import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()





class      A							(				unittest.TestCase     ):
   __UpperCAmelCase : List[str]      =			StableDiffusionLDMaDPipeline
   __UpperCAmelCase : Optional[Any]      =			TEXT_TO_IMAGE_PARAMS
   __UpperCAmelCase : Optional[int]      =			TEXT_TO_IMAGE_BATCH_PARAMS
   __UpperCAmelCase : str      =			TEXT_TO_IMAGE_IMAGE_PARAMS


   def 			lowercase_      (self     :						Any		)						->				Optional[int]:
         """simple docstring"""



         torch.manual_seed(0		)
         UpperCAmelCase__								=  UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)       ,  layers_per_block=2       ,  sample_size=3_2       ,  in_channels=4       ,  out_channels=4       ,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,  cross_attention_dim=3_2       ,  )
         UpperCAmelCase__								=  DDIMScheduler(
             beta_start=0.00085       ,  beta_end=0.012       ,  beta_schedule="scaled_linear"       ,  clip_sample=__UpperCAmelCase       ,  set_alpha_to_one=__UpperCAmelCase       ,  )
         torch.manual_seed(0		)
         UpperCAmelCase__								=  AutoencoderKL(
             block_out_channels=[3_2, 6_4]       ,  in_channels=6       ,  out_channels=6       ,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]       ,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]       ,  latent_channels=4       ,  )
         torch.manual_seed(0		)
         UpperCAmelCase__								=  CLIPTextConfig(
             bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=3_2       ,  intermediate_size=3_7       ,  layer_norm_eps=1E-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1_0_0_0       ,  )
         UpperCAmelCase__								=  CLIPTextModel(__UpperCAmelCase		)
         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 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						Optional[Any]       ,  __UpperCAmelCase     :						Dict=0		)						->				Optional[Any]:
         """simple docstring"""



         if str(__UpperCAmelCase		).startswith("mps"		):
               UpperCAmelCase__								=  torch.manual_seed(__UpperCAmelCase		)
         else:
               UpperCAmelCase__								=  torch.Generator(device=__UpperCAmelCase		).manual_seed(__UpperCAmelCase		)
         UpperCAmelCase__								=  {
             "prompt": "A painting of a squirrel eating a burger",
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
             "output_type": "numpy",
         }
         return inputs


   def 			lowercase_      (self     :						List[str]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  "cpu"  # ensure determinism for the device-dependent torch.Generator

         UpperCAmelCase__								=  self.get_dummy_components()
         UpperCAmelCase__								=  StableDiffusionLDMaDPipeline(**__UpperCAmelCase		)
         UpperCAmelCase__								=  ldmad_pipe.to(__UpperCAmelCase		)
         ldmad_pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         UpperCAmelCase__								=  self.get_dummy_inputs(__UpperCAmelCase		)
         UpperCAmelCase__								=  ldmad_pipe(**__UpperCAmelCase		)
         UpperCAmelCase__		,      UpperCAmelCase__								=  output.rgb, output.depth

         UpperCAmelCase__								=  rgb[0, -3:, -3:, -1]
         UpperCAmelCase__								=  depth[0, -3:, -1]

         assert rgb.shape == (1, 6_4, 6_4, 3)
         assert depth.shape == (1, 6_4, 6_4)

         UpperCAmelCase__								=  np.array(
             [0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262]		)
         UpperCAmelCase__								=  np.array([103.46727, 85.812004, 87.849236]		)

         assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb		).max() < 1E-2
         assert np.abs(image_slice_depth.flatten() - expected_slice_depth		).max() < 1E-2


   def 			lowercase_      (self     :						int		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_dummy_components()
         UpperCAmelCase__								=  StableDiffusionLDMaDPipeline(**__UpperCAmelCase		)
         UpperCAmelCase__								=  ldmad_pipe.to(__UpperCAmelCase		)
         ldmad_pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         UpperCAmelCase__								=  self.get_dummy_inputs(__UpperCAmelCase		)
         UpperCAmelCase__								=  3 * [inputs["prompt"]]

