'''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_:	Tuple    =16
SCREAMING_SNAKE_CASE_:	Tuple    =32





def       lowerCAmelCase_							(   snake_case_			:					Accelerator   ,       snake_case_			:					int = 16   ,       snake_case_			:					str = "bert-base-cased"     )       -> Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  AutoTokenizer.from_pretrained(snake_case_     )
     UpperCAmelCase_			       =  load_dataset("glue"   ,       "mrpc"     )

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

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

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

     def collate_fn(snake_case_			:					Union[str, Any]     ):
          # 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(snake_case_   ,       padding="max_length"   ,       max_length=1_28   ,       return_tensors="pt"     )
          return tokenizer.pad(snake_case_   ,       padding="longest"   ,       return_tensors="pt"     )

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

     return train_dataloader, eval_dataloader





def       lowerCAmelCase_							(   snake_case_			:					int   ,       snake_case_			:					str     )       -> Union[str, Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  Accelerator()

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

     set_seed(snake_case_     )
     UpperCAmelCase_       ,					UpperCAmelCase_			       =  get_dataloaders(snake_case_   ,       snake_case_   ,       snake_case_     )

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

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

     if accelerator.state.deepspeed_plugin is not None:
          UpperCAmelCase_			       =  accelerator.state.deepspeed_plugin.deepspeed_config[
              "gradient_accumulation_steps"
          ]
     else:
          UpperCAmelCase_			       =  1
     UpperCAmelCase_			       =  (len(snake_case_     ) * num_epochs) // gradient_accumulation_steps

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

     # We need to keep track of how many total steps we have iterated over
     UpperCAmelCase_			       =  0
     # We also need to keep track of the stating epoch so files are named properly
     UpperCAmelCase_			       =  0

     # Now we train the model
     UpperCAmelCase_			       =  evaluate.load("glue"   ,       "mrpc"     )
     UpperCAmelCase_			       =  0
     UpperCAmelCase_			       =  {}
     for epoch in range(snake_case_   ,       snake_case_     ):
          model.train()
          for step, batch in enumerate(snake_case_     ):
               UpperCAmelCase_			       =  model(**snake_case_     )
               UpperCAmelCase_			       =  outputs.loss
               UpperCAmelCase_			       =  loss / gradient_accumulation_steps
               accelerator.backward(snake_case_     )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

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

          UpperCAmelCase_			       =  metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f"""epoch {epoch}:"""   ,       snake_case_     )
          UpperCAmelCase_			       =  eval_metric["accuracy"]

          if best_performance < eval_metric["accuracy"]:
               UpperCAmelCase_			       =  eval_metric["accuracy"]

     if args.performance_lower_bound is not None:
          assert (
              args.performance_lower_bound <= best_performance
          ), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
          with open(os.path.join(args.output_dir   ,       "all_results.json"     )   ,       "w"     ) as f:
               json.dump(snake_case_   ,       snake_case_     )





def       lowerCAmelCase_							(   )       -> Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."     )
     parser.add_argument(
         "--model_name_or_path"   ,       type=snake_case_   ,       default="bert-base-cased"   ,       help="Path to pretrained model or model identifier from huggingface.co/models."   ,       required=snake_case_   ,       )
     parser.add_argument(
         "--output_dir"   ,       type=snake_case_   ,       default="."   ,       help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."   ,       )
     parser.add_argument(
         "--performance_lower_bound"   ,       type=snake_case_   ,       default=snake_case_   ,       help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."   ,       )
     parser.add_argument(
         "--num_epochs"   ,       type=snake_case_   ,       default=3   ,       help="Number of train epochs."   ,       )
     UpperCAmelCase_			       =  parser.parse_args()
     UpperCAmelCase_			       =  {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
     training_function(snake_case_   ,       snake_case_     )


if __name__ == "__main__":
    main()












import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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						( UpperCAmelCase_	,				unittest.TestCase ):
  lowercase_						:			int						=					KandinskyVaaPipeline
  lowercase_						:			Union[str, Any]						=					[
      '''image_embeds''',
      '''negative_image_embeds''',
  ]
  lowercase_						:			int						=					['''image_embeds''', '''negative_image_embeds''']
  lowercase_						:			str						=					[
      '''generator''',
      '''height''',
      '''width''',
      '''latents''',
      '''guidance_scale''',
      '''num_inference_steps''',
      '''return_dict''',
      '''guidance_scale''',
      '''num_images_per_prompt''',
      '''output_type''',
      '''return_dict''',
  ]
  lowercase_						:			str						=					False

  @property
  def    a					(    self						:	int			):
     """simple docstring"""

     return 32

  @property
  def    a					(    self						:	str			):
     """simple docstring"""

     return 32

  @property
  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     return self.time_input_dim

  @property
  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     return self.time_input_dim * 4

  @property
  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     return 1_00

  @property
  def    a					(    self						:	str			):
     """simple docstring"""

     torch.manual_seed(0			)

