



'''simple docstring'''







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

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


lowercase__  :	Any            =      logging.getLogger(__name__)

@dataclass
class    lowerCamelCase_				:






   '''simple docstring'''

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





def     snake_case__			():
    UpperCamelCase__	     =      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__     ,	UpperCamelCase__     ,	UpperCamelCase__	     =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
    else:
        UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__	     =      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__						)

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

    UpperCamelCase__	     =      AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,	cache_dir=model_args.cache_dir       ,	)
    UpperCamelCase__	     =      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=UpperCamelCase__       ,	)
    UpperCamelCase__	     =      AutoModelForQuestionAnswering.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
    UpperCamelCase__	     =      hasattr(model.config       ,	"lang2id"						)
    UpperCamelCase__	     =      (
        SquadDataset(
            UpperCamelCase__       ,	tokenizer=UpperCamelCase__       ,	is_language_sensitive=UpperCamelCase__       ,	cache_dir=model_args.cache_dir						)
        if training_args.do_train
        else None
    )
    UpperCamelCase__	     =      (
        SquadDataset(
            UpperCamelCase__       ,	tokenizer=UpperCamelCase__       ,	mode="dev"       ,	is_language_sensitive=UpperCamelCase__       ,	cache_dir=model_args.cache_dir       ,	)
        if training_args.do_eval
        else None
    )

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

    # Initialize our Trainer
    UpperCamelCase__	     =      Trainer(
        model=UpperCamelCase__       ,	args=UpperCamelCase__       ,	train_dataset=UpperCamelCase__       ,	eval_dataset=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						)





def     snake_case__			(lowerCAmelCase_						):
    main()


if __name__ == "__main__":
      main()









"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class    __snake_case(					unittest.TestCase       ):




    @property
    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''cpu'''
      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler()

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''    ,							return_dict=A_			)[0]

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

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array(
          [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]			)

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




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler(prediction_type='''sample'''			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_eps[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			1e-2 if torch_device != '''mps''' else 3e-2
      assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




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




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''google/ddpm-cifar10-32'''

      _SCREAMING_SNAKE_CASE        =			UNetaDModel.from_pretrained(A_			)
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler.from_pretrained(A_			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							output_type='''numpy'''			).images

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

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
