





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


lowercase_					     =	logging.getLogger(__name__)
@dataclass
class        SCREAMING_SNAKE_CASE     :
   _UpperCamelCase					:	str				      = field(
       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}   )
   _UpperCamelCase					:	Optional[str]				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
   _UpperCamelCase					:	Optional[str]				      = field(
       default='NER'    ,			metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}   )
   _UpperCamelCase					:	Optional[str]				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}   )
   _UpperCamelCase					:	bool				      = 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.
   _UpperCamelCase					:	Optional[str]				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}    ,			)






@dataclass
class        SCREAMING_SNAKE_CASE     :
   _UpperCamelCase					:	str				      = field(
       metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}   )
   _UpperCamelCase					:	Optional[str]				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}    ,			)
   _UpperCamelCase					:	int				      = field(
       default=1_28    ,			metadata={
           'help': (
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )
       }    ,			)
   _UpperCamelCase					:	bool				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}   )






def     __UpperCamelCase						()       ->     Union[str, Any]:
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

  lowercase__			    =						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.
    lowercase__    ,					lowercase__    ,					lowercase__			    =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
  else:
    lowercase__    ,					lowercase__    ,					lowercase__			    =						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.'	)

  lowercase__			    =						import_module('tasks'	)
  try:
    lowercase__			    =						getattr(_SCREAMING_SNAKE_CASE	,		model_args.task_type	)
    lowercase__			    =						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'	,		_SCREAMING_SNAKE_CASE	)

  # Set seed
  set_seed(training_args.seed	)

  # Prepare CONLL-2003 task
  lowercase__			    =						token_classification_task.get_labels(data_args.labels	)
  lowercase__			    =						dict(enumerate(_SCREAMING_SNAKE_CASE	)	)
  lowercase__			    =						len(_SCREAMING_SNAKE_CASE	)

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

  lowercase__			    =						AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path	,		num_labels=_SCREAMING_SNAKE_CASE	,		idalabel=_SCREAMING_SNAKE_CASE	,		labelaid={label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE	)}	,		cache_dir=model_args.cache_dir	,		)
  lowercase__			    =						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	,		)
  lowercase__			    =						AutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path	,		from_tf=bool('.ckpt' in model_args.model_name_or_path	)	,		config=_SCREAMING_SNAKE_CASE	,		cache_dir=model_args.cache_dir	,		)

  # Get datasets
  lowercase__			    =						(
      TokenClassificationDataset(
          token_classification_task=_SCREAMING_SNAKE_CASE	,		data_dir=data_args.data_dir	,		tokenizer=_SCREAMING_SNAKE_CASE	,		labels=_SCREAMING_SNAKE_CASE	,		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
  )
  lowercase__			    =						(
      TokenClassificationDataset(
          token_classification_task=_SCREAMING_SNAKE_CASE	,		data_dir=data_args.data_dir	,		tokenizer=_SCREAMING_SNAKE_CASE	,		labels=_SCREAMING_SNAKE_CASE	,		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(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	) -> Tuple[List[int], List[int]]:
    lowercase__			    =						np.argmax(_SCREAMING_SNAKE_CASE	,		axis=2	)

    lowercase__    ,					lowercase__			    =						preds.shape

    lowercase__			    =						[[] for _ in range(_SCREAMING_SNAKE_CASE	)]
    lowercase__			    =						[[] for _ in range(_SCREAMING_SNAKE_CASE	)]

    for i in range(_SCREAMING_SNAKE_CASE	):
      for j in range(_SCREAMING_SNAKE_CASE	):
        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(_SCREAMING_SNAKE_CASE	) -> Dict:
    lowercase__    ,					lowercase__			    =						align_predictions(p.predictions	,		p.label_ids	)
    return {
        "accuracy_score": accuracy_score(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	),
        "precision": precision_score(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	),
        "recall": recall_score(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	),
        "f1": fa_score(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	),
    }

  # Data collator
  lowercase__			    =						DataCollatorWithPadding(_SCREAMING_SNAKE_CASE	,		pad_to_multiple_of=8	) if training_args.fpaa else None

  # Initialize our Trainer
  lowercase__			    =						Trainer(
      model=_SCREAMING_SNAKE_CASE	,		args=_SCREAMING_SNAKE_CASE	,		train_dataset=_SCREAMING_SNAKE_CASE	,		eval_dataset=_SCREAMING_SNAKE_CASE	,		compute_metrics=_SCREAMING_SNAKE_CASE	,		data_collator=_SCREAMING_SNAKE_CASE	,		)

  # 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
  lowercase__			    =						{}
  if training_args.do_eval:
    logger.info('*** Evaluate ***'	)

    lowercase__			    =						trainer.evaluate()

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

      results.update(_SCREAMING_SNAKE_CASE	)

    # Predict
  if training_args.do_predict:
    lowercase__			    =						TokenClassificationDataset(
        token_classification_task=_SCREAMING_SNAKE_CASE	,		data_dir=data_args.data_dir	,		tokenizer=_SCREAMING_SNAKE_CASE	,		labels=_SCREAMING_SNAKE_CASE	,		model_type=config.model_type	,		max_seq_length=data_args.max_seq_length	,		overwrite_cache=data_args.overwrite_cache	,		mode=Split.test	,		)

    lowercase__    ,					lowercase__    ,					lowercase__			    =						trainer.predict(_SCREAMING_SNAKE_CASE	)
    lowercase__    ,					lowercase__			    =						align_predictions(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)

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

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

  return results




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Optional[Any]:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
      main()









import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowercase_					     =	False
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):
   pass






@nightly
@require_torch_gpu
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Optional[int]:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,  torch_dtype=torch.floataa )
     pipe.to(a )
     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )

     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						pipe.dual_guided(
         prompt='first prompt'    ,  image=a    ,  text_to_image_strength=0.75    ,  generator=a    ,  guidance_scale=7.5    ,  num_inference_steps=2    ,  output_type='numpy'    ,  ).images

     with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(a )
       lowercase__			    =						VersatileDiffusionPipeline.from_pretrained(a    ,  torch_dtype=torch.floataa )
     pipe.to(a )
     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						generator.manual_seed(0 )
     lowercase__			    =						pipe.dual_guided(
         prompt='first prompt'    ,  image=a    ,  text_to_image_strength=0.75    ,  generator=a    ,  guidance_scale=7.5    ,  num_inference_steps=2    ,  output_type='numpy'    ,  ).images

     assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     List[str]:





     """simple docstring"""







     lowercase__			    =						VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,  torch_dtype=torch.floataa )
     pipe.to(a )
     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						'cyberpunk 2077'
     lowercase__			    =						load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						pipe.dual_guided(
         prompt=a    ,  image=a    ,  text_to_image_strength=0.75    ,  generator=a    ,  guidance_scale=7.5    ,  num_inference_steps=50    ,  output_type='numpy'    ,  ).images

     lowercase__			    =						image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1

     lowercase__			    =						'A painting of a squirrel eating a burger '
     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						pipe.text_to_image(
         prompt=a    ,  generator=a    ,  guidance_scale=7.5    ,  num_inference_steps=50    ,  output_type='numpy' ).images

     lowercase__			    =						image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1

     lowercase__			    =						pipe.image_variation(a    ,  generator=a    ,  output_type='numpy' ).images

     lowercase__			    =						image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1


