"""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


lowerCamelCase      : List[str]       =logging.getLogger(__name__)






@dataclass
class 							__snake_case:


    '''simple docstring'''





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







def 						_lowercase				(  )    ->     int:







     '''simple docstring'''


     __A					:					int								=		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.
          __A						,		__A						,		__A					:					Optional[Any]								=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
     else:
          __A						,		__A						,		__A					:					int								=		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'  ,       _SCREAMING_SNAKE_CASE						)

     # 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.

     __A					:					int								=		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  ,       )
     __A					:					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  ,       use_fast=_SCREAMING_SNAKE_CASE  ,       )
     __A					:					Union[str, Any]								=		AutoModelForQuestionAnswering.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
     __A					:					List[Any]								=		hasattr(model.config  ,       'lang2id'						)
     __A					:					str								=		(
         SquadDataset(
             _SCREAMING_SNAKE_CASE  ,       tokenizer=_SCREAMING_SNAKE_CASE  ,       is_language_sensitive=_SCREAMING_SNAKE_CASE  ,       cache_dir=model_args.cache_dir						)
         if training_args.do_train
         else None
     )
     __A					:					List[Any]								=		(
         SquadDataset(
             _SCREAMING_SNAKE_CASE  ,       tokenizer=_SCREAMING_SNAKE_CASE  ,       mode='dev'  ,       is_language_sensitive=_SCREAMING_SNAKE_CASE  ,       cache_dir=model_args.cache_dir  ,       )
         if training_args.do_eval
         else None
     )

     # Data collator
     __A					:					List[Any]								=		DataCollatorWithPadding(_SCREAMING_SNAKE_CASE  ,       pad_to_multiple_of=8						) if training_args.fpaa else None

     # Initialize our Trainer
     __A					:					int								=		Trainer(
         model=_SCREAMING_SNAKE_CASE  ,       args=_SCREAMING_SNAKE_CASE  ,       train_dataset=_SCREAMING_SNAKE_CASE  ,       eval_dataset=_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_master():
               tokenizer.save_pretrained(training_args.output_dir						)




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple						)    ->     List[Any]:







     '''simple docstring'''


     main()


if __name__ == "__main__":
       main()




"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 							__snake_case(					A_				,	A_				,	unittest.TestCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			IFImgaImgPipeline
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
    _UpperCAmelCase       =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    _UpperCAmelCase       =			PipelineTesterMixin.required_optional_params - {"latents"}
    def 						_a							(  self	):




         '''simple docstring'''



         return self._get_dummy_components()
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase=0	):




         '''simple docstring'''



         if str(__lowerCamelCase	).startswith('mps'	):
              __A					:					Tuple								=		torch.manual_seed(__lowerCamelCase	)
         else:
              __A					:					int								=		torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)

         __A					:					str								=		floats_tensor((1, 3, 32, 32)					,       rng=random.Random(__lowerCamelCase	)	).to(__lowerCamelCase	)

         __A					:					str								=		{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_optional_components()
    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()					,       reason='XFormers attention is only available with CUDA and `xformers` installed'					,       )
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_save_load_floataa(expected_max_diff=1e-1	)
    @unittest.skipIf(torch_device != 'cuda'					,       reason='float16 requires CUDA'	)
    def 						_a							(  self	):




         '''simple docstring'''



         super().test_floataa_inference(expected_max_diff=1e-1	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)
    def 						_a							(  self	):




         '''simple docstring'''



         self._test_save_load_local()

    def 						_a							(  self	):




         '''simple docstring'''



         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2					,       )



