






'''simple docstring'''



import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


def 				lowercase_					(     __A			:							List[Any]      )							->   Dict:



 """simple docstring"""




 lowercase	,		lowercase       : List[Any]						=image.size
 lowercase	,		lowercase       : str						=(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
 lowercase       : Tuple						=image.resize((w, h)       ,   resample=PIL_INTERPOLATION['''lanczos''']      )
 lowercase       : int						=np.array(__A      ).astype(np.floataa      ) / 255.0
 lowercase       : Union[str, Any]						=image[None].transpose(0       ,   3       ,   1       ,   2      )
 lowercase       : Optional[int]						=torch.from_numpy(__A      )
 return 2.0 * image - 1.0





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      def __init__(			self      :		Union[str, Any]  ,   UpperCAmelCase      :		VQModel  ,   UpperCAmelCase      :		UNetaDModel  ,   UpperCAmelCase      :		Union[
              DDIMScheduler,
              PNDMScheduler,
              LMSDiscreteScheduler,
              EulerDiscreteScheduler,
              EulerAncestralDiscreteScheduler,
              DPMSolverMultistepScheduler,
          ]  ,   )	->	Tuple:


       '''simple docstring'''

       super().__init__()
       self.register_modules(vqvae=UpperCAmelCase  ,   unet=UpperCAmelCase  ,   scheduler=UpperCAmelCase		)






      @torch.no_grad()
      def __call__(			self      :		Union[str, Any]  ,   UpperCAmelCase      :		Union[torch.Tensor, PIL.Image.Image] = None  ,   UpperCAmelCase      :		Optional[int] = 1  ,   UpperCAmelCase      :		Optional[int] = 100  ,   UpperCAmelCase      :		Optional[float] = 0.0  ,   UpperCAmelCase      :		Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,   UpperCAmelCase      :		Optional[str] = "pil"  ,   UpperCAmelCase      :		bool = True  ,   )	->	Union[Tuple, ImagePipelineOutput]:


       '''simple docstring'''

       if isinstance(UpperCAmelCase  ,   PIL.Image.Image		):
        lowercase       : Tuple						=1
       elif isinstance(UpperCAmelCase  ,   torch.Tensor		):
        lowercase       : Optional[Any]						=image.shape[0]
       else:
        raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCAmelCase		)}'		)

       if isinstance(UpperCAmelCase  ,   PIL.Image.Image		):
        lowercase       : List[str]						=preprocess(UpperCAmelCase		)

       lowercase	,		lowercase       : int						=image.shape[-2:]

       # in_channels should be 6: 3 for latents, 3 for low resolution image
       lowercase       : Any						=(batch_size, self.unet.config.in_channels // 2, height, width)
       lowercase       : Any						=next(self.unet.parameters()		).dtype

       lowercase       : Union[str, Any]						=randn_tensor(UpperCAmelCase  ,   generator=UpperCAmelCase  ,   device=self.device  ,   dtype=UpperCAmelCase		)

       lowercase       : Optional[int]						=image.to(device=self.device  ,   dtype=UpperCAmelCase		)

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(UpperCAmelCase  ,   device=self.device		)
       lowercase       : List[str]						=self.scheduler.timesteps

       # scale the initial noise by the standard deviation required by the scheduler
       lowercase       : Optional[int]						=latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       lowercase       : int						='''eta''' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
       lowercase       : Any						={}
       if accepts_eta:
        lowercase       : List[str]						=eta

       for t in self.progress_bar(UpperCAmelCase		):
        # concat latents and low resolution image in the channel dimension.
        lowercase       : str						=torch.cat([latents, image]  ,   dim=1		)
        lowercase       : Tuple						=self.scheduler.scale_model_input(UpperCAmelCase  ,   UpperCAmelCase		)
        # predict the noise residual
        lowercase       : List[str]						=self.unet(UpperCAmelCase  ,   UpperCAmelCase		).sample
        # compute the previous noisy sample x_t -> x_t-1
        lowercase       : int						=self.scheduler.step(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   **UpperCAmelCase		).prev_sample

       # decode the image latents with the VQVAE
       lowercase       : Any						=self.vqvae.decode(UpperCAmelCase		).sample
       lowercase       : Union[str, Any]						=torch.clamp(UpperCAmelCase  ,   -1.0  ,   1.0		)
       lowercase       : List[str]						=image / 2 + 0.5
       lowercase       : Dict						=image.cpu().permute(0  ,   2  ,   3  ,   1		).numpy()

       if output_type == "pil":
        lowercase       : Any						=self.numpy_to_pil(UpperCAmelCase		)

       if not return_dict:
        return (image,)

       return ImagePipelineOutput(images=UpperCAmelCase		)











'''simple docstring'''



from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
      from .deepspeed import (
          DeepSpeedEngineWrapper,
          DeepSpeedOptimizerWrapper,
          DeepSpeedSchedulerWrapper,
          DummyOptim,
          DummyScheduler,
          HfDeepSpeedConfig,
      )

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers



