import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


A     :  Union[str, Any]					     =       False


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







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







  @property
  def    __A     (							self :       Optional[int]   )  ->	Optional[Any]:
        return 12







  @property
  def    __A     (							self :       str   )  ->	Optional[int]:
        return 12







  @property
  def    __A     (							self :       Optional[Any]   )  ->	Optional[int]:
        return 32







  @property
  def    __A     (							self :       Tuple   )  ->	Any:
        torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  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  ,     num_vq_embeddings=self.num_embed  ,     vq_embed_dim=3  ,     )
        return model







  @property
  def    __A     (							self :       Dict   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )
        return tokenizer







  @property
  def    __A     (							self :       Any   )  ->	int:
        torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  CLIPTextConfig(
            bos_token_id=0  ,     eos_token_id=2  ,     hidden_size=self.text_embedder_hidden_size  ,     intermediate_size=37  ,     layer_norm_eps=1e-05  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     pad_token_id=1  ,     vocab_size=1_000  ,     )
        return CLIPTextModel(__magic_name__   )







  @property
  def    __A     (							self :       Any   )  ->	List[Any]:
        torch.manual_seed(0   )

        SCREAMING_SNAKE_CASE_          =  12
        SCREAMING_SNAKE_CASE_          =  12

        SCREAMING_SNAKE_CASE_          =  {
            "attention_bias": True,
            "cross_attention_dim": 32,
            "attention_head_dim": height * width,
            "num_attention_heads": 1,
            "num_vector_embeds": self.num_embed,
            "num_embeds_ada_norm": self.num_embeds_ada_norm,
            "norm_num_groups": 32,
            "sample_size": width,
            "activation_fn": "geglu-approximate",
        }

        SCREAMING_SNAKE_CASE_          =  TransformeraDModel(**__magic_name__   )
        return model







  def    __A     (							self :       Any   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  "cpu"

        SCREAMING_SNAKE_CASE_          =  self.dummy_vqvae
        SCREAMING_SNAKE_CASE_          =  self.dummy_text_encoder
        SCREAMING_SNAKE_CASE_          =  self.dummy_tokenizer
        SCREAMING_SNAKE_CASE_          =  self.dummy_transformer
        SCREAMING_SNAKE_CASE_          =  VQDiffusionScheduler(self.num_embed   )
        SCREAMING_SNAKE_CASE_          =  LearnedClassifierFreeSamplingEmbeddings(learnable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  VQDiffusionPipeline(
            vqvae=__magic_name__  ,     text_encoder=__magic_name__  ,     tokenizer=__magic_name__  ,     transformer=__magic_name__  ,     scheduler=__magic_name__  ,     learned_classifier_free_sampling_embeddings=__magic_name__  ,     )
        SCREAMING_SNAKE_CASE_          =  pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "teddy bear playing in the pool"

        SCREAMING_SNAKE_CASE_          =  torch.Generator(device=__magic_name__   ).manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe([prompt]  ,     generator=__magic_name__  ,     num_inference_steps=2  ,     output_type="np"   )
        SCREAMING_SNAKE_CASE_          =  output.images

        SCREAMING_SNAKE_CASE_          =  torch.Generator(device=__magic_name__   ).manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            [prompt]  ,     generator=__magic_name__  ,     output_type="np"  ,     return_dict=__magic_name__  ,     num_inference_steps=2   )[0]

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

        assert image.shape == (1, 24, 24, 3)

        SCREAMING_SNAKE_CASE_          =  np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]   )

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







  def    __A     (							self :       Optional[Any]   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  "cpu"

        SCREAMING_SNAKE_CASE_          =  self.dummy_vqvae
        SCREAMING_SNAKE_CASE_          =  self.dummy_text_encoder
        SCREAMING_SNAKE_CASE_          =  self.dummy_tokenizer
        SCREAMING_SNAKE_CASE_          =  self.dummy_transformer
        SCREAMING_SNAKE_CASE_          =  VQDiffusionScheduler(self.num_embed   )
        SCREAMING_SNAKE_CASE_          =  LearnedClassifierFreeSamplingEmbeddings(
            learnable=__magic_name__  ,     hidden_size=self.text_embedder_hidden_size  ,     length=tokenizer.model_max_length   )

