


"""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


__lowercase    					=      16
__lowercase    					=      32


def      lowercase			(				A_  ,   A_ = 16  ,   A_ = "bert-base-cased"       )->      Dict:
  '''simple docstring'''







  a						:		str      							=			AutoTokenizer.from_pretrained(A_       )
  a						:		int      							=			load_dataset("glue"  ,   "mrpc"       )

  def tokenize_function(A_       ):
    # max_length=None => use the model max length (it's actually the default)
    a						:		List[str]      							=			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						:		Any      							=			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						:		List[Any]      							=			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						:		Dict      							=			DataLoader(
      tokenized_datasets["train"]  ,   shuffle=A_  ,   collate_fn=A_  ,   batch_size=A_       )
  a						:		str      							=			DataLoader(
      tokenized_datasets["validation"]  ,   shuffle=A_  ,   collate_fn=A_  ,   batch_size=A_       )

  return train_dataloader, eval_dataloader


def      lowercase			(				A_  ,   A_  ,   A_  ,   A_       )->      Tuple:
  '''simple docstring'''







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

  a						:		Tuple      							=			metric.compute()
  return eval_metric["accuracy"]


def      lowercase			(				A_  ,   A_       )->      Any:
  '''simple docstring'''







  a						:		int      							=			Accelerator()

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

  set_seed(A_       )
  a							,		a						:		Tuple      							=			get_dataloaders(A_  ,   A_  ,   A_       )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  a						:		Optional[int]      							=			AutoModelForSequenceClassification.from_pretrained(A_  ,   return_dict=A_       )

  # Instantiate optimizer
  a						:		Optional[int]      							=			(
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  a						:		Optional[int]      							=			optimizer_cls(params=model.parameters()  ,   lr=A_       )

  if accelerator.state.deepspeed_plugin is not None:
    a						:		str      							=			accelerator.state.deepspeed_plugin.deepspeed_config[
        "gradient_accumulation_steps"
    ]
  else:
    a						:		str      							=			1
  a						:		Union[str, Any]      							=			(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						:		List[Any]      							=			get_linear_schedule_with_warmup(
        optimizer=A_  ,   num_warmup_steps=0  ,   num_training_steps=A_  ,   )
  else:
    a						:		Union[str, Any]      							=			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						:		List[Any]      							=			accelerator.prepare(
      A_  ,   A_  ,   A_  ,   A_  ,   A_       )

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

  if args.partial_train_epoch is not None:
    a						:		Optional[Any]      							=			args.partial_train_epoch

  if args.resume_from_checkpoint:
    accelerator.load_state(args.resume_from_checkpoint       )
    a						:		Tuple      							=			args.resume_from_checkpoint.split("epoch_"       )[1]
    a						:		Dict      							=			""
    for char in epoch_string:
      if char.isdigit():
        state_epoch_num += char
      else:
        break
    a						:		List[str]      							=			int(A_       ) + 1
    a						:		List[Any]      							=			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						:		Optional[int]      							=			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						:		int      							=			{}
  for epoch in range(A_  ,   A_       ):
    model.train()
    for step, batch in enumerate(A_       ):
      a						:		List[Any]      							=			model(**A_       )
      a						:		int      							=			outputs.loss
      a						:		Union[str, Any]      							=			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						:		Tuple      							=			F'''epoch_{epoch}'''
    a						:		Dict      							=			os.path.join(args.output_dir  ,   A_       )
    accelerator.save_state(A_       )
    a						:		List[Any]      							=			evaluation_loop(A_  ,   A_  ,   A_  ,   A_       )
    a						:		Union[str, Any]      							=			accuracy
    a						:		Optional[Any]      							=			lr_scheduler.get_lr()[0]
    a						:		List[Any]      							=			optimizer.param_groups[0]["lr"]
    a						:		Optional[Any]      							=			epoch
    a						:		Dict      							=			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      lowercase			(				)->      Optional[int]:
  '''simple docstring'''







  a						:		List[str]      							=			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						:		Union[str, Any]      							=			parser.parse_args()
  a						:		Union[str, Any]      							=			{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

  training_function(A_  ,   A_       )


if __name__ == "__main__":
 main()










"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 						_A  (  _a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							Dict	=       KandinskyVaaControlnetPipeline
    UpperCAmelCase       :							List[str]	=       ["""image_embeds""", """negative_image_embeds""", """hint"""]
    UpperCAmelCase       :							Optional[Any]	=       ["""image_embeds""", """negative_image_embeds""", """hint"""]
    UpperCAmelCase       :							Dict	=       [
        """generator""",
        """height""",
        """width""",
        """latents""",
        """guidance_scale""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    UpperCAmelCase       :							Optional[int]	=       False






    @property
    def 	__snake_case							(					self				:    Optional[Any]):
      return 32






    @property
    def 	__snake_case							(					self				:    Dict):
      return 32






    @property
    def 	__snake_case							(					self				:    Dict):
      return self.time_input_dim






    @property
    def 	__snake_case							(					self				:    Any):
      return self.time_input_dim * 4






