
from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowercase						:      Any									= 8




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]       ,						_lowerCamelCase				:    List[Any]=BITS)      ->					List[str]:






							'''simple docstring'''





							__UpperCamelCase			:     List[str]         =      x.device

							__UpperCamelCase			:     List[str]         =      (x * 255).int().clamp(0       ,						255)

							__UpperCamelCase			:     Tuple         =      2 ** torch.arange(bits - 1       ,						-1       ,						-1       ,						device=__UpperCAmelCase)
							__UpperCamelCase			:     Union[str, Any]         =      rearrange(__UpperCAmelCase       ,						"d -> d 1 1")
							__UpperCamelCase			:     str         =      rearrange(__UpperCAmelCase       ,						"b c h w -> b c 1 h w")

							__UpperCamelCase			:     Dict         =      ((x & mask) != 0).float()
							__UpperCamelCase			:     Tuple         =      rearrange(__UpperCAmelCase       ,						"b c d h w -> b (c d) h w")
							__UpperCamelCase			:     List[str]         =      bits * 2 - 1
							return bits




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]       ,						_lowerCamelCase				:    Optional[int]=BITS)      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase			:     List[str]         =      x.device

							__UpperCamelCase			:     List[str]         =      (x > 0).int()
							__UpperCamelCase			:     int         =      2 ** torch.arange(bits - 1       ,						-1       ,						-1       ,						device=__UpperCAmelCase       ,						dtype=torch.intaa)

							__UpperCamelCase			:     List[str]         =      rearrange(__UpperCAmelCase       ,						"d -> d 1 1")
							__UpperCamelCase			:     int         =      rearrange(__UpperCAmelCase       ,						"b (c d) h w -> b c d h w"       ,						d=8)
							__UpperCamelCase			:     Optional[int]         =      reduce(x * mask       ,						"b c d h w -> b c h w"       ,						"sum")
							return (dec / 255).clamp(0.0       ,						1.0)




def 			_SCREAMING_SNAKE_CASE      (	self				:    Tuple       ,						_lowerCamelCase				:    Any       ,						_lowerCamelCase				:    Tuple       ,						_lowerCamelCase				:    Any       ,						_lowerCamelCase				:    List[str] = 0.0       ,						_lowerCamelCase				:    Optional[Any] = True       ,						_lowerCamelCase				:    int=None       ,						_lowerCamelCase				:    Union[str, Any] = True       ,						)      ->					Union[DDIMSchedulerOutput, Tuple]:






							'''simple docstring'''





							if self.num_inference_steps is None:
														raise ValueError(
														    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler")

							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
							# Ideally, read DDIM paper in-detail understanding

							# Notation (<variable name> -> <name in paper>
							# - pred_noise_t -> e_theta(x_t, t)
							# - pred_original_sample -> f_theta(x_t, t) or x_0
							# - std_dev_t -> sigma_t
							# - eta -> η
							# - pred_sample_direction -> "direction pointing to x_t"
							# - pred_prev_sample -> "x_t-1"

							# 1. get previous step value (=t-1)
							__UpperCamelCase			:     Dict         =      timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							__UpperCamelCase			:     Any         =      self.alphas_cumprod[timestep]
							__UpperCamelCase			:     int         =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

							__UpperCamelCase			:     Optional[int]         =      1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							__UpperCamelCase			:     str         =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

							# 4. Clip "predicted x_0"
							__UpperCamelCase			:     List[str]         =      self.bit_scale
							if self.config.clip_sample:
														__UpperCamelCase			:     Any         =      torch.clamp(__UpperCAmelCase       ,						-scale       ,						__UpperCAmelCase)

							# 5. compute variance: "sigma_t(η)" -> see formula (16)
							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
							__UpperCamelCase			:     str         =      self._get_variance(__UpperCAmelCase       ,						__UpperCAmelCase)
							__UpperCamelCase			:     str         =      eta * variance ** 0.5

							if use_clipped_model_output:
														# the model_output is always re-derived from the clipped x_0 in Glide
														__UpperCamelCase			:     Optional[Any]         =      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

							# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							__UpperCamelCase			:     Dict         =      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							__UpperCamelCase			:     Dict         =      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if eta > 0:
														# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
														__UpperCamelCase			:     List[str]         =      model_output.device if torch.is_tensor(__UpperCAmelCase) else "cpu"
														__UpperCamelCase			:     List[str]         =      torch.randn(model_output.shape       ,						dtype=model_output.dtype       ,						generator=__UpperCAmelCase).to(__UpperCAmelCase)
														__UpperCamelCase			:     Dict         =      self._get_variance(__UpperCAmelCase       ,						__UpperCAmelCase) ** 0.5 * eta * noise

														__UpperCamelCase			:     int         =      prev_sample + variance

							if not return_dict:
														return (prev_sample,)

							return DDIMSchedulerOutput(prev_sample=__UpperCAmelCase       ,						pred_original_sample=__UpperCAmelCase)




def 			_SCREAMING_SNAKE_CASE      (	self				:    Union[str, Any]       ,						_lowerCamelCase				:    Tuple       ,						_lowerCamelCase				:    Tuple       ,						_lowerCamelCase				:    Optional[int]       ,						_lowerCamelCase				:    Optional[Any]="epsilon"       ,						_lowerCamelCase				:    List[Any]=None       ,						_lowerCamelCase				:    Any = True       ,						)      ->					Union[DDPMSchedulerOutput, Tuple]:






							'''simple docstring'''





							__UpperCamelCase			:     int         =      timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
														__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      torch.split(__UpperCAmelCase       ,						sample.shape[1]       ,						dim=1)
							else:
														__UpperCamelCase			:     Tuple         =      None

							# 1. compute alphas, betas
							__UpperCamelCase			:     Optional[int]         =      self.alphas_cumprod[t]
							__UpperCamelCase			:     str         =      self.alphas_cumprod[t - 1] if t > 0 else self.one
							__UpperCamelCase			:     Any         =      1 - alpha_prod_t
							__UpperCamelCase			:     int         =      1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if prediction_type == "epsilon":
														__UpperCamelCase			:     str         =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif prediction_type == "sample":
														__UpperCamelCase			:     Tuple         =      model_output
							else:
														raise ValueError(F'Unsupported prediction_type {prediction_type}.')

