
"""simple docstring"""


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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


__A       						=       logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''
			lowerCamelCase  					=		42







class 		lowerCamelCase__					(					__magic_name__ ,	__magic_name__				):





			'''simple docstring'''
			lowerCamelCase  					=		1



			@register_to_config
			def __init__(							self     ,     __UpperCAmelCase = 40     ,     __UpperCAmelCase = 0.0_0_2     ,     __UpperCAmelCase = 8_0.0     ,     __UpperCAmelCase = 0.5     ,     __UpperCAmelCase = 1.0     ,     __UpperCAmelCase = 7.0     ,     __UpperCAmelCase = True     ,     ) ->     List[str]:
				# standard deviation of the initial noise distribution
				_lowerCAmelCase								=sigma_max

				_lowerCAmelCase								=np.linspace(0     ,     1     ,     __UpperCAmelCase  )
				_lowerCAmelCase								=self._convert_to_karras(__UpperCAmelCase  )
				_lowerCAmelCase								=self.sigma_to_t(__UpperCAmelCase  )

				# setable values
				_lowerCAmelCase								=None
				_lowerCAmelCase								=torch.from_numpy(__UpperCAmelCase  )
				_lowerCAmelCase								=torch.from_numpy(__UpperCAmelCase  )
				_lowerCAmelCase								=False
				_lowerCAmelCase								=False



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase=None  ) ->     List[Any]:
				if schedule_timesteps is None:
					_lowerCAmelCase								=self.timesteps

				_lowerCAmelCase								=(schedule_timesteps == timestep).nonzero()
				return indices.item()



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase  ) ->     torch.FloatTensor:
				# Get sigma corresponding to timestep
				if isinstance(__UpperCAmelCase     ,     torch.Tensor  ):
					_lowerCAmelCase								=timestep.to(self.timesteps.device  )
				_lowerCAmelCase								=self.index_for_timestep(__UpperCAmelCase  )
				_lowerCAmelCase								=self.sigmas[step_idx]

				_lowerCAmelCase								=sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

				_lowerCAmelCase								=True
				return sample



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     Union[str, Any]:
				if not isinstance(__UpperCAmelCase     ,     np.ndarray  ):
					_lowerCAmelCase								=np.array(__UpperCAmelCase     ,     dtype=np.floataa  )

				_lowerCAmelCase								=10_00 * 0.2_5 * np.log(sigmas + 1e-44  )

				return timesteps



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase = None     ,     __UpperCAmelCase = None     ,     __UpperCAmelCase = None     ,     ) ->     Any:
				if num_inference_steps is None and timesteps is None:
					raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""  )

				if num_inference_steps is not None and timesteps is not None:
					raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""  )

				# Follow DDPMScheduler custom timesteps logic
				if timesteps is not None:
					for i in range(1     ,     len(__UpperCAmelCase  )  ):
						if timesteps[i] >= timesteps[i - 1]:
							raise ValueError("""`timesteps` must be in descending order."""  )

					if timesteps[0] >= self.config.num_train_timesteps:
						raise ValueError(
						    f'''`timesteps` must start before `self.config.train_timesteps`:'''
						    f''' {self.config.num_train_timesteps}.'''  )

					_lowerCAmelCase								=np.array(__UpperCAmelCase     ,     dtype=np.intaa  )
					_lowerCAmelCase								=True
				else:
					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								=num_inference_steps

					_lowerCAmelCase								=self.config.num_train_timesteps // self.num_inference_steps
					_lowerCAmelCase								=(np.arange(0     ,     __UpperCAmelCase  ) * step_ratio).round()[::-1].copy().astype(np.intaa  )
					_lowerCAmelCase								=False

				# Map timesteps to Karras sigmas directly for multistep sampling
				# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
				_lowerCAmelCase								=self.config.num_train_timesteps
				_lowerCAmelCase								=timesteps[::-1].copy()
				_lowerCAmelCase								=ramp / (num_train_timesteps - 1)
				_lowerCAmelCase								=self._convert_to_karras(__UpperCAmelCase  )
				_lowerCAmelCase								=self.sigma_to_t(__UpperCAmelCase  )

				_lowerCAmelCase								=np.concatenate([sigmas, [self.sigma_min]]  ).astype(np.floataa  )
				_lowerCAmelCase								=torch.from_numpy(__UpperCAmelCase  ).to(device=__UpperCAmelCase  )

				if str(__UpperCAmelCase  ).startswith("""mps"""  ):
					# mps does not support float64
					_lowerCAmelCase								=torch.from_numpy(__UpperCAmelCase  ).to(__UpperCAmelCase     ,     dtype=torch.floataa  )
				else:
					_lowerCAmelCase								=torch.from_numpy(__UpperCAmelCase  ).to(device=__UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     int:
				_lowerCAmelCase								=self.config.sigma_min
				_lowerCAmelCase								=self.config.sigma_max

				_lowerCAmelCase								=self.config.rho
				_lowerCAmelCase								=sigma_min ** (1 / rho)
				_lowerCAmelCase								=sigma_max ** (1 / rho)
				_lowerCAmelCase								=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
				return sigmas