    @property
    def 	__snake_case							(					self				:    str):
      return 100






    @property
    def 	__snake_case							(					self				:    str):
      torch.manual_seed(0)

      a						:		str      							=			{
          "in_channels": 8,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 8,
          "addition_embed_type": "image_hint",
          "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
          "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
          "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "layers_per_block": 1,
          "encoder_hid_dim": self.text_embedder_hidden_size,
          "encoder_hid_dim_type": "image_proj",
          "cross_attention_dim": self.cross_attention_dim,
          "attention_head_dim": 4,
          "resnet_time_scale_shift": "scale_shift",
          "class_embed_type": None,
      }

      a						:		Dict      							=			UNetaDConditionModel(**__UpperCAmelCase)
      return model






    @property
    def 	__snake_case							(					self				:    str):
      return {
          "block_out_channels": [32, 32, 64, 64],
          "down_block_types": [
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "AttnDownEncoderBlock2D",
          ],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
          "vq_embed_dim": 4,
      }






    @property
    def 	__snake_case							(					self				:    Union[str, Any]):
      torch.manual_seed(0)
      a						:		Dict      							=			VQModel(**self.dummy_movq_kwargs)
      return model






    def 	__snake_case							(					self				:    Optional[Any]):
      a						:		Optional[Any]      							=			self.dummy_unet
      a						:		int      							=			self.dummy_movq

      a						:		str      							=			DDIMScheduler(
          num_train_timesteps=1000				, beta_schedule="linear"				, beta_start=0.00_085				, beta_end=0.012				, clip_sample=__UpperCAmelCase				, set_alpha_to_one=__UpperCAmelCase				, steps_offset=1				, prediction_type="epsilon"				, thresholding=__UpperCAmelCase				, )

      a						:		Optional[Any]      							=			{
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }
      return components






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    Optional[Any]				, __UpperCAmelCase				:    int=0):
      a						:		Optional[int]      							=			floats_tensor((1, self.text_embedder_hidden_size)				, rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)
      a						:		List[str]      							=			floats_tensor((1, self.text_embedder_hidden_size)				, rng=random.Random(seed + 1)).to(
          __UpperCAmelCase)

      # create hint
      a						:		Union[str, Any]      							=			floats_tensor((1, 3, 64, 64)				, rng=random.Random(__UpperCAmelCase)).to(__UpperCAmelCase)

      if str(__UpperCAmelCase).startswith("mps"):
        a						:		Union[str, Any]      							=			torch.manual_seed(__UpperCAmelCase)
      else:
        a						:		List[Any]      							=			torch.Generator(device=__UpperCAmelCase).manual_seed(__UpperCAmelCase)
      a						:		str      							=			{
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "hint": hint,
          "generator": generator,
          "height": 64,
          "width": 64,
          "guidance_scale": 4.0,
          "num_inference_steps": 2,
          "output_type": "np",
      }
      return inputs






    def 	__snake_case							(					self				:    Dict):
      a						:		str      							=			"cpu"

      a						:		Tuple      							=			self.get_dummy_components()

      a						:		Dict      							=			self.pipeline_class(**__UpperCAmelCase)
      a						:		Optional[int]      							=			pipe.to(__UpperCAmelCase)

      pipe.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Optional[Any]      							=			pipe(**self.get_dummy_inputs(__UpperCAmelCase))
      a						:		Any      							=			output.images

      a						:		Any      							=			pipe(
          **self.get_dummy_inputs(__UpperCAmelCase)				, return_dict=__UpperCAmelCase				, )[0]

      a						:		Union[str, Any]      							=			image[0, -3:, -3:, -1]
      a						:		int      							=			image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      a						:		Tuple      							=			np.array(
          [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595])

      assert (
          np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
      ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
      ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






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







    """simple docstring"""




    def 	__snake_case							(					self				:    Optional[int]):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def 	__snake_case							(					self				:    List[str]):
      a						:		List[Any]      							=			load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy")

      a						:		Union[str, Any]      							=			load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/hint_image_cat.png")
      a						:		List[Any]      							=			torch.from_numpy(np.array(__UpperCAmelCase)).float() / 255.0
      a						:		str      							=			hint.permute(2				, 0				, 1).unsqueeze(0)

      a						:		Optional[int]      							=			KandinskyVaaPriorPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-prior"				, torch_dtype=torch.floataa)
      pipe_prior.to(__UpperCAmelCase)

      a						:		List[str]      							=			KandinskyVaaControlnetPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-controlnet-depth"				, torch_dtype=torch.floataa)
      a						:		int      							=			pipeline.to(__UpperCAmelCase)
      pipeline.set_progress_bar_config(disable=__UpperCAmelCase)

      a						:		Tuple      							=			"A robot, 4k photo"

      a						:		Any      							=			torch.Generator(device="cuda").manual_seed(0)
      a							,		a						:		int      							=			pipe_prior(
          __UpperCAmelCase				, generator=__UpperCAmelCase				, num_inference_steps=5				, negative_prompt=""				, ).to_tuple()

      a						:		str      							=			torch.Generator(device="cuda").manual_seed(0)
      a						:		Union[str, Any]      							=			pipeline(
          image_embeds=__UpperCAmelCase				, negative_image_embeds=__UpperCAmelCase				, hint=__UpperCAmelCase				, generator=__UpperCAmelCase				, num_inference_steps=100				, output_type="np"				, )

      a						:		str      							=			output.images[0]

      assert image.shape == (512, 512, 3)

      assert_mean_pixel_difference(__UpperCAmelCase				, __UpperCAmelCase)






