





import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


a_					      = logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

a_					      = {
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
a_					      = {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
a_					      = sorted(arg_to_scheduler.keys())
a_					      = '{' + ', '.join(arg_to_scheduler_choices) + '}'





class 	_lowercase		(      pl.LightningModule ):

 def __init__(				self			:							str     ,   snake_case			:							argparse.Namespace     ,   snake_case			:							List[Any]=None     ,   snake_case			:							Dict="base"     ,   snake_case			:							Dict=None     ,   snake_case			:							Dict=None     ,   snake_case			:							Any=None     ,   **snake_case			:							Optional[Any]     ,   )     ->		Any:

  """simple docstring"""





  super().__init__()
  # TODO: move to self.save_hyperparameters()
  # self.save_hyperparameters()
  # can also expand arguments into trainer signature for easier reading

  self.save_hyperparameters(snake_case    )
  UpperCamelCase_      :		List[str]				=      0
  UpperCamelCase_      :		List[Any]				=      Path(self.hparams.output_dir    )
  UpperCamelCase_      :		Optional[Any]				=      self.hparams.cache_dir if self.hparams.cache_dir else None
  if config is None:
   UpperCamelCase_      :		Dict				=      AutoConfig.from_pretrained(
       self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path     ,   **({'num_labels': num_labels} if num_labels is not None else {})     ,   cache_dir=snake_case     ,   **snake_case     ,   )
  else:
   UpperCamelCase_      :		PretrainedConfig				=      config

  UpperCamelCase_      :		Any				=      ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
  for p in extra_model_params:
   if getattr(self.hparams     ,   snake_case     ,   snake_case    ):
    assert hasattr(self.config     ,   snake_case    ), f"model config doesn't have a `{p}` attribute"
    setattr(self.config     ,   snake_case     ,   getattr(self.hparams     ,   snake_case    )    )

  if tokenizer is None:
   UpperCamelCase_      :		Union[str, Any]				=      AutoTokenizer.from_pretrained(
       self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,   cache_dir=snake_case     ,   )
  else:
   UpperCamelCase_      :		PreTrainedTokenizer				=      tokenizer
  UpperCamelCase_      :		List[str]				=      MODEL_MODES[mode]
  if model is None:
   UpperCamelCase_      :		Optional[Any]				=      self.model_type.from_pretrained(
       self.hparams.model_name_or_path     ,   from_tf=bool('.ckpt' in self.hparams.model_name_or_path    )     ,   config=self.config     ,   cache_dir=snake_case     ,   )
  else:
   UpperCamelCase_      :		str				=      model

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   *snake_case			:							Any     ,   **snake_case			:							int    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		str				=      self.model_type.from_pretrained(*snake_case     ,   **snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Tuple:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      arg_to_scheduler[self.hparams.lr_scheduler]
  UpperCamelCase_      :		Any				=      get_schedule_func(
      self.opt     ,   num_warmup_steps=self.hparams.warmup_steps     ,   num_training_steps=self.total_steps()    )
  UpperCamelCase_      :		Tuple				=      {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
  return scheduler

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

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      self.model
  UpperCamelCase_      :		Optional[int]				=      ['bias', 'LayerNorm.weight']
  UpperCamelCase_      :		str				=      [
      {
          'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay    )],
          'weight_decay': self.hparams.weight_decay,
      },
      {
          'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay    )],
          'weight_decay': 0.0,
      },
  ]
  if self.hparams.adafactor:
   UpperCamelCase_      :		Union[str, Any]				=      Adafactor(
       snake_case     ,   lr=self.hparams.learning_rate     ,   scale_parameter=snake_case     ,   relative_step=snake_case    )

  else:
   UpperCamelCase_      :		List[Any]				=      AdamW(
       snake_case     ,   lr=self.hparams.learning_rate     ,   eps=self.hparams.adam_epsilon    )
  UpperCamelCase_      :		Optional[Any]				=      optimizer

