



'''simple docstring'''







from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)

class       lowerCamelCase_							(	lowerCAmelCase_  ):






			'''simple docstring'''

			def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						):
							super().__init__()

							self.register_modules(vae=__UpperCamelCase     ,						text_encoder=__UpperCamelCase     ,						tokenizer=__UpperCamelCase     ,						unet=__UpperCamelCase     ,						scheduler=__UpperCamelCase )


			def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
							with torch.no_grad():
											UpperCamelCase__      =			self.vae.encode(tfms.ToTensor()(__UpperCamelCase ).unsqueeze(0 ).to(self.device ) * 2 - 1 )
											UpperCamelCase__      =			0.18_215 * latent.latent_dist.sample()
							return latent


			def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
							UpperCamelCase__      =			(1 / 0.18_215) * latent
							with torch.no_grad():
											UpperCamelCase__      =			self.vae.decode(__UpperCamelCase ).sample
							UpperCamelCase__      =			(img / 2 + 0.5).clamp(0     ,						1 )
							UpperCamelCase__      =			img.detach().cpu().permute(0     ,						2     ,						3     ,						1 ).numpy()
							UpperCamelCase__      =			(img * 2_55).round().astype("uint8" )
							return Image.fromarray(img[0] )


			def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
							UpperCamelCase__      =			self.tokenizer(
							    __UpperCamelCase     ,						padding="max_length"     ,						max_length=self.tokenizer.model_max_length     ,						truncation=__UpperCamelCase     ,						return_tensors="pt"     ,						)

							UpperCamelCase__      =			self.text_encoder(text_input.input_ids.to(self.device ) )[0]

							UpperCamelCase__      =			self.tokenizer(
							    ""     ,						padding="max_length"     ,						max_length=self.tokenizer.model_max_length     ,						truncation=__UpperCamelCase     ,						return_tensors="pt"     ,						)

							UpperCamelCase__      =			self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]

							return torch.cat([uncond_embedding, text_embedding] )







			def __call__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase = 0.3     ,						__UpperCamelCase = 0.6     ,						__UpperCamelCase = 0.5     ,						__UpperCamelCase = 42     ,						__UpperCamelCase = 50     ,						__UpperCamelCase = 7.5     ,						):
							UpperCamelCase__      =			steps - int(kmin * steps )
							UpperCamelCase__      =			steps - int(kmax * steps )

							UpperCamelCase__      =			self.prep_text(__UpperCamelCase )

							self.scheduler.set_timesteps(__UpperCamelCase )

							UpperCamelCase__    ,  UpperCamelCase__      =			img.size
							UpperCamelCase__      =			self.encode(__UpperCamelCase )

							torch.manual_seed(__UpperCamelCase )
							UpperCamelCase__      =			torch.randn(
							    (1, self.unet.config.in_channels, height // 8, width // 8)     ,						).to(self.device )

							UpperCamelCase__      =			self.scheduler.add_noise(
							    __UpperCamelCase     ,						__UpperCamelCase     ,						timesteps=self.scheduler.timesteps[tmax]     ,						)

							UpperCamelCase__      =			torch.cat([latents] * 2 )

							UpperCamelCase__      =			self.scheduler.scale_model_input(__UpperCamelCase     ,						self.scheduler.timesteps[tmax] )

							with torch.no_grad():
											UpperCamelCase__      =			self.unet(
											    __UpperCamelCase     ,						self.scheduler.timesteps[tmax]     ,						encoder_hidden_states=__UpperCamelCase     ,						).sample

							UpperCamelCase__    ,  UpperCamelCase__      =			pred.chunk(2 )
							UpperCamelCase__      =			pred_uncond + guidance_scale * (pred_text - pred_uncond)

							UpperCamelCase__      =			self.scheduler.step(__UpperCamelCase     ,						self.scheduler.timesteps[tmax]     ,						__UpperCamelCase ).prev_sample

							for i, t in enumerate(tqdm(self.scheduler.timesteps ) ):
											if i > tmax:
															if i < tmin:  # layout generation phase
																			UpperCamelCase__      =			self.scheduler.add_noise(
																			    __UpperCamelCase     ,						__UpperCamelCase     ,						timesteps=__UpperCamelCase     ,						)

