import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowercase_     :   int 				=				logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')






@dataclass
class 		_lowerCamelCase		:
    __a  =      field(
        default="cifar10"    ,     metadata={"help": "Name of a dataset from the datasets package"}    )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}    )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "The column name of the images in the files."}    )
    __a  =      field(default=UpperCamelCase_    ,     metadata={"help": "A folder containing the training data."}    )
    __a  =      field(default=UpperCamelCase_    ,     metadata={"help": "A folder containing the validation data."}    )
    __a  =      field(
        default=0.15    ,     metadata={"help": "Percent to split off of train for validation."}    )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        }    ,     )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        }    ,     )
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      {}
     if self.train_dir is not None:
      SCREAMING_SNAKE_CASE__:   List[str]=      self.train_dir
     if self.validation_dir is not None:
      SCREAMING_SNAKE_CASE__:   str=      self.validation_dir
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      data_files if data_files else None






@dataclass
class 		_lowerCamelCase		:
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={
            "help": (
                "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
            )
        }    ,     )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}    )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={
            "help": (
                "Override some existing default config settings when a model is trained from scratch. Example: "
                "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
            )
        }    ,     )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}    )
    __a  =      field(
        default="main"    ,     metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,     )
    __a  =      field(default=UpperCamelCase_    ,     metadata={"help": "Name or path of preprocessor config."}    )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        }    ,     )
    __a  =      field(
        default=0.75    ,     metadata={"help": "The ratio of the number of masked tokens in the input sequence."}    )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "Whether or not to train with normalized pixel values as target."}    )






@dataclass
class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      field(
        default=1e-3    ,     metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}    )







def 			A__      (	snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   Optional[int]=      torch.stack([example['''pixel_values'''] for example in examples]      )
 return {"pixel_values": pixel_values}







def 			A__      (	):
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 SCREAMING_SNAKE_CASE__:   List[Any]=      HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)      )
 if len(sys.argv      ) == 2 and sys.argv[1].endswith('''.json'''      ):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
 else:
  SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Dict=      parser.parse_args_into_dataclasses()

 # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
 # information sent is the one passed as arguments along with your Python/PyTorch versions.
 send_example_telemetry('''run_mae'''       ,				snake_case_       ,				snake_case_      )

 # Setup logging
 logging.basicConfig(
     format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,				datefmt='''%m/%d/%Y %H:%M:%S'''       ,				handlers=[logging.StreamHandler(sys.stdout      )]       ,				)

 if training_args.should_log:
  # The default of training_args.log_level is passive, so we set log level at info here to have that default.
  transformers.utils.logging.set_verbosity_info()

 SCREAMING_SNAKE_CASE__:   Optional[Any]=      training_args.get_process_log_level()
 logger.setLevel(snake_case_      )
 transformers.utils.logging.set_verbosity(snake_case_      )
 transformers.utils.logging.enable_default_handler()
 transformers.utils.logging.enable_explicit_format()

 # Log on each process the small summary:
 logger.warning(
     F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
     + F'distributed training: {bool(training_args.local_rank != -1      )}, 16-bits training: {training_args.fpaa}'      )
 logger.info(F'Training/evaluation parameters {training_args}'      )

 # Detecting last checkpoint.
 SCREAMING_SNAKE_CASE__:   List[str]=      None
 if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
  SCREAMING_SNAKE_CASE__:   Tuple=      get_last_checkpoint(training_args.output_dir      )
  if last_checkpoint is None and len(os.listdir(training_args.output_dir      )      ) > 0:
   raise ValueError(
       F'Output directory ({training_args.output_dir}) already exists and is not empty. '
       '''Use --overwrite_output_dir to overcome.'''      )
  elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
   logger.info(
       F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
       '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''      )

    # Initialize our dataset.
 SCREAMING_SNAKE_CASE__:   str=      load_dataset(
     data_args.dataset_name       ,				data_args.dataset_config_name       ,				data_files=data_args.data_files       ,				cache_dir=model_args.cache_dir       ,				use_auth_token=True if model_args.use_auth_token else None       ,				)