  UpperCamelCase_      :		Tuple				=      self.get_lr_scheduler()

  return [optimizer], [scheduler]

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							Any     ,   snake_case			:							Any    )     ->		Optional[Any]:

  """simple docstring"""





  return self.validation_step(snake_case     ,   snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   snake_case			:							List[str]    )     ->		int:

  """simple docstring"""





  return self.validation_end(snake_case    )

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

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      max(1     ,   self.hparams.gpus    )  # TODO: consider num_tpu_cores
  UpperCamelCase_      :		Dict				=      self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
  return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]     ,   snake_case			:							Dict    )     ->		str:

  """simple docstring"""





  if stage == "test":
   UpperCamelCase_      :		Any				=      len(self.test_dataloader().dataset    )
  else:
   UpperCamelCase_      :		int				=      self.get_dataloader('train'     ,   self.hparams.train_batch_size     ,   shuffle=snake_case    )
   UpperCamelCase_      :		Any				=      len(self.train_dataloader().dataset    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]     ,   snake_case			:							str     ,   snake_case			:							int     ,   snake_case			:							bool = False    )     ->		Tuple:

  """simple docstring"""





  raise NotImplementedError('You must implement this for your task'    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		Optional[Any]:

  """simple docstring"""





  return self.train_loader

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		int:

  """simple docstring"""





  return self.get_dataloader('dev'     ,   self.hparams.eval_batch_size     ,   shuffle=snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		List[Any]:

  """simple docstring"""





  return self.get_dataloader('test'     ,   self.hparams.eval_batch_size     ,   shuffle=snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   snake_case			:							int    )     ->		Union[str, Any]:

  """simple docstring"""





  return os.path.join(
      self.hparams.data_dir     ,   'cached_{}_{}_{}'.format(
          snake_case     ,   list(filter(snake_case     ,   self.hparams.model_name_or_path.split('/'    )    )    ).pop()     ,   str(self.hparams.max_seq_length    )     ,   )     ,   )

 @pl.utilities.rank_zero_only
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]     ,   snake_case			:							Dict[str, Any]    )     ->		None:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      self.output_dir.joinpath('best_tfmr'    )
  UpperCamelCase_      :		Optional[Any]				=      self.step_count
  self.model.save_pretrained(snake_case    )
  self.tokenizer.save_pretrained(snake_case    )


 @staticmethod
 def 		SCREAMING_SNAKE_CASE__     (				snake_case			:							List[str]     ,   snake_case			:							Optional[int]    )     ->		Optional[int]:

  """simple docstring"""





  parser.add_argument(
      '--model_name_or_path'     ,   default=snake_case     ,   type=snake_case     ,   required=snake_case     ,   help='Path to pretrained model or model identifier from huggingface.co/models'     ,   )
  parser.add_argument(
      '--config_name'     ,   default=''     ,   type=snake_case     ,   help='Pretrained config name or path if not the same as model_name'    )
  parser.add_argument(
      '--tokenizer_name'     ,   default=snake_case     ,   type=snake_case     ,   help='Pretrained tokenizer name or path if not the same as model_name'     ,   )
  parser.add_argument(
      '--cache_dir'     ,   default=''     ,   type=snake_case     ,   help='Where do you want to store the pre-trained models downloaded from huggingface.co'     ,   )
  parser.add_argument(
      '--encoder_layerdrop'     ,   type=snake_case     ,   help='Encoder layer dropout probability (Optional). Goes into model.config'     ,   )
  parser.add_argument(
      '--decoder_layerdrop'     ,   type=snake_case     ,   help='Decoder layer dropout probability (Optional). Goes into model.config'     ,   )
  parser.add_argument(
      '--dropout'     ,   type=snake_case     ,   help='Dropout probability (Optional). Goes into model.config'     ,   )
  parser.add_argument(
      '--attention_dropout'     ,   type=snake_case     ,   help='Attention dropout probability (Optional). Goes into model.config'     ,   )
  parser.add_argument('--learning_rate'     ,   default=5e-5     ,   type=snake_case     ,   help='The initial learning rate for Adam.'    )
  parser.add_argument(
      '--lr_scheduler'     ,   default='linear'     ,   choices=snake_case     ,   metavar=snake_case     ,   type=snake_case     ,   help='Learning rate scheduler'     ,   )
  parser.add_argument('--weight_decay'     ,   default=0.0     ,   type=snake_case     ,   help='Weight decay if we apply some.'    )
  parser.add_argument('--adam_epsilon'     ,   default=1e-8     ,   type=snake_case     ,   help='Epsilon for Adam optimizer.'    )
  parser.add_argument('--warmup_steps'     ,   default=0     ,   type=snake_case     ,   help='Linear warmup over warmup_steps.'    )
  parser.add_argument('--num_workers'     ,   default=4     ,   type=snake_case     ,   help='kwarg passed to DataLoader'    )
  parser.add_argument('--num_train_epochs'     ,   dest='max_epochs'     ,   default=3     ,   type=snake_case    )
  parser.add_argument('--train_batch_size'     ,   default=3_2     ,   type=snake_case    )
  parser.add_argument('--eval_batch_size'     ,   default=3_2     ,   type=snake_case    )
  parser.add_argument('--adafactor'     ,   action='store_true'    )





