
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   lowerCamelCase					(__lowercase					):
  """simple docstring"""







  lowerCamelCase__     =						4_2
  lowerCamelCase__     =						4_2
  lowerCamelCase__     =						None



class   lowerCamelCase					(__lowercase  ,      __lowercase					):
  """simple docstring"""







  lowerCamelCase__     =						2







  @register_to_config
  def __init__(							self :       Any  ,     __magic_name__ :       float = 0.02  ,     __magic_name__ :       float = 100  ,     __magic_name__ :       float = 1.007  ,     __magic_name__ :       float = 80  ,     __magic_name__ :       float = 0.05  ,     __magic_name__ :       float = 50  ,     )  ->	List[str]:
        # standard deviation of the initial noise distribution
        SCREAMING_SNAKE_CASE_          =  sigma_max

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







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       torch.FloatTensor  ,     __magic_name__ :       Optional[int] = None   )  ->	int:
        return sample







  def    __A     (							self :       Tuple  ,     __magic_name__ :       int  ,     __magic_name__ :       Union[str, torch.device] = None   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  num_inference_steps
        SCREAMING_SNAKE_CASE_          =  np.arange(0  ,     self.num_inference_steps   )[::-1].copy()
        SCREAMING_SNAKE_CASE_          =  torch.from_numpy(_A   ).to(_A   )
        SCREAMING_SNAKE_CASE_          =  [
            (
                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
        ]
        SCREAMING_SNAKE_CASE_          =  torch.tensor(_A  ,     dtype=torch.floataa  ,     device=_A   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       torch.FloatTensor  ,     __magic_name__ :       float  ,     __magic_name__ :       Optional[torch.Generator] = None   )  ->	int:
        if self.config.s_min <= sigma <= self.config.s_max:
              SCREAMING_SNAKE_CASE_          =  min(self.config.s_churn / self.num_inference_steps  ,     2**0.5 - 1   )
        else:
              SCREAMING_SNAKE_CASE_          =  0

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

        return sample_hat, sigma_hat







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       torch.FloatTensor  ,     __magic_name__ :       float  ,     __magic_name__ :       float  ,     __magic_name__ :       torch.FloatTensor  ,     __magic_name__ :       bool = True  ,     )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  sample_hat + sigma_hat * model_output
        SCREAMING_SNAKE_CASE_          =  (sample_hat - pred_original_sample) / sigma_hat
        SCREAMING_SNAKE_CASE_          =  sample_hat + (sigma_prev - sigma_hat) * derivative

        if not return_dict:
              return (sample_prev, derivative)

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







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       torch.FloatTensor  ,     __magic_name__ :       float  ,     __magic_name__ :       float  ,     __magic_name__ :       torch.FloatTensor  ,     __magic_name__ :       torch.FloatTensor  ,     __magic_name__ :       torch.FloatTensor  ,     __magic_name__ :       bool = True  ,     )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  sample_prev + sigma_prev * model_output
        SCREAMING_SNAKE_CASE_          =  (sample_prev - pred_original_sample) / sigma_prev
        SCREAMING_SNAKE_CASE_          =  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=_A  ,     derivative=_A  ,     pred_original_sample=_A   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       int  ,     __magic_name__ :       Tuple   )  ->	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   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       List[Any]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  logging.get_logger()

        # the current default level is logging.WARNING
        SCREAMING_SNAKE_CASE_          =  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(__magic_name__   )







  def    __A     (							self :       Union[str, Any]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  logging.get_verbosity()

        SCREAMING_SNAKE_CASE_          =  logging.get_logger("transformers.models.bart.tokenization_bart"   )
        SCREAMING_SNAKE_CASE_          =  "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(__magic_name__   ) as cl:
                    logger.warning(__magic_name__   )
              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(__magic_name__   ) as cl:
              logger.warning(__magic_name__   )
        self.assertEqual(cl.out  ,     ""   )

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

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







  @mockenv(TRANSFORMERS_VERBOSITY="error"   )
  def    __A     (							self :       int   )  ->	Optional[Any]:
        # 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
        SCREAMING_SNAKE_CASE_          =  logging.get_logger("transformers.models.bart.tokenization_bart"   )

        SCREAMING_SNAKE_CASE_          =  os.getenv("TRANSFORMERS_VERBOSITY"  ,     __magic_name__   )
        SCREAMING_SNAKE_CASE_          =  logging.log_levels[env_level_str]

        SCREAMING_SNAKE_CASE_          =  logging.get_verbosity()
        self.assertEqual(
            __magic_name__  ,     __magic_name__  ,     F'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''  ,     )

        # restore to the original level
        SCREAMING_SNAKE_CASE_          =  ""
        transformers.utils.logging._reset_library_root_logger()







  @mockenv(TRANSFORMERS_VERBOSITY="super-error"   )
  def    __A     (							self :       Dict   )  ->	List[Any]:
        # reset for the env var to take effect, next time some logger call is made
        transformers.utils.logging._reset_library_root_logger()
        SCREAMING_SNAKE_CASE_          =  logging.logging.getLogger()
        with CaptureLogger(__magic_name__   ) 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    __A     (							self :       Dict   )  ->	Optional[int]:
        # testing `logger.warning_advice()`
        transformers.utils.logging._reset_library_root_logger()

        SCREAMING_SNAKE_CASE_          =  logging.get_logger("transformers.models.bart.tokenization_bart"   )
        SCREAMING_SNAKE_CASE_          =  "Testing 1, 2, 3"

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

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



def      a__ (		):
      disable_progress_bar()
      assert are_progress_bars_disabled()

      enable_progress_bar()
      assert not are_progress_bars_disabled()