 # If we don't have a validation split, split off a percentage of train as validation.
 SCREAMING_SNAKE_CASE__:   Any=      None if '''validation''' in ds.keys() else data_args.train_val_split
 if isinstance(data_args.train_val_split       ,				snake_case_      ) and data_args.train_val_split > 0.0:
  SCREAMING_SNAKE_CASE__:   List[Any]=      ds['''train'''].train_test_split(data_args.train_val_split      )
  SCREAMING_SNAKE_CASE__:   int=      split['''train''']
  SCREAMING_SNAKE_CASE__:   Optional[int]=      split['''test''']

 # Load pretrained model and image processor
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 SCREAMING_SNAKE_CASE__:   Optional[int]=      {
     '''cache_dir''': model_args.cache_dir,
     '''revision''': model_args.model_revision,
     '''use_auth_token''': True if model_args.use_auth_token else None,
 }
 if model_args.config_name:
  SCREAMING_SNAKE_CASE__:   Dict=      ViTMAEConfig.from_pretrained(model_args.config_name       ,				**snake_case_      )
 elif model_args.model_name_or_path:
  SCREAMING_SNAKE_CASE__:   Any=      ViTMAEConfig.from_pretrained(model_args.model_name_or_path       ,				**snake_case_      )
 else:
  SCREAMING_SNAKE_CASE__:   Any=      ViTMAEConfig()
  logger.warning('''You are instantiating a new config instance from scratch.'''      )
  if model_args.config_overrides is not None:
   logger.info(F'Overriding config: {model_args.config_overrides}'      )
   config.update_from_string(model_args.config_overrides      )
   logger.info(F'New config: {config}'      )

    # adapt config
 config.update(
     {
         '''mask_ratio''': model_args.mask_ratio,
         '''norm_pix_loss''': model_args.norm_pix_loss,
     }      )

 # create image processor
 if model_args.image_processor_name:
  SCREAMING_SNAKE_CASE__:   List[str]=      ViTImageProcessor.from_pretrained(model_args.image_processor_name       ,				**snake_case_      )
 elif model_args.model_name_or_path:
  SCREAMING_SNAKE_CASE__:   Optional[Any]=      ViTImageProcessor.from_pretrained(model_args.model_name_or_path       ,				**snake_case_      )
 else:
  SCREAMING_SNAKE_CASE__:   Any=      ViTImageProcessor()

 # create model
 if model_args.model_name_or_path:
  SCREAMING_SNAKE_CASE__:   int=      ViTMAEForPreTraining.from_pretrained(
      model_args.model_name_or_path       ,				from_tf=bool('''.ckpt''' in model_args.model_name_or_path      )       ,				config=snake_case_       ,				cache_dir=model_args.cache_dir       ,				revision=model_args.model_revision       ,				use_auth_token=True if model_args.use_auth_token else None       ,				)
 else:
  logger.info('''Training new model from scratch'''      )
  SCREAMING_SNAKE_CASE__:   Union[str, Any]=      ViTMAEForPreTraining(snake_case_      )

 if training_args.do_train:
  SCREAMING_SNAKE_CASE__:   int=      ds['''train'''].column_names
 else:
  SCREAMING_SNAKE_CASE__:   Optional[Any]=      ds['''validation'''].column_names

 if data_args.image_column_name is not None:
  SCREAMING_SNAKE_CASE__:   Optional[int]=      data_args.image_column_name
 elif "image" in column_names:
  SCREAMING_SNAKE_CASE__:   List[Any]=      '''image'''
 elif "img" in column_names:
  SCREAMING_SNAKE_CASE__:   Dict=      '''img'''
 else:
  SCREAMING_SNAKE_CASE__:   List[str]=      column_names[0]

