

'''simple docstring'''

import math
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 KarrasDiffusionSchedulers, SchedulerMixin


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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class lowerCamelCase   (    __lowerCAmelCase				):
				snake_case_							     = 42
				snake_case_							     = None

def      __magic_name__							(			A       , A=0.999       , A="cosine"       , )      ->		Optional[int]:
	if alpha_transform_type == "cosine":

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

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(A				):
			return math.exp(t * -12.0				)

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

	snake_case							=					[]
	for i in range(A				):
		snake_case							=					i / num_diffusion_timesteps
		snake_case							=					(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(A				) / alpha_bar_fn(A				)       , A				)				)
	return torch.tensor(A       , dtype=torch.floataa				)







class lowerCamelCase   (    __lowerCAmelCase				,				__lowerCAmelCase				):
				snake_case_							     = [e.name for e in KarrasDiffusionSchedulers]
				snake_case_							     = 1

				@register_to_config
				def __init__(       self,						lowercase_ = 1000,						lowercase_ = 0.0_001,						lowercase_ = 0.02,						lowercase_ = "linear",						lowercase_ = None,						lowercase_ = "epsilon",						lowercase_ = "linspace",						lowercase_ = 0,						)    ->							List[str]:
					if trained_betas is not None:
						snake_case							=					torch.tensor(lowercase_,						dtype=torch.floataa )
					elif beta_schedule == "linear":
						snake_case							=					torch.linspace(lowercase_,						lowercase_,						lowercase_,						dtype=torch.floataa )
					elif beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						snake_case							=					(
						    torch.linspace(beta_start**0.5,						beta_end**0.5,						lowercase_,						dtype=torch.floataa ) ** 2
						)
					elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						snake_case							=					betas_for_alpha_bar(lowercase_ )
					else:
						raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )

					snake_case							=					1.0 - self.betas
					snake_case							=					torch.cumprod(self.alphas,						dim=0 )

					snake_case							=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
					snake_case							=					np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
					snake_case							=					torch.from_numpy(lowercase_ )

					# setable values
					snake_case							=					None
					snake_case							=					np.linspace(0,						num_train_timesteps - 1,						lowercase_,						dtype=lowercase_ )[::-1].copy()
					snake_case							=					torch.from_numpy(lowercase_ )
					snake_case							=					False

				@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,						lowercase_,						lowercase_ )    ->							torch.FloatTensor:
					if isinstance(lowercase_,						torch.Tensor ):
						snake_case							=					timestep.to(self.timesteps.device )
					snake_case							=					(self.timesteps == timestep).nonzero().item()
					snake_case							=					self.sigmas[step_index]
					snake_case							=					sample / ((sigma**2 + 1) ** 0.5)
					snake_case							=					True
					return sample

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ = None )    ->							List[Any]:
					snake_case							=					num_inference_steps

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

					snake_case							=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
					snake_case							=					np.interp(lowercase_,						np.arange(0,						len(lowercase_ ) ),						lowercase_ )
					snake_case							=					np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
					snake_case							=					torch.from_numpy(lowercase_ ).to(device=lowercase_ )
					if str(lowercase_ ).startswith('mps' ):
						# mps does not support float64
						snake_case							=					torch.from_numpy(lowercase_ ).to(lowercase_,						dtype=torch.floataa )
					else:
						snake_case							=					torch.from_numpy(lowercase_ ).to(device=lowercase_ )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_ = None,						lowercase_ = True,						)    ->							Union[EulerAncestralDiscreteSchedulerOutput, 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'
						        ' `EulerDiscreteScheduler.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 ):
						snake_case							=					timestep.to(self.timesteps.device )

					snake_case							=					(self.timesteps == timestep).nonzero().item()
					snake_case							=					self.sigmas[step_index]

					# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
					if self.config.prediction_type == "epsilon":
						snake_case							=					sample - sigma * model_output
					elif self.config.prediction_type == "v_prediction":
						# * c_out + input * c_skip
						snake_case							=					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
					elif self.config.prediction_type == "sample":
						raise NotImplementedError('prediction_type not implemented yet: sample' )
					else:
						raise ValueError(
						    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )

					snake_case							=					self.sigmas[step_index]
					snake_case							=					self.sigmas[step_index + 1]
					snake_case							=					(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
					snake_case							=					(sigma_to**2 - sigma_up**2) ** 0.5

					# 2. Convert to an ODE derivative
					snake_case							=					(sample - pred_original_sample) / sigma

					snake_case							=					sigma_down - sigma

					snake_case							=					sample + derivative * dt

					snake_case							=					model_output.device
					snake_case							=					randn_tensor(model_output.shape,						dtype=model_output.dtype,						device=lowercase_,						generator=lowercase_ )

					snake_case							=					prev_sample + noise * sigma_up

					if not return_dict:
						return (prev_sample,)

					return EulerAncestralDiscreteSchedulerOutput(
					    prev_sample=lowercase_,						pred_original_sample=lowercase_ )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						)    ->							torch.FloatTensor:
					# 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(lowercase_ ):
						# 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 )    ->							List[str]:
					return self.config.num_train_timesteps










import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A_		:  int										=  '''src/transformers'''