class 	_lowercase		(      pl.Callback ):

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]     ,   snake_case			:							Tuple     ,   snake_case			:							int    )     ->		int:

  """simple docstring"""





  if (
      trainer.is_global_zero and trainer.global_rank == 0
  ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
   pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.





class 	_lowercase		(      pl.Callback ):

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							Dict     ,   snake_case			:							Any    )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      trainer.lr_schedulers[0]['scheduler']
  UpperCamelCase_      :		Union[str, Any]				=      {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
  pl_module.logger.log_metrics(snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   snake_case			:							pl.Trainer     ,   snake_case			:							pl.LightningModule    )     ->		Optional[Any]:

  """simple docstring"""





  rank_zero_info('***** Validation results *****'    )
  UpperCamelCase_      :		Tuple				=      trainer.callback_metrics
  # Log results
  for key in sorted(snake_case    ):
   if key not in ["log", "progress_bar"]:
    rank_zero_info('{} = {}\n'.format(snake_case     ,   str(metrics[key]    )    )    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]     ,   snake_case			:							pl.Trainer     ,   snake_case			:							pl.LightningModule    )     ->		str:

  """simple docstring"""





  rank_zero_info('***** Test results *****'    )
  UpperCamelCase_      :		List[str]				=      trainer.callback_metrics
  # Log and save results to file
  UpperCamelCase_      :		Union[str, Any]				=      os.path.join(pl_module.hparams.output_dir     ,   'test_results.txt'    )
  with open(snake_case     ,   'w'    ) as writer:
   for key in sorted(snake_case    ):
    if key not in ["log", "progress_bar"]:
     rank_zero_info('{} = {}\n'.format(snake_case     ,   str(metrics[key]    )    )    )
     writer.write('{} = {}\n'.format(snake_case     ,   str(metrics[key]    )    )    )





def      __lowercase  (		lowerCamelCase      :  List[Any]						,						lowerCamelCase      :  Dict			):
 #  To allow all pl args uncomment the following line
 #  parser = pl.Trainer.add_argparse_args(parser)
 parser.add_argument(
     '--output_dir'						,						default=lowerCamelCase						,						type=lowerCamelCase						,						required=lowerCamelCase						,						help='The output directory where the model predictions and checkpoints will be written.'						,						)
 parser.add_argument(
     '--fp16'						,						action='store_true'						,						help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'						,						)

