





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






@dataclass
class      UpperCAmelCase			( UpperCamelCase__       ):
 __lowercase     	=       42






class      UpperCAmelCase			( UpperCamelCase__    , UpperCamelCase__       ):
 __lowercase     	=       1
 @register_to_config
 def __init__(       self   :Dict		, lowercase_   :int = 40		, lowercase_   :float = 0.0_0_2		, lowercase_   :float = 8_0.0		, lowercase_   :float = 0.5		, lowercase_   :float = 1.0		, lowercase_   :float = 7.0		, lowercase_   :bool = True		, )->							Any:
     # standard deviation of the initial noise distribution
     A__       =    sigma_max

     A__       =    np.linspace(0		, 1		, lowercase_							)
     A__       =    self._convert_to_karras(lowercase_							)
     A__       =    self.sigma_to_t(lowercase_							)

     # setable values
     A__       =    None
     A__       =    torch.from_numpy(lowercase_							)
     A__       =    torch.from_numpy(lowercase_							)
     A__       =    False
     A__       =    False
 def   UpperCAmelCase_							(       self   :str		, lowercase_   :Union[str, Any]		, lowercase_   :Optional[int]=None							)->							Union[str, Any]:
     if schedule_timesteps is None:
         A__       =    self.timesteps

     A__       =    (schedule_timesteps == timestep).nonzero()
     return indices.item()
 def   UpperCAmelCase_							(       self   :Any		, lowercase_   :torch.FloatTensor		, lowercase_   :Union[float, torch.FloatTensor]							)->							torch.FloatTensor:
     # Get sigma corresponding to timestep
     if isinstance(lowercase_		, torch.Tensor							):
         A__       =    timestep.to(self.timesteps.device							)
     A__       =    self.index_for_timestep(lowercase_							)
     A__       =    self.sigmas[step_idx]

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

     A__       =    True
     return sample
 def   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :Union[float, np.ndarray]							)->							Dict:
     if not isinstance(lowercase_		, np.ndarray							):
         A__       =    np.array(lowercase_		, dtype=np.floataa							)

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

     return timesteps
 def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Optional[int] = None		, lowercase_   :Union[str, torch.device] = None		, lowercase_   :Optional[List[int]] = None		, )->							Optional[int]:
     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(lowercase_							)							):
             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}."							)

         A__       =    np.array(lowercase_		, dtype=np.intaa							)
         A__       =    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."							)

         A__       =    num_inference_steps

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

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

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

     if str(lowercase_							).startswith("mps"							):
         # mps does not support float64
         A__       =    torch.from_numpy(lowercase_							).to(lowercase_		, dtype=torch.floataa							)
     else:
         A__       =    torch.from_numpy(lowercase_							).to(device=lowercase_							)
 def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :str							)->							str:
     A__       =    self.config.sigma_min
     A__       =    self.config.sigma_max

     A__       =    self.config.rho
     A__       =    sigma_min ** (1 / rho)
     A__       =    sigma_max ** (1 / rho)
     A__       =    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas
 def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :Optional[Any]							)->							Optional[int]:
     A__       =    self.config.sigma_data

     A__       =    sigma_data**2 / (sigma**2 + sigma_data**2)
     A__       =    sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
     return c_skip, c_out
 def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :int							)->							Any:
     A__       =    self.config.sigma_min
     A__       =    self.config.sigma_data

     A__       =    sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
     A__       =    (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
     return c_skip, c_out
 def   UpperCAmelCase_							(       self   :str		, lowercase_   :torch.FloatTensor		, lowercase_   :Union[float, torch.FloatTensor]		, lowercase_   :torch.FloatTensor		, lowercase_   :Optional[torch.Generator] = None		, lowercase_   :bool = True		, )->							Union[CMStochasticIterativeSchedulerOutput, Tuple]:

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

     A__       =    self.config.sigma_min
     A__       =    self.config.sigma_max

     A__       =    self.index_for_timestep(lowercase_							)

