



"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def  lowerCamelCase_						(UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		Dict=0.999     ,       UpperCamelCase__		:		Any="cosine"     ,       ):
    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(UpperCamelCase__		:		Optional[int]							):
            return math.exp(t * -12.0							)

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

    _UpperCAmelCase							:  Dict											=    []
    for i in range(UpperCamelCase__							):
        _UpperCAmelCase							:  List[str]											=    i / num_diffusion_timesteps
        _UpperCAmelCase							:  Any											=    (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(UpperCamelCase__							) / alpha_bar_fn(UpperCamelCase__							)     ,       UpperCamelCase__							)							)
    return torch.tensor(UpperCamelCase__     ,       dtype=torch.floataa							)


class 							_UpperCAmelCase							( a		,a      ):





     '''simple docstring'''



     a__									=[e.name for e in KarrasDiffusionSchedulers]
     a__									=2





     @register_to_config
     def __init__(					self       ,	A = 1_0_0_0       ,	A = 0.00_085       ,	A = 0.012       ,	A = "linear"       ,	A = None       ,	A = "epsilon"       ,	A = False       ,	A = False       ,	A = 1.0       ,	A = "linspace"       ,	A = 0       ,	)							->					Optional[int]:
         if trained_betas is not None:
             _UpperCAmelCase							:  Optional[Any]											=    torch.tensor(A       ,	dtype=torch.floataa		)
         elif beta_schedule == "linear":
             _UpperCAmelCase							:  Union[str, Any]											=    torch.linspace(A       ,	A       ,	A       ,	dtype=torch.floataa		)
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             _UpperCAmelCase							:  Optional[Any]											=    (
                 torch.linspace(beta_start**0.5       ,	beta_end**0.5       ,	A       ,	dtype=torch.floataa		) ** 2
             )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             _UpperCAmelCase							:  str											=    betas_for_alpha_bar(A       ,	alpha_transform_type='''cosine'''		)
         elif beta_schedule == "exp":
             _UpperCAmelCase							:  int											=    betas_for_alpha_bar(A       ,	alpha_transform_type='''exp'''		)
         else:
             raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'		)

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

         #  set all values
         self.set_timesteps(A       ,	A       ,	A		)
         _UpperCAmelCase							:  Any											=    use_karras_sigmas





     def  __lowerCAmelCase	(					self       ,	A       ,	A=None		)							->					Optional[int]:
         if schedule_timesteps is None:
             _UpperCAmelCase							:  List[str]											=    self.timesteps

         _UpperCAmelCase							:  Dict											=    (schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter		) == 0:
             _UpperCAmelCase							:  Any											=    1 if len(A		) > 1 else 0
         else:
             _UpperCAmelCase							:  Tuple											=    timestep.cpu().item() if torch.is_tensor(A		) else timestep
             _UpperCAmelCase							:  Tuple											=    self._index_counter[timestep_int]

         return indices[pos].item()





     @property
     def  __lowerCAmelCase	(					self		)							->					Any:
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	)							->					torch.FloatTensor:
         _UpperCAmelCase							:  Tuple											=    self.index_for_timestep(A		)

         _UpperCAmelCase							:  Any											=    self.sigmas[step_index]
         _UpperCAmelCase							:  Tuple											=    sample / ((sigma**2 + 1) ** 0.5)
         return sample





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None       ,	A = None       ,	)							->					Union[str, Any]:
         _UpperCAmelCase							:  int											=    num_inference_steps

         _UpperCAmelCase							:  List[Any]											=    num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
             _UpperCAmelCase							:  Optional[int]											=    np.linspace(0       ,	num_train_timesteps - 1       ,	A       ,	dtype=A		)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
             _UpperCAmelCase							:  str											=    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
             _UpperCAmelCase							:  List[Any]											=    (np.arange(0       ,	A		) * step_ratio).round()[::-1].copy().astype(A		)
             timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
             _UpperCAmelCase							:  Optional[int]											=    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
             _UpperCAmelCase							:  Tuple											=    (np.arange(A       ,	0       ,	-step_ratio		)).round().copy().astype(A		)
             timesteps -= 1
         else:
             raise ValueError(
                 f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'		)

         _UpperCAmelCase							:  List[str]											=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
         _UpperCAmelCase							:  Union[str, Any]											=    np.log(A		)
         _UpperCAmelCase							:  str											=    np.interp(A       ,	np.arange(0       ,	len(A		)		)       ,	A		)

         if self.config.use_karras_sigmas:
             _UpperCAmelCase							:  Any											=    self._convert_to_karras(in_sigmas=A       ,	num_inference_steps=self.num_inference_steps		)
             _UpperCAmelCase							:  int											=    np.array([self._sigma_to_t(A       ,	A		) for sigma in sigmas]		)

