
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


_UpperCAmelCase					: Union[str, Any]      =       logging.getLogger(__name__)

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


@dataclasses.dataclass
class 				lowercase					:
		__lowercase       :       str	     =				dataclasses.field(
		    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}   )
		__lowercase       :       Optional[str]	     =				dataclasses.field(
		    default=_SCREAMING_SNAKE_CASE  ,   metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}  ,   )






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






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






def       A       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

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

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

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

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

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


def       A       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       **lowercase )   ->  Any:
		'''simple docstring'''

		UpperCamelCase		=     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()

		UpperCamelCase		=     STModelArguments(model_name_or_path=lowercase )
		UpperCamelCase		=     STDataArguments(train_file=lowercase    ,       infer_file=lowercase )
		UpperCamelCase		=     STTrainingArguments(output_dir=lowercase )
		UpperCamelCase		=     argparse.Namespace()

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

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

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

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

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

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

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

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

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

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

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

				UpperCamelCase		=     iteration
				UpperCamelCase		=     data_dir_format(iteration + 1 )

				UpperCamelCase		=     AutoConfig.from_pretrained(os.path.join(lowercase    ,       'best-checkpoint' ) )
				UpperCamelCase		=     config.idalabel
				UpperCamelCase		=     os.path.join(lowercase    ,       'eval_results_best-checkpoint.json' )
				UpperCamelCase		=     os.path.join(lowercase    ,       'test_results_best-checkpoint.json' )
				assert os.path.exists(lowercase )

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

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

				UpperCamelCase		=     os.path.join(lowercase    ,       f'''train_pseudo.{args.data_file_extension}''' )

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

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

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


import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       int	     =				IFInpaintingPipeline
		__lowercase       :       str	     =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
		__lowercase       :       Optional[int]	     =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		__lowercase       :       Optional[int]	     =				PipelineTesterMixin.required_optional_params - {"latents"}



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return self._get_dummy_components()



		def __UpperCamelCase ( self				,      A_				,      A_=0       )  ->					List[Any]:
				"""simple docstring"""
				if str(A_       ).startswith('mps'       ):
						UpperCamelCase		=     torch.manual_seed(A_       )
				else:
						UpperCamelCase		=     torch.Generator(device=A_       ).manual_seed(A_       )

				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )
				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )

				UpperCamelCase		=     {
				    'prompt': 'A painting of a squirrel eating a burger',
				    'image': image,
				    'mask_image': mask_image,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'output_type': 'numpy',
				}

				return inputs



		@unittest.skipIf(
		    torch_device != 'cuda' or not is_xformers_available()				,      reason='XFormers attention is only available with CUDA and `xformers` installed'				,      )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != 'cuda'				,      reason='float16 requires CUDA'       )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
				super().test_save_load_floataa(expected_max_diff=1e-1       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_save_load_local()



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2				,      )
