


"""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_   :   Dict									=  logging.getLogger(__name__)

lowerCamelCase_   :   Tuple									=  """pytorch_model.bin"""






@dataclasses.dataclass
class       __A				:






 """simple docstring"""




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






@dataclasses.dataclass
class       __A				:






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






@dataclasses.dataclass
class       __A				:






 """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=_SCREAMING_SNAKE_CASE,    metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."},    )
 __lowerCAmelCase 					=  dataclasses.field(
     default=_SCREAMING_SNAKE_CASE,    metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."},    )
 __lowerCAmelCase 					=  dataclasses.field(
     default=_SCREAMING_SNAKE_CASE,    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=_SCREAMING_SNAKE_CASE,    metadata={"help": "Random seed for initialization."},    )

def _A						(						lowercase       ,    lowercase       ,    lowercase       ,    lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=datasets.concatenate_datasets([infer_input, infer_output]       ,    axis=1		)

    if args.do_filter_by_confidence:
        a 		=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
        a 		=int(eval_result * len(lowercase		)		)
        print(lowercase		)
        a 		=dataset.sort('''probability'''       ,    reverse=lowercase		)
        a 		=dataset.select(range(lowercase		)		)

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

    a 		=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		):







    """simple docstring"""
    a 		=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 		=STModelArguments(model_name_or_path=lowercase		)
    a 		=STDataArguments(train_file=lowercase       ,    infer_file=lowercase		)
    a 		=STTrainingArguments(output_dir=lowercase		)
    a 		=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
    a 		={}
    a 		=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 		=args.train_file
    a 		=args.infer_file

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

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

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

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

        a 		=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
            a 		=os.path.join(lowercase       ,    '''best-checkpoint'''		)
            a 		=os.path.join(lowercase       ,    '''stage-2'''		)
            # Update arguments_dict
            a 		=model_path
            a 		=data_files['''train''']
            a 		=current_output_dir

            a 		=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		)

        a 		=iteration
        a 		=data_dir_format(iteration + 1		)

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

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

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

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

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

                if early_stopping_patience_counter >= args.early_stopping_patience:
                    a 		=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 gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class 			a__    (					snake_case__ ,				snake_case__ ,				snake_case__ ,				unittest.TestCase      ):
						_a							:    str					=    StableUnCLIPPipeline
						_a							:    Union[str, Any]					=    TEXT_TO_IMAGE_PARAMS
						_a							:    Dict					=    TEXT_TO_IMAGE_BATCH_PARAMS
						_a							:    Optional[int]					=    TEXT_TO_IMAGE_IMAGE_PARAMS
						_a							:    Dict					=    TEXT_TO_IMAGE_IMAGE_PARAMS

						# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
						_a							:    Optional[Any]					=    False






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       3_2
											__lowerCAmelCase				   =       embedder_hidden_size

											# prior components

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       CLIPTextModelWithProjection(
											    CLIPTextConfig(
											        bos_token_id=0		,			eos_token_id=2		,			hidden_size=_A		,			projection_dim=_A		,			intermediate_size=3_7		,			layer_norm_eps=1E-0_5		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=1_0_0_0		,			)			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       PriorTransformer(
											    num_attention_heads=2		,			attention_head_dim=1_2		,			embedding_dim=_A		,			num_layers=1		,			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       DDPMScheduler(
											    variance_type="fixed_small_log"		,			prediction_type="sample"		,			num_train_timesteps=1_0_0_0		,			clip_sample=_A		,			clip_sample_range=5.0		,			beta_schedule="squaredcos_cap_v2"		,			)

											# regular denoising components

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       StableUnCLIPImageNormalizer(embedding_dim=_A			)
											__lowerCAmelCase				   =       DDPMScheduler(beta_schedule="squaredcos_cap_v2"			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       CLIPTextModel(
											    CLIPTextConfig(
											        bos_token_id=0		,			eos_token_id=2		,			hidden_size=_A		,			projection_dim=3_2		,			intermediate_size=3_7		,			layer_norm_eps=1E-0_5		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=1_0_0_0		,			)			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       UNetaDConditionModel(
											    sample_size=3_2		,			in_channels=4		,			out_channels=4		,			down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")		,			up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")		,			block_out_channels=(3_2, 6_4)		,			attention_head_dim=(2, 4)		,			class_embed_type="projection"		,			projection_class_embeddings_input_dim=embedder_projection_dim * 2		,			cross_attention_dim=_A		,			layers_per_block=1		,			upcast_attention=_A		,			use_linear_projection=_A		,			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       DDIMScheduler(
											    beta_schedule="scaled_linear"		,			beta_start=0.0_00_85		,			beta_end=0.0_12		,			prediction_type="v_prediction"		,			set_alpha_to_one=_A		,			steps_offset=1		,			)

											torch.manual_seed(0			)
											__lowerCAmelCase				   =       AutoencoderKL()

											__lowerCAmelCase				   =       {
											    # prior components
											    "prior_tokenizer": prior_tokenizer,
											    "prior_text_encoder": prior_text_encoder,
											    "prior": prior,
											    "prior_scheduler": prior_scheduler,
											    # image noising components
											    "image_normalizer": image_normalizer,
											    "image_noising_scheduler": image_noising_scheduler,
											    # regular denoising components
											    "tokenizer": tokenizer,
											    "text_encoder": text_encoder,
											    "unet": unet,
											    "scheduler": scheduler,
											    "vae": vae,
											}

											return components






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=0			):




											"""simple docstring"""



											if str(_A			).startswith("mps"			):
																__lowerCAmelCase				   =       torch.manual_seed(_A			)
											else:
																__lowerCAmelCase				   =       torch.Generator(device=_A			).manual_seed(_A			)
											__lowerCAmelCase				   =       {
											    "prompt": "A painting of a squirrel eating a burger",
											    "generator": generator,
											    "num_inference_steps": 2,
											    "prior_num_inference_steps": 2,
											    "output_type": "numpy",
											}
											return inputs






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       torch_device == "cpu"

											self._test_attention_slicing_forward_pass(test_max_difference=_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       torch_device in ["cpu", "mps"]

											self._test_inference_batch_single_identical(test_max_difference=_A			)






@slow
@require_torch_gpu
class 			a__    (					unittest.TestCase      ):






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"			)

											__lowerCAmelCase				   =       StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"		,			torch_dtype=torch.floataa			)
											pipe.to(_A			)
											pipe.set_progress_bar_config(disable=_A			)
											# stable unclip will oom when integration tests are run on a V100,
											# so turn on memory savings
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											__lowerCAmelCase				   =       torch.Generator(device="cpu"			).manual_seed(0			)
											__lowerCAmelCase				   =       pipe("anime turle"		,			generator=_A		,			output_type="np"			)

											__lowerCAmelCase				   =       output.images[0]

											assert image.shape == (7_6_8, 7_6_8, 3)

											assert_mean_pixel_difference(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											__lowerCAmelCase				   =       StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"		,			torch_dtype=torch.floataa			)
											__lowerCAmelCase				   =       pipe.to(_A			)
											pipe.set_progress_bar_config(disable=_A			)
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											__lowerCAmelCase				   =       pipe(
											    "anime turtle"		,			prior_num_inference_steps=2		,			num_inference_steps=2		,			output_type="np"		,			)

											__lowerCAmelCase				   =       torch.cuda.max_memory_allocated()
											# make sure that less than 7 GB is allocated
											assert mem_bytes < 7 * 1_0**9