 # transformations as done in original MAE paper
 # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
 if "shortest_edge" in image_processor.size:
  SCREAMING_SNAKE_CASE__:   Optional[int]=      image_processor.size['''shortest_edge''']
 else:
  SCREAMING_SNAKE_CASE__:   Union[str, Any]=      (image_processor.size['''height'''], image_processor.size['''width'''])
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      Compose(
     [
         Lambda(lambda snake_case_      : img.convert('''RGB'''      ) if img.mode != "RGB" else img      ),
         RandomResizedCrop(snake_case_       ,				scale=(0.2, 1.0)       ,				interpolation=InterpolationMode.BICUBIC      ),
         RandomHorizontalFlip(),
         ToTensor(),
         Normalize(mean=image_processor.image_mean       ,				std=image_processor.image_std      ),
     ]      )

 def preprocess_images(snake_case_ :		List[Any]      ):
  SCREAMING_SNAKE_CASE__:   Any=      [transforms(snake_case_      ) for image in examples[image_column_name]]
  return examples

 if training_args.do_train:
  if "train" not in ds:
   raise ValueError('''--do_train requires a train dataset'''      )
  if data_args.max_train_samples is not None:
   SCREAMING_SNAKE_CASE__:   Any=      ds['''train'''].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
  # Set the training transforms
  ds["train"].set_transform(snake_case_      )

 if training_args.do_eval:
  if "validation" not in ds:
   raise ValueError('''--do_eval requires a validation dataset'''      )
  if data_args.max_eval_samples is not None:
   SCREAMING_SNAKE_CASE__:   Union[str, Any]=      (
       ds['''validation'''].shuffle(seed=training_args.seed      ).select(range(data_args.max_eval_samples      )      )
   )
  # Set the validation transforms
  ds["validation"].set_transform(snake_case_      )

 # Compute absolute learning rate
 SCREAMING_SNAKE_CASE__:   Dict=      (
     training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
 )
 if training_args.base_learning_rate is not None:
  SCREAMING_SNAKE_CASE__:   int=      training_args.base_learning_rate * total_train_batch_size / 256

 # Initialize our trainer
 SCREAMING_SNAKE_CASE__:   Dict=      Trainer(
     model=snake_case_       ,				args=snake_case_       ,				train_dataset=ds['''train'''] if training_args.do_train else None       ,				eval_dataset=ds['''validation'''] if training_args.do_eval else None       ,				tokenizer=snake_case_       ,				data_collator=snake_case_       ,				)

 # Training
 if training_args.do_train:
  SCREAMING_SNAKE_CASE__:   Optional[Any]=      None
  if training_args.resume_from_checkpoint is not None:
   SCREAMING_SNAKE_CASE__:   List[str]=      training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   SCREAMING_SNAKE_CASE__:   List[str]=      last_checkpoint
  SCREAMING_SNAKE_CASE__:   Any=      trainer.train(resume_from_checkpoint=snake_case_      )
  trainer.save_model()
  trainer.log_metrics('''train'''       ,				train_result.metrics      )
  trainer.save_metrics('''train'''       ,				train_result.metrics      )
  trainer.save_state()

 # Evaluation
 if training_args.do_eval:
  SCREAMING_SNAKE_CASE__:   Dict=      trainer.evaluate()
  trainer.log_metrics('''eval'''       ,				snake_case_      )
  trainer.save_metrics('''eval'''       ,				snake_case_      )

 # Write model card and (optionally) push to hub
 SCREAMING_SNAKE_CASE__:   int=      {
     '''tasks''': '''masked-auto-encoding''',
     '''dataset''': data_args.dataset_name,
     '''tags''': ['''masked-auto-encoding'''],
 }
 if training_args.push_to_hub:
  trainer.push_to_hub(**snake_case_      )
 else:
  trainer.create_model_card(**snake_case_      )







def 			A__      (	snake_case_ :		Tuple      ):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
      main()


from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)






@flax.struct.dataclass
class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      42
    __a  =      42






class 		_lowerCamelCase		(   nn.Module    ):
    __a  =      42
    __a  =      (16, 32, 96, 256)
    __a  =      jnp.floataa
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   List[str]=      nn.Conv(
         self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )

     SCREAMING_SNAKE_CASE__:   Optional[int]=      []
     for i in range(len(self.block_out_channels   ) - 1   ):
      SCREAMING_SNAKE_CASE__:   str=      self.block_out_channels[i]
      SCREAMING_SNAKE_CASE__:   List[str]=      self.block_out_channels[i + 1]
      SCREAMING_SNAKE_CASE__:   List[Any]=      nn.Conv(
          lowerCAmelCase , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
      blocks.append(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   str=      nn.Conv(
          lowerCAmelCase , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
      blocks.append(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   str=      blocks