 parser.add_argument(
     '--fp16_opt_level'						,						type=lowerCamelCase						,						default='O2'						,						help=(
         'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
         'See details at https://nvidia.github.io/apex/amp.html'
     )						,						)
 parser.add_argument('--n_tpu_cores'						,						dest='tpu_cores'						,						type=lowerCamelCase			)
 parser.add_argument('--max_grad_norm'						,						dest='gradient_clip_val'						,						default=1.0						,						type=lowerCamelCase						,						help='Max gradient norm'			)
 parser.add_argument('--do_train'						,						action='store_true'						,						help='Whether to run training.'			)
 parser.add_argument('--do_predict'						,						action='store_true'						,						help='Whether to run predictions on the test set.'			)
 parser.add_argument(
     '--gradient_accumulation_steps'						,						dest='accumulate_grad_batches'						,						type=lowerCamelCase						,						default=1						,						help='Number of updates steps to accumulate before performing a backward/update pass.'						,						)
 parser.add_argument('--seed'						,						type=lowerCamelCase						,						default=42						,						help='random seed for initialization'			)
 parser.add_argument(
     '--data_dir'						,						default=lowerCamelCase						,						type=lowerCamelCase						,						required=lowerCamelCase						,						help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'						,						)






def      __lowercase  (		lowerCamelCase      :  BaseTransformer						,						lowerCamelCase      :  argparse.Namespace						,						lowerCamelCase      :  Union[str, Any]=None						,						lowerCamelCase      :  int=True						,						lowerCamelCase      :  Dict=None						,						lowerCamelCase      :  str=[]						,						lowerCamelCase      :  List[Any]=None						,						lowerCamelCase      :  List[Any]=None						,						**lowerCamelCase      :  List[str]						,						):
 pl.seed_everything(args.seed			)

 # init model
 UpperCamelCase_      :		Optional[Any]				=      Path(model.hparams.output_dir			)
 odir.mkdir(exist_ok=lowerCamelCase			)

 # add custom checkpoints
 if checkpoint_callback is None:
  UpperCamelCase_      :		Union[str, Any]				=      pl.callbacks.ModelCheckpoint(
      filepath=args.output_dir						,						prefix='checkpoint'						,						monitor='val_loss'						,						mode='min'						,						save_top_k=1			)
 if early_stopping_callback:
  extra_callbacks.append(lowerCamelCase			)
 if logging_callback is None:
  UpperCamelCase_      :		Tuple				=      LoggingCallback()

 UpperCamelCase_      :		Optional[Any]				=      {}

 # TODO: remove with PyTorch 1.6 since pl uses native amp
 if args.fpaa:
  UpperCamelCase_      :		Any				=      16
  # train_params["amp_level"] = args.fp16_opt_level

 if args.gpus > 1:
  UpperCamelCase_      :		Union[str, Any]				=      'auto'  # "ddp"
  UpperCamelCase_      :		str				=      'ddp'

 UpperCamelCase_      :		Optional[int]				=      args.accumulate_grad_batches
 UpperCamelCase_      :		int				=      None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
 UpperCamelCase_      :		Any				=      'auto'

 UpperCamelCase_      :		Optional[int]				=      pl.Trainer.from_argparse_args(
     lowerCamelCase						,						weights_summary=lowerCamelCase						,						callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]						,						logger=lowerCamelCase						,						**lowerCamelCase						,						)

 if args.do_train:
  trainer.fit(lowerCamelCase			)

 return trainer




from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


a_					      = logging.get_logger(__name__)  # pylint: disable=invalid-name

a_					      = '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'





def      __lowercase  (		lowerCamelCase      :  str						,						lowerCamelCase      :  List[str]						,						lowerCamelCase      :  str=8			):
 UpperCamelCase_      :		Union[str, Any]				=      height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 UpperCamelCase_      :		List[str]				=      width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor





def      __lowercase  (		lowerCamelCase      :  Optional[Any]						,						lowerCamelCase      :  Optional[int]=512						,						lowerCamelCase      :  str=512			):
 UpperCamelCase_      :		Any				=      pil_image.resize((w, h)						,						resample=Image.BICUBIC						,						reducing_gap=1			)
 UpperCamelCase_      :		Tuple				=      np.array(pil_image.convert('RGB'			)			)
 UpperCamelCase_      :		Optional[int]				=      arr.astype(np.floataa			) / 1_2_7.5 - 1
 UpperCamelCase_      :		int				=      np.transpose(lowerCamelCase						,						[2, 0, 1]			)
 UpperCamelCase_      :		List[Any]				=      torch.from_numpy(lowerCamelCase			).unsqueeze(0			)
 return image






class 	_lowercase		(      snake_case_ ):

 def __init__(				self			:							Union[str, Any]     ,   snake_case			:							UNetaDConditionModel     ,   snake_case			:							DDPMScheduler     ,   snake_case			:							VQModel     ,   )     ->		str:

  """simple docstring"""





  super().__init__()

  self.register_modules(
      unet=snake_case     ,   scheduler=snake_case     ,   movq=snake_case     ,   )
  UpperCamelCase_      :		Optional[Any]				=      2 ** (len(self.movq.config.block_out_channels    ) - 1)

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							str     ,   snake_case			:							List[Any]     ,   snake_case			:							int    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      min(int(num_inference_steps * strength    )     ,   snake_case    )

  UpperCamelCase_      :		Optional[Any]				=      max(num_inference_steps - init_timestep     ,   0    )
  UpperCamelCase_      :		Union[str, Any]				=      self.scheduler.timesteps[t_start:]

  return timesteps, num_inference_steps - t_start

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							int     ,   snake_case			:							Union[str, Any]     ,   snake_case			:							List[str]     ,   snake_case			:							str     ,   snake_case			:							Optional[int]     ,   snake_case			:							Dict     ,   snake_case			:							List[Any]=None    )     ->		Union[str, Any]:

  """simple docstring"""





  if not isinstance(snake_case     ,   (torch.Tensor, PIL.Image.Image, list)    ):
   raise ValueError(
       f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(snake_case    )}"    )

  UpperCamelCase_      :		Union[str, Any]				=      image.to(device=snake_case     ,   dtype=snake_case    )

  UpperCamelCase_      :		Any				=      batch_size * num_images_per_prompt

  if image.shape[1] == 4:
   UpperCamelCase_      :		List[Any]				=      image

  else:
   if isinstance(snake_case     ,   snake_case    ) and len(snake_case    ) != batch_size:
    raise ValueError(
        f"You have passed a list of generators of length {len(snake_case    )}, but requested an effective batch"
        f" size of {batch_size}. Make sure the batch size matches the length of the generators."    )

   elif isinstance(snake_case     ,   snake_case    ):
    UpperCamelCase_      :		Any				=      [
        self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(snake_case    )
    ]
    UpperCamelCase_      :		Union[str, Any]				=      torch.cat(snake_case     ,   dim=0    )
   else:
    UpperCamelCase_      :		Optional[Any]				=      self.movq.encode(snake_case    ).latent_dist.sample(snake_case    )

   UpperCamelCase_      :		List[Any]				=      self.movq.config.scaling_factor * init_latents

  UpperCamelCase_      :		List[str]				=      torch.cat([init_latents]     ,   dim=0    )

  UpperCamelCase_      :		Optional[Any]				=      init_latents.shape
  UpperCamelCase_      :		Optional[int]				=      randn_tensor(snake_case     ,   generator=snake_case     ,   device=snake_case     ,   dtype=snake_case    )

  # get latents
  UpperCamelCase_      :		Union[str, Any]				=      self.scheduler.add_noise(snake_case     ,   snake_case     ,   snake_case    )

  UpperCamelCase_      :		Optional[Any]				=      init_latents

  return latents

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]     ,   snake_case			:							Dict=0    )     ->		Optional[Any]:

  """simple docstring"""





  if is_accelerate_available():
   from accelerate import cpu_offload
  else:
   raise ImportError('Please install accelerate via `pip install accelerate`'    )

  UpperCamelCase_      :		str				=      torch.device(f"cuda:{gpu_id}"    )

  UpperCamelCase_      :		Dict				=      [
      self.unet,
      self.movq,
  ]
  for cpu_offloaded_model in models:
   if cpu_offloaded_model is not None:
    cpu_offload(snake_case     ,   snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							Tuple=0    )     ->		Optional[Any]:

  """simple docstring"""





  if is_accelerate_available() and is_accelerate_version('>='     ,   '0.17.0.dev0'    ):
   from accelerate import cpu_offload_with_hook
  else:
   raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'    )

  UpperCamelCase_      :		Any				=      torch.device(f"cuda:{gpu_id}"    )

  if self.device.type != "cpu":
   self.to('cpu'     ,   silence_dtype_warnings=snake_case    )
   torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

  UpperCamelCase_      :		Any				=      None
  for cpu_offloaded_model in [self.unet, self.movq]:
   UpperCamelCase_,				UpperCamelCase_      :		Any				=      cpu_offload_with_hook(snake_case     ,   snake_case     ,   prev_module_hook=snake_case    )

  # We'll offload the last model manually.
  UpperCamelCase_      :		Optional[int]				=      hook

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		Optional[Any]:

  """simple docstring"""





  if not hasattr(self.unet     ,   '_hf_hook'    ):
   return self.device
  for module in self.unet.modules():
   if (
       hasattr(snake_case     ,   '_hf_hook'    )
       and hasattr(module._hf_hook     ,   'execution_device'    )
       and module._hf_hook.execution_device is not None
   ):
    return torch.device(module._hf_hook.execution_device    )
  return self.device


 @torch.no_grad()
 @replace_example_docstring(snake_case    )
 def __call__(				self			:							Optional[int]     ,   snake_case			:							Union[torch.FloatTensor, List[torch.FloatTensor]]     ,   snake_case			:							Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]     ,   snake_case			:							Union[torch.FloatTensor, List[torch.FloatTensor]]     ,   snake_case			:							torch.FloatTensor     ,   snake_case			:							int = 5_1_2     ,   snake_case			:							int = 5_1_2     ,   snake_case			:							int = 1_0_0     ,   snake_case			:							float = 4.0     ,   snake_case			:							float = 0.3     ,   snake_case			:							int = 1     ,   snake_case			:							Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,   snake_case			:							Optional[str] = "pil"     ,   snake_case			:							bool = True     ,   )     ->		Any:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      self._execution_device

  UpperCamelCase_      :		str				=      guidance_scale > 1.0

  if isinstance(snake_case     ,   snake_case    ):
   UpperCamelCase_      :		Dict				=      torch.cat(snake_case     ,   dim=0    )
  if isinstance(snake_case     ,   snake_case    ):
   UpperCamelCase_      :		Optional[int]				=      torch.cat(snake_case     ,   dim=0    )
  if isinstance(snake_case     ,   snake_case    ):
   UpperCamelCase_      :		Dict				=      torch.cat(snake_case     ,   dim=0    )

  UpperCamelCase_      :		int				=      image_embeds.shape[0]

  if do_classifier_free_guidance:
   UpperCamelCase_      :		Optional[Any]				=      image_embeds.repeat_interleave(snake_case     ,   dim=0    )
   UpperCamelCase_      :		Any				=      negative_image_embeds.repeat_interleave(snake_case     ,   dim=0    )
   UpperCamelCase_      :		Tuple				=      hint.repeat_interleave(snake_case     ,   dim=0    )

  UpperCamelCase_      :		int				=      torch.cat([negative_image_embeds, image_embeds]     ,   dim=0    ).to(dtype=self.unet.dtype     ,   device=snake_case    )
  UpperCamelCase_      :		Union[str, Any]				=      torch.cat([hint, hint]     ,   dim=0    ).to(dtype=self.unet.dtype     ,   device=snake_case    )

  if not isinstance(snake_case     ,   snake_case    ):
   UpperCamelCase_      :		Optional[Any]				=      [image]
  if not all(isinstance(snake_case     ,   (PIL.Image.Image, torch.Tensor)    ) for i in image    ):
   raise ValueError(
       f"Input is in incorrect format: {[type(snake_case    ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"    )

  UpperCamelCase_      :		Union[str, Any]				=      torch.cat([prepare_image(snake_case     ,   snake_case     ,   snake_case    ) for i in image]     ,   dim=0    )
  UpperCamelCase_      :		Dict				=      image.to(dtype=image_embeds.dtype     ,   device=snake_case    )

  UpperCamelCase_      :		List[Any]				=      self.movq.encode(snake_case    )['latents']
  UpperCamelCase_      :		str				=      latents.repeat_interleave(snake_case     ,   dim=0    )
  self.scheduler.set_timesteps(snake_case     ,   device=snake_case    )
  UpperCamelCase_,				UpperCamelCase_      :		int				=      self.get_timesteps(snake_case     ,   snake_case     ,   snake_case    )
  UpperCamelCase_      :		Any				=      timesteps[:1].repeat(batch_size * num_images_per_prompt    )
  UpperCamelCase_,				UpperCamelCase_      :		Optional[Any]				=      downscale_height_and_width(snake_case     ,   snake_case     ,   self.movq_scale_factor    )
  UpperCamelCase_      :		List[Any]				=      self.prepare_latents(
      snake_case     ,   snake_case     ,   snake_case     ,   snake_case     ,   image_embeds.dtype     ,   snake_case     ,   snake_case    )
  for i, t in enumerate(self.progress_bar(snake_case    )    ):
   # expand the latents if we are doing classifier free guidance
   UpperCamelCase_      :		int				=      torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

   UpperCamelCase_      :		Optional[Any]				=      {'image_embeds': image_embeds, 'hint': hint}
   UpperCamelCase_      :		Optional[Any]				=      self.unet(
       sample=snake_case     ,   timestep=snake_case     ,   encoder_hidden_states=snake_case     ,   added_cond_kwargs=snake_case     ,   return_dict=snake_case     ,   )[0]

   if do_classifier_free_guidance:
    UpperCamelCase_,				UpperCamelCase_      :		Dict				=      noise_pred.split(latents.shape[1]     ,   dim=1    )
    UpperCamelCase_,				UpperCamelCase_      :		List[Any]				=      noise_pred.chunk(2    )
    UpperCamelCase_,				UpperCamelCase_      :		Union[str, Any]				=      variance_pred.chunk(2    )
    UpperCamelCase_      :		Union[str, Any]				=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
    UpperCamelCase_      :		Tuple				=      torch.cat([noise_pred, variance_pred_text]     ,   dim=1    )

   if not (
       hasattr(self.scheduler.config     ,   'variance_type'    )
       and self.scheduler.config.variance_type in ["learned", "learned_range"]
   ):
    UpperCamelCase_,				UpperCamelCase_      :		int				=      noise_pred.split(latents.shape[1]     ,   dim=1    )

   # compute the previous noisy sample x_t -> x_t-1

   UpperCamelCase_      :		List[Any]				=      self.scheduler.step(
       snake_case     ,   snake_case     ,   snake_case     ,   generator=snake_case     ,   )[0]

  # post-processing
  UpperCamelCase_      :		Any				=      self.movq.decode(snake_case     ,   force_not_quantize=snake_case    )['sample']

  if output_type not in ["pt", "np", "pil"]:
   raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"    )

  if output_type in ["np", "pil"]:
   UpperCamelCase_      :		List[Any]				=      image * 0.5 + 0.5
   UpperCamelCase_      :		Optional[int]				=      image.clamp(0     ,   1    )
   UpperCamelCase_      :		Dict				=      image.cpu().permute(0     ,   2     ,   3     ,   1    ).float().numpy()

  if output_type == "pil":
   UpperCamelCase_      :		Optional[Any]				=      self.numpy_to_pil(snake_case    )

  if not return_dict:
   return (image,)

  return ImagePipelineOutput(images=snake_case    )