         _UpperCAmelCase							:  int											=    np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
         _UpperCAmelCase							:  Dict											=    torch.from_numpy(A		).to(device=A		)
         _UpperCAmelCase							:  Tuple											=    torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2		), sigmas[-1:]]		)

         _UpperCAmelCase							:  Optional[int]											=    torch.from_numpy(A		)
         _UpperCAmelCase							:  List[Any]											=    torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2		)]		)

         if str(A		).startswith('''mps'''		):
             # mps does not support float64
             _UpperCAmelCase							:  List[Any]											=    timesteps.to(A       ,	dtype=torch.floataa		)
         else:
             _UpperCAmelCase							:  Tuple											=    timesteps.to(device=A		)

         # empty dt and derivative
         _UpperCAmelCase							:  Dict											=    None
         _UpperCAmelCase							:  Dict											=    None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         _UpperCAmelCase							:  Optional[Any]											=    defaultdict(A		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					Tuple:
         # get log sigma
         _UpperCAmelCase							:  Dict											=    np.log(A		)

         # get distribution
         _UpperCAmelCase							:  Union[str, Any]											=    log_sigma - log_sigmas[:, np.newaxis]

         # get sigmas range
         _UpperCAmelCase							:  str											=    np.cumsum((dists >= 0)       ,	axis=0		).argmax(axis=0		).clip(max=log_sigmas.shape[0] - 2		)
         _UpperCAmelCase							:  List[str]											=    low_idx + 1

         _UpperCAmelCase							:  int											=    log_sigmas[low_idx]
         _UpperCAmelCase							:  Any											=    log_sigmas[high_idx]

         # interpolate sigmas
         _UpperCAmelCase							:  Optional[Any]											=    (low - log_sigma) / (low - high)
         _UpperCAmelCase							:  Optional[int]											=    np.clip(A       ,	0       ,	1		)

         # transform interpolation to time range
         _UpperCAmelCase							:  List[str]											=    (1 - w) * low_idx + w * high_idx
         _UpperCAmelCase							:  List[str]											=    t.reshape(sigma.shape		)
         return t





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					torch.FloatTensor:
         _UpperCAmelCase							:  float											=    in_sigmas[-1].item()
         _UpperCAmelCase							:  float											=    in_sigmas[0].item()

         _UpperCAmelCase							:  List[Any]											=    7.0  # 7.0 is the value used in the paper
         _UpperCAmelCase							:  Optional[Any]											=    np.linspace(0       ,	1       ,	A		)
         _UpperCAmelCase							:  Any											=    sigma_min ** (1 / rho)
         _UpperCAmelCase							:  str											=    sigma_max ** (1 / rho)
         _UpperCAmelCase							:  Optional[int]											=    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas





     @property
     def  __lowerCAmelCase	(					self		)							->					Any:
         return self.dt is None





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	A = True       ,	)							->					Union[SchedulerOutput, Tuple]:
         _UpperCAmelCase							:  Union[str, Any]											=    self.index_for_timestep(A		)

         # advance index counter by 1
         _UpperCAmelCase							:  int											=    timestep.cpu().item() if torch.is_tensor(A		) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
             _UpperCAmelCase							:  List[Any]											=    self.sigmas[step_index]
             _UpperCAmelCase							:  List[str]											=    self.sigmas[step_index + 1]
         else:
             # 2nd order / Heun's method
             _UpperCAmelCase							:  Dict											=    self.sigmas[step_index - 1]
             _UpperCAmelCase							:  List[Any]											=    self.sigmas[step_index]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         _UpperCAmelCase							:  List[Any]											=    0
         _UpperCAmelCase							:  Union[str, Any]											=    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
             _UpperCAmelCase							:  Optional[Any]											=    sigma_hat if self.state_in_first_order else sigma_next
             _UpperCAmelCase							:  List[Any]											=    sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
             _UpperCAmelCase							:  Any											=    sigma_hat if self.state_in_first_order else sigma_next
             _UpperCAmelCase							:  Any											=    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
         elif self.config.prediction_type == "sample":
             _UpperCAmelCase							:  int											=    model_output
         else:
             raise ValueError(
                 f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'		)

         if self.config.clip_sample:
             _UpperCAmelCase							:  int											=    pred_original_sample.clamp(
                 -self.config.clip_sample_range       ,	self.config.clip_sample_range		)

         if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             _UpperCAmelCase							:  List[str]											=    (sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             _UpperCAmelCase							:  Optional[int]											=    sigma_next - sigma_hat

