




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class     a__					(      snake_case__   ):
						_a							:					torch.FloatTensor
						_a							:					torch.FloatTensor






class     a__					(      snake_case__				,       snake_case__   ):
						_a							:					Optional[Any]      =					1






						@register_to_config
						def __init__(     self	,    _A = 2_0_0_0	,    _A = 0.15	,    _A = 0.01	,    _A = 13_48.0	,    _A = 1E-5	,    _A = 1	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = sigma_max

											# setable values
											__lowerCAmelCase            = None

											self.set_sigmas(_A	,    _A	,    _A	,    _A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A = None    ):




											"""simple docstring"""



											return sample






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A = None	,    _A = None    ):




											"""simple docstring"""



											__lowerCAmelCase            = sampling_eps if sampling_eps is not None else self.config.sampling_eps

											__lowerCAmelCase            = torch.linspace(1	,    _A	,    _A	,    device=_A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A = None	,    _A = None	,    _A = None    ):




											"""simple docstring"""



											__lowerCAmelCase            = sigma_min if sigma_min is not None else self.config.sigma_min
											__lowerCAmelCase            = sigma_max if sigma_max is not None else self.config.sigma_max
											__lowerCAmelCase            = sampling_eps if sampling_eps is not None else self.config.sampling_eps
											if self.timesteps is None:
																self.set_timesteps(_A	,    _A    )

											__lowerCAmelCase            = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
											__lowerCAmelCase            = torch.exp(torch.linspace(math.log(_A    )	,    math.log(_A    )	,    _A    )    )
											__lowerCAmelCase            = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



											return torch.where(
											    timesteps == 0	,    torch.zeros_like(t.to(timesteps.device    )    )	,    self.discrete_sigmas[timesteps - 1].to(timesteps.device    )	,    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A = None	,    _A = True	,    ):




											"""simple docstring"""



											if self.timesteps is None:
																raise ValueError(
																    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

											__lowerCAmelCase            = timestep * torch.ones(
											    sample.shape[0]	,    device=sample.device    )  # torch.repeat_interleave(timestep, sample.shape[0])
											__lowerCAmelCase            = (timestep * (len(self.timesteps    ) - 1)).long()

											# mps requires indices to be in the same device, so we use cpu as is the default with cuda
											__lowerCAmelCase            = timesteps.to(self.discrete_sigmas.device    )

											__lowerCAmelCase            = self.discrete_sigmas[timesteps].to(sample.device    )
											__lowerCAmelCase            = self.get_adjacent_sigma(_A	,    _A    ).to(sample.device    )
											__lowerCAmelCase            = torch.zeros_like(_A    )
											__lowerCAmelCase            = (sigma**2 - adjacent_sigma**2) ** 0.5

											# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
											# also equation 47 shows the analog from SDE models to ancestral sampling methods
											__lowerCAmelCase            = diffusion.flatten()
											while len(diffusion.shape    ) < len(sample.shape    ):
																__lowerCAmelCase            = diffusion.unsqueeze(-1    )
											__lowerCAmelCase            = drift - diffusion**2 * model_output

											#  equation 6: sample noise for the diffusion term of
											__lowerCAmelCase            = randn_tensor(
											    sample.shape	,    layout=sample.layout	,    generator=_A	,    device=sample.device	,    dtype=sample.dtype    )
											__lowerCAmelCase            = sample - drift  # subtract because `dt` is a small negative timestep
											# TODO is the variable diffusion the correct scaling term for the noise?
											__lowerCAmelCase            = prev_sample_mean + diffusion * noise  # add impact of diffusion field g

											if not return_dict:
																return (prev_sample, prev_sample_mean)

											return SdeVeOutput(prev_sample=_A	,    prev_sample_mean=_A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A = None	,    _A = True	,    ):




											"""simple docstring"""



											if self.timesteps is None:
																raise ValueError(
																    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"    )

											# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
											# sample noise for correction
											__lowerCAmelCase            = randn_tensor(sample.shape	,    layout=sample.layout	,    generator=_A    ).to(sample.device    )

											# compute step size from the model_output, the noise, and the snr
											__lowerCAmelCase            = torch.norm(model_output.reshape(model_output.shape[0]	,    -1    )	,    dim=-1    ).mean()
											__lowerCAmelCase            = torch.norm(noise.reshape(noise.shape[0]	,    -1    )	,    dim=-1    ).mean()
											__lowerCAmelCase            = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
											__lowerCAmelCase            = step_size * torch.ones(sample.shape[0]    ).to(sample.device    )
											# self.repeat_scalar(step_size, sample.shape[0])

