


"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


SCREAMING_SNAKE_CASE :    Optional[Any]									=				logging.getLogger(__name__)

SCREAMING_SNAKE_CASE :    Union[str, Any]									=				"""pytorch_model.bin"""



@dataclasses.dataclass
class 			_UpperCAmelCase :






		'''simple docstring'''

		lowerCamelCase__				=dataclasses.field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'}				)
		lowerCamelCase__				=dataclasses.field(
		    default=_UpperCAmelCase,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'},				)



@dataclasses.dataclass
class 			_UpperCAmelCase :






		'''simple docstring'''

		lowerCamelCase__				=dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'}				)
		lowerCamelCase__				=dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'}				)
		lowerCamelCase__				=dataclasses.field(
		    default=_UpperCAmelCase,				metadata={'help': 'A csv or a json file containing the validation data.'}				)
		lowerCamelCase__				=dataclasses.field(
		    default=_UpperCAmelCase,				metadata={'help': 'The name of the task to train on.'},				)
		lowerCamelCase__				=dataclasses.field(
		    default=_UpperCAmelCase,				metadata={'help': 'The list of labels for the task.'}				)



@dataclasses.dataclass
class 			_UpperCAmelCase :






		'''simple docstring'''

		lowerCamelCase__				=dataclasses.field(
		    metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'}				)
		lowerCamelCase__				=dataclasses.field(
		    default='accuracy',				metadata={'help': 'The evaluation metric used for the task.'}				)
		lowerCamelCase__				=dataclasses.field(
		    default='no',				metadata={
		        'help': 'The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]'
		    },				)
		lowerCamelCase__				=dataclasses.field(
		    default=10,				metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'},				)
		lowerCamelCase__				=dataclasses.field(
		    default=0.0,				metadata={
		        'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
		    },				)
		lowerCamelCase__				=dataclasses.field(
		    default=_UpperCAmelCase,				metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'},				)
		lowerCamelCase__				=dataclasses.field(
		    default=_UpperCAmelCase,				metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'},				)
		lowerCamelCase__				=dataclasses.field(
		    default=_UpperCAmelCase,				metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'},				)
		lowerCamelCase__				=dataclasses.field(
		    default=0.0,				metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'},				)
		lowerCamelCase__				=dataclasses.field(
		    default=100,				metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'},				)
		lowerCamelCase__				=dataclasses.field(
		    default=_UpperCAmelCase,				metadata={'help': 'Random seed for initialization.'},				)






def 		lowercase							(     _snake_case						:      Tuple     ,   _snake_case						:      Any     ,   _snake_case						:      Optional[Any]     ,   _snake_case						:      int     ,   _snake_case						:      str     ,   _snake_case						:      Optional[Any]     )     ->Any:
	"""simple docstring"""





	__snake_case	:						Dict						=				datasets.concatenate_datasets([infer_input, infer_output]     ,   axis=1     )

	if args.do_filter_by_confidence:
		__snake_case	:						Optional[Any]						=				dataset.filter(lambda _snake_case     : example["probability"] > args.confidence_threshold     )

	if args.do_filter_by_val_performance:
		assert eval_result >= 0.0 and eval_result <= 1.0
		__snake_case	:						int						=				int(eval_result * len(UpperCamelCase_     )     )
		print(UpperCamelCase_     )
		__snake_case	:						List[str]						=				dataset.sort('''probability'''     ,   reverse=UpperCamelCase_     )
		__snake_case	:						Union[str, Any]						=				dataset.select(range(UpperCamelCase_     )     )

	__snake_case	:						int						=				dataset.remove_columns(['''label''', '''probability''']     )
	__snake_case	:						str						=				dataset.rename_column('''prediction'''     ,   '''label'''     )
	__snake_case	:						Tuple						=				dataset.map(lambda _snake_case     : {"label": idalabel[example["label"]]}     )
	__snake_case	:						List[Any]						=				dataset.shuffle(seed=args.seed     )

