





'''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 						UpperCamelCase    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : Any=0.9_9_9						,	_lowerCamelCase : List[str]="cosine"						,	):
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(_lowerCamelCase : Dict	):
            return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2	) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(_lowerCamelCase : Optional[Any]	):
            return math.exp(t * -1_2.0	)

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

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






class        UpperCAmelCase     (	UpperCamelCase__      ,	UpperCamelCase__    ):
 __lowercase       		=    [e.name for e in KarrasDiffusionSchedulers]
 __lowercase       		=    2
 @register_to_config
 def __init__(    self				:Optional[int]						,	lowercase_				:int = 10_00						,	lowercase_				:float = 0.0_0_0_8_5						,	lowercase_				:float = 0.0_1_2						,	lowercase_				:str = "linear"						,	lowercase_				:Optional[Union[np.ndarray, List[float]]] = None						,	lowercase_				:str = "epsilon"						,	lowercase_				:Optional[bool] = False						,	lowercase_				:Optional[bool] = False						,	lowercase_				:float = 1.0						,	lowercase_				:str = "linspace"						,	lowercase_				:int = 0						,	)->							Tuple:
     if trained_betas is not None:
         A__    =      torch.tensor(lowercase_						,	dtype=torch.floataa							)
     elif beta_schedule == "linear":
         A__    =      torch.linspace(lowercase_						,	lowercase_						,	lowercase_						,	dtype=torch.floataa							)
     elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         A__    =      (
             torch.linspace(beta_start**0.5						,	beta_end**0.5						,	lowercase_						,	dtype=torch.floataa							) ** 2
         )
     elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         A__    =      betas_for_alpha_bar(lowercase_						,	alpha_transform_type="cosine"							)
     elif beta_schedule == "exp":
         A__    =      betas_for_alpha_bar(lowercase_						,	alpha_transform_type="exp"							)
     else:
         raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"							)

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

     #  set all values
     self.set_timesteps(lowercase_						,	lowercase_						,	lowercase_							)
     A__    =      use_karras_sigmas
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Union[str, Any]						,	lowercase_				:Optional[Any]=None							)->							Optional[int]:
     if schedule_timesteps is None:
         A__    =      self.timesteps

     A__    =      (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:
         A__    =      1 if len(lowercase_							) > 1 else 0
     else:
         A__    =      timestep.cpu().item() if torch.is_tensor(lowercase_							) else timestep
         A__    =      self._index_counter[timestep_int]

     return indices[pos].item()
 @property
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Dict:
     # 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 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:torch.FloatTensor						,	lowercase_				:Union[float, torch.FloatTensor]						,	)->							torch.FloatTensor:
     A__    =      self.index_for_timestep(lowercase_							)

     A__    =      self.sigmas[step_index]
     A__    =      sample / ((sigma**2 + 1) ** 0.5)
     return sample
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:int						,	lowercase_				:Union[str, torch.device] = None						,	lowercase_				:Optional[int] = None						,	)->							Optional[int]:
     A__    =      num_inference_steps

     A__    =      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":
         A__    =      np.linspace(0						,	num_train_timesteps - 1						,	lowercase_						,	dtype=lowercase_							)[::-1].copy()
     elif self.config.timestep_spacing == "leading":
         A__    =      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
         A__    =      (np.arange(0						,	lowercase_							) * step_ratio).round()[::-1].copy().astype(lowercase_							)
         timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
         A__    =      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
         A__    =      (np.arange(lowercase_						,	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'."							)

     A__    =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
     A__    =      np.log(lowercase_							)
     A__    =      np.interp(lowercase_						,	np.arange(0						,	len(lowercase_							)							)						,	lowercase_							)

     if self.config.use_karras_sigmas:
         A__    =      self._convert_to_karras(in_sigmas=lowercase_						,	num_inference_steps=self.num_inference_steps							)
         A__    =      np.array([self._sigma_to_t(lowercase_						,	lowercase_							) for sigma in sigmas]							)

     A__    =      np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
     A__    =      torch.from_numpy(lowercase_							).to(device=lowercase_							)
     A__    =      torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2							), sigmas[-1:]]							)