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     int:
				_lowerCAmelCase								=self.config.sigma_data

				_lowerCAmelCase								=sigma_data**2 / (sigma**2 + sigma_data**2)
				_lowerCAmelCase								=sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
				return c_skip, c_out



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase  ) ->     int:
				_lowerCAmelCase								=self.config.sigma_min
				_lowerCAmelCase								=self.config.sigma_data

				_lowerCAmelCase								=sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
				_lowerCAmelCase								=(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
				return c_skip, c_out



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None     ,     __UpperCAmelCase = True     ,     ) ->     Union[CMStochasticIterativeSchedulerOutput, Tuple]:

				if (
				    isinstance(__UpperCAmelCase     ,     __UpperCAmelCase  )
				    or isinstance(__UpperCAmelCase     ,     torch.IntTensor  )
				    or isinstance(__UpperCAmelCase     ,     torch.LongTensor  )
				):
					raise ValueError(
					    (
					        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
					        f''' `{self.__class__}.step()` is not supported. Make sure to pass'''
					        """ one of the `scheduler.timesteps` as a timestep."""
					    )     ,     )

				if not self.is_scale_input_called:
					logger.warning(
					    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
					    """See `StableDiffusionPipeline` for a usage example."""  )

				if isinstance(__UpperCAmelCase     ,     torch.Tensor  ):
					_lowerCAmelCase								=timestep.to(self.timesteps.device  )

				_lowerCAmelCase								=self.config.sigma_min
				_lowerCAmelCase								=self.config.sigma_max

				_lowerCAmelCase								=self.index_for_timestep(__UpperCAmelCase  )

				# sigma_next corresponds to next_t in original implementation
				_lowerCAmelCase								=self.sigmas[step_index]
				if step_index + 1 < self.config.num_train_timesteps:
					_lowerCAmelCase								=self.sigmas[step_index + 1]
				else:
					# Set sigma_next to sigma_min
					_lowerCAmelCase								=self.sigmas[-1]

				# Get scalings for boundary conditions
				_lowerCAmelCase						,							_lowerCAmelCase								=self.get_scalings_for_boundary_condition(__UpperCAmelCase  )

				# 1. Denoise model output using boundary conditions
				_lowerCAmelCase								=c_out * model_output + c_skip * sample
				if self.config.clip_denoised:
					_lowerCAmelCase								=denoised.clamp(-1     ,     1  )

				# 2. Sample z ~ N(0, s_noise^2 * I)
				# Noise is not used for onestep sampling.
				if len(self.timesteps  ) > 1:
					_lowerCAmelCase								=randn_tensor(
					    model_output.shape     ,     dtype=model_output.dtype     ,     device=model_output.device     ,     generator=__UpperCAmelCase  )
				else:
					_lowerCAmelCase								=torch.zeros_like(__UpperCAmelCase  )
				_lowerCAmelCase								=noise * self.config.s_noise

				_lowerCAmelCase								=sigma_next.clamp(min=__UpperCAmelCase     ,     max=__UpperCAmelCase  )

				# 3. Return noisy sample
				# tau = sigma_hat, eps = sigma_min
				_lowerCAmelCase								=denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

				if not return_dict:
					return (prev_sample,)

				return CMStochasticIterativeSchedulerOutput(prev_sample=__UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     ) ->     torch.FloatTensor:
				# Make sure sigmas and timesteps have the same device and dtype as original_samples
				_lowerCAmelCase								=self.sigmas.to(device=original_samples.device     ,     dtype=original_samples.dtype  )
				if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase  ):
					# mps does not support float64
					_lowerCAmelCase								=self.timesteps.to(original_samples.device     ,     dtype=torch.floataa  )
					_lowerCAmelCase								=timesteps.to(original_samples.device     ,     dtype=torch.floataa  )
				else:
					_lowerCAmelCase								=self.timesteps.to(original_samples.device  )
					_lowerCAmelCase								=timesteps.to(original_samples.device  )

				_lowerCAmelCase								=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

				_lowerCAmelCase								=sigmas[step_indices].flatten()
				while len(sigma.shape  ) < len(original_samples.shape  ):
					_lowerCAmelCase								=sigma.unsqueeze(-1  )

				_lowerCAmelCase								=original_samples + noise * sigma
				return noisy_samples



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





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__A       						=       {'configuration_bert_generation': ['BertGenerationConfig']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A       						=       ['BertGenerationTokenizer']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A       						=       [
			    'BertGenerationDecoder',
			    'BertGenerationEncoder',
			    'BertGenerationPreTrainedModel',
			    'load_tf_weights_in_bert_generation',
			]


if TYPE_CHECKING:
			from .configuration_bert_generation import BertGenerationConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bert_generation import BertGenerationTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bert_generation import (
						    BertGenerationDecoder,
						    BertGenerationEncoder,
						    BertGenerationPreTrainedModel,
						    load_tf_weights_in_bert_generation,
						)

else:
			import sys

			__A       						=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



