





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


lowercase      :       List[Any]									=  logging.getLogger(__name__)

lowercase      :       str									=  """pytorch_model.bin"""





@dataclasses.dataclass
class  A__    :




   """simple docstring"""



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





@dataclasses.dataclass
class  A__    :




   """simple docstring"""



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





@dataclasses.dataclass
class  A__    :




   """simple docstring"""



   __A   :				str         =  dataclasses.field(
       metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}	)
   __A   :				Optional[str]         =  dataclasses.field(
       default='''accuracy''' ,  metadata={'''help''': '''The evaluation metric used for the task.'''}	)
   __A   :				Optional[str]         =  dataclasses.field(
       default='''no''' ,  metadata={
           '''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
       } ,  )
   __A   :				Optional[int]         =  dataclasses.field(
       default=1_0 ,  metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} ,  )
   __A   :				Optional[float]         =  dataclasses.field(
       default=0.0 ,  metadata={
           '''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
       } ,  )
   __A   :				Optional[bool]         =  dataclasses.field(
       default=__UpperCAmelCase ,  metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''} ,  )
   __A   :				Optional[bool]         =  dataclasses.field(
       default=__UpperCAmelCase ,  metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''} ,  )
   __A   :				Optional[bool]         =  dataclasses.field(
       default=__UpperCAmelCase ,  metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''} ,  )
   __A   :				Optional[float]         =  dataclasses.field(
       default=0.0 ,  metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''} ,  )
   __A   :				Optional[int]         =  dataclasses.field(
       default=1_0_0 ,  metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} ,  )
   __A   :				Optional[int]         =  dataclasses.field(
       default=__UpperCAmelCase ,  metadata={'''help''': '''Random seed for initialization.'''} ,  )



def A_	(		A__		,      A__		,      A__		,      A__		,      A__		,      A__				)  ->		List[Any]:
       a__						:	int =				datasets.concatenate_datasets([infer_input, infer_output]		,      axis=1				)

       if args.do_filter_by_confidence:
              a__						:	List[str] =				dataset.filter(lambda A__				: example["probability"] > args.confidence_threshold				)

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

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

       a__						:	Dict =				os.path.join(A__		,      F'train_pseudo.{args.data_file_extension}'				)
       if args.data_file_extension == "csv":
              dataset.to_csv(A__		,      index=A__				)
       else:
              dataset.to_json(A__				)



def A_	(		A__		,      A__		,      A__		,      A__		,      **A__				)  ->		Union[str, Any]:
       a__						:	Any =				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()

       a__						:	List[Any] =				STModelArguments(model_name_or_path=A__				)
       a__						:	Union[str, Any] =				STDataArguments(train_file=A__		,      infer_file=A__				)
       a__						:	str =				STTrainingArguments(output_dir=A__				)
       a__						:	Optional[int] =				argparse.Namespace()

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

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

    # Sanity checks
       a__						:	Union[str, Any] =				{}
       a__						:	Optional[int] =				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
       a__						:	int =				args.train_file
       a__						:	Any =				args.infer_file

       if args.evaluation_strategy != IntervalStrategy.NO.value:
              assert args.eval_file is not None
              a__						:	Dict =				args.eval_file

       for key in data_files:
              a__						:	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:
                     a__						:	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...'				)
       a__						:	Optional[Any] =				F'{args.output_dir}/self-train_iter-{{}}'.format
       a__						:	List[str] =				data_dir_format(0				)

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

       a__						:	List[Any] =				None
       a__						:	Tuple =				None
       a__						:	Optional[Any] =				0
       a__						:	int =				False
       # Show the progress bar
       a__						:	List[Any] =				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				)				):
              a__						:	Any =				data_dir_format(A__				)
              assert os.path.exists(A__				)

              # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
              # iteration > 0
              a__						:	Union[str, Any] =				os.path.join(A__		,      'stage-1'				)
              a__						:	int =				{
                  '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(A__		,      A__				):
                            arguments_dict.update({key: value}				)