         # forward
         UpperCAmelCase__								=  ldmad_pipe(**__UpperCAmelCase		)
         UpperCAmelCase__		,      UpperCAmelCase__								=  output.rgb, output.depth
         UpperCAmelCase__								=  rgb_slice_a[0, -3:, -3:, -1]
         UpperCAmelCase__								=  depth_slice_a[0, -3:, -1]

         UpperCAmelCase__								=  self.get_dummy_inputs(__UpperCAmelCase		)
         UpperCAmelCase__								=  3 * [inputs.pop("prompt"		)]

         UpperCAmelCase__								=  ldmad_pipe.tokenizer(
             __UpperCAmelCase       ,  padding="max_length"       ,  max_length=ldmad_pipe.tokenizer.model_max_length       ,  truncation=__UpperCAmelCase       ,  return_tensors="pt"       ,  )
         UpperCAmelCase__								=  text_inputs["input_ids"].to(__UpperCAmelCase		)

         UpperCAmelCase__								=  ldmad_pipe.text_encoder(__UpperCAmelCase		)[0]

         UpperCAmelCase__								=  prompt_embeds

         # forward
         UpperCAmelCase__								=  ldmad_pipe(**__UpperCAmelCase		)
         UpperCAmelCase__		,      UpperCAmelCase__								=  output.rgb, output.depth
         UpperCAmelCase__								=  rgb_slice_a[0, -3:, -3:, -1]
         UpperCAmelCase__								=  depth_slice_a[0, -3:, -1]

         assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()		).max() < 1E-4
         assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()		).max() < 1E-4





   def 			lowercase_      (self     :						List[Any]		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__								=  self.get_dummy_components()
         UpperCAmelCase__								=  PNDMScheduler(skip_prk_steps=__UpperCAmelCase		)
         UpperCAmelCase__								=  StableDiffusionLDMaDPipeline(**__UpperCAmelCase		)
         UpperCAmelCase__								=  ldmad_pipe.to(__UpperCAmelCase		)
         ldmad_pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         UpperCAmelCase__								=  self.get_dummy_inputs(__UpperCAmelCase		)
         UpperCAmelCase__								=  "french fries"
         UpperCAmelCase__								=  ldmad_pipe(**__UpperCAmelCase       ,  negative_prompt=__UpperCAmelCase		)

         UpperCAmelCase__		,      UpperCAmelCase__								=  output.rgb, output.depth
         UpperCAmelCase__								=  rgb[0, -3:, -3:, -1]
         UpperCAmelCase__								=  depth[0, -3:, -1]

         assert rgb.shape == (1, 6_4, 6_4, 3)
         assert depth.shape == (1, 6_4, 6_4)

         UpperCAmelCase__								=  np.array(
             [0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217]		)
         UpperCAmelCase__								=  np.array([107.84738, 84.62802, 89.962135]		)
         assert np.abs(rgb_slice.flatten() - expected_slice_rgb		).max() < 1E-2
         assert np.abs(depth_slice.flatten() - expected_slice_depth		).max() < 1E-2





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


   def 			lowercase_      (self     :						List[Any]		)						->				Union[str, Any]:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						Optional[int]       ,  __UpperCAmelCase     :						Tuple="cpu"       ,  __UpperCAmelCase     :						Tuple=torch.floataa       ,  __UpperCAmelCase     :						Optional[int]=0		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  torch.Generator(device=__UpperCAmelCase		).manual_seed(__UpperCAmelCase		)
         UpperCAmelCase__								=  np.random.RandomState(__UpperCAmelCase		).standard_normal((1, 4, 6_4, 6_4)		)
         UpperCAmelCase__								=  torch.from_numpy(__UpperCAmelCase		).to(device=__UpperCAmelCase       ,  dtype=__UpperCAmelCase		)
         UpperCAmelCase__								=  {
             "prompt": "a photograph of an astronaut riding a horse",
             "latents": latents,
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 7.5,
             "output_type": "numpy",
         }
         return inputs





