





import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
  import onnxruntime as ort






@nightly
@require_onnxruntime
@require_torch_gpu
class     lowercase__       (	unittest.TestCase				):

   '''simple docstring'''


   @property
   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






       """simple docstring"""
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )




   @property
   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				int          =      ort.SessionOptions()
       UpperCamelCase__							:				Optional[Any]          =      False
       return options




   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				int          =      load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo.png'''							)
       UpperCamelCase__							:				List[Any]          =      load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''							)
       UpperCamelCase__							:				Optional[int]          =      load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'''							)

       # using the PNDM scheduler by default
       UpperCamelCase__							:				Optional[Any]          =      OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           '''CompVis/stable-diffusion-v1-4''',		revision='''onnx''',		safety_checker=__magic_name__,		feature_extractor=__magic_name__,		provider=self.gpu_provider,		sess_options=self.gpu_options,		)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:				List[str]          =      '''A red cat sitting on a park bench'''

       UpperCamelCase__							:				int          =      np.random.RandomState(0							)
       UpperCamelCase__							:				List[str]          =      pipe(
           prompt=__magic_name__,		image=__magic_name__,		mask_image=__magic_name__,		strength=0.75,		guidance_scale=7.5,		num_inference_steps=15,		generator=__magic_name__,		output_type='''np''',		)

       UpperCamelCase__							:				int          =      output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image							).max() < 1E-2








import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			str		,   __UpperCAmelCase:			str		,   __UpperCAmelCase:			PreTrainedTokenizer		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			Optional[int] = None		,   )      ->	List[Any]:
    UpperCamelCase__							:				Dict          =      {}

    if train_file is not None:
        UpperCamelCase__							:				str          =      [train_file]
    if eval_file is not None:
        UpperCamelCase__							:				Union[str, Any]          =      [eval_file]
    if test_file is not None:
        UpperCamelCase__							:				Tuple          =      [test_file]

    UpperCamelCase__							:				Optional[Any]          =      datasets.load_dataset('''csv'''		,   data_files=__UpperCAmelCase      )
    UpperCamelCase__							:				List[Any]          =      list(ds[list(files.keys()      )[0]].features.keys()      )
    UpperCamelCase__							:				str          =      features_name.pop(__UpperCAmelCase      )
    UpperCamelCase__							:				List[str]          =      list(set(ds[list(files.keys()      )[0]][label_name]      )      )
    UpperCamelCase__							:				Optional[Any]          =      {label: i for i, label in enumerate(__UpperCAmelCase      )}
    UpperCamelCase__							:				Union[str, Any]          =      tokenizer.model_input_names
    UpperCamelCase__							:				str          =      {}

    if len(__UpperCAmelCase      ) == 1:
        for k in files.keys():
            UpperCamelCase__							:				Optional[int]          =      ds[k].map(
                lambda __UpperCAmelCase      : tokenizer.batch_encode_plus(
                    example[features_name[0]]		,   truncation=__UpperCAmelCase		,   max_length=__UpperCAmelCase		,   padding='''max_length'''      )		,   batched=__UpperCAmelCase		,   )
    elif len(__UpperCAmelCase      ) == 2:
        for k in files.keys():
            UpperCamelCase__							:				Dict          =      ds[k].map(
                lambda __UpperCAmelCase      : tokenizer.batch_encode_plus(
                    (example[features_name[0]], example[features_name[1]])		,   truncation=__UpperCAmelCase		,   max_length=__UpperCAmelCase		,   padding='''max_length'''		,   )		,   batched=__UpperCAmelCase		,   )

    def gen_train():
        for ex in transformed_ds[datasets.Split.TRAIN]:
            UpperCamelCase__							:				Any          =      {k: v for k, v in ex.items() if k in input_names}
            UpperCamelCase__							:				str          =      labelaid[ex[label_name]]
            yield (d, label)

    def gen_val():
        for ex in transformed_ds[datasets.Split.VALIDATION]:
            UpperCamelCase__							:				Union[str, Any]          =      {k: v for k, v in ex.items() if k in input_names}
            UpperCamelCase__							:				str          =      labelaid[ex[label_name]]
            yield (d, label)

    def gen_test():
        for ex in transformed_ds[datasets.Split.TEST]:
            UpperCamelCase__							:				Optional[Any]          =      {k: v for k, v in ex.items() if k in input_names}
            UpperCamelCase__							:				int          =      labelaid[ex[label_name]]
            yield (d, label)

    UpperCamelCase__							:				Tuple          =      (
        tf.data.Dataset.from_generator(
            __UpperCAmelCase		,   ({k: tf.intaa for k in input_names}, tf.intaa)		,   ({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      ))		,   )
        if datasets.Split.TRAIN in transformed_ds
        else None
    )

    if train_ds is not None:
        UpperCamelCase__							:				Optional[Any]          =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

