





import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class   lowerCAmelCase				(   _a     ):
 UpperCAmelCase__						=						42
 UpperCAmelCase__						=						None


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase=0.999		,      _UpperCAmelCase="cosine"		,      )     ->	Optional[int]:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(_UpperCAmelCase					):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(_UpperCAmelCase					):
            return math.exp(t * -12.0					)

    else:
        raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""					)

    lowerCamelCase__       : Tuple   					=						[]
    for i in range(_lowerCAmelCase					):
        lowerCamelCase__       : Union[str, Any]   					=						i / num_diffusion_timesteps
        lowerCamelCase__       : str   					=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(_lowerCAmelCase					) / alpha_bar_fn(_lowerCAmelCase					)		,      _lowerCAmelCase					)					)
    return torch.tensor(_lowerCAmelCase		,      dtype=torch.floataa					)


class   lowerCAmelCase				(   _a,    _a     ):
 UpperCAmelCase__						=						1
 @register_to_config
 def __init__( self    : Optional[int]     ,	UpperCAmelCase    : int = 1000     ,	UpperCAmelCase    : float = 0.0_0_0_1     ,	UpperCAmelCase    : float = 0.0_2     ,	UpperCAmelCase    : str = "linear"     ,	UpperCAmelCase    : Optional[Union[np.ndarray, List[float]]] = None     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : int = 0     ,	UpperCAmelCase    : str = "epsilon"     ,	UpperCAmelCase    : float = 1.0     ,	**UpperCAmelCase    : List[Any]     ,	)     ->      Union[str, Any]:
     if kwargs.get('set_alpha_to_one'     ,	snake_case_      ) is not None:
         lowerCamelCase__       : Union[str, Any]   					=						(
             """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
         )
         deprecate('set_alpha_to_one'     ,	'1.0.0'     ,	snake_case_     ,	standard_warn=snake_case_      )
         lowerCamelCase__       : List[Any]   					=						kwargs["""set_alpha_to_one"""]
     if trained_betas is not None:
         lowerCamelCase__       : List[str]   					=						torch.tensor(snake_case_     ,	dtype=torch.floataa      )
     elif beta_schedule == "linear":
         lowerCamelCase__       : Union[str, Any]   					=						torch.linspace(snake_case_     ,	snake_case_     ,	snake_case_     ,	dtype=torch.floataa      )
     elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         lowerCamelCase__       : Any   					=						(
             torch.linspace(beta_start**0.5     ,	beta_end**0.5     ,	snake_case_     ,	dtype=torch.floataa      ) ** 2
         )
     elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         lowerCamelCase__       : List[str]   					=						betas_for_alpha_bar(snake_case_      )
     else:
         raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""      )

     lowerCamelCase__       : Dict   					=						1.0 - self.betas
     lowerCamelCase__       : List[str]   					=						torch.cumprod(self.alphas     ,	dim=0      )

     # At every step in inverted ddim, we are looking into the next alphas_cumprod
     # For the final step, there is no next alphas_cumprod, and the index is out of bounds
     # `set_alpha_to_zero` decides whether we set this parameter simply to zero
     # in this case, self.step() just output the predicted noise
     # or whether we use the final alpha of the "non-previous" one.
     lowerCamelCase__       : Optional[Any]   					=						torch.tensor(0.0      ) if set_alpha_to_zero else self.alphas_cumprod[-1]

     # standard deviation of the initial noise distribution
     lowerCamelCase__       : Any   					=						1.0

     # setable values
     lowerCamelCase__       : Any   					=						None
     lowerCamelCase__       : Tuple   					=						torch.from_numpy(np.arange(0     ,	snake_case_      ).copy().astype(np.intaa      )      )
 def 	A_      ( self    : int     ,	UpperCAmelCase    : torch.FloatTensor     ,	UpperCAmelCase    : Optional[int] = None      )     ->      str:
     return sample
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : int     ,	UpperCAmelCase    : Union[str, torch.device] = None      )     ->      Tuple:

     if num_inference_steps > self.config.num_train_timesteps:
         raise ValueError(
             F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
             F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
             F""" maximal {self.config.num_train_timesteps} timesteps."""      )