# Matches is_xxx_available()
A_		:  Union[str, Any]										=  re.compile(r'''is\_([a-z_]*)_available()''')
# Matches from xxx import bla
A_		:  int										=  re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
A_		:  Tuple										=  re.compile(r'''^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)''')


A_		:  str										=  '''
{0} = None
'''

A_		:  Tuple										=  '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


A_		:  Any										=  '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]       ):






 '''simple docstring'''

 if _re_test_backend.search(UpperCAmelCase__       ) is None:
  return None
 a							=		[b[0] for b in _re_backend.findall(UpperCAmelCase__       )]
 backends.sort()
 return "_and_".join(UpperCAmelCase__       )


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 with open(os.path.join(UpperCAmelCase__			,       "__init__.py"       )			,       "r"			,       encoding="utf-8"			,       newline="\n"       ) as f:
  a							=		f.readlines()

 # Get to the point we do the actual imports for type checking
 a							=		0
 while not lines[line_index].startswith("if TYPE_CHECKING"       ):
  line_index += 1

 a							=		{}
 # Go through the end of the file
 while line_index < len(UpperCAmelCase__       ):
  # If the line is an if is_backend_available, we grab all objects associated.
  a							=		find_backend(lines[line_index]       )
  if backend is not None:
   while not lines[line_index].startswith("    else:"       ):
    line_index += 1
   line_index += 1

   a							=		[]
   # Until we unindent, add backend objects to the list
   while len(lines[line_index]       ) <= 1 or lines[line_index].startswith(" " * 8       ):
    a							=		lines[line_index]
    a							=		_re_single_line_import.search(UpperCAmelCase__       )
    if single_line_import_search is not None:
     objects.extend(single_line_import_search.groups()[0].split(", "       )       )
    elif line.startswith(" " * 12       ):
     objects.append(line[12:-2]       )
    line_index += 1

   a							=		objects
  else:
   line_index += 1

 return backend_specific_objects


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:Any       ):






 '''simple docstring'''

 if name.isupper():
  return DUMMY_CONSTANT.format(UpperCAmelCase__       )
 elif name.islower():
  return DUMMY_FUNCTION.format(UpperCAmelCase__			,       UpperCAmelCase__       )
 else:
  return DUMMY_CLASS.format(UpperCAmelCase__			,       UpperCAmelCase__       )


def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[str]=None       ):






 '''simple docstring'''

 if backend_specific_objects is None:
  a							=		read_init()
 # For special correspondence backend to module name as used in the function requires_modulename
 a							=		{}

 for backend, objects in backend_specific_objects.items():
  a							=		"[" + ", ".join(F"""\"{b}\"""" for b in backend.split("_and_"       )       ) + "]"
  a							=		"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
  dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
  dummy_file += "\n".join([create_dummy_object(UpperCAmelCase__			,       UpperCAmelCase__       ) for o in objects]       )
  a							=		dummy_file

 return dummy_files


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int=False       ):






 '''simple docstring'''

 a							=		create_dummy_files()
 # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
 a							=		{"torch": "pt"}

 # Locate actual dummy modules and read their content.
 a							=		os.path.join(UpperCAmelCase__			,       "utils"       )
 a							=		{
     backend: os.path.join(UpperCAmelCase__			,       F"""dummy_{short_names.get(UpperCAmelCase__			,       UpperCAmelCase__       )}_objects.py"""       )
     for backend in dummy_files.keys()
 }

 a							=		{}
 for backend, file_path in dummy_file_paths.items():
  if os.path.isfile(UpperCAmelCase__       ):
   with open(UpperCAmelCase__			,       "r"			,       encoding="utf-8"			,       newline="\n"       ) as f:
    a							=		f.read()
  else:
   a							=		""

 for backend in dummy_files.keys():
  if dummy_files[backend] != actual_dummies[backend]:
   if overwrite:
    print(
        F"""Updating transformers.utils.dummy_{short_names.get(UpperCAmelCase__			,       UpperCAmelCase__       )}_objects.py as the main """
        "__init__ has new objects."       )
    with open(dummy_file_paths[backend]			,       "w"			,       encoding="utf-8"			,       newline="\n"       ) as f:
     f.write(dummy_files[backend]       )
   else:
    raise ValueError(
        "The main __init__ has objects that are not present in "
        F"""transformers.utils.dummy_{short_names.get(UpperCAmelCase__			,       UpperCAmelCase__       )}_objects.py. Run `make fix-copies` """
        "to fix this."       )


if __name__ == "__main__":
     A_		:  Dict										=  argparse.ArgumentParser()
     parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
     A_		:  List[str]										=  parser.parse_args()

     check_dummies(args.fix_and_overwrite)

