



'''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


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

lowerCAmelCase	:		Any				=   '''pytorch_model.bin'''






@dataclasses.dataclass
class 					UpperCAmelCase__	:
					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 					UpperCAmelCase__	:
					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 					UpperCAmelCase__	:
					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   __lowerCAmelCase					(							lowerCamelCase   :						List[Any]				,		lowerCamelCase   :						Optional[Any]				,		lowerCamelCase   :						List[str]				,		lowerCamelCase   :						Optional[Any]				,		lowerCamelCase   :						Tuple				,		lowerCamelCase   :						int					):






	'''simple docstring'''




	__lowerCAmelCase												=  datasets.concatenate_datasets([infer_input, infer_output]				,		axis=1					)

	if args.do_filter_by_confidence:
		__lowerCAmelCase												=  dataset.filter(lambda lowerCamelCase					: example["probability"] > args.confidence_threshold					)

	if args.do_filter_by_val_performance:
		assert eval_result >= 0.0 and eval_result <= 1.0
		__lowerCAmelCase												=  int(eval_result * len(lowerCamelCase					)					)
		print(lowerCamelCase					)
		__lowerCAmelCase												=  dataset.sort("probability"				,		reverse=lowerCamelCase					)
		__lowerCAmelCase												=  dataset.select(range(lowerCamelCase					)					)

	__lowerCAmelCase												=  dataset.remove_columns(["label", "probability"]					)
	__lowerCAmelCase												=  dataset.rename_column("prediction"				,		"label"					)
	__lowerCAmelCase												=  dataset.map(lambda lowerCamelCase					: {"label": idalabel[example["label"]]}					)
	__lowerCAmelCase												=  dataset.shuffle(seed=args.seed					)

	__lowerCAmelCase												=  os.path.join(lowerCamelCase				,		f'''train_pseudo.{args.data_file_extension}'''					)
	if args.data_file_extension == "csv":
		dataset.to_csv(lowerCamelCase				,		index=lowerCamelCase					)
	else:
		dataset.to_json(lowerCamelCase					)




def   __lowerCAmelCase					(							lowerCamelCase   :						Tuple				,		lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						Dict				,		lowerCamelCase   :						Optional[Any]				,		**lowerCamelCase   :						Union[str, Any]					):






	'''simple docstring'''




	__lowerCAmelCase												=  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()

	__lowerCAmelCase												=  STModelArguments(model_name_or_path=lowerCamelCase					)
	__lowerCAmelCase												=  STDataArguments(train_file=lowerCamelCase				,		infer_file=lowerCamelCase					)
	__lowerCAmelCase												=  STTrainingArguments(output_dir=lowerCamelCase					)
	__lowerCAmelCase												=  argparse.Namespace()

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

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

    # Sanity checks
	__lowerCAmelCase												=  {}
	__lowerCAmelCase												=  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
	__lowerCAmelCase												=  args.train_file
	__lowerCAmelCase												=  args.infer_file

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

	for key in data_files:
		__lowerCAmelCase												=  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:
			__lowerCAmelCase												=  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..."					)
	__lowerCAmelCase												=  f'''{args.output_dir}/self-train_iter-{{}}'''.format
	__lowerCAmelCase												=  data_dir_format(0					)

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

	__lowerCAmelCase												=  None
	__lowerCAmelCase												=  None
	__lowerCAmelCase												=  0
	__lowerCAmelCase												=  False
	# Show the progress bar
	__lowerCAmelCase												=  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					)					):
		__lowerCAmelCase												=  data_dir_format(lowerCamelCase					)
		assert os.path.exists(lowerCamelCase					)

		# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
		# iteration > 0
		__lowerCAmelCase												=  os.path.join(lowerCamelCase				,		"stage-1"					)
		__lowerCAmelCase												=  {
		    "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(lowerCamelCase				,		lowerCamelCase					):
				arguments_dict.update({key: value}					)

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

		if iteration > 0 and args.finetune_on_labeled_data:
			# Stage 2 (optional): fine-tuning on the original labeled data
			__lowerCAmelCase												=  os.path.join(lowerCamelCase				,		"best-checkpoint"					)
			__lowerCAmelCase												=  os.path.join(lowerCamelCase				,		"stage-2"					)
			# Update arguments_dict
			__lowerCAmelCase												=  model_path
			__lowerCAmelCase												=  data_files["train"]
			__lowerCAmelCase												=  current_output_dir