     A__    =      torch.from_numpy(lowercase_							)
     A__    =      torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2							)]							)

     if str(lowercase_							).startswith("mps"							):
         # mps does not support float64
         A__    =      timesteps.to(lowercase_						,	dtype=torch.floataa							)
     else:
         A__    =      timesteps.to(device=lowercase_							)

     # empty dt and derivative
     A__    =      None
     A__    =      None

     # for exp beta schedules, such as the one for `pipeline_shap_e.py`
     # we need an index counter
     A__    =      defaultdict(lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:Any						,	lowercase_				:Tuple							)->							Any:
     # get log sigma
     A__    =      np.log(lowercase_							)

     # get distribution
     A__    =      log_sigma - log_sigmas[:, np.newaxis]

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

     A__    =      log_sigmas[low_idx]
     A__    =      log_sigmas[high_idx]

     # interpolate sigmas
     A__    =      (low - log_sigma) / (low - high)
     A__    =      np.clip(lowercase_						,	0						,	1							)

     # transform interpolation to time range
     A__    =      (1 - w) * low_idx + w * high_idx
     A__    =      t.reshape(sigma.shape							)
     return t
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:torch.FloatTensor						,	lowercase_				:Optional[Any]							)->							torch.FloatTensor:
     A__    =      in_sigmas[-1].item()
     A__    =      in_sigmas[0].item()

     A__    =      7.0  # 7.0 is the value used in the paper
     A__    =      np.linspace(0						,	1						,	lowercase_							)
     A__    =      sigma_min ** (1 / rho)
     A__    =      sigma_max ** (1 / rho)
     A__    =      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas
 @property
 def 					UpperCAmelCase_							(    self				:Dict							)->							Tuple:
     return self.dt is None
 def 					UpperCAmelCase_							(    self				:List[Any]						,	lowercase_				:Union[torch.FloatTensor, np.ndarray]						,	lowercase_				:Union[float, torch.FloatTensor]						,	lowercase_				:Union[torch.FloatTensor, np.ndarray]						,	lowercase_				:bool = True						,	)->							Union[SchedulerOutput, Tuple]:
     A__    =      self.index_for_timestep(lowercase_							)

     # advance index counter by 1
     A__    =      timestep.cpu().item() if torch.is_tensor(lowercase_							) else timestep
     self._index_counter[timestep_int] += 1

     if self.state_in_first_order:
         A__    =      self.sigmas[step_index]
         A__    =      self.sigmas[step_index + 1]
     else:
         # 2nd order / Heun's method
         A__    =      self.sigmas[step_index - 1]
         A__    =      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
     A__    =      0
     A__    =      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":
         A__    =      sigma_hat if self.state_in_first_order else sigma_next
         A__    =      sample - sigma_input * model_output
     elif self.config.prediction_type == "v_prediction":
         A__    =      sigma_hat if self.state_in_first_order else sigma_next
         A__    =      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**2 + 1)
         )
     elif self.config.prediction_type == "sample":
         A__    =      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:
         A__    =      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
         A__    =      (sample - pred_original_sample) / sigma_hat
         # 3. delta timestep
         A__    =      sigma_next - sigma_hat

         # store for 2nd order step
         A__    =      derivative
         A__    =      dt
         A__    =      sample
     else:
         # 2. 2nd order / Heun's method
         A__    =      (sample - pred_original_sample) / sigma_next
         A__    =      (self.prev_derivative + derivative) / 2

         # 3. take prev timestep & sample
         A__    =      self.dt
         A__    =      self.sample

         # free dt and derivative
         # Note, this puts the scheduler in "first order mode"
         A__    =      None
         A__    =      None
         A__    =      None

     A__    =      sample + derivative * dt

     if not return_dict:
         return (prev_sample,)

     return SchedulerOutput(prev_sample=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	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__    =      [self.index_for_timestep(lowercase_						,	lowercase_							) 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				:Dict							)->							Any:
     return self.config.num_train_timesteps










'''simple docstring'''

import argparse
import os
import re

import packaging.version


__lowerCAmelCase							:     List[Any]		     ="examples/"
__lowerCAmelCase							:     Dict		     ={
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
__lowerCAmelCase							:     List[str]		     ={
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
__lowerCAmelCase							:     str		     ="README.md"





