
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        _UpperCamelCase				(					lowercase__  ):






			'''simple docstring'''

			__UpperCamelCase      :						Dict					      = 42
			__UpperCamelCase      :						Any					      = 42
			__UpperCamelCase      :						List[str]					      = None




class        _UpperCamelCase				(					lowercase__     ,       lowercase__  ):






			'''simple docstring'''

			__UpperCamelCase      :						int					      = 2

			@register_to_config
			def __init__(       self :       List[str]						,	snake_case_ :       float = 0.02						,	snake_case_ :       float = 100						,	snake_case_ :       float = 1.007						,	snake_case_ :       float = 80						,	snake_case_ :       float = 0.05						,	snake_case_ :       float = 50						,	):
							UpperCamelCase_:							List[str]	=						sigma_max

							# setable values
							UpperCamelCase_:							int	=						None
							UpperCamelCase_:							np.IntTensor	=						None
							UpperCamelCase_:							torch.FloatTensor	=						None  # sigma(t_i)

			def       lowerCAmelCase__	(       self :       Any						,	snake_case_ :       torch.FloatTensor						,	snake_case_ :       Optional[int] = None      ):
							return sample

			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       int						,	snake_case_ :       Union[str, torch.device] = None      ):
							UpperCamelCase_:							Tuple	=						num_inference_steps
							UpperCamelCase_:							int	=						np.arange(0						,	self.num_inference_steps      )[::-1].copy()
							UpperCamelCase_:							Union[str, Any]	=						torch.from_numpy(__lowerCamelCase      ).to(__lowerCamelCase      )
							UpperCamelCase_:							Any	=						[
							    (
							        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
							]
							UpperCamelCase_:							Optional[int]	=						torch.tensor(__lowerCamelCase						,	dtype=torch.floataa						,	device=__lowerCamelCase      )

			def       lowerCAmelCase__	(       self :       Optional[int]						,	snake_case_ :       torch.FloatTensor						,	snake_case_ :       float						,	snake_case_ :       Optional[torch.Generator] = None      ):



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

							# sample eps ~ N(0, S_noise^2 * I)
							UpperCamelCase_:							Tuple	=						self.config.s_noise * randn_tensor(sample.shape						,	generator=__lowerCamelCase      ).to(sample.device      )
							UpperCamelCase_:							str	=						sigma + gamma * sigma
							UpperCamelCase_:							List[Any]	=						sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat

			def       lowerCAmelCase__	(       self :       Union[str, Any]						,	snake_case_ :       torch.FloatTensor						,	snake_case_ :       float						,	snake_case_ :       float						,	snake_case_ :       torch.FloatTensor						,	snake_case_ :       bool = True						,	):
							UpperCamelCase_:							str	=						sample_hat + sigma_hat * model_output
							UpperCamelCase_:							Optional[Any]	=						(sample_hat - pred_original_sample) / sigma_hat
							UpperCamelCase_:							List[Any]	=						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       lowerCAmelCase__	(       self :       Union[str, Any]						,	snake_case_ :       torch.FloatTensor						,	snake_case_ :       float						,	snake_case_ :       float						,	snake_case_ :       torch.FloatTensor						,	snake_case_ :       torch.FloatTensor						,	snake_case_ :       torch.FloatTensor						,	snake_case_ :       bool = True						,	):
							UpperCamelCase_:							Any	=						sample_prev + sigma_prev * model_output
							UpperCamelCase_:							Any	=						(sample_prev - pred_original_sample) / sigma_prev
							UpperCamelCase_:							List[Any]	=						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       lowerCAmelCase__	(       self :       Union[str, Any]						,	snake_case_ :       Optional[Any]						,	snake_case_ :       Optional[int]						,	snake_case_ :       Dict      ):
							raise NotImplementedError()








import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar

class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Optional[int]	=						logging.get_logger()

							# the current default level is logging.WARNING
							UpperCamelCase_:							Dict	=						logging.get_verbosity()

							logging.set_verbosity_error()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_warning()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_info()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_debug()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							# restore to the original level
							logging.set_verbosity(snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Union[str, Any]	=						logging.get_verbosity()

							UpperCamelCase_:							int	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Union[str, Any]	=						"""Testing 1, 2, 3"""

							# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
							if level_origin <= logging.WARNING:
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning(snake_case_      )
											self.assertEqual(cl.out						,	msg + """\n"""      )

							# this is setting the level for all of `transformers.*` loggers
							logging.set_verbosity_error()

							# should not be able to log warnings
							with CaptureLogger(snake_case_      ) as cl:
											logger.warning(snake_case_      )
							self.assertEqual(cl.out						,	""""""      )

							# should be able to log warnings again
							logging.set_verbosity_warning()
							with CaptureLogger(snake_case_      ) as cl:
											logger.warning(snake_case_      )
							self.assertEqual(cl.out						,	msg + """\n"""      )

							# restore to the original level
							logging.set_verbosity(snake_case_      )

			@mockenv(TRANSFORMERS_VERBOSITY="""error"""      )
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							# this action activates the env var
							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )

							UpperCamelCase_:							str	=						os.getenv("""TRANSFORMERS_VERBOSITY"""						,	snake_case_      )
							UpperCamelCase_:							Any	=						logging.log_levels[env_level_str]

							UpperCamelCase_:							Dict	=						logging.get_verbosity()
							self.assertEqual(
							    snake_case_						,	snake_case_						,	f'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''						,	)

							# restore to the original level
							UpperCamelCase_:							str	=						""""""
							transformers.utils.logging._reset_library_root_logger()

			@mockenv(TRANSFORMERS_VERBOSITY="""super-error"""      )
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							UpperCamelCase_:							str	=						logging.logging.getLogger()
							with CaptureLogger(snake_case_      ) as cl:
											# this action activates the env var
											logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error"""						,	cl.out      )

							# no need to restore as nothing was changed




			def       lowerCAmelCase__	(       self :       List[Any]      ):
							# testing `logger.warning_advice()`
							transformers.utils.logging._reset_library_root_logger()

							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Any	=						"""Testing 1, 2, 3"""

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1"""      ):
											# nothing should be logged as env var disables this method
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning_advice(snake_case_      )
											self.assertEqual(cl.out						,	""""""      )

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""""""      ):
											# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning_advice(snake_case_      )
											self.assertEqual(cl.out						,	msg + """\n"""      )







def        A__					(						)  ->	Union[str, Any]:
				disable_progress_bar()
				assert are_progress_bars_disabled()

				enable_progress_bar()
				assert not are_progress_bars_disabled()






