




'''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   _snake_case( UpperCAmelCase			):
 __snake_case:       torch.FloatTensor
 __snake_case:       Optional[torch.FloatTensor]									=				None







def 	_A			(   UpperCAmelCase			,UpperCAmelCase=0.9_99			,UpperCAmelCase="cosine"			,):







   if alpha_transform_type == "cosine":

      def alpha_bar_fn(UpperCAmelCase	):
         return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2	) ** 2

   elif alpha_transform_type == "exp":

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

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

   A__         =    []
   for i in range(UpperCAmelCase	):
      A__         =    i / num_diffusion_timesteps
      A__         =    (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   _snake_case( UpperCAmelCase					,   UpperCAmelCase			):
 __snake_case:       Tuple									=				[e.name for e in KarrasDiffusionSchedulers]
 __snake_case:       List[str]									=				1






 @register_to_config
 def __init__(self					:     Union[str, Any]						,		a					:     int = 10_00						,		a					:     float = 0.0001						,		a					:     float = 0.02						,		a					:     str = "linear"						,		a					:     Optional[Union[np.ndarray, List[float]]] = None						,		a					:     str = "epsilon"						,		a					:     str = "linspace"						,		a					:     int = 0						,		)					->      Tuple:






    """simple docstring"""


    if trained_betas is not None:
       A__         =    torch.tensor(a						,		dtype=torch.floataa						)
    elif beta_schedule == "linear":
       A__         =    torch.linspace(a						,		a						,		a						,		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						,		a						,		dtype=torch.floataa						) ** 2
       )
    elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       A__         =    betas_for_alpha_bar(a						)
    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						)

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

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






 @property
 def 	_UpperCamelCase   (self					:     int						)					->      List[Any]:






    """simple docstring"""


    if self.config.timestep_spacing in ["linspace", "trailing"]:
       return self.sigmas.max()

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






 def 	_UpperCamelCase   (self					:     Any						,		a					:     torch.FloatTensor						,		a					:     Union[float, torch.FloatTensor]						)					->      torch.FloatTensor:






    """simple docstring"""


    if isinstance(a						,		torch.Tensor						):
       A__         =    timestep.to(self.timesteps.device						)
    A__         =    (self.timesteps == timestep).nonzero().item()
    A__         =    self.sigmas[step_index]
    A__         =    sample / ((sigma**2 + 1) ** 0.5)
    A__         =    True
    return sample






 def 	_UpperCamelCase   (self					:     Any						,		a					:     int						,		a					:     Union[str, torch.device] = None						)					->      Dict:






    """simple docstring"""


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

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






 def 	_UpperCamelCase   (self					:     Dict						,		a					:     torch.FloatTensor						,		a					:     Union[float, torch.FloatTensor]						,		a					:     torch.FloatTensor						,		a					:     Optional[torch.Generator] = None						,		a					:     bool = True						,		)					->      Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:






    """simple docstring"""


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

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

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
       A__         =    sample - sigma * model_output
    elif self.config.prediction_type == "v_prediction":
       # * c_out + input * c_skip
       A__         =    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`"""						)

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

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

    A__         =    sigma_down - sigma

    A__         =    sample + derivative * dt

    A__         =    model_output.device
    A__         =    randn_tensor(model_output.shape						,		dtype=model_output.dtype						,		device=a						,		generator=a						)

    A__         =    prev_sample + noise * sigma_up

    if not return_dict:
       return (prev_sample,)

    return EulerAncestralDiscreteSchedulerOutput(
        prev_sample=a						,		pred_original_sample=a						)






 def 	_UpperCamelCase   (self					:     int						,		a					:     torch.FloatTensor						,		a					:     torch.FloatTensor						,		a					:     torch.FloatTensor						,		)					->      torch.FloatTensor:






    """simple docstring"""


    A__         =    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
       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					:     Dict						)					->      int:






    """simple docstring"""


    return self.config.num_train_timesteps








'''simple docstring'''







import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


lowerCAmelCase_    	=							logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
lowerCAmelCase_    	=							direct_transformers_import(Path(__file__).parent)


lowerCAmelCase_    	=							{
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}

class   _snake_case( UpperCAmelCase			):
 __snake_case:       str									=				['''feature_extractor''', '''tokenizer''']
 # Names need to be attr_class for attr in attributes
 __snake_case:       Optional[Any]									=				None
 __snake_case:       List[str]									=				None
 __snake_case:       Any									=				None






 def __init__(self					:     List[Any]						,		*a					:     Union[str, Any]						,		**a					:     Optional[int]						)					->      Any:






    """simple docstring"""


    for key in kwargs:
       if key not in self.attributes:
          raise TypeError(f"""Unexpected keyword argument {key}."""						)
    for arg, attribute_name in zip(a						,		self.attributes						):
       if attribute_name in kwargs:
          raise TypeError(f"""Got multiple values for argument {attribute_name}."""						)
       else:
          A__         =    arg

    if len(a						) != len(self.attributes						):
       raise ValueError(
           f"""This processor requires {len(self.attributes						)} arguments: {", ".join(self.attributes						)}. Got """
           f"""{len(a						)} arguments instead."""						)

    # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
    for attribute_name, arg in kwargs.items():
       A__         =    getattr(self						,		f"""{attribute_name}_class"""						)
       # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
       A__         =    AUTO_TO_BASE_CLASS_MAPPING.get(a						,		a						)
       if isinstance(a						,		a						):
          A__         =    tuple(getattr(a						,		a						) for n in class_name if n is not None						)
       else:
          A__         =    getattr(a						,		a						)

       if not isinstance(a						,		a						):
          raise ValueError(
              f"""Received a {type(a						).__name__} for argument {attribute_name}, but a {class_name} was expected."""						)

       setattr(self						,		a						,		a						)






 def __repr__(self					:     Optional[int]						)					->      Any:






    """simple docstring"""


    A__         =    [f"""- {name}: {repr(getattr(self						,		a						)						)}""" for name in self.attributes]
    A__         =    '\n'.join(a						)
    return f"""{self.__class__.__name__}:\n{attributes_repr}"""






 def 	_UpperCamelCase   (self					:     Union[str, Any]						,		a					:     Tuple						,		a					:     bool = False						,		**a					:     int						)					->      List[str]:






    """simple docstring"""


    os.makedirs(a						,		exist_ok=a						)

    if push_to_hub:
       A__         =    kwargs.pop('commit_message'						,		a						)
       A__         =    kwargs.pop('repo_id'						,		save_directory.split(os.path.sep						)[-1]						)
       A__         =    self._create_repo(a						,		**a						)
       A__         =    self._get_files_timestamps(a						)
    # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
    # loaded from the Hub.
    if self._auto_class is not None:
       A__         =    [getattr(self						,		a						) for attribute_name in self.attributes]
       A__         =    [(a.init_kwargs if isinstance(a						,		a						) else a) for a in attrs]
       custom_object_save(self						,		a						,		config=a						)

    for attribute_name in self.attributes:
       A__         =    getattr(self						,		a						)
       # Include the processor class in the attribute config so this processor can then be reloaded with the
       # `AutoProcessor` API.
       if hasattr(a						,		'_set_processor_class'						):
          attribute._set_processor_class(self.__class__.__name__						)
       attribute.save_pretrained(a						)

    if self._auto_class is not None:
       # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
       for attribute_name in self.attributes:
          A__         =    getattr(self						,		a						)
          if isinstance(a						,		a						):
             del attribute.init_kwargs["auto_map"]

    if push_to_hub:
       self._upload_modified_files(
           a						,		a						,		a						,		commit_message=a						,		token=kwargs.get('use_auth_token'						)						,		)






 @classmethod
 def 	_UpperCamelCase   (cls					:     int						,		a					:     Union[str, os.PathLike]						,		a					:     Optional[Union[str, os.PathLike]] = None						,		a					:     bool = False						,		a					:     bool = False						,		a					:     Optional[Union[str, bool]] = None						,		a					:     str = "main"						,		**a					:     Union[str, Any]						,		)					->      Optional[Any]:






    """simple docstring"""


    A__         =    cache_dir
    A__         =    force_download
    A__         =    local_files_only
    A__         =    revision

    A__         =    kwargs.pop('use_auth_token'						,		a						)
    if use_auth_token is not None:
       warnings.warn(
           'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'						,		a						)
       if token is not None:
          raise ValueError(
              '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'						)
       A__         =    use_auth_token

    if token is not None:
       # change to `token` in a follow-up PR
       A__         =    token

    A__         =    cls._get_arguments_from_pretrained(a						,		**a						)
    return cls(*a						)






 @classmethod
 def 	_UpperCamelCase   (cls					:     Optional[int]						,		a					:     Union[str, Any]="AutoProcessor"						)					->      Any:






    """simple docstring"""


    if not isinstance(a						,		a						):
       A__         =    auto_class.__name__

    import transformers.models.auto as auto_module

    if not hasattr(a						,		a						):
       raise ValueError(f"""{auto_class} is not a valid auto class."""						)

    A__         =    auto_class






 @classmethod
 def 	_UpperCamelCase   (cls					:     Optional[Any]						,		a					:     Optional[Any]						,		**a					:     Tuple						)					->      int:






    """simple docstring"""


    A__         =    []
    for attribute_name in cls.attributes:
       A__         =    getattr(cls						,		f"""{attribute_name}_class"""						)
       if isinstance(a						,		a						):
          A__         =    tuple(getattr(a						,		a						) if n is not None else None for n in class_name						)
          A__         =    kwargs.get('use_fast'						,		a						)
          if use_fast and classes[1] is not None:
             A__         =    classes[1]
          else:
             A__         =    classes[0]
       else:
          A__         =    getattr(a						,		a						)

       args.append(attribute_class.from_pretrained(a						,		**a						)						)
    return args






 @property
 def 	_UpperCamelCase   (self					:     str						)					->      List[str]:






    """simple docstring"""


    A__         =    getattr(self						,		self.attributes[0]						)
    return getattr(a						,		'model_input_names'						,		a						)




lowerCAmelCase_    	=							copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
    lowerCAmelCase_    	=							ProcessorMixin.push_to_hub.__doc__.format(
        object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
    )


