"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 				snake_case       ( __UpperCAmelCase      ,    __UpperCAmelCase      ,    __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = StableDiffusionInpaintPipeline
					snake_case__				    = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					snake_case__				    = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					snake_case__				    = frozenset(
					    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					snake_case__				    = frozenset([]				)







					def 		__lowerCAmelCase						(				self   :	str			):
									torch.manual_seed(0			)
									UpperCAmelCase__             =							UNetaDConditionModel(
									    block_out_channels=(32, 64)    ,layers_per_block=2    ,sample_size=32    ,in_channels=9    ,out_channels=4    ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,cross_attention_dim=32    ,attention_head_dim=(2, 4)    ,use_linear_projection=lowerCamelCase__    ,)
									UpperCAmelCase__             =							PNDMScheduler(skip_prk_steps=lowerCamelCase__			)
									torch.manual_seed(0			)
									UpperCAmelCase__             =							AutoencoderKL(
									    block_out_channels=[32, 64]    ,in_channels=3    ,out_channels=3    ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,latent_channels=4    ,sample_size=128    ,)
									torch.manual_seed(0			)
									UpperCAmelCase__             =							CLIPTextConfig(
									    bos_token_id=0    ,eos_token_id=2    ,hidden_size=32    ,intermediate_size=37    ,layer_norm_eps=1e-05    ,num_attention_heads=4    ,num_hidden_layers=5    ,pad_token_id=1    ,vocab_size=1_000    ,hidden_act='gelu'    ,projection_dim=512    ,)
									UpperCAmelCase__             =							CLIPTextModel(lowerCamelCase__			)
									UpperCAmelCase__             =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

									UpperCAmelCase__             =							{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Tuple    ,lowerCamelCase__   :	int=0			):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									UpperCAmelCase__             =							floats_tensor((1, 3, 32, 32)    ,rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
									UpperCAmelCase__             =							image.cpu().permute(0    ,2    ,3    ,1			)[0]
									UpperCAmelCase__             =							Image.fromarray(np.uinta(lowerCamelCase__			)			).convert('RGB'			).resize((64, 64)			)
									UpperCAmelCase__             =							Image.fromarray(np.uinta(image + 4			)			).convert('RGB'			).resize((64, 64)			)
									if str(lowerCamelCase__			).startswith('mps'			):
													UpperCAmelCase__             =							torch.manual_seed(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
									UpperCAmelCase__             =							{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': init_image,
									    'mask_image': mask_image,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}
									return inputs







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							'cpu'  # ensure determinism for the device-dependent torch.Generator
									UpperCAmelCase__             =							self.get_dummy_components()
									UpperCAmelCase__             =							StableDiffusionInpaintPipeline(**lowerCamelCase__			)
									UpperCAmelCase__             =							sd_pipe.to(lowerCamelCase__			)
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							self.get_dummy_inputs(lowerCamelCase__			)
									UpperCAmelCase__             =							sd_pipe(**lowerCamelCase__			).images
									UpperCAmelCase__             =							image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									UpperCAmelCase__             =							np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]			)

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







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3			)






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




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'			)
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'			)
									UpperCAmelCase__             =							load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench.npy'			)

									UpperCAmelCase__             =							'stabilityai/stable-diffusion-2-inpainting'
									UpperCAmelCase__             =							StableDiffusionInpaintPipeline.from_pretrained(lowerCamelCase__    ,safety_checker=lowerCamelCase__			)
									pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)
									pipe.enable_attention_slicing()

									UpperCAmelCase__             =							'Face of a yellow cat, high resolution, sitting on a park bench'

									UpperCAmelCase__             =							torch.manual_seed(0			)
									UpperCAmelCase__             =							pipe(
									    prompt=lowerCamelCase__    ,image=lowerCamelCase__    ,mask_image=lowerCamelCase__    ,generator=lowerCamelCase__    ,output_type='np'    ,)
									UpperCAmelCase__             =							output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image			).max() < 9e-3







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'			)
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'			)
									UpperCAmelCase__             =							load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'			)

									UpperCAmelCase__             =							'stabilityai/stable-diffusion-2-inpainting'
									UpperCAmelCase__             =							StableDiffusionInpaintPipeline.from_pretrained(
									    lowerCamelCase__    ,torch_dtype=torch.floataa    ,safety_checker=lowerCamelCase__    ,)
									pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)
									pipe.enable_attention_slicing()

									UpperCAmelCase__             =							'Face of a yellow cat, high resolution, sitting on a park bench'

									UpperCAmelCase__             =							torch.manual_seed(0			)
									UpperCAmelCase__             =							pipe(
									    prompt=lowerCamelCase__    ,image=lowerCamelCase__    ,mask_image=lowerCamelCase__    ,generator=lowerCamelCase__    ,output_type='np'    ,)
									UpperCAmelCase__             =							output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image			).max() < 5e-1







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'			)
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'			)

