'''simple docstring'''





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


SCREAMING_SNAKE_CASE :	Optional[Any]							     =		16
SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		32



def   __A		(			_A							,				_A = 16							,				_A = "bert-base-cased"					):



 """simple docstring"""


 __a       =  AutoTokenizer.from_pretrained(_A					)
 __a       =  load_dataset("glue"							,				"mrpc"					)

 def tokenize_function(_A					):
  # max_length=None => use the model max length (it's actually the default)
  __a       =  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
 __a       =  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
 __a       =  tokenized_datasets.rename_column("label"							,				"labels"					)

 def collate_fn(_A					):
  # 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.
 __a       =  DataLoader(
     tokenized_datasets["train"]							,				shuffle=_A							,				collate_fn=_A							,				batch_size=_A					)
 __a       =  DataLoader(
     tokenized_datasets["validation"]							,				shuffle=_A							,				collate_fn=_A							,				batch_size=_A					)

 return train_dataloader, eval_dataloader



def   __A		(			_A							,				_A							,				_A							,				_A					):



 """simple docstring"""


 model.eval()
 __a       =  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():
   __a       =  model(**_A					)
  __a       =  outputs.logits.argmax(dim=-1					)
  # It is slightly faster to call this once, than multiple times
  __a       ,     __a       =  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:
    __a       =  predictions[: len(eval_dataloader.dataset					) - samples_seen]
    __a       =  references[: len(eval_dataloader.dataset					) - samples_seen]
   else:
    samples_seen += references.shape[0]
  metric.add_batch(
      predictions=_A							,				references=_A							,				)

 __a       =  metric.compute()
 return eval_metric["accuracy"]



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  Accelerator()

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

 set_seed(_A					)
 __a       ,     __a       =  get_dataloaders(_A							,				_A							,				_A					)

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

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

 if accelerator.state.deepspeed_plugin is not None:
  __a       =  accelerator.state.deepspeed_plugin.deepspeed_config[
      "gradient_accumulation_steps"
  ]
 else:
  __a       =  1
 __a       =  (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
 ):
  __a       =  get_linear_schedule_with_warmup(
      optimizer=_A							,				num_warmup_steps=0							,				num_training_steps=_A							,				)
 else:
  __a       =  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.
 __a       ,     __a       ,     __a       ,     __a       ,     __a       =  accelerator.prepare(
     _A							,				_A							,				_A							,				_A							,				_A					)

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

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

 if args.resume_from_checkpoint:
  accelerator.load_state(args.resume_from_checkpoint					)
  __a       =  args.resume_from_checkpoint.split("epoch_"					)[1]
  __a       =  ""
  for char in epoch_string:
   if char.isdigit():
    state_epoch_num += char
   else:
    break
  __a       =  int(_A					) + 1
  __a       =  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:
   __a       =  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
 __a       =  {}
 for epoch in range(_A							,				_A					):
  model.train()
  for step, batch in enumerate(_A					):
   __a       =  model(**_A					)
   __a       =  outputs.loss
   __a       =  loss / gradient_accumulation_steps
   accelerator.backward(_A					)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1
  __a       =  f"""epoch_{epoch}"""
  __a       =  os.path.join(args.output_dir							,				_A					)
  accelerator.save_state(_A					)
  __a       =  evaluation_loop(_A							,				_A							,				_A							,				_A					)
  __a       =  accuracy
  __a       =  lr_scheduler.get_lr()[0]
  __a       =  optimizer.param_groups[0]["lr"]
  __a       =  epoch
  __a       =  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		(			):



 """simple docstring"""


 __a       =  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."							,				)
 __a       =  parser.parse_args()
 __a       =  {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

 training_function(_A							,				_A					)


if __name__ == "__main__":
  main()



import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class    A_ (       unittest.TestCase ):

  @property
  def 		_UpperCAmelCase   (		self :      str       ):
   torch.manual_seed(0       )
   __a       =  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

  @property
  def 		_UpperCAmelCase   (		self :      Dict       ):
   torch.manual_seed(0       )
   __a       =  VQModel(
       block_out_channels=[32, 64]     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,							latent_channels=3     ,							)
   return model

  @property
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   torch.manual_seed(0       )
   __a       =  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=10_00     ,							)
   return CLIPTextModel(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  self.dummy_uncond_unet
   __a       =  DDIMScheduler()
   __a       =  self.dummy_vq_model

   __a       =  LDMPipeline(unet=__SCREAMING_SNAKE_CASE     ,							vqvae=__SCREAMING_SNAKE_CASE     ,							scheduler=__SCREAMING_SNAKE_CASE       )
   ldm.to(__SCREAMING_SNAKE_CASE       )
   ldm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="numpy"       ).images

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="numpy"     ,							return_dict=__SCREAMING_SNAKE_CASE       )[0]

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

   assert image.shape == (1, 64, 64, 3)
   __a       =  np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]       )
   __a       =  1E-2 if torch_device != "mps" else 3E-2

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < tolerance
@slow
@require_torch
class    A_ (       unittest.TestCase ):

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"       )
   ldm.to(__SCREAMING_SNAKE_CASE       )
   ldm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=5     ,							output_type="numpy"       ).images

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

   assert image.shape == (1, 2_56, 2_56, 3)
   __a       =  np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]       )
   __a       =  1E-2 if torch_device != "mps" else 3E-2

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance