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      nn.Conv(
         self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )



    def __call__(  self , lowerCAmelCase   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   Dict=      self.conv_in(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      nn.silu(lowerCAmelCase   )

     for block in self.blocks:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      block(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      nn.silu(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Any=      self.conv_out(lowerCAmelCase   )

     return embedding






@flax_register_to_config
class 		_lowerCamelCase		(   nn.Module    ,     UpperCamelCase_    ,     UpperCamelCase_    ):
    __a  =      32
    __a  =      4
    __a  =      (
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "DownBlock2D",
    )
    __a  =      False
    __a  =      (320, 640, 1280, 1280)
    __a  =      2
    __a  =      8
    __a  =      None
    __a  =      1280
    __a  =      0.0
    __a  =      False
    __a  =      jnp.floataa
    __a  =      True
    __a  =      0
    __a  =      "rgb"
    __a  =      (16, 32, 96, 256)
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					FrozenDict:
     # init input tensors
     SCREAMING_SNAKE_CASE__:   Any=      (1, self.in_channels, self.sample_size, self.sample_size)
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      jnp.zeros(lowerCAmelCase , dtype=jnp.floataa   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      jnp.ones((1,) , dtype=jnp.intaa   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa   )
     SCREAMING_SNAKE_CASE__:   int=      (1, 3, self.sample_size * 8, self.sample_size * 8)
     SCREAMING_SNAKE_CASE__:   Optional[int]=      jnp.zeros(lowerCAmelCase , dtype=jnp.floataa   )

     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Any=      jax.random.split(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      {'''params''': params_rng, '''dropout''': dropout_rng}

     return self.init(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )["params"]
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     SCREAMING_SNAKE_CASE__:   Dict=      self.block_out_channels
     SCREAMING_SNAKE_CASE__:   str=      block_out_channels[0] * 4

     # If `num_attention_heads` is not defined (which is the case for most models)
     # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
     # The reason for this behavior is to correct for incorrectly named variables that were introduced
     # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
     # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
     # which is why we correct for the naming here.
     SCREAMING_SNAKE_CASE__:   int=      self.num_attention_heads or self.attention_head_dim

     # input
     SCREAMING_SNAKE_CASE__:   Any=      nn.Conv(
         block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )

     # time
     SCREAMING_SNAKE_CASE__:   str=      FlaxTimesteps(
         block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      FlaxTimestepEmbedding(lowerCAmelCase , dtype=self.dtype   )

     SCREAMING_SNAKE_CASE__:   str=      FlaxControlNetConditioningEmbedding(
         conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )

     SCREAMING_SNAKE_CASE__:   List[str]=      self.only_cross_attention
     if isinstance(lowerCAmelCase , lowerCAmelCase   ):
      SCREAMING_SNAKE_CASE__:   str=      (only_cross_attention,) * len(self.down_block_types   )

     if isinstance(lowerCAmelCase , lowerCAmelCase   ):
      SCREAMING_SNAKE_CASE__:   Dict=      (num_attention_heads,) * len(self.down_block_types   )

     # down
     SCREAMING_SNAKE_CASE__:   Optional[int]=      []
     SCREAMING_SNAKE_CASE__:   List[str]=      []

     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      block_out_channels[0]

     SCREAMING_SNAKE_CASE__:   Dict=      nn.Conv(
         lowerCAmelCase , kernel_size=(1, 1) , padding='''VALID''' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
     controlnet_down_blocks.append(lowerCAmelCase   )

     for i, down_block_type in enumerate(self.down_block_types   ):
      SCREAMING_SNAKE_CASE__:   Tuple=      output_channel
      SCREAMING_SNAKE_CASE__:   Any=      block_out_channels[i]
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      i == len(lowerCAmelCase   ) - 1

      if down_block_type == "CrossAttnDownBlock2D":
       SCREAMING_SNAKE_CASE__:   Any=      FlaxCrossAttnDownBlockaD(
           in_channels=lowerCAmelCase , out_channels=lowerCAmelCase , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
      else:
       SCREAMING_SNAKE_CASE__:   Union[str, Any]=      FlaxDownBlockaD(
           in_channels=lowerCAmelCase , out_channels=lowerCAmelCase , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )

      down_blocks.append(lowerCAmelCase   )

      for _ in range(self.layers_per_block   ):
       SCREAMING_SNAKE_CASE__:   str=      nn.Conv(
           lowerCAmelCase , kernel_size=(1, 1) , padding='''VALID''' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
       controlnet_down_blocks.append(lowerCAmelCase   )

      if not is_final_block:
       SCREAMING_SNAKE_CASE__:   str=      nn.Conv(
           lowerCAmelCase , kernel_size=(1, 1) , padding='''VALID''' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
       controlnet_down_blocks.append(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   int=      down_blocks
     SCREAMING_SNAKE_CASE__:   int=      controlnet_down_blocks

     # mid
     SCREAMING_SNAKE_CASE__:   Optional[int]=      block_out_channels[-1]
     SCREAMING_SNAKE_CASE__:   int=      FlaxUNetMidBlockaDCrossAttn(
         in_channels=lowerCAmelCase , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )

     SCREAMING_SNAKE_CASE__:   str=      nn.Conv(
         lowerCAmelCase , kernel_size=(1, 1) , padding='''VALID''' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )



    def __call__(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = 1.0 , lowerCAmelCase = True , lowerCAmelCase = False , )       ->					Union[FlaxControlNetOutput, Tuple]:
     SCREAMING_SNAKE_CASE__:   Dict=      self.controlnet_conditioning_channel_order
     if channel_order == "bgr":
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      jnp.flip(lowerCAmelCase , axis=1   )

     # 1. time
     if not isinstance(lowerCAmelCase , jnp.ndarray   ):
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      jnp.array([timesteps] , dtype=jnp.intaa   )
     elif isinstance(lowerCAmelCase , jnp.ndarray   ) and len(timesteps.shape   ) == 0:
      SCREAMING_SNAKE_CASE__:   int=      timesteps.astype(dtype=jnp.floataa   )
      SCREAMING_SNAKE_CASE__:   Any=      jnp.expand_dims(lowerCAmelCase , 0   )

     SCREAMING_SNAKE_CASE__:   Dict=      self.time_proj(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Dict=      self.time_embedding(lowerCAmelCase   )

     # 2. pre-process
     SCREAMING_SNAKE_CASE__:   int=      jnp.transpose(lowerCAmelCase , (0, 2, 3, 1)   )
     SCREAMING_SNAKE_CASE__:   str=      self.conv_in(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      jnp.transpose(lowerCAmelCase , (0, 2, 3, 1)   )
     SCREAMING_SNAKE_CASE__:   str=      self.controlnet_cond_embedding(lowerCAmelCase   )
     sample += controlnet_cond

     # 3. down
     SCREAMING_SNAKE_CASE__:   Tuple=      (sample,)
     for down_block in self.down_blocks:
      if isinstance(lowerCAmelCase , lowerCAmelCase   ):
       SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Tuple=      down_block(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , deterministic=not train   )
      else:
       SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   int=      down_block(lowerCAmelCase , lowerCAmelCase , deterministic=not train   )
      down_block_res_samples += res_samples

     # 4. mid
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.mid_block(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , deterministic=not train   )

     # 5. contronet blocks
     SCREAMING_SNAKE_CASE__:   Optional[int]=      ()
     for down_block_res_sample, controlnet_block in zip(lowerCAmelCase , self.controlnet_down_blocks   ):
      SCREAMING_SNAKE_CASE__:   List[str]=      controlnet_block(lowerCAmelCase   )
      controlnet_down_block_res_samples += (down_block_res_sample,)

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      controlnet_down_block_res_samples

     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.controlnet_mid_block(lowerCAmelCase   )

     # 6. scaling
     SCREAMING_SNAKE_CASE__:   Tuple=      [sample * conditioning_scale for sample in down_block_res_samples]
     mid_block_res_sample *= conditioning_scale

     if not return_dict:
      return (down_block_res_samples, mid_block_res_sample)

     return FlaxControlNetOutput(
         down_block_res_samples=lowerCAmelCase , mid_block_res_sample=lowerCAmelCase   )