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

     # Get scalings for boundary conditions
     A__,			A__       =    self.get_scalings_for_boundary_condition(lowercase_							)

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

     A__       =    sigma_next.clamp(min=lowercase_		, max=lowercase_							)

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

     if not return_dict:
         return (prev_sample,)

     return CMStochasticIterativeSchedulerOutput(prev_sample=lowercase_							)
 def   UpperCAmelCase_							(       self   :List[str]		, lowercase_   :torch.FloatTensor		, lowercase_   :torch.FloatTensor		, lowercase_   :torch.FloatTensor		, )->							torch.FloatTensor:
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     A__       =    self.sigmas.to(device=original_samples.device		, dtype=original_samples.dtype							)
     if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_							):
         # mps does not support float64
         A__       =    self.timesteps.to(original_samples.device		, dtype=torch.floataa							)
         A__       =    timesteps.to(original_samples.device		, dtype=torch.floataa							)
     else:
         A__       =    self.timesteps.to(original_samples.device							)
         A__       =    timesteps.to(original_samples.device							)

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

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

     A__       =    original_samples + noise * sigma
     return noisy_samples



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










'''simple docstring'''

import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
  import torch


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






class      UpperCAmelCase			:
 def __init__(       self   :Optional[int]		, lowercase_   :Any		, lowercase_   :Any=21		, lowercase_   :Any=[]		, lowercase_   :Dict=None							)->							Tuple:
     A__       =    model
     A__       =    trace_batch_nums
     A__       =    abort_after_batch_num

     # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
     A__       =    collections.deque([]		, lowercase_							)
     A__       =    []
     A__       =    0
     A__       =    0
     A__       =    False
     A__       =    "                 "

     self.analyse_model()

     self.register_forward_hook()
 def   UpperCAmelCase_							(       self   :Any		, lowercase_   :Any=None							)->							Optional[Any]:
     if frame is not None:
         self.expand_frame(lowercase_							)
     self.frames.append("\n".join(self.frame							)							)
     A__       =    []  # start a new frame
 def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :Dict							)->							List[str]:
     self.frame.append(lowercase_							)
 def   UpperCAmelCase_							(       self   :int							)->							Dict:
     print("\n".join(self.frames							)							)
     A__       =    []
 def   UpperCAmelCase_							(       self   :str							)->							Union[str, Any]:
     A__       =    []
 def   UpperCAmelCase_							(       self   :List[str]							)->							Optional[Any]:
     print(F"\nDetected inf/nan during batch_number={self.batch_number}"							)
     print(F"Last {len(self.frames							)} forward frames:"							)
     print(F"{'abs min':8} {'abs max':8} metadata"							)
     print("\n".join(self.frames							)							)
     print("\n\n"							)
     A__       =    []
 def   UpperCAmelCase_							(       self   :Any							)->							Optional[Any]:
     # extract the fully qualified module names, to be able to report at run time. e.g.:
     # encoder.block.2.layer.0.SelfAttention.o
     #
     # for shared weights only the first shared module name will be registered
     A__       =    {m: name for name, m in self.model.named_modules()}
     # self.longest_module_name = max(len(v) for v in self.module_names.values())
 def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Dict		, lowercase_   :Any							)->							str:
     if torch.is_tensor(lowercase_							):
         self.expand_frame(get_abs_min_max(lowercase_		, lowercase_							)							)
         if detect_overflow(lowercase_		, lowercase_							):
             A__       =    True
     elif var is None:
         self.expand_frame(F"{'None':>17} {ctx}"							)
     else:
         self.expand_frame(F"{'not a tensor':>17} {ctx}"							)
 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							str:
     self.expand_frame(F"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"							)
     self.expand_frame(F"{'abs min':8} {'abs max':8} metadata"							)
 def   UpperCAmelCase_							(       self   :int							)->							int:
     self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"							)
 def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :Dict		, lowercase_   :Dict		, lowercase_   :str							)->							int:
     self.expand_frame(F"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"							)

     # params
     for name, p in module.named_parameters(recurse=lowercase_							):
         self.analyse_variable(lowercase_		, lowercase_							)

