





import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCAmelCase_					=					logging.getLogger(__name__)






@dataclass
class        lowercase__			:

   '''simple docstring'''


   a		:				str  						=						field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							)
   a		:				List[str]  						=						field(
       default=_UpperCamelCase     ,							metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
   a		:				Optional[Any]  						=						field(
       default="NER"     ,							metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}							)
   a		:				List[str]  						=						field(
       default=_UpperCamelCase     ,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
   a		:				Any  						=						field(default=_UpperCamelCase     ,							metadata={"help": "Set this flag to use fast tokenization."}							)
   # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
   # or just modify its tokenizer_config.json.
   a		:				Any  						=						field(
       default=_UpperCamelCase     ,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}     ,							)






@dataclass
class        lowercase__			:

   '''simple docstring'''


   a		:				List[str]  						=						field(
       metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}							)
   a		:				Optional[int]  						=						field(
       default=_UpperCamelCase     ,							metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}     ,							)
   a		:				str  						=						field(
       default=128     ,							metadata={
           "help": (
               "The maximum total input sequence length after tokenization. Sequences longer "
               "than this will be truncated, sequences shorter will be padded."
           )
       }     ,							)
   a		:				Tuple  						=						field(
       default=_UpperCamelCase     ,							metadata={"help": "Overwrite the cached training and evaluation sets"}							)




def 						lowerCAmelCase_       (    ) ->      Union[str, Any]:
    UpperCamelCase__				:							str			  = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
    if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        UpperCamelCase__				:							str			  = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
    else:
        UpperCamelCase__				:							Union[str, Any]			  = parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir )
        and os.listdir(training_args.output_dir )
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
            ''' --overwrite_output_dir to overcome.''' )

    UpperCamelCase__				:							List[str]			  = import_module('''tasks''' )
    try:
        UpperCamelCase__				:							str			  = getattr(_lowercase						,  model_args.task_type )
        UpperCamelCase__				:							TokenClassificationTask			  = token_classification_task_clazz()
    except AttributeError:
        raise ValueError(
            f"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
            f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}" )

    # Setup logging
    logging.basicConfig(
        format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''						,  datefmt='''%m/%d/%Y %H:%M:%S'''						,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN						,  )
    logger.warning(
        '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''						,  training_args.local_rank						,  training_args.device						,  training_args.n_gpu						,  bool(training_args.local_rank != -1 )						,  training_args.fpaa						,  )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank ):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info('''Training/evaluation parameters %s'''						,  _lowercase )

    # Set seed
    set_seed(training_args.seed )

    # Prepare CONLL-2003 task
    UpperCamelCase__				:							Tuple			  = token_classification_task.get_labels(data_args.labels )
    UpperCamelCase__				:							Dict[int, str]			  = dict(enumerate(_lowercase ) )
    UpperCamelCase__				:							Optional[int]			  = len(_lowercase )

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    UpperCamelCase__				:							Dict			  = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path						,  num_labels=_lowercase						,  idalabel=_lowercase						,  labelaid={label: i for i, label in enumerate(_lowercase )}						,  cache_dir=model_args.cache_dir						,  )
    UpperCamelCase__				:							Dict			  = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path						,  cache_dir=model_args.cache_dir						,  use_fast=model_args.use_fast						,  )
    UpperCamelCase__				:							Any			  = AutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path						,  from_tf=bool('''.ckpt''' in model_args.model_name_or_path )						,  config=_lowercase						,  cache_dir=model_args.cache_dir						,  )

    # Get datasets
    UpperCamelCase__				:							int			  = (
        TokenClassificationDataset(
            token_classification_task=_lowercase						,  data_dir=data_args.data_dir						,  tokenizer=_lowercase						,  labels=_lowercase						,  model_type=config.model_type						,  max_seq_length=data_args.max_seq_length						,  overwrite_cache=data_args.overwrite_cache						,  mode=Split.train						,  )
        if training_args.do_train
        else None
    )
    UpperCamelCase__				:							List[Any]			  = (
        TokenClassificationDataset(
            token_classification_task=_lowercase						,  data_dir=data_args.data_dir						,  tokenizer=_lowercase						,  labels=_lowercase						,  model_type=config.model_type						,  max_seq_length=data_args.max_seq_length						,  overwrite_cache=data_args.overwrite_cache						,  mode=Split.dev						,  )
        if training_args.do_eval
        else None
    )

    def align_predictions(__UpperCAmelCase:     np.ndarray						,  __UpperCAmelCase:     np.ndarray ) -> Tuple[List[int], List[int]]:
        UpperCamelCase__				:							str			  = np.argmax(_lowercase						,  axis=2 )