																			UpperCamelCase__      =			(mix_factor * latents) + (
																			    1 - mix_factor
																			) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																			UpperCamelCase__      =			torch.cat([input] * 2 )

															else:  # content generation phase
																			UpperCamelCase__      =			torch.cat([latents] * 2 )

															UpperCamelCase__      =			self.scheduler.scale_model_input(__UpperCamelCase     ,						__UpperCamelCase )

															with torch.no_grad():
																			UpperCamelCase__      =			self.unet(
																			    __UpperCamelCase     ,						__UpperCamelCase     ,						encoder_hidden_states=__UpperCamelCase     ,						).sample

															UpperCamelCase__    ,  UpperCamelCase__      =			pred.chunk(2 )
															UpperCamelCase__      =			pred_uncond + guidance_scale * (pred_text - pred_uncond)

															UpperCamelCase__      =			self.scheduler.step(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ).prev_sample

							return self.decode(__UpperCamelCase )









'''simple docstring'''







import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
      import torch

lowercase__     :  List[Any]    					=			logging.get_logger(__name__)





def 						snake_case__ ():
    return importlib.util.find_spec("deepspeed"	) is not None


if is_accelerate_available() and is_deepspeed_available():
      from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
      # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
      # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
      from builtins import object as DeepSpeedConfig

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase ):
       # set global weakref object
       set_hf_deepspeed_config(self )
       dep_version_check("accelerate" )
       dep_version_check("deepspeed" )
       super().__init__(__UpperCamelCase )

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase ):
       super().__init__(__UpperCamelCase )
       UpperCamelCase__      =			None
       UpperCamelCase__      =			[]


   def 	_UpperCamelCase       (  self ):
       if self._dtype is None:
           raise ValueError("trainer_config_process() wasn't called yet to tell dtype" )
       return self._dtype


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.get_value(__UpperCamelCase )
       if val is None:
           return False
       else:
           return val == "auto"


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase=None     ,						__UpperCamelCase=True ):
       UpperCamelCase__    ,  UpperCamelCase__      =			self.find_config_node(__UpperCamelCase )
       if config is None:
           return

       if config.get(__UpperCamelCase ) == "auto":
           UpperCamelCase__      =			hf_val
           return

       if not must_match:
           return

       UpperCamelCase__      =			config.get(__UpperCamelCase )
       if ds_val is not None and ds_val != hf_val:
           self.mismatches.append(f'- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}' )

   A :     Any				=	partialmethod(lowerCAmelCase_					,    must_match=lowerCAmelCase_  )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
       self.fill_match(
           "train_micro_batch_size_per_gpu"     ,						args.per_device_train_batch_size     ,						"per_device_train_batch_size" )
       self.fill_match("gradient_accumulation_steps"     ,						args.gradient_accumulation_steps     ,						"gradient_accumulation_steps" )
       self.fill_match("train_batch_size"     ,						__UpperCamelCase     ,						"train_batch_size (calculated)" )
       self.fill_match("gradient_clipping"     ,						args.max_grad_norm     ,						"max_grad_norm" )

       self.fill_match("optimizer.params.lr"     ,						args.learning_rate     ,						"learning_rate" )
       self.fill_match("optimizer.params.betas"     ,						[args.adam_betaa, args.adam_betaa]     ,						"adam_beta1+adam_beta2" )
       self.fill_match("optimizer.params.eps"     ,						args.adam_epsilon     ,						"adam_epsilon" )
       self.fill_match("optimizer.params.weight_decay"     ,						args.weight_decay     ,						"weight_decay" )

       self.fill_only("scheduler.params.warmup_min_lr"     ,						0 )  # not a trainer arg
       self.fill_match("scheduler.params.warmup_max_lr"     ,						args.learning_rate     ,						"learning_rate" )
       # total_num_steps - will get set in trainer_config_finalize

       # fp16
       if args.fpaa or args.fpaa_full_eval:
           UpperCamelCase__      =			"apex" if args.fpaa_backend == "apex" else "amp"
       else:
           UpperCamelCase__      =			None

       if args.save_on_each_node:
           # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
           UpperCamelCase__      =			self.config.get("checkpoint"     ,						{} )
           UpperCamelCase__      =			args.save_on_each_node

       # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
       # any here unless the user did the work
       self.fill_match(
           "fp16.enabled"     ,						((args.fpaa or args.fpaa_full_eval) and fpaa_backend == "amp")     ,						"fp16|fp16_full_eval+fp16_backend(amp)"     ,						)

       # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
       # ZeRO features
       self.fill_match("amp.enabled"     ,						fpaa_backend == "apex"     ,						"fp16+fp16_backend(apex)" )
       self.fill_match("amp.opt_level"     ,						args.fpaa_opt_level     ,						"fp16_opt_level" )

       self.fill_match("bf16.enabled"     ,						(args.bfaa or args.bfaa_full_eval)     ,						"bf16|bf16_full_eval" )

       # deepspeed's default mode is fp16 unless there is a config that says differently
       if self.is_true("bf16.enabled" ):
           UpperCamelCase__      =			torch.bfloataa
       elif self.is_false("fp16.enabled" ):
           UpperCamelCase__      =			torch.floataa
       else:
           UpperCamelCase__      =			torch.floataa







   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       UpperCamelCase__      =			[
           "zero_optimization.reduce_bucket_size",
           "zero_optimization.stage3_prefetch_bucket_size",
           "zero_optimization.stage3_param_persistence_threshold",
       ]
       UpperCamelCase__      =			[x for x in hidden_size_based_keys if self.is_auto(__UpperCamelCase )]

       if len(__UpperCamelCase ) > 0:
           if hasattr(model.config     ,						"hidden_size" ):
               UpperCamelCase__      =			model.config.hidden_size
           elif hasattr(model.config     ,						"hidden_sizes" ):
               # if there are many hidden sizes pick the largest one
               UpperCamelCase__      =			max(model.config.hidden_sizes )
           else:
               raise ValueError(
                   "The model's config file has neither `hidden_size` nor `hidden_sizes` entry, "
                   "therefore it's not possible to automatically fill out the following `auto` entries "
                   f'in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing '
                   "`auto` values for these keys with an integer value of your choice." )

           self.fill_only("zero_optimization.reduce_bucket_size"     ,						hidden_size * hidden_size )
           if self.is_zeroa():
               # automatically assign the optimal config values based on model config
               self.fill_only("zero_optimization.stage3_prefetch_bucket_size"     ,						0.9 * hidden_size * hidden_size )
               self.fill_only("zero_optimization.stage3_param_persistence_threshold"     ,						10 * hidden_size )

        # scheduler
       self.fill_match("scheduler.params.total_num_steps"     ,						__UpperCamelCase     ,						"num_training_steps (calculated)" )
       self.fill_match("scheduler.params.warmup_num_steps"     ,						args.get_warmup_steps(__UpperCamelCase )     ,						"warmup_steps" )

       if len(self.mismatches ) > 0:
           UpperCamelCase__      =			"\n".join(self.mismatches )
           raise ValueError(
               "Please correct the following DeepSpeed config values that mismatch TrainingArguments"
               f' values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to \'auto\'.' )

lowercase__     :  List[str]    					=			None





def 						snake_case__ (lowerCAmelCase_	):
    # this is a special weakref global object to allow us to get to Deepspeed config from APIs
    # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
    global _hf_deepspeed_config_weak_ref
    # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
    UpperCamelCase__      =			weakref.ref(lowerCAmelCase_	)





def 						snake_case__ ():
    # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
    global _hf_deepspeed_config_weak_ref
    UpperCamelCase__      =			None





def 						snake_case__ ():
    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().is_zeroa()
    else:
        return False





def 						snake_case__ ():
    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().config
    else:
        return None





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    from accelerate.utils import DummyOptim, DummyScheduler