        SCREAMING_SNAKE_CASE_          =  VQDiffusionPipeline(
            vqvae=__magic_name__  ,     text_encoder=__magic_name__  ,     tokenizer=__magic_name__  ,     transformer=__magic_name__  ,     scheduler=__magic_name__  ,     learned_classifier_free_sampling_embeddings=__magic_name__  ,     )
        SCREAMING_SNAKE_CASE_          =  pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "teddy bear playing in the pool"

        SCREAMING_SNAKE_CASE_          =  torch.Generator(device=__magic_name__   ).manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe([prompt]  ,     generator=__magic_name__  ,     num_inference_steps=2  ,     output_type="np"   )
        SCREAMING_SNAKE_CASE_          =  output.images

        SCREAMING_SNAKE_CASE_          =  torch.Generator(device=__magic_name__   ).manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            [prompt]  ,     generator=__magic_name__  ,     output_type="np"  ,     return_dict=__magic_name__  ,     num_inference_steps=2   )[0]

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

        assert image.shape == (1, 24, 24, 3)

        SCREAMING_SNAKE_CASE_          =  np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]   )

        assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2.0
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2



@slow
@require_torch_gpu
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       Union[str, Any]   )  ->	Optional[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def    __A     (							self :       str   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"   )

        SCREAMING_SNAKE_CASE_          =  VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"   )
        SCREAMING_SNAKE_CASE_          =  pipeline.to(__magic_name__   )
        pipeline.set_progress_bar_config(disable=__magic_name__   )

        # requires GPU generator for gumbel softmax
        # don't use GPU generator in tests though
        SCREAMING_SNAKE_CASE_          =  torch.Generator(device=__magic_name__   ).manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipeline(
            "teddy bear playing in the pool"  ,     num_images_per_prompt=1  ,     generator=__magic_name__  ,     output_type="np"  ,     )

        SCREAMING_SNAKE_CASE_          =  output.images[0]

        assert image.shape == (256, 256, 3)
        assert np.abs(expected_image - image   ).max() < 2.0



import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A     :  Optional[Any]					     =       16
A     :  Optional[Any]					     =       32


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase = 1_6				):
      SCREAMING_SNAKE_CASE_          =  AutoTokenizer.from_pretrained("bert-base-cased"				)
      SCREAMING_SNAKE_CASE_          =  load_dataset("glue"     ,      "mrpc"				)

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

      # Apply the method we just defined to all the examples in all the splits of the dataset
      # starting with the main process first:
      with accelerator.main_process_first():
            SCREAMING_SNAKE_CASE_          =  datasets.map(
                __UpperCamelCase     ,      batched=__UpperCamelCase     ,      remove_columns=["idx", "sentence1", "sentence2"]     ,      )

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

      def collate_fn(__UpperCamelCase				):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE_          =  1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  SCREAMING_SNAKE_CASE_          =  1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE_          =  8
            else:
                  SCREAMING_SNAKE_CASE_          =  None

            return tokenizer.pad(
                __UpperCamelCase     ,      padding="longest"     ,      max_length=__UpperCamelCase     ,      pad_to_multiple_of=__UpperCamelCase     ,      return_tensors="pt"     ,      )

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE_          =  DataLoader(
          tokenized_datasets["train"]     ,      shuffle=__UpperCamelCase     ,      collate_fn=__UpperCamelCase     ,      batch_size=__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  DataLoader(
          tokenized_datasets["validation"]     ,      shuffle=__UpperCamelCase     ,      collate_fn=__UpperCamelCase     ,      batch_size=__UpperCamelCase				)

      return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      A     :  Tuple					     =       mocked_dataloaders  # noqa: F811