        UpperCamelCase__				:							Tuple			  = preds.shape

        UpperCamelCase__				:							Tuple			  = [[] for _ in range(_lowercase )]
        UpperCamelCase__				:							Dict			  = [[] for _ in range(_lowercase )]

        for i in range(_lowercase ):
            for j in range(_lowercase ):
                if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                    out_label_list[i].append(label_map[label_ids[i][j]] )
                    preds_list[i].append(label_map[preds[i][j]] )

        return preds_list, out_label_list

    def compute_metrics(__UpperCAmelCase:     EvalPrediction ) -> Dict:
        UpperCamelCase__				:							str			  = align_predictions(p.predictions						,  p.label_ids )
        return {
            "accuracy_score": accuracy_score(_lowercase						,  _lowercase ),
            "precision": precision_score(_lowercase						,  _lowercase ),
            "recall": recall_score(_lowercase						,  _lowercase ),
            "f1": fa_score(_lowercase						,  _lowercase ),
        }

    # Data collator
    UpperCamelCase__				:							List[str]			  = DataCollatorWithPadding(_lowercase						,  pad_to_multiple_of=8 ) if training_args.fpaa else None

    # Initialize our Trainer
    UpperCamelCase__				:							List[str]			  = Trainer(
        model=_lowercase						,  args=_lowercase						,  train_dataset=_lowercase						,  eval_dataset=_lowercase						,  compute_metrics=_lowercase						,  data_collator=_lowercase						,  )

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_process_zero():
            tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
    UpperCamelCase__				:							List[str]			  = {}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***''' )

        UpperCamelCase__				:							Union[str, Any]			  = trainer.evaluate()

        UpperCamelCase__				:							Tuple			  = os.path.join(training_args.output_dir						,  '''eval_results.txt''' )
        if trainer.is_world_process_zero():
            with open(_lowercase						,  '''w''' ) as writer:
                logger.info('''***** Eval results *****''' )
                for key, value in result.items():
                    logger.info('''  %s = %s'''						,  _lowercase						,  _lowercase )
                    writer.write('''%s = %s\n''' % (key, value) )

            results.update(_lowercase )

    # Predict
    if training_args.do_predict:
        UpperCamelCase__				:							List[Any]			  = TokenClassificationDataset(
            token_classification_task=_lowercase						,  data_dir=data_args.data_dir						,  tokenizer=_lowercase						,  labels=_lowercase						,  model_type=config.model_type						,  max_seq_length=data_args.max_seq_length						,  overwrite_cache=data_args.overwrite_cache						,  mode=Split.test						,  )

        UpperCamelCase__				:							Any			  = trainer.predict(_lowercase )
        UpperCamelCase__				:							Optional[int]			  = align_predictions(_lowercase						,  _lowercase )

        UpperCamelCase__				:							Optional[Any]			  = os.path.join(training_args.output_dir						,  '''test_results.txt''' )
        if trainer.is_world_process_zero():
            with open(_lowercase						,  '''w''' ) as writer:
                for key, value in metrics.items():
                    logger.info('''  %s = %s'''						,  _lowercase						,  _lowercase )
                    writer.write('''%s = %s\n''' % (key, value) )