											# compute corrected sample: model_output term and noise term
											__lowerCAmelCase            = step_size.flatten()
											while len(step_size.shape    ) < len(sample.shape    ):
																__lowerCAmelCase            = step_size.unsqueeze(-1    )
											__lowerCAmelCase            = sample + step_size * model_output
											__lowerCAmelCase            = prev_sample_mean + ((step_size * 2) ** 0.5) * noise

											if not return_dict:
																return (prev_sample,)

											return SchedulerOutput(prev_sample=_A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = timesteps.to(original_samples.device    )
											__lowerCAmelCase            = self.discrete_sigmas.to(original_samples.device    )[timesteps]
											__lowerCAmelCase            = (
											    noise * sigmas[:, None, None, None]
											    if noise is not None
											    else torch.randn_like(_A    ) * sigmas[:, None, None, None]
											)
											__lowerCAmelCase            = noise + original_samples
											return noisy_samples






						def __len__(     self    ):




											"""simple docstring"""



											return self.config.num_train_timesteps










from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__					    =    logging.get_logger(__name__)

UpperCamelCase__					    =    {
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
class     a__					(      snake_case__   ):
						_a							:					Tuple      =					"""nllb-moe"""
						_a							:					Dict      =					["""past_key_values"""]
						_a							:					Optional[int]      =					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






						def __init__(     self	,    _A=1_2_8_1_1_2	,    _A=1_0_2_4	,    _A=1_2	,    _A=4_0_9_6	,    _A=1_6	,    _A=1_2	,    _A=4_0_9_6	,    _A=1_6	,    _A=0.05	,    _A=0.05	,    _A=True	,    _A=True	,    _A="relu"	,    _A=1_0_2_4	,    _A=0.1	,    _A=0.1	,    _A=0.0	,    _A=0.02	,    _A=2	,    _A=True	,    _A=False	,    _A="float32"	,    _A=False	,    _A=1_2_8	,    _A=6_4	,    _A=4	,    _A=4	,    _A=0.0_01	,    _A=0.0_01	,    _A="all"	,    _A=False	,    _A=False	,    _A=1.0	,    _A=0.2	,    _A=1	,    _A=0	,    _A=2	,    _A=False	,    **_A	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = vocab_size
											__lowerCAmelCase            = max_position_embeddings
											__lowerCAmelCase            = d_model
											__lowerCAmelCase            = encoder_ffn_dim
											__lowerCAmelCase            = encoder_layers
											__lowerCAmelCase            = encoder_attention_heads
											__lowerCAmelCase            = decoder_ffn_dim
											__lowerCAmelCase            = decoder_layers
											__lowerCAmelCase            = decoder_attention_heads
											__lowerCAmelCase            = dropout
											__lowerCAmelCase            = attention_dropout
											__lowerCAmelCase            = activation_dropout
											__lowerCAmelCase            = activation_function
											__lowerCAmelCase            = init_std
											__lowerCAmelCase            = encoder_layerdrop
											__lowerCAmelCase            = decoder_layerdrop
											__lowerCAmelCase            = use_cache
											__lowerCAmelCase            = encoder_layers
											__lowerCAmelCase            = scale_embedding  # scale factor will be sqrt(d_model) if True
											__lowerCAmelCase            = router_z_loss_coef
											__lowerCAmelCase            = router_aux_loss_coef
											__lowerCAmelCase            = decoder_sparse_step
											__lowerCAmelCase            = encoder_sparse_step
											__lowerCAmelCase            = num_experts
											__lowerCAmelCase            = expert_capacity
											__lowerCAmelCase            = router_bias
											if router_dtype not in ["float32", "float16", "bfloat16"]:
																raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""    )
											__lowerCAmelCase            = router_dtype

											__lowerCAmelCase            = router_ignore_padding_tokens
											__lowerCAmelCase            = batch_prioritized_routing
											__lowerCAmelCase            = second_expert_policy
											__lowerCAmelCase            = normalize_router_prob_before_dropping
											__lowerCAmelCase            = moe_eval_capacity_token_fraction
											__lowerCAmelCase            = moe_token_dropout
											__lowerCAmelCase            = output_router_logits
											super().__init__(
											    pad_token_id=_A	,    bos_token_id=_A	,    eos_token_id=_A	,    is_encoder_decoder=_A	,    decoder_start_token_id=_A	,    **_A	,    )