	__snake_case	:						List[str]						=				os.path.join(UpperCamelCase_     ,   f"""train_pseudo.{args.data_file_extension}"""     )
	if args.data_file_extension == "csv":
		dataset.to_csv(UpperCamelCase_     ,   index=UpperCamelCase_     )
	else:
		dataset.to_json(UpperCamelCase_     )






def 		lowercase							(     _snake_case						:      List[str]     ,   _snake_case						:      int     ,   _snake_case						:      Tuple     ,   _snake_case						:      Tuple     ,   **_snake_case						:      List[str]     )     ->Tuple:
	"""simple docstring"""





	__snake_case	:						str						=				Accelerator()
	# Make one log on every process with the configuration for debugging.
	logging.basicConfig(
	    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''     ,   datefmt='''%m/%d/%Y %H:%M:%S'''     ,   level=logging.INFO     ,   )
	logger.info(accelerator.state     )

	# Setup logging, we only want one process per machine to log things on the
	# screen. accelerator.is_local_main_process is only True for one process per
	# machine.
	logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR     )

	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_info()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()

	__snake_case	:						Union[str, Any]						=				STModelArguments(model_name_or_path=UpperCamelCase_     )
	__snake_case	:						Tuple						=				STDataArguments(train_file=UpperCamelCase_     ,   infer_file=UpperCamelCase_     )
	__snake_case	:						Union[str, Any]						=				STTrainingArguments(output_dir=UpperCamelCase_     )
	__snake_case	:						Any						=				argparse.Namespace()

	for arg_class in (model_args, data_args, training_args):
		for key, value in vars(UpperCamelCase_     ).items():
			setattr(UpperCamelCase_     ,   UpperCamelCase_     ,   UpperCamelCase_     )

	for key, value in kwargs.items():
		if hasattr(UpperCamelCase_     ,   UpperCamelCase_     ):
			setattr(UpperCamelCase_     ,   UpperCamelCase_     ,   UpperCamelCase_     )

    # Sanity checks
	__snake_case	:						str						=				{}
	__snake_case	:						List[str]						=				None

	# You need to provide the training data and the data to predict on
	assert args.train_file is not None
	assert args.infer_file is not None
	__snake_case	:						Tuple						=				args.train_file
	__snake_case	:						Optional[Any]						=				args.infer_file

	if args.evaluation_strategy != IntervalStrategy.NO.value:
		assert args.eval_file is not None
		__snake_case	:						List[str]						=				args.eval_file

	for key in data_files:
		__snake_case	:						Union[str, Any]						=				data_files[key].split('''.'''     )[-1]
		assert extension in ["csv", "json"], f"""`{key}_file` should be a csv or a json file."""
		if args.data_file_extension is None:
			__snake_case	:						List[Any]						=				extension
		else:
			assert extension == args.data_file_extension, f"""`{key}_file` should be a {args.data_file_extension} file`."""

	assert (
	    args.eval_metric in datasets.list_metrics()
	), f"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""

	# If passed along, set the training seed now.
	if args.seed is not None:
		set_seed(args.seed     )

	logger.info('''Creating the initial data directory for self-training...'''     )
	__snake_case	:						List[Any]						=				f"""{args.output_dir}/self-train_iter-{{}}""".format
	__snake_case	:						Any						=				data_dir_format(0     )

	if accelerator.is_main_process:
		if args.output_dir is not None:
			os.makedirs(args.output_dir     ,   exist_ok=UpperCamelCase_     )
			os.makedirs(UpperCamelCase_     ,   exist_ok=UpperCamelCase_     )
	accelerator.wait_for_everyone()

	__snake_case	:						Optional[int]						=				None
	__snake_case	:						Any						=				None
	__snake_case	:						Union[str, Any]						=				0
	__snake_case	:						Any						=				False
	# Show the progress bar
	__snake_case	:						str						=				tqdm(range(args.max_selftrain_iterations     )     ,   disable=not accelerator.is_local_main_process     )

	# Self-train
	for iteration in range(0     ,   int(args.max_selftrain_iterations     )     ):
		__snake_case	:						List[str]						=				data_dir_format(UpperCamelCase_     )
		assert os.path.exists(UpperCamelCase_     )

		# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
		# iteration > 0
		__snake_case	:						int						=				os.path.join(UpperCamelCase_     ,   '''stage-1'''     )
		__snake_case	:						Optional[Any]						=				{
		    '''accelerator''': accelerator,
		    '''model_name_or_path''': args.model_name_or_path,
		    '''cache_dir''': args.cache_dir,
		    '''do_train''': True,
		    '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
		    '''do_eval''': True if args.eval_file is not None else False,
		    '''eval_file''': data_files['''eval'''],
		    '''do_predict''': True,
		    '''infer_file''': data_files['''infer'''],
		    '''task_name''': args.task_name,
		    '''label_list''': args.label_list,
		    '''output_dir''': current_output_dir,
		    '''eval_metric''': args.eval_metric,
		    '''evaluation_strategy''': args.evaluation_strategy,
		    '''early_stopping_patience''': args.early_stopping_patience,
		    '''early_stopping_threshold''': args.early_stopping_threshold,
		    '''seed''': args.seed,
		}
		# Add additional training arguments
		for key, value in kwargs.items():
			if key not in arguments_dict and not hasattr(UpperCamelCase_     ,   UpperCamelCase_     ):
				arguments_dict.update({key: value}     )

		__snake_case	:						Any						=				os.path.join(UpperCamelCase_     ,   '''best-checkpoint'''     ,   UpperCamelCase_     )
		if os.path.exists(UpperCamelCase_     ):
			logger.info(
			    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''     ,   UpperCamelCase_     ,   UpperCamelCase_     ,   )
		else:
			logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''     ,   UpperCamelCase_     )
			finetune(**UpperCamelCase_     )
			accelerator.wait_for_everyone()
			assert os.path.exists(UpperCamelCase_     )
			logger.info('''Self-training job completed: iteration: %d, stage: 1.'''     ,   UpperCamelCase_     )

		if iteration > 0 and args.finetune_on_labeled_data:
			# Stage 2 (optional): fine-tuning on the original labeled data
			__snake_case	:						Optional[int]						=				os.path.join(UpperCamelCase_     ,   '''best-checkpoint'''     )
			__snake_case	:						int						=				os.path.join(UpperCamelCase_     ,   '''stage-2'''     )
			# Update arguments_dict
			__snake_case	:						Tuple						=				model_path
			__snake_case	:						int						=				data_files['''train''']
			__snake_case	:						Any						=				current_output_dir

			__snake_case	:						List[str]						=				os.path.join(UpperCamelCase_     ,   '''best-checkpoint'''     ,   UpperCamelCase_     )
			if os.path.exists(UpperCamelCase_     ):
				logger.info(
				    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''     ,   UpperCamelCase_     ,   UpperCamelCase_     ,   )
			else:
				logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''     ,   UpperCamelCase_     )
				finetune(**UpperCamelCase_     )
				accelerator.wait_for_everyone()
				assert os.path.exists(UpperCamelCase_     )
				logger.info('''Self-training job completed: iteration: %d, stage: 2.'''     ,   UpperCamelCase_     )

		__snake_case	:						List[Any]						=				iteration
		__snake_case	:						Union[str, Any]						=				data_dir_format(iteration + 1     )

		__snake_case	:						str						=				AutoConfig.from_pretrained(os.path.join(UpperCamelCase_     ,   '''best-checkpoint'''     )     )
		__snake_case	:						Optional[int]						=				config.idalabel
		__snake_case	:						Dict						=				os.path.join(UpperCamelCase_     ,   '''eval_results_best-checkpoint.json'''     )
		__snake_case	:						str						=				os.path.join(UpperCamelCase_     ,   '''test_results_best-checkpoint.json'''     )
		assert os.path.exists(UpperCamelCase_     )

		with open(UpperCamelCase_     ,   '''r'''     ) as f:
			__snake_case	:						List[Any]						=				float(json.load(UpperCamelCase_     )[args.eval_metric]     )
		__snake_case	:						int						=				os.path.join(UpperCamelCase_     ,   '''infer_output_best-checkpoint.csv'''     )
		assert os.path.exists(UpperCamelCase_     )
		# Loading the dataset from local csv or json files.
		__snake_case	:						Optional[Any]						=				load_dataset(args.data_file_extension     ,   data_files={'''data''': data_files['''infer''']}     )['''data''']
		__snake_case	:						List[str]						=				load_dataset('''csv'''     ,   data_files={'''data''': infer_output_file}     )['''data''']

