




'''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


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





@dataclass
class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__   =			42





class 					lowerCamelCase_							(	UpperCAmelCase_,	UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__   =			1






					@register_to_config
					def __init__(   self,  __UpperCamelCase = 40,  __UpperCamelCase = 0.0_02,  __UpperCamelCase = 80.0,  __UpperCamelCase = 0.5,  __UpperCamelCase = 1.0,  __UpperCamelCase = 7.0,  __UpperCamelCase = True,  ):
							# standard deviation of the initial noise distribution
							_snake_case     	=	sigma_max

							_snake_case     	=	np.linspace(0,  1,  __UpperCamelCase			)
							_snake_case     	=	self._convert_to_karras(__UpperCamelCase			)
							_snake_case     	=	self.sigma_to_t(__UpperCamelCase			)

							# setable values
							_snake_case     	=	None
							_snake_case     	=	torch.from_numpy(__UpperCamelCase			)
							_snake_case     	=	torch.from_numpy(__UpperCamelCase			)
							_snake_case     	=	False
							_snake_case     	=	False






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase=None			):
							if schedule_timesteps is None:
									_snake_case     	=	self.timesteps

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






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase			):
							# Get sigma corresponding to timestep
							if isinstance(__UpperCamelCase,  torch.Tensor			):
									_snake_case     	=	timestep.to(self.timesteps.device			)
							_snake_case     	=	self.index_for_timestep(__UpperCamelCase			)
							_snake_case     	=	self.sigmas[step_idx]

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

							_snake_case     	=	True
							return sample






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase			):
							if not isinstance(__UpperCamelCase,  np.ndarray			):
									_snake_case     	=	np.array(__UpperCamelCase,  dtype=np.floataa			)

							_snake_case     	=	1_000 * 0.25 * np.log(sigmas + 1E-44			)

							return timesteps






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase = None,  __UpperCamelCase = None,  __UpperCamelCase = None,  ):
							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}.'''			)

									_snake_case     	=	np.array(__UpperCamelCase,  dtype=np.intaa			)
									_snake_case     	=	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.'''			)

									_snake_case     	=	num_inference_steps

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

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

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

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






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase			):
							_snake_case     	=	self.config.sigma_min
							_snake_case     	=	self.config.sigma_max

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






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase			):
							_snake_case     	=	self.config.sigma_data

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






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase			):
							_snake_case     	=	self.config.sigma_min
							_snake_case     	=	self.config.sigma_data

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






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

							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			):
									_snake_case     	=	timestep.to(self.timesteps.device			)

							_snake_case     	=	self.config.sigma_min
							_snake_case     	=	self.config.sigma_max

							_snake_case     	=	self.index_for_timestep(__UpperCamelCase			)

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

							# Get scalings for boundary conditions
							_snake_case	,   _snake_case     	=	self.get_scalings_for_boundary_condition(__UpperCamelCase			)

							# 1. Denoise model output using boundary conditions
							_snake_case     	=	c_out * model_output + c_skip * sample
							if self.config.clip_denoised:
									_snake_case     	=	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:
									_snake_case     	=	randn_tensor(
									    model_output.shape,  dtype=model_output.dtype,  device=model_output.device,  generator=__UpperCamelCase			)
							else:
									_snake_case     	=	torch.zeros_like(__UpperCamelCase			)
							_snake_case     	=	noise * self.config.s_noise

							_snake_case     	=	sigma_next.clamp(min=__UpperCamelCase,  max=__UpperCamelCase			)

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

							if not return_dict:
									return (prev_sample,)

							return CMStochasticIterativeSchedulerOutput(prev_sample=__UpperCamelCase			)






					def     __SCREAMING_SNAKE_CASE     (   self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase,  ):
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							_snake_case     	=	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
									_snake_case     	=	self.timesteps.to(original_samples.device,  dtype=torch.floataa			)
									_snake_case     	=	timesteps.to(original_samples.device,  dtype=torch.floataa			)
							else:
									_snake_case     	=	self.timesteps.to(original_samples.device			)
									_snake_case     	=	timesteps.to(original_samples.device			)

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

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

							_snake_case     	=	original_samples + noise * sigma
							return noisy_samples






					def __len__(   self			):
							return self.config.num_train_timesteps











'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase							:						Optional[int]							     =  logging.get_logger(__name__)

_lowerCamelCase							:						int							     =  {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}





class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__   =			"""flaubert"""
					lowercase__   =			{
					    """hidden_size""": """emb_dim""",
					    """num_attention_heads""": """n_heads""",
					    """num_hidden_layers""": """n_layers""",
					    """n_words""": """vocab_size""",  # For backward compatibility
					}






					def __init__(   self,  __UpperCamelCase=False,  __UpperCamelCase=0.0,  __UpperCamelCase=30_145,  __UpperCamelCase=2_048,  __UpperCamelCase=12,  __UpperCamelCase=16,  __UpperCamelCase=0.1,  __UpperCamelCase=0.1,  __UpperCamelCase=True,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=1,  __UpperCamelCase=True,  __UpperCamelCase=512,  __UpperCamelCase=2_048**-0.5,  __UpperCamelCase=1E-12,  __UpperCamelCase=0.02,  __UpperCamelCase=0,  __UpperCamelCase=1,  __UpperCamelCase=2,  __UpperCamelCase=3,  __UpperCamelCase=5,  __UpperCamelCase=True,  __UpperCamelCase="first",  __UpperCamelCase=True,  __UpperCamelCase=None,  __UpperCamelCase=True,  __UpperCamelCase=0.1,  __UpperCamelCase=5,  __UpperCamelCase=5,  __UpperCamelCase=0,  __UpperCamelCase=0,  __UpperCamelCase=2,  __UpperCamelCase=0,  **__UpperCamelCase,  ):
							_snake_case     	=	pre_norm
							_snake_case     	=	layerdrop
							_snake_case     	=	vocab_size
							_snake_case     	=	emb_dim
							_snake_case     	=	n_layers
							_snake_case     	=	n_heads
							_snake_case     	=	dropout
							_snake_case     	=	attention_dropout
							_snake_case     	=	gelu_activation
							_snake_case     	=	sinusoidal_embeddings
							_snake_case     	=	causal
							_snake_case     	=	asm
							_snake_case     	=	n_langs
							_snake_case     	=	use_lang_emb
							_snake_case     	=	layer_norm_eps
							_snake_case     	=	bos_index
							_snake_case     	=	eos_index
							_snake_case     	=	pad_index
							_snake_case     	=	unk_index
							_snake_case     	=	mask_index
							_snake_case     	=	is_encoder
							_snake_case     	=	max_position_embeddings
							_snake_case     	=	embed_init_std
							_snake_case     	=	init_std
							_snake_case     	=	summary_type
							_snake_case     	=	summary_use_proj
							_snake_case     	=	summary_activation
							_snake_case     	=	summary_proj_to_labels
							_snake_case     	=	summary_first_dropout
							_snake_case     	=	start_n_top
							_snake_case     	=	end_n_top
							_snake_case     	=	mask_token_id
							_snake_case     	=	lang_id

							if "n_words" in kwargs:
									_snake_case     	=	kwargs["n_words"]

							super().__init__(pad_token_id=__UpperCamelCase,  bos_token_id=__UpperCamelCase,  **__UpperCamelCase			)





class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							if self.task == "multiple-choice":
									_snake_case     	=	{0: "batch", 1: "choice", 2: "sequence"}
							else:
									_snake_case     	=	{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]			)





