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 :     int          =  logging.getLogger(__name__)






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







def 				_snake_case     (       ):
  """simple docstring"""







  SCREAMING_SNAKE_CASE_  :		Dict    				=   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.
    SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		List[str]    				=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
  else:
    SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		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"       ,			lowerCamelCase_     )

  # 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.

  SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   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       ,			)
  SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   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=lowerCamelCase_       ,			)
  SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   AutoModelForQuestionAnswering.from_pretrained(
      model_args.model_name_or_path       ,			from_tf=bool(".ckpt" in model_args.model_name_or_path     )       ,			config=lowerCamelCase_       ,			cache_dir=model_args.cache_dir       ,			)

  # Get datasets
  SCREAMING_SNAKE_CASE_  :		Tuple    				=   hasattr(model.config       ,			"lang2id"     )
  SCREAMING_SNAKE_CASE_  :		str    				=   (
      SquadDataset(
          lowerCamelCase_       ,			tokenizer=lowerCamelCase_       ,			is_language_sensitive=lowerCamelCase_       ,			cache_dir=model_args.cache_dir     )
      if training_args.do_train
      else None
  )
  SCREAMING_SNAKE_CASE_  :		str    				=   (
      SquadDataset(
          lowerCamelCase_       ,			tokenizer=lowerCamelCase_       ,			mode="dev"       ,			is_language_sensitive=lowerCamelCase_       ,			cache_dir=model_args.cache_dir       ,			)
      if training_args.do_eval
      else None
  )

  # Data collator
  SCREAMING_SNAKE_CASE_  :		Any    				=   DataCollatorWithPadding(lowerCamelCase_       ,			pad_to_multiple_of=8     ) if training_args.fpaa else None

  # Initialize our Trainer
  SCREAMING_SNAKE_CASE_  :		List[Any]    				=   Trainer(
      model=lowerCamelCase_       ,			args=lowerCamelCase_       ,			train_dataset=lowerCamelCase_       ,			eval_dataset=lowerCamelCase_       ,			data_collator=lowerCamelCase_       ,			)

  # 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							:       Dict     ):
  """simple docstring"""







  main()


if __name__ == "__main__":
  main()

import gc
import tempfile
import unittest

import numpy as np
import torch

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


__lowerCamelCase :     List[Any]          =  False






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

					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
							# remove text_unet
							pipe.remove_unused_weights()
							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   torch.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		int    				=   pipe(
							    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   )
									SCREAMING_SNAKE_CASE_  :		Dict    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained(_A   )

							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		List[str]    				=   generator.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		str    				=   pipe(
							    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     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
							pipe.remove_unused_weights()
							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   "cyberpunk 2077"
							SCREAMING_SNAKE_CASE_  :		Dict    				=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   torch.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		Dict    				=   pipe(
							    prompt=_A,image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=50,output_type="numpy",).images

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]   )

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