		if accelerator.is_main_process:
			os.makedirs(UpperCamelCase_     ,   exist_ok=UpperCamelCase_     )
			shutil.copy(UpperCamelCase_     ,   os.path.join(UpperCamelCase_     ,   f"""eval_results_iter-{iteration}.json"""     )     )
			if os.path.exists(UpperCamelCase_     ):
				shutil.copy(UpperCamelCase_     ,   os.path.join(UpperCamelCase_     ,   f"""test_results_iter-{iteration}.json"""     )     )
			create_pseudo_labeled_data(UpperCamelCase_     ,   UpperCamelCase_     ,   UpperCamelCase_     ,   UpperCamelCase_     ,   UpperCamelCase_     ,   UpperCamelCase_     )
		accelerator.wait_for_everyone()

		__snake_case	:						str						=				os.path.join(UpperCamelCase_     ,   f"""train_pseudo.{args.data_file_extension}"""     )

		if args.evaluation_strategy != IntervalStrategy.NO.value:
			__snake_case	:						Optional[int]						=				eval_result

			if best_iteration is None:
				__snake_case	:						List[Any]						=				new_iteration
				__snake_case	:						Any						=				new_eval_result
			else:
				if new_eval_result - best_eval_result > args.early_stopping_threshold:
					__snake_case	:						Union[str, Any]						=				new_iteration
					__snake_case	:						Optional[Any]						=				new_eval_result
					__snake_case	:						List[Any]						=				0
				else:
					if new_eval_result == best_eval_result:
						__snake_case	:						List[str]						=				new_iteration
						__snake_case	:						str						=				new_eval_result
					early_stopping_patience_counter += 1

				if early_stopping_patience_counter >= args.early_stopping_patience:
					__snake_case	:						List[Any]						=				True

		progress_bar.update(1     )

		if should_training_stop:
			break

	if best_iteration is not None:
		# Save the best iteration
		logger.info('''Best iteration: %d'''     ,   UpperCamelCase_     )
		logger.info('''Best evaluation result: %s = %f'''     ,   args.eval_metric     ,   UpperCamelCase_     )
		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			shutil.copy(
			    os.path.join(UpperCamelCase_     ,   f"""eval_results_iter-{iteration}.json"""     )     ,   os.path.join(UpperCamelCase_     ,   '''eval_results_best-iteration.json'''     )     ,   )
	else:
		# Assume that the last iteration is the best
		logger.info('''Best iteration: %d'''     ,   args.max_selftrain_iterations - 1     )
		logger.info('''Best evaluation result: %s = %f'''     ,   args.eval_metric     ,   UpperCamelCase_     )
		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			shutil.copy(
			    os.path.join(UpperCamelCase_     ,   f"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""     )     ,   os.path.join(UpperCamelCase_     ,   '''eval_results_best-iteration.json'''     )     ,   )







import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD




class 						lowercase__  (						nn.Module  ):
	A__    :   int
	A__    :   int
	A__    :   float				       =0.0
	A__    :   int				       =1
	A__    :   int				       =1
	A__    :   bool				       =True
	A__    :   bool				       =False
	A__    :   bool				       =False
	A__    :   bool				       =False
	A__    :   jnp.dtype				       =jnp.floataa
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= []
						SCREAMING_SNAKE_CASE__													= []

						for i in range(self.num_layers							):
											SCREAMING_SNAKE_CASE__													= self.in_channels if i == 0 else self.out_channels

											SCREAMING_SNAKE_CASE__													= FlaxResnetBlockaD(
											    in_channels=UpperCAmelCase_       ,   out_channels=self.out_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
											resnets.append(UpperCAmelCase_							)