   def 			lowercase_      (self     :						Tuple		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"		)
         UpperCAmelCase__								=  ldmad_pipe.to(__UpperCAmelCase		)
         ldmad_pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         UpperCAmelCase__								=  self.get_inputs(__UpperCAmelCase		)
         UpperCAmelCase__								=  ldmad_pipe(**__UpperCAmelCase		)
         UpperCAmelCase__		,      UpperCAmelCase__								=  output.rgb, output.depth
         UpperCAmelCase__								=  rgb[0, -3:, -3:, -1].flatten()
         UpperCAmelCase__								=  rgb[0, -3:, -1].flatten()

         assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
         assert depth.shape == (1, 5_1_2, 5_1_2)

         UpperCAmelCase__								=  np.array(
             [0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706]		)
         UpperCAmelCase__								=  np.array(
             [0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706]		)
         assert np.abs(rgb_slice - expected_slice_rgb		).max() < 3E-3
         assert np.abs(depth_slice - expected_slice_depth		).max() < 3E-3





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


   def 			lowercase_      (self     :						Union[str, Any]		)						->				Optional[Any]:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						Optional[Any]="cpu"       ,  __UpperCAmelCase     :						Optional[int]=torch.floataa       ,  __UpperCAmelCase     :						Optional[int]=0		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  torch.Generator(device=__UpperCAmelCase		).manual_seed(__UpperCAmelCase		)
         UpperCAmelCase__								=  np.random.RandomState(__UpperCAmelCase		).standard_normal((1, 4, 6_4, 6_4)		)
         UpperCAmelCase__								=  torch.from_numpy(__UpperCAmelCase		).to(device=__UpperCAmelCase       ,  dtype=__UpperCAmelCase		)
         UpperCAmelCase__								=  {
             "prompt": "a photograph of an astronaut riding a horse",
             "latents": latents,
             "generator": generator,
             "num_inference_steps": 5_0,
             "guidance_scale": 7.5,
             "output_type": "numpy",
         }
         return inputs


   def 			lowercase_      (self     :						Any		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"		).to(__UpperCAmelCase		)
         ldmad_pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         UpperCAmelCase__								=  self.get_inputs(__UpperCAmelCase		)
         UpperCAmelCase__								=  ldmad_pipe(**__UpperCAmelCase		)
         UpperCAmelCase__		,      UpperCAmelCase__								=  output.rgb, output.depth

         UpperCAmelCase__								=  0.495586
         UpperCAmelCase__								=  0.33795515
         UpperCAmelCase__								=  112.48518
         UpperCAmelCase__								=  98.489746
         assert np.abs(expected_rgb_mean - rgb.mean()		) < 1E-3
         assert np.abs(expected_rgb_std - rgb.std()		) < 1E-3
         assert np.abs(expected_depth_mean - depth.mean()		) < 1E-3
         assert np.abs(expected_depth_std - depth.std()		) < 1E-3





   def 			lowercase_      (self     :						Tuple		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c"		).to(__UpperCAmelCase		)
         ldmad_pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         UpperCAmelCase__								=  self.get_inputs(__UpperCAmelCase		)
         UpperCAmelCase__								=  ldmad_pipe(**__UpperCAmelCase		)
         UpperCAmelCase__		,      UpperCAmelCase__								=  output.rgb, output.depth

         UpperCAmelCase__								=  0.4194127
         UpperCAmelCase__								=  0.35375586
         UpperCAmelCase__								=  0.5638502
         UpperCAmelCase__								=  0.34686103

         assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
         assert depth.shape == (1, 5_1_2, 5_1_2, 1)
         assert np.abs(expected_rgb_mean - rgb.mean()		) < 1E-3
         assert np.abs(expected_rgb_std - rgb.std()		) < 1E-3
         assert np.abs(expected_depth_mean - depth.mean()		) < 1E-3
         assert np.abs(expected_depth_std - depth.std()		) < 1E-3






import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowercase_									=   1_6
lowercase_									=   3_2
def      a      (			A__	:   Accelerator      ,   A__	:   int = 16      ,   A__	:   str = "bert-base-cased"     )    ->      Optional[int]:





      """simple docstring"""



      _lowercase	       =AutoTokenizer.from_pretrained(A__     )
      _lowercase	       =load_dataset('glue'      ,   'mrpc'     )

      def tokenize_function(A__	:   Optional[int]     ):
            # max_length=None => use the model max length (it's actually the default)
            _lowercase	       =tokenizer(examples['sentence1']      ,   examples['sentence2']      ,   truncation=A__      ,   max_length=A__     )
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      _lowercase	       =datasets.map(
          A__      ,   batched=A__      ,   remove_columns=['idx', 'sentence1', 'sentence2']      ,   load_from_cache_file=A__     )

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      _lowercase	       =tokenized_datasets.rename_column('label'      ,   'labels'     )

      def collate_fn(A__	:   List[str]     ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(A__      ,   padding='max_length'      ,   max_length=128      ,   return_tensors='pt'     )
            return tokenizer.pad(A__      ,   padding='longest'      ,   return_tensors='pt'     )

      # Instantiate dataloaders.
      _lowercase	       =DataLoader(
          tokenized_datasets['train']      ,   shuffle=A__      ,   collate_fn=A__      ,   batch_size=A__     )
      _lowercase	       =DataLoader(
          tokenized_datasets['validation']      ,   shuffle=A__      ,   collate_fn=A__      ,   batch_size=A__     )

      return train_dataloader, eval_dataloader
def      a      (			A__	:   Optional[Any]      ,   A__	:   Optional[int]      ,   A__	:   List[str]      ,   A__	:   Dict     )    ->      Dict:





      """simple docstring"""



      model.eval()
      _lowercase	       =0
      for step, batch in enumerate(A__     ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device     )
            with torch.no_grad():
                  _lowercase	       =model(**A__     )
            _lowercase	       =outputs.logits.argmax(dim=-1     )
            # It is slightly faster to call this once, than multiple times
            _lowercase       ,       _lowercase	       =accelerator.gather(
                (predictions, batch['labels'])     )  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                  if step == len(A__     ) - 1:
                        _lowercase	       =predictions[: len(eval_dataloader.dataset     ) - samples_seen]
                        _lowercase	       =references[: len(eval_dataloader.dataset     ) - samples_seen]
                  else:
                        samples_seen += references.shape[0]
            metric.add_batch(
                predictions=A__      ,   references=A__      ,   )

      _lowercase	       =metric.compute()
      return eval_metric["accuracy"]
def      a      (			A__	:   str      ,   A__	:   Optional[Any]     )    ->      Optional[Any]:





      """simple docstring"""



      _lowercase	       =Accelerator()

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      _lowercase	       =config['lr']
      _lowercase	       =int(config['num_epochs']     )
      _lowercase	       =int(config['seed']     )
      _lowercase	       =int(config['batch_size']     )
      _lowercase	       =args.model_name_or_path

      set_seed(A__     )
      _lowercase       ,       _lowercase	       =get_dataloaders(A__      ,   A__      ,   A__     )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _lowercase	       =AutoModelForSequenceClassification.from_pretrained(A__      ,   return_dict=A__     )

      # Instantiate optimizer
      _lowercase	       =(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      _lowercase	       =optimizer_cls(params=model.parameters()      ,   lr=A__     )

      if accelerator.state.deepspeed_plugin is not None:
            _lowercase	       =accelerator.state.deepspeed_plugin.deepspeed_config[
                'gradient_accumulation_steps'
            ]
      else:
            _lowercase	       =1
      _lowercase	       =(len(A__     ) * num_epochs) // gradient_accumulation_steps

      # Instantiate scheduler
      if (
          accelerator.state.deepspeed_plugin is None
          or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
      ):
            _lowercase	       =get_linear_schedule_with_warmup(
                optimizer=A__      ,   num_warmup_steps=0      ,   num_training_steps=A__      ,   )
      else:
            _lowercase	       =DummyScheduler(A__      ,   total_num_steps=A__      ,   warmup_num_steps=0     )

