
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_			      =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_			      =					{
    '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
    '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
    '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
    '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
    '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
    '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
}
class      lowerCAmelCase_						(							_a				):



							'''simple docstring'''





							_snake_case		     =						'''opt'''
							_snake_case		     =						['''past_key_values''']




							def __init__(				self  ,							snake_case_=50_272  ,							snake_case_=768  ,							snake_case_=12  ,							snake_case_=3_072  ,							snake_case_=2_048  ,							snake_case_=True  ,							snake_case_=False  ,							snake_case_=None  ,							snake_case_=0.1  ,							snake_case_=0.0  ,							snake_case_=12  ,							snake_case_="relu"  ,							snake_case_=0.0  ,							snake_case_=0.02  ,							snake_case_=True  ,							snake_case_=1  ,							snake_case_=2  ,							snake_case_=2  ,							snake_case_=True  ,							snake_case_=True  ,							**snake_case_  ,							)   ->       Dict:
													super().__init__(
													    pad_token_id=snake_case_  ,							bos_token_id=snake_case_  ,							eos_token_id=snake_case_  ,							**snake_case_  ,							)
													__lowerCAmelCase        =       vocab_size
													__lowerCAmelCase        =       max_position_embeddings
													__lowerCAmelCase        =       num_attention_heads
													__lowerCAmelCase        =       word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
													__lowerCAmelCase        =       ffn_dim
													__lowerCAmelCase        =       hidden_size
													__lowerCAmelCase        =       num_hidden_layers
													__lowerCAmelCase        =       dropout
													__lowerCAmelCase        =       attention_dropout
													__lowerCAmelCase        =       activation_function
													__lowerCAmelCase        =       init_std
													__lowerCAmelCase        =       layerdrop
													__lowerCAmelCase        =       use_cache
													__lowerCAmelCase        =       do_layer_norm_before
													# We keep these variables at `True` for backward compatibility.
													__lowerCAmelCase        =       enable_bias
													__lowerCAmelCase        =       layer_norm_elementwise_affine

													# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
													# with checkpoints that have been fine-tuned before transformers v4.20.1
													# see https://github.com/facebookresearch/metaseq/pull/164
													__lowerCAmelCase        =       _remove_final_layer_norm


"""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


SCREAMING_SNAKE_CASE_			      =					logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						42
class      lowerCAmelCase_						(							A__							,							A__				):



							'''simple docstring'''





							_snake_case		     =						1




							@register_to_config
							def __init__(				self  ,							snake_case_ = 40  ,							snake_case_ = 0.002  ,							snake_case_ = 80.0  ,							snake_case_ = 0.5  ,							snake_case_ = 1.0  ,							snake_case_ = 7.0  ,							snake_case_ = True  ,							)   ->       List[str]:
													# standard deviation of the initial noise distribution
													__lowerCAmelCase        =       sigma_max

													__lowerCAmelCase        =       np.linspace(0  ,							1  ,							snake_case_							)
													__lowerCAmelCase        =       self._convert_to_karras(snake_case_							)
													__lowerCAmelCase        =       self.sigma_to_t(snake_case_							)

													# setable values
													__lowerCAmelCase        =       None
													__lowerCAmelCase        =       torch.from_numpy(snake_case_							)
													__lowerCAmelCase        =       torch.from_numpy(snake_case_							)
													__lowerCAmelCase        =       False
													__lowerCAmelCase        =       False




							def  A__		(				self  ,							snake_case_  ,							snake_case_=None							)   ->       str:
													if schedule_timesteps is None:
																			__lowerCAmelCase        =       self.timesteps

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




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

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

													__lowerCAmelCase        =       True
													return sample




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

													__lowerCAmelCase        =       1_000 * 0.25 * np.log(sigmas + 1e-4_4							)

													return timesteps




							def  A__		(				self  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = 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(snake_case_							)							):
																									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(snake_case_  ,							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  ,							snake_case_							) * 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(snake_case_							)
													__lowerCAmelCase        =       self.sigma_to_t(snake_case_							)

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

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




							def  A__		(				self  ,							snake_case_							)   ->       str:
													__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  A__		(				self  ,							snake_case_							)   ->       Optional[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  A__		(				self  ,							snake_case_							)   ->       Any:
													__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  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_ = None  ,							snake_case_ = True  ,							)   ->       Union[CMStochasticIterativeSchedulerOutput, Tuple]:

													if (
													    isinstance(snake_case_  ,							snake_case_							)
													    or isinstance(snake_case_  ,							torch.IntTensor							)
													    or isinstance(snake_case_  ,							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(snake_case_  ,							torch.Tensor							):
																			__lowerCAmelCase        =       timestep.to(self.timesteps.device							)

													__lowerCAmelCase        =       self.config.sigma_min
													__lowerCAmelCase        =       self.config.sigma_max

													__lowerCAmelCase        =       self.index_for_timestep(snake_case_							)

													# 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(snake_case_							)

													# 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=snake_case_							)
													else:
																			__lowerCAmelCase        =       torch.zeros_like(snake_case_							)
													__lowerCAmelCase        =       noise * self.config.s_noise

													__lowerCAmelCase        =       sigma_next.clamp(min=snake_case_  ,							max=snake_case_							)

													# 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=snake_case_							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							)   ->       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(snake_case_							):
																			# 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[int]:
													return self.config.num_train_timesteps