											SCREAMING_SNAKE_CASE__													= FlaxTransformeraDModel(
											    in_channels=self.out_channels       ,   n_heads=self.num_attention_heads       ,   d_head=self.out_channels // self.num_attention_heads       ,   depth=1       ,   use_linear_projection=self.use_linear_projection       ,   only_cross_attention=self.only_cross_attention       ,   use_memory_efficient_attention=self.use_memory_efficient_attention       ,   dtype=self.dtype       ,   )
											attentions.append(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= resnets
						SCREAMING_SNAKE_CASE__													= attentions

						if self.add_downsample:
											SCREAMING_SNAKE_CASE__													= FlaxDownsampleaD(self.out_channels       ,   dtype=self.dtype							)





	def __call__(			self							:   int       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   int=True							):
						SCREAMING_SNAKE_CASE__													= ()

						for resnet, attn in zip(self.resnets       ,   self.attentions							):
											SCREAMING_SNAKE_CASE__													= resnet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= attn(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)
											output_states += (hidden_states,)

						if self.add_downsample:
											SCREAMING_SNAKE_CASE__													= self.downsamplers_a(UpperCAmelCase_							)
											output_states += (hidden_states,)

						return hidden_states, output_states





class 						lowercase__  (						nn.Module  ):
	A__    :   int
	A__    :   int
	A__    :   float				       =0.0
	A__    :   int				       =1
	A__    :   bool				       =True
	A__    :   jnp.dtype				       =jnp.floataa
	def      A_				(			self							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= []

						for i in range(self.num_layers							):
											SCREAMING_SNAKE_CASE__													= self.in_channels if i == 0 else self.out_channels

											SCREAMING_SNAKE_CASE__													= FlaxResnetBlockaD(
											    in_channels=UpperCAmelCase_       ,   out_channels=self.out_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
											resnets.append(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= resnets

						if self.add_downsample:
											SCREAMING_SNAKE_CASE__													= FlaxDownsampleaD(self.out_channels       ,   dtype=self.dtype							)





	def __call__(			self							:   List[str]       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Union[str, Any]       ,   UpperCAmelCase_							:   List[Any]=True							):
						SCREAMING_SNAKE_CASE__													= ()

						for resnet in self.resnets:
											SCREAMING_SNAKE_CASE__													= resnet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)
											output_states += (hidden_states,)

						if self.add_downsample:
											SCREAMING_SNAKE_CASE__													= self.downsamplers_a(UpperCAmelCase_							)
											output_states += (hidden_states,)

						return hidden_states, output_states





class 						lowercase__  (						nn.Module  ):
	A__    :   int
	A__    :   int
	A__    :   int
	A__    :   float				       =0.0
	A__    :   int				       =1
	A__    :   int				       =1
	A__    :   bool				       =True
	A__    :   bool				       =False
	A__    :   bool				       =False
	A__    :   bool				       =False
	A__    :   jnp.dtype				       =jnp.floataa
	def      A_				(			self							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= []
						SCREAMING_SNAKE_CASE__													= []

						for i in range(self.num_layers							):
											SCREAMING_SNAKE_CASE__													= self.in_channels if (i == self.num_layers - 1) else self.out_channels
											SCREAMING_SNAKE_CASE__													= self.prev_output_channel if i == 0 else self.out_channels

											SCREAMING_SNAKE_CASE__													= FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels       ,   out_channels=self.out_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
											resnets.append(UpperCAmelCase_							)

											SCREAMING_SNAKE_CASE__													= FlaxTransformeraDModel(
											    in_channels=self.out_channels       ,   n_heads=self.num_attention_heads       ,   d_head=self.out_channels // self.num_attention_heads       ,   depth=1       ,   use_linear_projection=self.use_linear_projection       ,   only_cross_attention=self.only_cross_attention       ,   use_memory_efficient_attention=self.use_memory_efficient_attention       ,   dtype=self.dtype       ,   )
											attentions.append(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= resnets
						SCREAMING_SNAKE_CASE__													= attentions

						if self.add_upsample:
											SCREAMING_SNAKE_CASE__													= FlaxUpsampleaD(self.out_channels       ,   dtype=self.dtype							)





	def __call__(			self							:   str       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Union[str, Any]=True							):
						for resnet, attn in zip(self.resnets       ,   self.attentions							):
											# pop res hidden states
											SCREAMING_SNAKE_CASE__													= res_hidden_states_tuple[-1]
											SCREAMING_SNAKE_CASE__													= res_hidden_states_tuple[:-1]
											SCREAMING_SNAKE_CASE__													= jnp.concatenate((hidden_states, res_hidden_states)       ,   axis=-1							)