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

		__lowerCAmelCase												=  iteration
		__lowerCAmelCase												=  data_dir_format(iteration + 1					)

		__lowerCAmelCase												=  AutoConfig.from_pretrained(os.path.join(lowerCamelCase				,		"best-checkpoint"					)					)
		__lowerCAmelCase												=  config.idalabel
		__lowerCAmelCase												=  os.path.join(lowerCamelCase				,		"eval_results_best-checkpoint.json"					)
		__lowerCAmelCase												=  os.path.join(lowerCamelCase				,		"test_results_best-checkpoint.json"					)
		assert os.path.exists(lowerCamelCase					)

		with open(lowerCamelCase				,		"r"					) as f:
			__lowerCAmelCase												=  float(json.load(lowerCamelCase					)[args.eval_metric]					)
		__lowerCAmelCase												=  os.path.join(lowerCamelCase				,		"infer_output_best-checkpoint.csv"					)
		assert os.path.exists(lowerCamelCase					)
		# Loading the dataset from local csv or json files.
		__lowerCAmelCase												=  load_dataset(args.data_file_extension				,		data_files={"data": data_files["infer"]}					)["data"]
		__lowerCAmelCase												=  load_dataset("csv"				,		data_files={"data": infer_output_file}					)["data"]

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

		__lowerCAmelCase												=  os.path.join(lowerCamelCase				,		f'''train_pseudo.{args.data_file_extension}'''					)

		if args.evaluation_strategy != IntervalStrategy.NO.value:
			__lowerCAmelCase												=  eval_result

			if best_iteration is None:
				__lowerCAmelCase												=  new_iteration
				__lowerCAmelCase												=  new_eval_result
			else:
				if new_eval_result - best_eval_result > args.early_stopping_threshold:
					__lowerCAmelCase												=  new_iteration
					__lowerCAmelCase												=  new_eval_result
					__lowerCAmelCase												=  0
				else:
					if new_eval_result == best_eval_result:
						__lowerCAmelCase												=  new_iteration
						__lowerCAmelCase												=  new_eval_result
					early_stopping_patience_counter += 1

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




'''simple docstring'''



from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     torch.FloatTensor






class 					UpperCAmelCase__	(		UpperCamelCase__							,       UpperCamelCase__ ):



					@register_to_config
					def __init__(    self			,				UpperCamelCase = 16			,				UpperCamelCase = 88			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = 1			,				UpperCamelCase = 0.0			,				UpperCamelCase = 32			,				UpperCamelCase = None			,				UpperCamelCase = False			,				UpperCamelCase = None			,				UpperCamelCase = "geglu"			,				UpperCamelCase = True			,				UpperCamelCase = True			,				)     ->			List[str]:
						super().__init__()
						__lowerCAmelCase												=  num_attention_heads
						__lowerCAmelCase												=  attention_head_dim
						__lowerCAmelCase												=  num_attention_heads * attention_head_dim

						__lowerCAmelCase												=  in_channels

						__lowerCAmelCase												=  torch.nn.GroupNorm(num_groups=UpperCamelCase			,				num_channels=UpperCamelCase			,				eps=1E-6			,				affine=UpperCamelCase     )
						__lowerCAmelCase												=  nn.Linear(UpperCamelCase			,				UpperCamelCase     )

						# 3. Define transformers blocks
						__lowerCAmelCase												=  nn.ModuleList(
						    [
						        BasicTransformerBlock(
						            UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				dropout=UpperCamelCase			,				cross_attention_dim=UpperCamelCase			,				activation_fn=UpperCamelCase			,				attention_bias=UpperCamelCase			,				double_self_attention=UpperCamelCase			,				norm_elementwise_affine=UpperCamelCase			,				)
						        for d in range(UpperCamelCase     )
						    ]     )

						__lowerCAmelCase												=  nn.Linear(UpperCamelCase			,				UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=1			,				UpperCamelCase=None			,				UpperCamelCase = True			,				)     ->			List[str]:
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  hidden_states.shape
						__lowerCAmelCase												=  batch_frames // num_frames

						__lowerCAmelCase												=  hidden_states

						__lowerCAmelCase												=  hidden_states[None, :].reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
						__lowerCAmelCase												=  hidden_states.permute(0			,				2			,				1			,				3			,				4     )

						__lowerCAmelCase												=  self.norm(UpperCamelCase     )
						__lowerCAmelCase												=  hidden_states.permute(0			,				3			,				4			,				2			,				1     ).reshape(batch_size * height * width			,				UpperCamelCase			,				UpperCamelCase     )

						__lowerCAmelCase												=  self.proj_in(UpperCamelCase     )

						# 2. Blocks
						for block in self.transformer_blocks:
							__lowerCAmelCase												=  block(
							    UpperCamelCase			,				encoder_hidden_states=UpperCamelCase			,				timestep=UpperCamelCase			,				cross_attention_kwargs=UpperCamelCase			,				class_labels=UpperCamelCase			,				)

						# 3. Output
						__lowerCAmelCase												=  self.proj_out(UpperCamelCase     )
						__lowerCAmelCase												=  (
						    hidden_states[None, None, :]
						    .reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )
						    .permute(0			,				3			,				4			,				1			,				2     )
						    .contiguous()
						)
						__lowerCAmelCase												=  hidden_states.reshape(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )

						__lowerCAmelCase												=  hidden_states + residual

						if not return_dict:
							return (output,)

						return TransformerTemporalModelOutput(sample=UpperCamelCase     )