									UpperCAmelCase__             =							'stabilityai/stable-diffusion-2-inpainting'
									UpperCAmelCase__             =							PNDMScheduler.from_pretrained(lowerCamelCase__    ,subfolder='scheduler'			)
									UpperCAmelCase__             =							StableDiffusionInpaintPipeline.from_pretrained(
									    lowerCamelCase__    ,safety_checker=lowerCamelCase__    ,scheduler=lowerCamelCase__    ,torch_dtype=torch.floataa    ,)
									pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)
									pipe.enable_attention_slicing(1			)
									pipe.enable_sequential_cpu_offload()

									UpperCAmelCase__             =							'Face of a yellow cat, high resolution, sitting on a park bench'

									UpperCAmelCase__             =							torch.manual_seed(0			)
									UpperCAmelCase__             =							pipe(
									    prompt=lowerCamelCase__    ,image=lowerCamelCase__    ,mask_image=lowerCamelCase__    ,generator=lowerCamelCase__    ,num_inference_steps=2    ,output_type='np'    ,)

									UpperCAmelCase__             =							torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.6_5 * 10**9










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

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


snake_case_				=			logging.getLogger(__name__)

@dataclass
class 		SCREAMING_SNAKE_CASE__				:
      A_						:				str							=    field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}  )
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'Pretrained config name or path if not the same as model_name'}  )
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}  )
      A_						:				Optional[str]							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,      )
      A_						:				bool							=    field(default=_UpperCAmelCase						,      metadata={'help': 'Whether tp freeze the encoder.'}  )
      A_						:				bool							=    field(default=_UpperCAmelCase						,      metadata={'help': 'Whether  to freeze the embeddings.'}  )






@dataclass
class 		SCREAMING_SNAKE_CASE__				:
      A_						:				str							=    field(
          metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}  )
      A_						:				Optional[str]							=    field(
          default='summarization'						,      metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}						,      )
      A_						:				Optional[int]							=    field(
          default=1_024						,      metadata={
              'help': (
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }						,      )
      A_						:				Optional[int]							=    field(
          default=128						,      metadata={
              'help': (
                  'The maximum total sequence length for target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }						,      )
      A_						:				Optional[int]							=    field(
          default=142						,      metadata={
              'help': (
                  'The maximum total sequence length for validation target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded. '
                  'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
                  'during ``evaluate`` and ``predict``.'
              )
          }						,      )
      A_						:				Optional[int]							=    field(
          default=142						,      metadata={
              'help': (
                  'The maximum total sequence length for test target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }						,      )
      A_						:				Optional[int]							=    field(default=-1						,      metadata={'help': '# training examples. -1 means use all.'}  )
      A_						:				Optional[int]							=    field(default=-1						,      metadata={'help': '# validation examples. -1 means use all.'}  )
      A_						:				Optional[int]							=    field(default=-1						,      metadata={'help': '# test examples. -1 means use all.'}  )
      A_						:				Optional[str]							=    field(default=_UpperCAmelCase						,      metadata={'help': 'Source language id for translation.'}  )
      A_						:				Optional[str]							=    field(default=_UpperCAmelCase						,      metadata={'help': 'Target language id for translation.'}  )
      A_						:				Optional[int]							=    field(default=_UpperCAmelCase						,      metadata={'help': '# num_beams to use for evaluation.'}  )
      A_						:				bool							=    field(
          default=_UpperCAmelCase						,      metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}						,      )






def lowerCamelCase__    (							snake_case_	:  List[Any]       ,      snake_case_	:  List[str]       ,      snake_case_	:  Dict       )      ->				str:
   logger.info(f"""***** {split} metrics *****"""       )
   for key in sorted(metrics.keys()       ):
      logger.info(f"""  {key} = {metrics[key]}"""       )
   save_json(snake_case_       ,      os.path.join(snake_case_       ,      f"""{split}_results.json"""       )       )






def lowerCamelCase__    (							)      ->				Optional[Any]:
   # 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.

   __snake_case							       =   HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)       )

   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.
      __snake_case       ,	__snake_case       ,	__snake_case							       =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
   else:
      __snake_case       ,	__snake_case       ,	__snake_case							       =   parser.parse_args_into_dataclasses()

   check_output_dir(snake_case_       )

   # 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.parallel_mode == ParallelMode.DISTRIBUTED       )       ,      training_args.fpaa       ,      )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()
   # 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()
   logger.info('''Training/evaluation parameters %s'''       ,      snake_case_       )

   # Set seed
   set_seed(training_args.seed       )

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

   __snake_case							       =   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       ,      )

   __snake_case							       =   ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
   for p in extra_model_params:
      if getattr(snake_case_       ,      snake_case_       ,      snake_case_       ):
         assert hasattr(snake_case_       ,      snake_case_       ), f"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
         setattr(snake_case_       ,      snake_case_       ,      getattr(snake_case_       ,      snake_case_       )       )

   __snake_case							       =   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       ,      )
   __snake_case							       =   AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path       ,      from_tf='''.ckpt''' in model_args.model_name_or_path       ,      config=snake_case_       ,      cache_dir=model_args.cache_dir       ,      )