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      _lowercase       ,       _lowercase       ,       _lowercase       ,       _lowercase       ,       _lowercase	       =accelerator.prepare(
          A__      ,   A__      ,   A__      ,   A__      ,   A__     )

      # We need to keep track of how many total steps we have iterated over
      _lowercase	       =0
      # We also need to keep track of the stating epoch so files are named properly
      _lowercase	       =0
      _lowercase	       =evaluate.load('glue'      ,   'mrpc'     )
      _lowercase	       =num_epochs

      if args.partial_train_epoch is not None:
            _lowercase	       =args.partial_train_epoch

      if args.resume_from_checkpoint:
            accelerator.load_state(args.resume_from_checkpoint     )
            _lowercase	       =args.resume_from_checkpoint.split('epoch_'     )[1]
            _lowercase	       =''
            for char in epoch_string:
                  if char.isdigit():
                        state_epoch_num += char
                  else:
                        break
            _lowercase	       =int(A__     ) + 1
            _lowercase	       =evaluation_loop(A__      ,   A__      ,   A__      ,   A__     )
            accelerator.print('resumed checkpoint performance:'      ,   A__     )
            accelerator.print('resumed checkpoint\'s scheduler\'s lr:'      ,   lr_scheduler.get_lr()[0]     )
            accelerator.print('resumed optimizers\'s lr:'      ,   optimizer.param_groups[0]['lr']     )
            with open(os.path.join(args.output_dir      ,   F'''state_{starting_epoch-1}.json'''     )      ,   'r'     ) as f:
                  _lowercase	       =json.load(A__     )
                  assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["lr"] == lr_scheduler.get_lr()[0]
                  ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                  ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                  assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                  return

    # Now we train the model
      _lowercase	       ={}
      for epoch in range(A__      ,   A__     ):
            model.train()
            for step, batch in enumerate(A__     ):
                  _lowercase	       =model(**A__     )
                  _lowercase	       =outputs.loss
                  _lowercase	       =loss / gradient_accumulation_steps
                  accelerator.backward(A__     )
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1
            _lowercase	       =F'''epoch_{epoch}'''
            _lowercase	       =os.path.join(args.output_dir      ,   A__     )
            accelerator.save_state(A__     )
            _lowercase	       =evaluation_loop(A__      ,   A__      ,   A__      ,   A__     )
            _lowercase	       =accuracy
            _lowercase	       =lr_scheduler.get_lr()[0]
            _lowercase	       =optimizer.param_groups[0]['lr']
            _lowercase	       =epoch
            _lowercase	       =overall_step
            accelerator.print(F'''epoch {epoch}:'''      ,   A__     )

            accelerator.wait_for_everyone()
            if accelerator.is_main_process:
                  with open(os.path.join(args.output_dir      ,   F'''state_{epoch}.json'''     )      ,   'w'     ) as f:
                        json.dump(A__      ,   A__     )
def      a      (			)    ->      Tuple:





      """simple docstring"""



      _lowercase	       =argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'     )
      parser.add_argument(
          '--model_name_or_path'      ,   type=A__      ,   default='bert-base-cased'      ,   help='Path to pretrained model or model identifier from huggingface.co/models.'      ,   required=A__      ,   )
      parser.add_argument(
          '--output_dir'      ,   type=A__      ,   default='.'      ,   help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'      ,   )
      parser.add_argument(
          '--resume_from_checkpoint'      ,   type=A__      ,   default=A__      ,   help='If the training should continue from a checkpoint folder.'      ,   )
      parser.add_argument(
          '--partial_train_epoch'      ,   type=A__      ,   default=A__      ,   help='If passed, the training will stop after this number of epochs.'      ,   )
      parser.add_argument(
          '--num_epochs'      ,   type=A__      ,   default=2      ,   help='Number of train epochs.'      ,   )
      _lowercase	       =parser.parse_args()
      _lowercase	       ={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

      training_function(A__      ,   A__     )


if __name__ == "__main__":
  main()


