





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class    _UpperCAmelCase							(		_lowerCamelCase     ):
						a						       =							42
						a						       =							42
						a						       =							None






class    _UpperCAmelCase							(		_lowerCamelCase			,       _lowerCamelCase     ):
						a						       =							2
						@register_to_config
						def __init__(		self      ,       a__ = 0.02      ,       a__ = 100      ,       a__ = 1.007      ,       a__ = 80      ,       a__ = 0.05      ,       a__ = 50      ,       ):
									# standard deviation of the initial noise distribution
									A_     :				List[str]			      =	sigma_max

									# setable values
									A_     :				int			      =	None
									A_     :				np.IntTensor			      =	None
									A_     :				torch.FloatTensor			      =	None  # sigma(t_i)
						def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
									return sample
						def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
									A_     :				int			      =	num_inference_steps
									A_     :				Union[str, Any]			      =	np.arange(0      ,       self.num_inference_steps       )[::-1].copy()
									A_     :				Union[str, Any]			      =	torch.from_numpy(a__       ).to(a__       )
									A_     :				Optional[int]			      =	[
									    (
									        self.config.sigma_max**2
									        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
									    )
									    for i in self.timesteps
									]
									A_     :				Dict			      =	torch.tensor(a__      ,       dtype=torch.floataa      ,       device=a__       )
						def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__ = None       ):
									if self.config.s_min <= sigma <= self.config.s_max:
												A_     :				Any			      =	min(self.config.s_churn / self.num_inference_steps      ,       2**0.5 - 1       )
									else:
												A_     :				Union[str, Any]			      =	0

									# sample eps ~ N(0, S_noise^2 * I)
									A_     :				str			      =	self.config.s_noise * randn_tensor(sample.shape      ,       generator=a__       ).to(sample.device       )
									A_     :				Dict			      =	sigma + gamma * sigma
									A_     :				Any			      =	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat
						def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__ = True      ,       ):
									A_     :				Tuple			      =	sample_hat + sigma_hat * model_output
									A_     :				Tuple			      =	(sample_hat - pred_original_sample) / sigma_hat
									A_     :				Tuple			      =	sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
												return (sample_prev, derivative)

									return KarrasVeOutput(
									    prev_sample=a__      ,       derivative=a__      ,       pred_original_sample=a__       )
						def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__ = True      ,       ):
									A_     :				int			      =	sample_prev + sigma_prev * model_output
									A_     :				int			      =	(sample_prev - pred_original_sample) / sigma_prev
									A_     :				Dict			      =	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

									if not return_dict:
												return (sample_prev, derivative)

									return KarrasVeOutput(
									    prev_sample=a__      ,       derivative=a__      ,       pred_original_sample=a__       )






						def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__       ):
									raise NotImplementedError()













from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