   # use task specific params
   use_task_specific_params(snake_case_       ,      data_args.task       )

   # set num_beams for evaluation
   if data_args.eval_beams is None:
      __snake_case							       =   model.config.num_beams

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(snake_case_       ,      (MBartTokenizer, MBartTokenizerFast)       ):
      assert (
          data_args.tgt_lang is not None and data_args.src_lang is not None
      ), "mBart requires --tgt_lang and --src_lang"
      if isinstance(snake_case_       ,      snake_case_       ):
         __snake_case							       =   tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         __snake_case							       =   tokenizer.convert_tokens_to_ids(data_args.tgt_lang       )

   if model_args.freeze_embeds:
      freeze_embeds(snake_case_       )
   if model_args.freeze_encoder:
      freeze_params(model.get_encoder()       )
      assert_all_frozen(model.get_encoder()       )

   __snake_case							       =   SeqaSeqDataset

   # Get datasets
   __snake_case							       =   (
       dataset_class(
           snake_case_       ,      type_path='''train'''       ,      data_dir=data_args.data_dir       ,      n_obs=data_args.n_train       ,      max_target_length=data_args.max_target_length       ,      max_source_length=data_args.max_source_length       ,      prefix=model.config.prefix or ''''''       ,      )
       if training_args.do_train
       else None
   )
   __snake_case							       =   (
       dataset_class(
           snake_case_       ,      type_path='''val'''       ,      data_dir=data_args.data_dir       ,      n_obs=data_args.n_val       ,      max_target_length=data_args.val_max_target_length       ,      max_source_length=data_args.max_source_length       ,      prefix=model.config.prefix or ''''''       ,      )
       if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
       else None
   )
   __snake_case							       =   (
       dataset_class(
           snake_case_       ,      type_path='''test'''       ,      data_dir=data_args.data_dir       ,      n_obs=data_args.n_test       ,      max_target_length=data_args.test_max_target_length       ,      max_source_length=data_args.max_source_length       ,      prefix=model.config.prefix or ''''''       ,      )
       if training_args.do_predict
       else None
   )

   # Initialize our Trainer
   __snake_case							       =   (
       build_compute_metrics_fn(data_args.task       ,      snake_case_       ) if training_args.predict_with_generate else None
   )
   __snake_case							       =   SeqaSeqTrainer(
       model=snake_case_       ,      args=snake_case_       ,      data_args=snake_case_       ,      train_dataset=snake_case_       ,      eval_dataset=snake_case_       ,      data_collator=SeqaSeqDataCollator(
           snake_case_       ,      snake_case_       ,      model.config.decoder_start_token_id       ,      training_args.tpu_num_cores       )       ,      compute_metrics=snake_case_       ,      tokenizer=snake_case_       ,      )

   __snake_case							       =   {}
   # Training
   if training_args.do_train:
      logger.info('''*** Train ***'''       )

      __snake_case							       =   trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
      __snake_case							       =   train_result.metrics
      __snake_case							       =   data_args.n_train

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics('''train'''       ,      snake_case_       ,      training_args.output_dir       )
         all_metrics.update(snake_case_       )

         # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir       ,      '''trainer_state.json'''       )       )

         # For convenience, we also re-save the tokenizer to the same directory,
         # so that you can share your model easily on huggingface.co/models =)
         tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
   if training_args.do_eval:
      logger.info('''*** Evaluate ***'''       )

      __snake_case							       =   trainer.evaluate(metric_key_prefix='''val'''       )
      __snake_case							       =   data_args.n_val
      __snake_case							       =   round(metrics['''val_loss''']       ,      4       )

      if trainer.is_world_process_zero():
         handle_metrics('''val'''       ,      snake_case_       ,      training_args.output_dir       )
         all_metrics.update(snake_case_       )

   if training_args.do_predict:
      logger.info('''*** Predict ***'''       )

      __snake_case							       =   trainer.predict(test_dataset=snake_case_       ,      metric_key_prefix='''test'''       )
      __snake_case							       =   test_output.metrics
      __snake_case							       =   data_args.n_test

      if trainer.is_world_process_zero():
         __snake_case							       =   round(metrics['''test_loss''']       ,      4       )
         handle_metrics('''test'''       ,      snake_case_       ,      training_args.output_dir       )
         all_metrics.update(snake_case_       )

         if training_args.predict_with_generate:
            __snake_case							       =   tokenizer.batch_decode(
                test_output.predictions       ,      skip_special_tokens=snake_case_       ,      clean_up_tokenization_spaces=snake_case_       )
            __snake_case							       =   lmap(str.strip       ,      snake_case_       )
            write_txt_file(snake_case_       ,      os.path.join(training_args.output_dir       ,      '''test_generations.txt'''       )       )

   if trainer.is_world_process_zero():
      save_json(snake_case_       ,      os.path.join(training_args.output_dir       ,      '''all_results.json'''       )       )

   return all_metrics






def lowerCamelCase__    (							snake_case_	:  Optional[Any]       )      ->				Tuple:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()