     # inputs
     if isinstance(lowercase_		, lowercase_							):
         for i, x in enumerate(lowercase_							):
             self.analyse_variable(lowercase_		, F"input[{i}]"							)
     else:
         self.analyse_variable(lowercase_		, "input"							)

     # outputs
     if isinstance(lowercase_		, lowercase_							):
         for i, x in enumerate(lowercase_							):
             # possibly a tuple of tuples
             if isinstance(lowercase_		, lowercase_							):
                 for j, y in enumerate(lowercase_							):
                     self.analyse_variable(lowercase_		, F"output[{i}][{j}]"							)
             else:
                 self.analyse_variable(lowercase_		, F"output[{i}]"							)
     else:
         self.analyse_variable(lowercase_		, "output"							)

     self.save_frame()
 def   UpperCAmelCase_							(       self   :Any							)->							int:
     self.model.apply(self._register_forward_hook							)
 def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Optional[int]							)->							Tuple:
     module.register_forward_hook(self.forward_hook							)



 def   UpperCAmelCase_							(       self   :List[str]		, lowercase_   :int		, lowercase_   :Optional[Any]		, lowercase_   :Dict							)->							Union[str, Any]:
     # - input is a tuple of packed inputs (could be non-Tensors)
     # - output could be a Tensor or a tuple of Tensors and non-Tensors

     A__       =    False

     A__       =    True if self.batch_number in self.trace_batch_nums else False
     if trace_mode:
         self.reset_saved_frames()

     if self.total_calls == 0:
         self.batch_start_frame()
     self.total_calls += 1

     # count batch numbers - the very first forward hook of the batch will be called when the
     # batch completes - i.e. it gets called very last - we know this batch has finished
     if module == self.model:
         self.batch_number += 1
         A__       =    True

     self.create_frame(lowercase_		, lowercase_		, lowercase_							)

     # if last_frame_of_batch:
     #     self.batch_end_frame()

     if trace_mode:
         self.trace_frames()

     if last_frame_of_batch:
         self.batch_start_frame()

     if self.detected_overflow and not trace_mode:
         self.dump_saved_frames()

         # now we can abort, as it's pointless to continue running
         raise ValueError(
             "DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. "
             "Please scroll up above this traceback to see the activation values prior to this event."							)

     # abort after certain batch if requested to do so
     if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
         raise ValueError(
             F"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
             F" `abort_after_batch_num={self.abort_after_batch_num}` arg"							)





def 		UpperCamelCase       (							_lowerCamelCase      :      Dict		, _lowerCamelCase      :      int ):
    A__       =    var.abs()
    return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"





def 		UpperCamelCase       (							_lowerCamelCase      :      Tuple		, _lowerCamelCase      :      Optional[int] ):
    A__       =    False
    if torch.isnan(_lowerCamelCase ).any().item():
        A__       =    True
        print(F"{ctx} has nans" )
    if torch.isinf(_lowerCamelCase ).any().item():
        A__       =    True
        print(F"{ctx} has infs" )

    # if needed to monitor large elements can enable the following
    if 0:  # and detected:
        A__       =    var[torch.ge(var.abs()		, 1_00 )]
        if naaa.numel() > 0:
            print(F"{ctx}:  n100={naaa.numel()}" )
        A__       =    var[torch.ge(var.abs()		, 10_00 )]
        if naaaa.numel() > 0:
            print(F"{ctx}: n1000={naaaa.numel()}" )
        A__       =    var[torch.ge(var.abs()		, 1_00_00 )]
        if naaaaa.numel() > 0:
            print(F"{ctx}: n10000={naaaaa.numel()}" )

    if 0:
        print(F"min={var.min():9.2e} max={var.max():9.2e}" )

    if 0:
        print(F"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})" )

    return detected






class      UpperCAmelCase			( UpperCamelCase__       ):
 __lowercase     	=       """underflow_overflow"""
 __lowercase     	=       """tpu_metrics_debug"""