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

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

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

              a__						:	Optional[Any] =				iteration
              a__						:	Optional[Any] =				data_dir_format(iteration + 1				)

              a__						:	Union[str, Any] =				AutoConfig.from_pretrained(os.path.join(A__		,      'best-checkpoint'				)				)
              a__						:	Tuple =				config.idalabel
              a__						:	Tuple =				os.path.join(A__		,      'eval_results_best-checkpoint.json'				)
              a__						:	Optional[Any] =				os.path.join(A__		,      'test_results_best-checkpoint.json'				)
              assert os.path.exists(A__				)

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

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

              a__						:	Union[str, Any] =				os.path.join(A__		,      F'train_pseudo.{args.data_file_extension}'				)

              if args.evaluation_strategy != IntervalStrategy.NO.value:
                     a__						:	List[Any] =				eval_result

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

                            if early_stopping_patience_counter >= args.early_stopping_patience:
                                   a__						:	Optional[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'		,      A__				)
              logger.info('Best evaluation result: %s = %f'		,      args.eval_metric		,      A__				)
              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     shutil.copy(
                         os.path.join(A__		,      F'eval_results_iter-{iteration}.json'				)		,      os.path.join(A__		,      '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		,      A__				)
              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     shutil.copy(
                         os.path.join(A__		,      F'eval_results_iter-{args.max_selftrain_iterations - 1}.json'				)		,      os.path.join(A__		,      'eval_results_best-iteration.json'				)		,      )







from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings






@maybe_allow_in_graph
class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=0.0,		lowerCamelCase__ = None,		lowerCamelCase__ = "geglu",		lowerCamelCase__ = None,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		lowerCamelCase__ = True,		lowerCamelCase__ = "layer_norm",		lowerCamelCase__ = False,		):
        super().__init__()
        A :       Dict          =   only_cross_attention

        A :       Tuple          =   (num_embeds_ada_norm is not None) and norm_type == """ada_norm_zero"""
        A :       Union[str, Any]          =   (num_embeds_ada_norm is not None) and norm_type == """ada_norm"""

        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
            raise ValueError(
                f'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
                f''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'''		)

        # Define 3 blocks. Each block has its own normalization layer.
        # 1. Self-Attn
        if self.use_ada_layer_norm:
            A :       Dict          =   AdaLayerNorm(lowerCamelCase__,		lowerCamelCase__		)
        elif self.use_ada_layer_norm_zero:
            A :       List[str]          =   AdaLayerNormZero(lowerCamelCase__,		lowerCamelCase__		)
        else:
            A :       Tuple          =   nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__		)
        A :       Any          =   Attention(
            query_dim=lowerCamelCase__,		heads=lowerCamelCase__,		dim_head=lowerCamelCase__,		dropout=lowerCamelCase__,		bias=lowerCamelCase__,		cross_attention_dim=cross_attention_dim if only_cross_attention else None,		upcast_attention=lowerCamelCase__,		)

        # 2. Cross-Attn
        if cross_attention_dim is not None or double_self_attention:
            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
            # the second cross attention block.
            A :       int          =   (
                AdaLayerNorm(lowerCamelCase__,		lowerCamelCase__		)
                if self.use_ada_layer_norm
                else nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__		)
            )
            A :       Dict          =   Attention(
                query_dim=lowerCamelCase__,		cross_attention_dim=cross_attention_dim if not double_self_attention else None,		heads=lowerCamelCase__,		dim_head=lowerCamelCase__,		dropout=lowerCamelCase__,		bias=lowerCamelCase__,		upcast_attention=lowerCamelCase__,		)  # is self-attn if encoder_hidden_states is none
        else:
            A :       Dict          =   None
            A :       Dict          =   None

        # 3. Feed-forward
        A :       Optional[Any]          =   nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__		)
        A :       int          =   FeedForward(lowerCamelCase__,		dropout=lowerCamelCase__,		activation_fn=lowerCamelCase__,		final_dropout=lowerCamelCase__		)