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Union[str, Any]						,	_lowerCamelCase : Optional[Any]	):
    with open(_lowerCamelCase						,	"r"						,	encoding="utf-8"						,	newline="\n"	) as f:
        A__    =      f.read()
    A__,     A__    =      REPLACE_PATTERNS[pattern]
    A__    =      replace.replace("VERSION"						,	_lowerCamelCase	)
    A__    =      re_pattern.sub(_lowerCamelCase						,	_lowerCamelCase	)
    with open(_lowerCamelCase						,	"w"						,	encoding="utf-8"						,	newline="\n"	) as f:
        f.write(_lowerCamelCase	)





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    for folder, directories, fnames in os.walk(_lowerCamelCase	):
        # Removing some of the folders with non-actively maintained examples from the walk
        if "research_projects" in directories:
            directories.remove("research_projects"	)
        if "legacy" in directories:
            directories.remove("legacy"	)
        for fname in fnames:
            if fname.endswith(".py"	):
                update_version_in_file(os.path.join(_lowerCamelCase						,	_lowerCamelCase	)						,	_lowerCamelCase						,	pattern="examples"	)





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : Union[str, Any]=False	):
    for pattern, fname in REPLACE_FILES.items():
        update_version_in_file(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
    if not patch:
        update_version_in_examples(_lowerCamelCase	)





def 						UpperCamelCase    (							):
    A__    =      "🤗 Transformers currently provides the following architectures"
    A__    =      "1. Want to contribute a new model?"
    with open(_lowerCamelCase						,	"r"						,	encoding="utf-8"						,	newline="\n"	) as f:
        A__    =      f.readlines()

    # Find the start of the list.
    A__    =      0
    while not lines[start_index].startswith(_start_prompt	):
        start_index += 1
    start_index += 1

    A__    =      start_index
    # Update the lines in the model list.
    while not lines[index].startswith(_end_prompt	):
        if lines[index].startswith("1."	):
            A__    =      lines[index].replace(
                "https://huggingface.co/docs/transformers/main/model_doc"						,	"https://huggingface.co/docs/transformers/model_doc"						,	)
        index += 1

    with open(_lowerCamelCase						,	"w"						,	encoding="utf-8"						,	newline="\n"	) as f:
        f.writelines(_lowerCamelCase	)





def 						UpperCamelCase    (							):
    with open(REPLACE_FILES["init"]						,	"r"	) as f:
        A__    =      f.read()
    A__    =      REPLACE_PATTERNS["init"][0].search(_lowerCamelCase	).groups()[0]
    return packaging.version.parse(_lowerCamelCase	)





def 						UpperCamelCase    (							_lowerCamelCase : Dict=False	):
    A__    =      get_version()
    if patch and default_version.is_devrelease:
        raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"	)
    if default_version.is_devrelease:
        A__    =      default_version.base_version
    elif patch:
        A__    =      F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
    else:
        A__    =      F"{default_version.major}.{default_version.minor + 1}.0"

    # Now let's ask nicely if that's the right one.
    A__    =      input(F"Which version are you releasing? [{default_version}]"	)
    if len(_lowerCamelCase	) == 0:
        A__    =      default_version

    print(F"Updating version to {version}."	)
    global_version_update(_lowerCamelCase						,	patch=_lowerCamelCase	)
    if not patch:
        print("Cleaning main README, don't forget to run `make fix-copies`."	)
        clean_main_ref_in_model_list()





def 						UpperCamelCase    (							):
    A__    =      get_version()
    A__    =      F"{current_version.major}.{current_version.minor + 1}.0.dev0"
    A__    =      current_version.base_version

    # Check with the user we got that right.
    A__    =      input(F"Which version are we developing now? [{dev_version}]"	)
    if len(_lowerCamelCase	) == 0:
        A__    =      dev_version

    print(F"Updating version to {version}."	)
    global_version_update(_lowerCamelCase	)
    print("Cleaning main README, don't forget to run `make fix-copies`."	)
    clean_main_ref_in_model_list()


if __name__ == "__main__":
  __lowerCAmelCase							:     Union[str, Any]		     =argparse.ArgumentParser()
  parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
  parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
  __lowerCAmelCase							:     int		     =parser.parse_args()
  if not args.post_release:
    pre_release_work(patch=args.patch)
  elif args.patch:
    print("Nothing to do after a patch :-)")
  else:
    post_release_work()



