.dummy_sample_deter
		__lowercase             =  torch.manual_seed(0			)

		for i, t in enumerate(_lowerCAmelCase			):
			# 1. predict noise residual
			__lowercase             =  model(_lowerCAmelCase       ,				_lowerCAmelCase			)

			# 2. predict previous mean of sample x_t-1
			__lowercase             =  scheduler.step(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				generator=_lowerCAmelCase			).prev_sample

			__lowercase             =  pred_prev_sample

		__lowercase             =  torch.sum(torch.abs(_lowerCAmelCase			)			)
		__lowercase             =  torch.mean(torch.abs(_lowerCAmelCase			)			)

		assert abs(result_sum.item() - 252.2_682_495			) < 1e-2
		assert abs(result_mean.item() - 0.3_284_743			) < 1e-3







	def  _a   (  self      :							List[str]			)		-> str:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config()
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		scheduler.set_timesteps(25			)

		__lowercase             =  scheduler.timesteps

		__lowercase             =  self.dummy_model()
		__lowercase             =  self.dummy_sample_deter
		__lowercase             =  torch.manual_seed(0			)

		for i, t in enumerate(_lowerCAmelCase			):
			# 1. predict noise residual
			__lowercase             =  model(_lowerCAmelCase       ,				_lowerCAmelCase			)

			if i + 1 == timesteps.shape[0]:
				__lowercase             =  None
			else:
				__lowercase             =  timesteps[i + 1]

			# 2. predict previous mean of sample x_t-1
			__lowercase             =  scheduler.step(
			    _lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				prev_timestep=_lowerCAmelCase       ,				generator=_lowerCAmelCase			).prev_sample

			__lowercase             =  pred_prev_sample

		__lowercase             =  torch.sum(torch.abs(_lowerCAmelCase			)			)
		__lowercase             =  torch.mean(torch.abs(_lowerCAmelCase			)			)

		assert abs(result_sum.item() - 258.2_044_983			) < 1e-2
		assert abs(result_mean.item() - 0.3_362_038			) < 1e-3







	def  _a   (  self      :							str			)		-> Union[str, Any]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							int			)		-> List[str]:
		"""simple docstring"""







		pass