        # let chunk size default to None
        A :       Optional[Any]          =   None
        A :       int          =   0






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        # Sets chunk feed-forward
        A :       List[str]          =   chunk_size
        A :       int          =   dim






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		):
        # Notice that normalization is always applied before the real computation in the following blocks.
        # 1. Self-Attention
        if self.use_ada_layer_norm:
            A :       Optional[int]          =   self.norma(lowerCamelCase__,		lowerCamelCase__		)
        elif self.use_ada_layer_norm_zero:
            A     ,							A     ,							A     ,							A     ,							A :       Tuple          =   self.norma(
                lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		hidden_dtype=hidden_states.dtype		)
        else:
            A :       Tuple          =   self.norma(lowerCamelCase__		)

        A :       Dict          =   cross_attention_kwargs if cross_attention_kwargs is not None else {}

        A :       str          =   self.attna(
            lowerCamelCase__,		encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,		attention_mask=lowerCamelCase__,		**lowerCamelCase__,		)
        if self.use_ada_layer_norm_zero:
            A :       List[Any]          =   gate_msa.unsqueeze(1		) * attn_output
        A :       str          =   attn_output + hidden_states

        # 2. Cross-Attention
        if self.attna is not None:
            A :       Optional[int]          =   (
                self.norma(lowerCamelCase__,		lowerCamelCase__		) if self.use_ada_layer_norm else self.norma(lowerCamelCase__		)
            )

            A :       Optional[Any]          =   self.attna(
                lowerCamelCase__,		encoder_hidden_states=lowerCamelCase__,		attention_mask=lowerCamelCase__,		**lowerCamelCase__,		)
            A :       Dict          =   attn_output + hidden_states

        # 3. Feed-forward
        A :       str          =   self.norma(lowerCamelCase__		)

        if self.use_ada_layer_norm_zero:
            A :       Tuple          =   norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

        if self._chunk_size is not None:
            # "feed_forward_chunk_size" can be used to save memory
            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
                raise ValueError(
                    f'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'''		)

            A :       Any          =   norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
            A :       Optional[Any]          =   torch.cat(
                [self.ff(lowerCamelCase__		) for hid_slice in norm_hidden_states.chunk(lowerCamelCase__,		dim=self._chunk_dim		)],		dim=self._chunk_dim,		)
        else:
            A :       Dict          =   self.ff(lowerCamelCase__		)

        if self.use_ada_layer_norm_zero:
            A :       Optional[Any]          =   gate_mlp.unsqueeze(1		) * ff_output

        A :       Optional[int]          =   ff_output + hidden_states

        return hidden_states






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = 4,		lowerCamelCase__ = 0.0,		lowerCamelCase__ = "geglu",		lowerCamelCase__ = False,		):
        super().__init__()
        A :       str          =   int(dim * mult		)
        A :       Optional[Any]          =   dim_out if dim_out is not None else dim

        if activation_fn == "gelu":
            A :       Dict          =   GELU(lowerCamelCase__,		lowerCamelCase__		)
        if activation_fn == "gelu-approximate":
            A :       Optional[int]          =   GELU(lowerCamelCase__,		lowerCamelCase__,		approximate="""tanh"""		)
        elif activation_fn == "geglu":
            A :       Any          =   GEGLU(lowerCamelCase__,		lowerCamelCase__		)
        elif activation_fn == "geglu-approximate":
            A :       Optional[Any]          =   ApproximateGELU(lowerCamelCase__,		lowerCamelCase__		)

        A :       Dict          =   nn.ModuleList([]		)
        # project in
        self.net.append(lowerCamelCase__		)
        # project dropout
        self.net.append(nn.Dropout(lowerCamelCase__		)		)
        # project out
        self.net.append(nn.Linear(lowerCamelCase__,		lowerCamelCase__		)		)
        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
        if final_dropout:
            self.net.append(nn.Dropout(lowerCamelCase__		)		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        for module in self.net:
            A :       int          =   module(lowerCamelCase__		)
        return hidden_states