    UpperCamelCase__							:				int          =      (
        tf.data.Dataset.from_generator(
            __UpperCAmelCase		,   ({k: tf.intaa for k in input_names}, tf.intaa)		,   ({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      ))		,   )
        if datasets.Split.VALIDATION in transformed_ds
        else None
    )

    if val_ds is not None:
        UpperCamelCase__							:				Dict          =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]      )      )      )

    UpperCamelCase__							:				Optional[Any]          =      (
        tf.data.Dataset.from_generator(
            __UpperCAmelCase		,   ({k: tf.intaa for k in input_names}, tf.intaa)		,   ({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      ))		,   )
        if datasets.Split.TEST in transformed_ds
        else None
    )

    if test_ds is not None:
        UpperCamelCase__							:				Union[str, Any]          =      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

    return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase_     							= logging.getLogger(__name__)






@dataclass
class     lowercase__       :

   '''simple docstring'''


   a						:							int   		=		field(metadata={"help": "Which column contains the label"}				)
   a						:							str   		=		field(default=__lowerCamelCase			,				metadata={"help": "The path of the training file"}				)
   a						:							Optional[str]   		=		field(default=__lowerCamelCase			,				metadata={"help": "The path of the development file"}				)
   a						:							Optional[str]   		=		field(default=__lowerCamelCase			,				metadata={"help": "The path of the test file"}				)
   a						:							int   		=		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						:							bool   		=		field(
       default=__lowerCamelCase			,				metadata={"help": "Overwrite the cached training and evaluation sets"}				)






@dataclass
class     lowercase__       :

   '''simple docstring'''


   a						:							str   		=		field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}				)
   a						:							Optional[str]   		=		field(
       default=__lowerCamelCase			,				metadata={"help": "Pretrained config name or path if not the same as model_name"}				)
   a						:							Optional[str]   		=		field(
       default=__lowerCamelCase			,				metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}				)
   a						:							bool   		=		field(default=__lowerCamelCase			,				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						:							Optional[str]   		=		field(
       default=__lowerCamelCase			,				metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,				)




def 		lowerCAmelCase_    (  )      ->	int:
    # 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.
    UpperCamelCase__							:				Tuple          =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				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.'''      )

    # Setup logging
    logging.basicConfig(
        format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''		,   datefmt='''%m/%d/%Y %H:%M:%S'''		,   level=logging.INFO		,   )
    logger.info(
        f"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1      )}, "
        f"16-bits training: {training_args.fpaa}"      )
    logger.info(f"Training/evaluation parameters {training_args}"      )

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

    UpperCamelCase__							:				List[str]          =      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		,   )

    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Optional[int]          =      get_tfds(
        train_file=data_args.train_file		,   eval_file=data_args.dev_file		,   test_file=data_args.test_file		,   tokenizer=__UpperCAmelCase		,   label_column_id=data_args.label_column_id		,   max_seq_length=data_args.max_seq_length		,   )

    UpperCamelCase__							:				int          =      AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path		,   num_labels=len(__UpperCAmelCase      )		,   labelaid=__UpperCAmelCase		,   idalabel={id: label for label, id in labelaid.items()}		,   finetuning_task='''text-classification'''		,   cache_dir=model_args.cache_dir		,   )

    with training_args.strategy.scope():
        UpperCamelCase__							:				str          =      TFAutoModelForSequenceClassification.from_pretrained(
            model_args.model_name_or_path		,   from_pt=bool('''.bin''' in model_args.model_name_or_path      )		,   config=__UpperCAmelCase		,   cache_dir=model_args.cache_dir		,   )

    def compute_metrics(__UpperCAmelCase:			EvalPrediction      ) -> Dict:
        UpperCamelCase__							:				Optional[Any]          =      np.argmax(p.predictions		,   axis=1      )

        return {"acc": (preds == p.label_ids).mean()}

    # Initialize our Trainer
    UpperCamelCase__							:				Union[str, Any]          =      TFTrainer(
        model=__UpperCAmelCase		,   args=__UpperCAmelCase		,   train_dataset=__UpperCAmelCase		,   eval_dataset=__UpperCAmelCase		,   compute_metrics=__UpperCAmelCase		,   )

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
    UpperCamelCase__							:				List[str]          =      {}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''      )
        UpperCamelCase__							:				Tuple          =      trainer.evaluate()
        UpperCamelCase__							:				Optional[int]          =      os.path.join(training_args.output_dir		,   '''eval_results.txt'''      )

        with open(__UpperCAmelCase		,   '''w'''      ) as writer:
            logger.info('''***** Eval results *****'''      )

            for key, value in result.items():
                logger.info(f"  {key} = {value}"      )
                writer.write(f"{key} = {value}\n"      )

            results.update(__UpperCAmelCase      )

    return results


if __name__ == "__main__":
  main()