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      # For testing only
      if os.environ.get("TESTING_MOCKED_DATALOADERS"     ,      __UpperCamelCase				) == "1":
            SCREAMING_SNAKE_CASE_          =  2
      # New Code #
      SCREAMING_SNAKE_CASE_          =  int(args.gradient_accumulation_steps				)
      # Initialize accelerator
      SCREAMING_SNAKE_CASE_          =  Accelerator(
          cpu=args.cpu     ,      mixed_precision=args.mixed_precision     ,      gradient_accumulation_steps=__UpperCamelCase				)
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"				)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE_          =  config["lr"]
      SCREAMING_SNAKE_CASE_          =  int(config["num_epochs"]				)
      SCREAMING_SNAKE_CASE_          =  int(config["seed"]				)
      SCREAMING_SNAKE_CASE_          =  int(config["batch_size"]				)

      SCREAMING_SNAKE_CASE_          =  evaluate.load("glue"     ,      "mrpc"				)

      set_seed(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  get_dataloaders(__UpperCamelCase     ,      __UpperCamelCase				)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      SCREAMING_SNAKE_CASE_          =  AutoModelForSequenceClassification.from_pretrained("bert-base-cased"     ,      return_dict=__UpperCamelCase				)

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      SCREAMING_SNAKE_CASE_          =  model.to(accelerator.device				)

      # Instantiate optimizer
      SCREAMING_SNAKE_CASE_          =  AdamW(params=model.parameters()     ,      lr=__UpperCamelCase				)

      # Instantiate scheduler
      SCREAMING_SNAKE_CASE_          =  get_linear_schedule_with_warmup(
          optimizer=__UpperCamelCase     ,      num_warmup_steps=1_0_0     ,      num_training_steps=(len(__UpperCamelCase				) * num_epochs)     ,      )

      # 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.
      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  accelerator.prepare(
          __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)

      # Now we train the model
      for epoch in range(__UpperCamelCase				):
            model.train()
            for step, batch in enumerate(__UpperCamelCase				):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device				)
                  # New code #
                  # We use the new `accumulate` context manager to perform gradient accumulation
                  # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                  with accelerator.accumulate(__UpperCamelCase				):
                        SCREAMING_SNAKE_CASE_          =  model(**__UpperCamelCase				)
                        SCREAMING_SNAKE_CASE_          =  output.loss
                        accelerator.backward(__UpperCamelCase				)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(__UpperCamelCase				):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device				)
                  with torch.no_grad():
                        SCREAMING_SNAKE_CASE_          =  model(**__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  outputs.logits.argmax(dim=-1				)
                  SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  accelerator.gather_for_metrics((predictions, batch["labels"])				)
                  metric.add_batch(
                      predictions=__UpperCamelCase     ,      references=__UpperCamelCase     ,      )

            SCREAMING_SNAKE_CASE_          =  metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'''epoch {epoch}:'''     ,      __UpperCamelCase				)


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  argparse.ArgumentParser(description="Simple example of training script."				)
      parser.add_argument(
          "--mixed_precision"     ,      type=__UpperCamelCase     ,      default=__UpperCamelCase     ,      choices=["no", "fp16", "bf16", "fp8"]     ,      help="Whether to use mixed precision. Choose"
          "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
          "and an Nvidia Ampere GPU."     ,      )
      # New Code #
      parser.add_argument(
          "--gradient_accumulation_steps"     ,      type=__UpperCamelCase     ,      default=1     ,      help="The number of minibatches to be ran before gradients are accumulated."     ,      )
      parser.add_argument("--cpu"     ,      action="store_true"     ,      help="If passed, will train on the CPU."				)
      SCREAMING_SNAKE_CASE_          =  parser.parse_args()
      SCREAMING_SNAKE_CASE_          =  {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
      training_function(__UpperCamelCase     ,      __UpperCamelCase				)


if __name__ == "__main__":
      main()