             # store for 2nd order step
             _UpperCAmelCase							:  int											=    derivative
             _UpperCAmelCase							:  Optional[int]											=    dt
             _UpperCAmelCase							:  int											=    sample
         else:
             # 2. 2nd order / Heun's method
             _UpperCAmelCase							:  List[Any]											=    (sample - pred_original_sample) / sigma_next
             _UpperCAmelCase							:  str											=    (self.prev_derivative + derivative) / 2

             # 3. take prev timestep & sample
             _UpperCAmelCase							:  Dict											=    self.dt
             _UpperCAmelCase							:  Tuple											=    self.sample

             # free dt and derivative
             # Note, this puts the scheduler in "first order mode"
             _UpperCAmelCase							:  Optional[int]											=    None
             _UpperCAmelCase							:  int											=    None
             _UpperCAmelCase							:  int											=    None

         _UpperCAmelCase							:  int											=    sample + derivative * dt

         if not return_dict:
             return (prev_sample,)

         return SchedulerOutput(prev_sample=A		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	)							->					torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         _UpperCAmelCase							:  Dict											=    self.sigmas.to(device=original_samples.device       ,	dtype=original_samples.dtype		)
         if original_samples.device.type == "mps" and torch.is_floating_point(A		):
             # mps does not support float64
             _UpperCAmelCase							:  Tuple											=    self.timesteps.to(original_samples.device       ,	dtype=torch.floataa		)
             _UpperCAmelCase							:  List[Any]											=    timesteps.to(original_samples.device       ,	dtype=torch.floataa		)
         else:
             _UpperCAmelCase							:  Union[str, Any]											=    self.timesteps.to(original_samples.device		)
             _UpperCAmelCase							:  Dict											=    timesteps.to(original_samples.device		)

         _UpperCAmelCase							:  Optional[Any]											=    [self.index_for_timestep(A       ,	A		) for t in timesteps]

         _UpperCAmelCase							:  str											=    sigmas[step_indices].flatten()
         while len(sigma.shape		) < len(original_samples.shape		):
             _UpperCAmelCase							:  Optional[int]											=    sigma.unsqueeze(-1		)

         _UpperCAmelCase							:  Any											=    original_samples + noise * sigma
         return noisy_samples





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







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


_lowerCAmelCase    :int     =					logging.get_logger(__name__)

_lowerCAmelCase    :Union[str, Any]     =					{
    'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									='''mgp-str'''





     def __init__(					self       ,	A=[3_2, 1_2_8]       ,	A=4       ,	A=3       ,	A=2_7       ,	A=3_8       ,	A=5_0_2_5_7       ,	A=3_0_5_2_2       ,	A=7_6_8       ,	A=1_2       ,	A=1_2       ,	A=4.0       ,	A=True       ,	A=False       ,	A=1E-5       ,	A=0.0       ,	A=0.0       ,	A=0.0       ,	A=False       ,	A=0.02       ,	**A       ,	)							->					Union[str, Any]:
         super().__init__(**A		)

         _UpperCAmelCase							:  Any											=    image_size
         _UpperCAmelCase							:  str											=    patch_size
         _UpperCAmelCase							:  Dict											=    num_channels
         _UpperCAmelCase							:  Dict											=    max_token_length
         _UpperCAmelCase							:  Optional[Any]											=    num_character_labels
         _UpperCAmelCase							:  int											=    num_bpe_labels
         _UpperCAmelCase							:  List[str]											=    num_wordpiece_labels
         _UpperCAmelCase							:  Optional[int]											=    hidden_size
         _UpperCAmelCase							:  Any											=    num_hidden_layers
         _UpperCAmelCase							:  List[Any]											=    num_attention_heads
         _UpperCAmelCase							:  List[Any]											=    mlp_ratio
         _UpperCAmelCase							:  List[str]											=    distilled
         _UpperCAmelCase							:  Optional[int]											=    layer_norm_eps
         _UpperCAmelCase							:  str											=    drop_rate
         _UpperCAmelCase							:  List[Any]											=    qkv_bias
         _UpperCAmelCase							:  List[str]											=    attn_drop_rate
         _UpperCAmelCase							:  Dict											=    drop_path_rate
         _UpperCAmelCase							:  Union[str, Any]											=    output_aa_attentions
         _UpperCAmelCase							:  List[str]											=    initializer_range