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = "none"		):
        super().__init__()
        A :       Optional[int]          =   nn.Linear(lowerCamelCase__,		lowerCamelCase__		)
        A :       int          =   approximate






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if gate.device.type != "mps":
            return F.gelu(lowerCamelCase__,		approximate=self.approximate		)
        # mps: gelu is not implemented for float16
        return F.gelu(gate.to(dtype=torch.floataa		),		approximate=self.approximate		).to(dtype=gate.dtype		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       List[Any]          =   self.proj(lowerCamelCase__		)
        A :       Union[str, Any]          =   self.gelu(lowerCamelCase__		)
        return hidden_states






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()
        A :       Union[str, Any]          =   nn.Linear(lowerCamelCase__,		dim_out * 2		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if gate.device.type != "mps":
            return F.gelu(lowerCamelCase__		)
        # mps: gelu is not implemented for float16
        return F.gelu(gate.to(dtype=torch.floataa		)		).to(dtype=gate.dtype		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A     ,							A :       Any          =   self.proj(lowerCamelCase__		).chunk(2,		dim=-1		)
        return hidden_states * self.gelu(lowerCamelCase__		)






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()
        A :       Union[str, Any]          =   nn.Linear(lowerCamelCase__,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       List[Any]          =   self.proj(lowerCamelCase__		)
        return x * torch.sigmoid(1.702 * x		)






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        super().__init__()
        A :       Dict          =   nn.Embedding(lowerCamelCase__,		lowerCamelCase__		)
        A :       Tuple          =   nn.SiLU()
        A :       Tuple          =   nn.Linear(lowerCamelCase__,		embedding_dim * 2		)
        A :       List[str]          =   nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        A :       int          =   self.linear(self.silu(self.emb(lowerCamelCase__		)		)		)
        A     ,							A :       Optional[int]          =   torch.chunk(lowerCamelCase__,		2		)
        A :       Tuple          =   self.norm(lowerCamelCase__		) * (1 + scale) + shift
        return x






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






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

        A :       Union[str, Any]          =   CombinedTimestepLabelEmbeddings(lowerCamelCase__,		lowerCamelCase__		)

        A :       List[Any]          =   nn.SiLU()
        A :       str          =   nn.Linear(lowerCamelCase__,		6 * embedding_dim,		bias=lowerCamelCase__		)
        A :       List[Any]          =   nn.LayerNorm(lowerCamelCase__,		elementwise_affine=lowerCamelCase__,		eps=1e-6		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A :       Tuple          =   self.linear(self.silu(self.emb(lowerCamelCase__,		lowerCamelCase__,		hidden_dtype=lowerCamelCase__		)		)		)
        A     ,							A     ,							A     ,							A     ,							A     ,							A :       Optional[Any]          =   emb.chunk(6,		dim=1		)
        A :       List[str]          =   self.norm(lowerCamelCase__		) * (1 + scale_msa[:, None]) + shift_msa[:, None]
        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp






class SCREAMING_SNAKE_CASE__     (		nn.Module    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = 1e-5		):
        super().__init__()
        A :       int          =   num_groups
        A :       Dict          =   eps

        if act_fn is None:
            A :       Union[str, Any]          =   None
        else:
            A :       Optional[int]          =   get_activation(lowerCamelCase__		)

        A :       Dict          =   nn.Linear(lowerCamelCase__,		out_dim * 2		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        if self.act:
            A :       Dict          =   self.act(lowerCamelCase__		)
        A :       Optional[int]          =   self.linear(lowerCamelCase__		)
        A :       int          =   emb[:, :, None, None]
        A     ,							A :       Tuple          =   emb.chunk(2,		dim=1		)

        A :       Any          =   F.group_norm(lowerCamelCase__,		self.num_groups,		eps=self.eps		)
        A :       List[str]          =   x * (1 + scale) + shift
        return x