        # Save predictions
        UpperCamelCase__				:							Optional[int]			  = os.path.join(training_args.output_dir						,  '''test_predictions.txt''' )
        if trainer.is_world_process_zero():
            with open(_lowercase						,  '''w''' ) as writer:
                with open(os.path.join(data_args.data_dir						,  '''test.txt''' )						,  '''r''' ) as f:
                    token_classification_task.write_predictions_to_file(_lowercase						,  _lowercase						,  _lowercase )

    return results




def 						lowerCAmelCase_       (    __UpperCAmelCase:     Optional[int] ) ->      Tuple:
    main()


if __name__ == "__main__":
  main()








import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel


def       SCREAMING_SNAKE_CASE__  (       )   ->							int:




		'''simple docstring'''
		lowercase__					:      str      =		argparse.ArgumentParser()
		parser.add_argument(
		    '-m'   ,    '--pretrained_model_name_or_path'   ,    type=_lowercase   ,    default=_lowercase   ,    required=_lowercase   ,    help='Path to pretrained model or model identifier from huggingface.co/models.'   ,    )
		parser.add_argument(
		    '-c'   ,    '--caption'   ,    type=_lowercase   ,    default='robotic cat with wings'   ,    help='Text used to generate images.'   ,    )
		parser.add_argument(
		    '-n'   ,    '--images_num'   ,    type=_lowercase   ,    default=4   ,    help='How much images to generate.'   ,    )
		parser.add_argument(
		    '-s'   ,    '--seed'   ,    type=_lowercase   ,    default=42   ,    help='Seed for random process.'   ,    )
		parser.add_argument(
		    '-ci'   ,    '--cuda_id'   ,    type=_lowercase   ,    default=0   ,    help='cuda_id.'   ,    )
		lowercase__					:      Any      =		parser.parse_args()
		return args


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Union[str, Any]   ,    _lowercase     : Optional[int]   ,    _lowercase     : Union[str, Any]			)   ->							str:




		'''simple docstring'''
		if not len(_lowercase			) == rows * cols:
				raise ValueError('The specified number of rows and columns are not correct.'			)

		lowercase__   ,				lowercase__					:      Any      =		imgs[0].size
		lowercase__					:      int      =		Image.new('RGB'   ,    size=(cols * w, rows * h)			)
		lowercase__   ,				lowercase__					:      Any      =		grid.size

		for i, img in enumerate(_lowercase			):
				grid.paste(_lowercase   ,    box=(i % cols * w, i // cols * h)			)
		return grid


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]   ,    _lowercase     : Tuple="robotic cat with wings"   ,    _lowercase     : Optional[Any]=7.5   ,    _lowercase     : int=50   ,    _lowercase     : str=1   ,    _lowercase     : Any=42   ,    )   ->							str:




		'''simple docstring'''
		lowercase__					:      int      =		torch.Generator(pipeline.device			).manual_seed(_lowercase			)
		lowercase__					:      Any      =		pipeline(
		    _lowercase   ,    guidance_scale=_lowercase   ,    num_inference_steps=_lowercase   ,    generator=_lowercase   ,    num_images_per_prompt=_lowercase   ,    ).images
		lowercase__					:      Dict      =		int(math.sqrt(_lowercase			)			)
		lowercase__					:      int      =		image_grid(_lowercase   ,    rows=_rows   ,    cols=num_images_per_prompt // _rows			)
		return grid, images


__UpperCamelCase:	int = parse_args()
# Load models and create wrapper for stable diffusion
__UpperCamelCase:	Optional[int] = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
__UpperCamelCase:	List[str] = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
__UpperCamelCase:	int = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
__UpperCamelCase:	int = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

__UpperCamelCase:	Tuple = StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase:	List[str] = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
			__UpperCamelCase:	Dict = load(args.pretrained_model_name_or_path, model=unet)
			unet.eval()
			setattr(pipeline, """unet""", unet)
else:
			__UpperCamelCase:	Dict = unet.to(torch.device("""cuda""", args.cuda_id))
__UpperCamelCase:	Optional[int] = pipeline.to(unet.device)
__UpperCamelCase,	__UpperCamelCase:	Union[str, Any] = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, """{}.png""".format("""_""".join(args.caption.split()))))
__UpperCamelCase:	List[str] = os.path.join(args.pretrained_model_name_or_path, """_""".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
			image.save(os.path.join(dirname, """{}.png""".format(idx + 1)))


