

"""simple docstring"""



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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

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


SCREAMING_SNAKE_CASE_					:	int							  =						logging.getLogger(__name__)





def  _snake_case     (    UpperCAmelCase_  :					Optional[int]					,   UpperCAmelCase_  :					Union[str, Any]			):
     return (preds == labels).mean()



@dataclass
class 					a						:



    """simple docstring"""



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



@dataclass
class 					a						:



    """simple docstring"""



    UpperCAmelCase						 =					field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()				)}				)
    UpperCAmelCase						 =					field(metadata={"help": "Should contain the data files for the task."}				)
    UpperCAmelCase						 =					field(
        default=1_2_8,	metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },	)
    UpperCAmelCase						 =					field(
        default=_lowerCamelCase,	metadata={"help": "Overwrite the cached training and evaluation sets"}				)





def  _snake_case     (    ):
     # 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.

     A__										=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
     A__			,				A__			,				A__										=  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 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"""					,   UpperCAmelCase_			)

     # Set seed
     set_seed(training_args.seed			)

     try:
          A__										=  processors[data_args.task_name]()
          A__										=  processor.get_labels()
          A__										=  len(UpperCAmelCase_			)
     except KeyError:
          raise ValueError("""Task not found: %s""" % (data_args.task_name)			)

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

     A__										=  AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path					,   num_labels=UpperCAmelCase_					,   finetuning_task=data_args.task_name					,   cache_dir=model_args.cache_dir					,   )
     A__										=  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					,   )
     A__										=  AutoModelForMultipleChoice.from_pretrained(
         model_args.model_name_or_path					,   from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)					,   config=UpperCAmelCase_					,   cache_dir=model_args.cache_dir					,   )

     # Get datasets
     A__										=  (
         MultipleChoiceDataset(
             data_dir=data_args.data_dir					,   tokenizer=UpperCAmelCase_					,   task=data_args.task_name					,   max_seq_length=data_args.max_seq_length					,   overwrite_cache=data_args.overwrite_cache					,   mode=Split.train					,   )
         if training_args.do_train
         else None
     )
     A__										=  (
         MultipleChoiceDataset(
             data_dir=data_args.data_dir					,   tokenizer=UpperCAmelCase_					,   task=data_args.task_name					,   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 compute_metrics(UpperCAmelCase_  :					EvalPrediction			) -> Dict:
          A__										=  np.argmax(p.predictions					,   axis=1			)
          return {"acc": simple_accuracy(UpperCAmelCase_					,   p.label_ids			)}

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

     # Initialize our Trainer
     A__										=  Trainer(
         model=UpperCAmelCase_					,   args=UpperCAmelCase_					,   train_dataset=UpperCAmelCase_					,   eval_dataset=UpperCAmelCase_					,   compute_metrics=UpperCAmelCase_					,   data_collator=UpperCAmelCase_					,   )

     # 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_master():
               tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
     A__										=  {}
     if training_args.do_eval:
          logger.info("""*** Evaluate ***"""			)

          A__										=  trainer.evaluate()

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

                    results.update(UpperCAmelCase_			)

     return results





def  _snake_case     (    UpperCAmelCase_  :					int			):
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
 main()









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 					a						(       unittest.TestCase				):



    """simple docstring"""



    @property
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         torch.manual_seed(0       )
         A__										=  UNetaDModel(
             block_out_channels=(32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=3    ,						out_channels=3    ,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,						)
         return model





    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         A__										=  self.dummy_uncond_unet
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""       ).images

         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						num_inference_steps=20    ,						output_type="""numpy"""    ,						return_dict=UpperCamelCase       )[0]

         A__										=  image[0, -3:, -3:, -1]
         A__										=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2



@slow
@require_torch
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  """google/ddpm-cifar10-32"""

         A__										=  UNetaDModel.from_pretrained(UpperCamelCase       )
         A__										=  PNDMScheduler()

         A__										=  PNDMPipeline(unet=UpperCamelCase    ,						scheduler=UpperCamelCase       )
         pndm.to(UpperCamelCase       )
         pndm.set_progress_bar_config(disable=UpperCamelCase       )
         A__										=  torch.manual_seed(0       )
         A__										=  pndm(generator=UpperCamelCase    ,						output_type="""numpy"""       ).images

         A__										=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A__										=  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2