							# 3. Clip "predicted x_0"
							__UpperCamelCase			:     Union[str, Any]         =      self.bit_scale
							if self.config.clip_sample:
														__UpperCamelCase			:     Optional[int]         =      torch.clamp(__UpperCAmelCase       ,						-scale       ,						__UpperCAmelCase)

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							__UpperCamelCase			:     List[Any]         =      (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
							__UpperCamelCase			:     int         =      self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							__UpperCamelCase			:     Dict         =      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							__UpperCamelCase			:     Dict         =      0
							if t > 0:
														__UpperCamelCase			:     str         =      torch.randn(
														    model_output.size()       ,						dtype=model_output.dtype       ,						layout=model_output.layout       ,						generator=__UpperCAmelCase).to(model_output.device)
														__UpperCamelCase			:     Union[str, Any]         =      (self._get_variance(__UpperCAmelCase       ,						predicted_variance=__UpperCAmelCase) ** 0.5) * noise

							__UpperCamelCase			:     int         =      pred_prev_sample + variance

							if not return_dict:
														return (pred_prev_sample,)

							return DDPMSchedulerOutput(prev_sample=__UpperCAmelCase       ,						pred_original_sample=__UpperCAmelCase)




class 				lowerCamelCase__							(       _snake_case):




	'''simple docstring'''




	def __init__(      self	:Optional[Any]  ,     a	:UNetaDConditionModel  ,     a	:Union[DDIMScheduler, DDPMScheduler]  ,     a	:Optional[float] = 1.0  ,     )	->				Optional[Any]:
								super().__init__()
								__UpperCamelCase			:     int         =      bit_scale
								__UpperCamelCase			:     int         =      (
								    ddim_bit_scheduler_step if isinstance(a  ,     a		) else ddpm_bit_scheduler_step
								)

								self.register_modules(unet=a  ,     scheduler=a		)







	@torch.no_grad()
	def __call__(      self	:Optional[Any]  ,     a	:Optional[int] = 2_5_6  ,     a	:Optional[int] = 2_5_6  ,     a	:Optional[int] = 5_0  ,     a	:Optional[torch.Generator] = None  ,     a	:Optional[int] = 1  ,     a	:Optional[str] = "pil"  ,     a	:bool = True  ,     **a	:List[Any]  ,     )	->				List[Any]:
								__UpperCamelCase			:     Optional[int]         =      torch.randn(
								    (batch_size, self.unet.config.in_channels, height, width)  ,     generator=a  ,     )
								__UpperCamelCase			:     Dict         =      decimal_to_bits(a		) * self.bit_scale
								__UpperCamelCase			:     Optional[Any]         =      latents.to(self.device		)

								self.scheduler.set_timesteps(a		)

								for t in self.progress_bar(self.scheduler.timesteps		):
															# predict the noise residual
															__UpperCamelCase			:     str         =      self.unet(a  ,     a		).sample

															# compute the previous noisy sample x_t -> x_t-1
															__UpperCamelCase			:     str         =      self.scheduler.step(a  ,     a  ,     a		).prev_sample

								__UpperCamelCase			:     Optional[int]         =      bits_to_decimal(a		)

								if output_type == "pil":
															__UpperCamelCase			:     Tuple         =      self.numpy_to_pil(a		)

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a		)

# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_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_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


lowercase						:      Any									= 'pytorch_model.bin'
lowercase						:      List[str]									= 'pytorch_model.bin.index.json'
lowercase						:      List[Any]									= 'adapter_config.json'
lowercase						:      str									= 'adapter_model.bin'
lowercase						:      List[str]									= 'adapter_model.safetensors'
lowercase						:      Any									= 'tf_model.h5'
lowercase						:      str									= 'tf_model.h5.index.json'
lowercase						:      Any									= 'model.ckpt'
lowercase						:      Optional[int]									= 'flax_model.msgpack'
lowercase						:      Dict									= 'flax_model.msgpack.index.json'
lowercase						:      Dict									= 'model.safetensors'
lowercase						:      Dict									= 'model.safetensors.index.json'
lowercase						:      Union[str, Any]									= 'config.json'
lowercase						:      Tuple									= 'preprocessor_config.json'
lowercase						:      Tuple									= FEATURE_EXTRACTOR_NAME
lowercase						:      Dict									= 'generation_config.json'
lowercase						:      Dict									= 'modelcard.json'

lowercase						:      Optional[int]									= '▁'
lowercase						:      Any									= SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

lowercase						:      List[Any]									= [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
lowercase						:      Tuple									= [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
lowercase						:      Any									= [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[str, Any])      ->					List[str]:






							'''simple docstring'''





							if version.parse(_lowerCamelCase) < version.parse(_lowerCamelCase):
														if "dev" in min_version:
																					__UpperCamelCase			:     List[str]         =      (
																					    "This example requires a source install from HuggingFace Transformers (see "
																					    "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
																					)
														else:
																					__UpperCamelCase			:     Optional[Any]         =      F'This example requires a minimum version of {min_version},'
														error_message += F' but the version found is {__version__}.\n'
														raise ImportError(
														    error_message
														    + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
														    "versions of HuggingFace Transformers.")