     lowerCamelCase__       : List[str]   					=						num_inference_steps
     lowerCamelCase__       : Tuple   					=						self.config.num_train_timesteps // self.num_inference_steps
     # creates integer timesteps by multiplying by ratio
     # casting to int to avoid issues when num_inference_step is power of 3
     lowerCamelCase__       : List[Any]   					=						(np.arange(0     ,	snake_case_      ) * step_ratio).round().copy().astype(np.intaa      )
     lowerCamelCase__       : Tuple   					=						torch.from_numpy(snake_case_      ).to(snake_case_      )
     self.timesteps += self.config.steps_offset
 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : torch.FloatTensor     ,	UpperCAmelCase    : int     ,	UpperCAmelCase    : torch.FloatTensor     ,	UpperCAmelCase    : float = 0.0     ,	UpperCAmelCase    : bool = False     ,	UpperCAmelCase    : Optional[torch.FloatTensor] = None     ,	UpperCAmelCase    : bool = True     ,	)     ->      Tuple:
     # 1. get previous step value (=t+1)
     lowerCamelCase__       : Any   					=						timestep + self.config.num_train_timesteps // self.num_inference_steps

     # 2. compute alphas, betas
     # change original implementation to exactly match noise levels for analogous forward process
     lowerCamelCase__       : Optional[int]   					=						self.alphas_cumprod[timestep]
     lowerCamelCase__       : Union[str, Any]   					=						(
         self.alphas_cumprod[prev_timestep]
         if prev_timestep < self.config.num_train_timesteps
         else self.final_alpha_cumprod
     )

     lowerCamelCase__       : int   					=						1 - alpha_prod_t

     # 3. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     if self.config.prediction_type == "epsilon":
         lowerCamelCase__       : Any   					=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         lowerCamelCase__       : Optional[int]   					=						model_output
     elif self.config.prediction_type == "sample":
         lowerCamelCase__       : Union[str, Any]   					=						model_output
         lowerCamelCase__       : Optional[int]   					=						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
     elif self.config.prediction_type == "v_prediction":
         lowerCamelCase__       : str   					=						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
         lowerCamelCase__       : Any   					=						(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
     else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
             ' `v_prediction`'      )

     # 4. Clip or threshold "predicted x_0"
     if self.config.clip_sample:
         lowerCamelCase__       : Tuple   					=						pred_original_sample.clamp(
             -self.config.clip_sample_range     ,	self.config.clip_sample_range      )

     # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     lowerCamelCase__       : Any   					=						(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

     # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     lowerCamelCase__       : Optional[int]   					=						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

     if not return_dict:
         return (prev_sample, pred_original_sample)
     return DDIMSchedulerOutput(prev_sample=snake_case_     ,	pred_original_sample=snake_case_      )







 def __len__( self    : Tuple      )     ->      List[Any]:
     return self.config.num_train_timesteps










'''simple docstring'''





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   :	Optional[int]		):
          snake_case__    :       int       		= logging.get_logger()

          # the current default level is logging.WARNING
          snake_case__    :       str       		= 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   :	Union[str, Any]		):
          snake_case__    :       List[Any]       		= logging.get_verbosity()

          snake_case__    :       Any       		= logging.get_logger("""transformers.models.bart.tokenization_bart"""		)
          snake_case__    :       int       		= """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   :	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
          snake_case__    :       Union[str, Any]       		= logging.get_logger("""transformers.models.bart.tokenization_bart"""		)

          snake_case__    :       int       		= os.getenv("""TRANSFORMERS_VERBOSITY"""					,	snake_case_		)
          snake_case__    :       List[Any]       		= logging.log_levels[env_level_str]

          snake_case__    :       str       		= 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
          snake_case__    :       List[Any]       		= """"""
          transformers.utils.logging._reset_library_root_logger()




    @mockenv(TRANSFORMERS_VERBOSITY="""super-error"""		)
    def lowerCamelCase			(       self   :	int		):
          # reset for the env var to take effect, next time some logger call is made
          transformers.utils.logging._reset_library_root_logger()
          snake_case__    :       List[Any]       		= 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   :	int		):
          # testing `logger.warning_advice()`
          transformers.utils.logging._reset_library_root_logger()

          snake_case__    :       Optional[int]       		= logging.get_logger("""transformers.models.bart.tokenization_bart"""		)
          snake_case__    :       Tuple       		= """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 		__snake_case(							)						->					List[str]:
      disable_progress_bar()
      assert are_progress_bars_disabled()

      enable_progress_bar()
      assert not are_progress_bars_disabled()