     __UpperCamelCase	:			int					=					{
         """in_channels""": 4,
         # Out channels is double in channels because predicts mean and variance
         """out_channels""": 8,
         """addition_embed_type""": """image""",
         """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,
     }

     __UpperCamelCase	:			Any					=					UNetaDConditionModel(**lowerCamelCase__			)
     return model

  @property
  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["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",
         ],
         "vq_embed_dim": 4,
     }

  @property
  def    a					(    self						:	int			):
     """simple docstring"""

     torch.manual_seed(0			)
     __UpperCamelCase	:			Tuple					=					VQModel(**self.dummy_movq_kwargs			)
     return model

  def    a					(    self						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					self.dummy_unet
     __UpperCamelCase	:			Optional[int]					=					self.dummy_movq

     __UpperCamelCase	:			List[str]					=					DDIMScheduler(
         num_train_timesteps=10_00   ,				beta_schedule="""linear"""   ,				beta_start=0.0_0085   ,				beta_end=0.012   ,				clip_sample=lowerCamelCase__   ,				set_alpha_to_one=lowerCamelCase__   ,				steps_offset=1   ,				prediction_type="""epsilon"""   ,				thresholding=lowerCamelCase__   ,				)

     __UpperCamelCase	:			str					=					{
         """unet""": unet,
         """scheduler""": scheduler,
         """movq""": movq,
     }
     return components

  def    a					(    self						:	Optional[int]   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	Any=0			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					floats_tensor((1, self.text_embedder_hidden_size)   ,				rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
     __UpperCamelCase	:			Optional[int]					=					floats_tensor((1, self.text_embedder_hidden_size)   ,				rng=random.Random(seed + 1			)			).to(
         lowerCamelCase__			)
     if str(lowerCamelCase__			).startswith("""mps"""			):
        __UpperCamelCase	:			Optional[int]					=					torch.manual_seed(lowerCamelCase__			)
     else:
        __UpperCamelCase	:			int					=					torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
     __UpperCamelCase	:			Dict					=					{
         """image_embeds""": image_embeds,
         """negative_image_embeds""": negative_image_embeds,
         """generator""": generator,
         """height""": 64,
         """width""": 64,
         """guidance_scale""": 4.0,
         """num_inference_steps""": 2,
         """output_type""": """np""",
     }
     return inputs

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					"""cpu"""

     __UpperCamelCase	:			Dict					=					self.get_dummy_components()

     __UpperCamelCase	:			Union[str, Any]					=					self.pipeline_class(**lowerCamelCase__			)
     __UpperCamelCase	:			int					=					pipe.to(lowerCamelCase__			)

     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			List[Any]					=					pipe(**self.get_dummy_inputs(lowerCamelCase__			)			)
     __UpperCamelCase	:			List[Any]					=					output.images

     __UpperCamelCase	:			int					=					pipe(
         **self.get_dummy_inputs(lowerCamelCase__			)   ,				return_dict=lowerCamelCase__   ,				)[0]

     __UpperCamelCase	:			List[str]					=					image[0, -3:, -3:, -1]
     __UpperCamelCase	:			Tuple					=					image_from_tuple[0, -3:, -3:, -1]

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

     __UpperCamelCase	:			int					=					np.array(
         [0.623_7976, 1.0, 0.3644_1332, 1.0, 0.7063_9634, 0.2987_7186, 0.8565_2125, 0.521_6843, 0.5445_4046]			)

     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 ):

  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

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

  def    a					(    self						:	List[Any]			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""			)

     __UpperCamelCase	:			List[str]					=					KandinskyVaaPriorPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-prior"""   ,				torch_dtype=torch.floataa			)
     pipe_prior.to(lowerCamelCase__			)

     __UpperCamelCase	:			int					=					KandinskyVaaPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-decoder"""   ,				torch_dtype=torch.floataa			)
     __UpperCamelCase	:			Optional[int]					=					pipeline.to(lowerCamelCase__			)
     pipeline.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			Optional[Any]					=					"""red cat, 4k photo"""

     __UpperCamelCase	:			Tuple					=					torch.Generator(device="""cuda"""			).manual_seed(0			)
     __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					pipe_prior(
         lowerCamelCase__   ,				generator=lowerCamelCase__   ,				num_inference_steps=5   ,				negative_prompt=""""""   ,				).to_tuple()

     __UpperCamelCase	:			Any					=					torch.Generator(device="""cuda"""			).manual_seed(0			)
     __UpperCamelCase	:			Union[str, Any]					=					pipeline(
         image_embeds=lowerCamelCase__   ,				negative_image_embeds=lowerCamelCase__   ,				generator=lowerCamelCase__   ,				num_inference_steps=1_00   ,				output_type="""np"""   ,				)

     __UpperCamelCase	:			Any					=					output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(lowerCamelCase__   ,				lowerCamelCase__			)