											SCREAMING_SNAKE_CASE__													= resnet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= attn(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)

						if self.add_upsample:
											SCREAMING_SNAKE_CASE__													= self.upsamplers_a(UpperCAmelCase_							)

						return hidden_states





class 						lowercase__  (						nn.Module  ):
	A__    :   int
	A__    :   int
	A__    :   int
	A__    :   float				       =0.0
	A__    :   int				       =1
	A__    :   bool				       =True
	A__    :   jnp.dtype				       =jnp.floataa
	def      A_				(			self							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= []

						for i in range(self.num_layers							):
											SCREAMING_SNAKE_CASE__													= self.in_channels if (i == self.num_layers - 1) else self.out_channels
											SCREAMING_SNAKE_CASE__													= self.prev_output_channel if i == 0 else self.out_channels

											SCREAMING_SNAKE_CASE__													= FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels       ,   out_channels=self.out_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
											resnets.append(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= resnets

						if self.add_upsample:
											SCREAMING_SNAKE_CASE__													= FlaxUpsampleaD(self.out_channels       ,   dtype=self.dtype							)





	def __call__(			self							:   Tuple       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Any       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Dict=True							):
						for resnet in self.resnets:
											# pop res hidden states
											SCREAMING_SNAKE_CASE__													= res_hidden_states_tuple[-1]
											SCREAMING_SNAKE_CASE__													= res_hidden_states_tuple[:-1]
											SCREAMING_SNAKE_CASE__													= jnp.concatenate((hidden_states, res_hidden_states)       ,   axis=-1							)

											SCREAMING_SNAKE_CASE__													= resnet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)

						if self.add_upsample:
											SCREAMING_SNAKE_CASE__													= self.upsamplers_a(UpperCAmelCase_							)

						return hidden_states





class 						lowercase__  (						nn.Module  ):
	A__    :   int
	A__    :   float				       =0.0
	A__    :   int				       =1
	A__    :   int				       =1
	A__    :   bool				       =False
	A__    :   bool				       =False
	A__    :   jnp.dtype				       =jnp.floataa
	def      A_				(			self							:   Optional[int]							):
						# there is always at least one resnet
						SCREAMING_SNAKE_CASE__													= [
						    FlaxResnetBlockaD(
						        in_channels=self.in_channels       ,   out_channels=self.in_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
						]

						SCREAMING_SNAKE_CASE__													= []

						for _ in range(self.num_layers							):
											SCREAMING_SNAKE_CASE__													= FlaxTransformeraDModel(
											    in_channels=self.in_channels       ,   n_heads=self.num_attention_heads       ,   d_head=self.in_channels // self.num_attention_heads       ,   depth=1       ,   use_linear_projection=self.use_linear_projection       ,   use_memory_efficient_attention=self.use_memory_efficient_attention       ,   dtype=self.dtype       ,   )
											attentions.append(UpperCAmelCase_							)

											SCREAMING_SNAKE_CASE__													= FlaxResnetBlockaD(
											    in_channels=self.in_channels       ,   out_channels=self.in_channels       ,   dropout_prob=self.dropout       ,   dtype=self.dtype       ,   )
											resnets.append(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= resnets
						SCREAMING_SNAKE_CASE__													= attentions





	def __call__(			self							:   str       ,   UpperCAmelCase_							:   Union[str, Any]       ,   UpperCAmelCase_							:   Optional[int]       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   Any=True							):
						SCREAMING_SNAKE_CASE__													= self.resnets[0](UpperCAmelCase_       ,   UpperCAmelCase_							)
						for attn, resnet in zip(self.attentions       ,   self.resnets[1:]							):
											SCREAMING_SNAKE_CASE__													= attn(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= resnet(UpperCAmelCase_       ,   UpperCAmelCase_       ,   deterministic=UpperCAmelCase_							)

						return hidden_states