    UpperCamelCase__      =			hf_deepspeed_config.config

    # Optimizer + Scheduler
    # Currently supported combos:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Yes
    # 3. DS scheduler + HF optimizer: Yes
    # 4. HF scheduler + DS optimizer: No
    #
    # Unless Offload is enabled in which case it's:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Mostly*
    # 3. DS scheduler + HF optimizer: Mostly*
    # 4. HF scheduler + DS optimizer: No
    #
    # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

    UpperCamelCase__      =			None
    if "optimizer" in config:
        if args.adafactor:
            raise ValueError(
                "--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. "
                "Only one optimizer can be configured."	)
        UpperCamelCase__      =			DummyOptim(params=lowerCAmelCase_	)
    else:
        if hf_deepspeed_config.is_offload():
            logger.info(
                "Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the"
                " custom optimizer has both CPU and GPU implementation (except LAMB)"	)

        # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
        # But trainer uses AdamW by default.
        UpperCamelCase__      =			trainer.create_optimizer()
        # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
        UpperCamelCase__      =			True

    UpperCamelCase__      =			None
    if "scheduler" in config:
        UpperCamelCase__      =			DummyScheduler(lowerCAmelCase_	)
    else:
        if isinstance(lowerCAmelCase_					,  lowerCAmelCase_	):
            raise ValueError(
                "Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. "
                "Please configure a scheduler in the DeepSpeed config."	)
        UpperCamelCase__      =			trainer.create_scheduler(num_training_steps=lowerCAmelCase_					,  optimizer=lowerCAmelCase_	)

    return optimizer, lr_scheduler





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_=False	):
    from deepspeed.utils import logger as ds_logger

    UpperCamelCase__      =			trainer.model
    UpperCamelCase__      =			trainer.args

    UpperCamelCase__      =			trainer.accelerator.state.deepspeed_plugin.hf_ds_config

    # resume config update - some bits like `model` and `num_training_steps` only become available during train
    hf_deepspeed_config.trainer_config_finalize(lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	)

    # set the Deepspeed log level consistent with the Trainer
    ds_logger.setLevel(args.get_process_log_level()	)

    if inference:
        # only Z3 makes sense for the inference
        if not hf_deepspeed_config.is_zeroa():
            raise ValueError("ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config"	)

        # in case the training config is re-used for inference
        hf_deepspeed_config.del_config_sub_tree("optimizer"	)
        hf_deepspeed_config.del_config_sub_tree("lr_scheduler"	)
        UpperCamelCase__    ,  UpperCamelCase__      =			None, None
        UpperCamelCase__      =			None
    else:
        UpperCamelCase__      =			None  # important for when deepspeed_init is used as re-init
        UpperCamelCase__      =			list(filter(lambda lowerCAmelCase_	: p.requires_grad					,  model.parameters()	)	)
        UpperCamelCase__    ,  UpperCamelCase__      =			deepspeed_optim_sched(
            lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	)

    # keep for quick debug:
    # from pprint import pprint; pprint(config)

    return optimizer, lr_scheduler





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_	):
    # it's possible that the user is trying to resume from model_path, which doesn't necessarily
    # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
    # a resume from a checkpoint and not just a local pretrained weight. So we check here if the
    # path contains what looks like a deepspeed checkpoint
    import glob

    UpperCamelCase__      =			sorted(glob.glob(F'{checkpoint_path}/global_step*'	)	)

    if len(lowerCAmelCase_	) > 0:
        logger.info(F'Attempting to resume from {checkpoint_path}'	)
        # this magically updates self.optimizer and self.lr_scheduler
        UpperCamelCase__    ,  UpperCamelCase__      =			deepspeed_engine.load_checkpoint(
            lowerCAmelCase_					,  load_optimizer_states=lowerCAmelCase_					,  load_lr_scheduler_states=lowerCAmelCase_	)
        if load_path is None:
            raise ValueError(F'[deepspeed] failed to resume from checkpoint {checkpoint_path}'	)
    else:
        raise ValueError(F'Can\'t find a valid checkpoint at {checkpoint_path}'	)




