





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_						       =	1_6
SCREAMING_SNAKE_CASE_						       =	3_2



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = 16			,						_SCREAMING_SNAKE_CASE = "bert-base-cased"     )			->       Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						load_dataset("""glue"""			,						"""mrpc"""     )

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

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

      # 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(_SCREAMING_SNAKE_CASE     ):
            # 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(_SCREAMING_SNAKE_CASE			,						padding="""max_length"""			,						max_length=1_28			,						return_tensors="""pt"""     )
            return tokenizer.pad(_SCREAMING_SNAKE_CASE			,						padding="""longest"""			,						return_tensors="""pt"""     )

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

      return train_dataloader, eval_dataloader



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						Accelerator()

      # 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		=						args.model_name_or_path

      set_seed(_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						get_dataloaders(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )

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

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

      if accelerator.state.deepspeed_plugin is not None:
            SCREAMING_SNAKE_CASE		=						accelerator.state.deepspeed_plugin.deepspeed_config[
                """gradient_accumulation_steps"""
            ]
      else:
            SCREAMING_SNAKE_CASE		=						1
      SCREAMING_SNAKE_CASE		=						(len(_SCREAMING_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
      ):
            SCREAMING_SNAKE_CASE		=						get_linear_schedule_with_warmup(
                optimizer=_SCREAMING_SNAKE_CASE			,						num_warmup_steps=0			,						num_training_steps=_SCREAMING_SNAKE_CASE			,						)
      else:
            SCREAMING_SNAKE_CASE		=						DummyScheduler(_SCREAMING_SNAKE_CASE			,						total_num_steps=_SCREAMING_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.
      SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						accelerator.prepare(
          _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )

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

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

                  overall_step += 1

            model.eval()
            SCREAMING_SNAKE_CASE		=						0
            for step, batch in enumerate(_SCREAMING_SNAKE_CASE     ):
                  # 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(**_SCREAMING_SNAKE_CASE     )
                  SCREAMING_SNAKE_CASE		=						outputs.logits.argmax(dim=-1     )
                  # It is slightly faster to call this once, than multiple times
                  SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						accelerator.gather(
                      (predictions, batch["""labels"""])     )  # If we are in a multiprocess environment, the last batch has duplicates
                  if accelerator.use_distributed:
                        if step == len(_SCREAMING_SNAKE_CASE     ) - 1:
                              SCREAMING_SNAKE_CASE		=						predictions[: len(eval_dataloader.dataset     ) - samples_seen]
                              SCREAMING_SNAKE_CASE		=						references[: len(eval_dataloader.dataset     ) - samples_seen]
                        else:
                              samples_seen += references.shape[0]
                  metric.add_batch(
                      predictions=_SCREAMING_SNAKE_CASE			,						references=_SCREAMING_SNAKE_CASE			,						)

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

            if best_performance < eval_metric["accuracy"]:
                  SCREAMING_SNAKE_CASE		=						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(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )



def        __lowercase       (				)			->       Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""     )
      parser.add_argument(
          """--model_name_or_path"""			,						type=_SCREAMING_SNAKE_CASE			,						default="""bert-base-cased"""			,						help="""Path to pretrained model or model identifier from huggingface.co/models."""			,						required=_SCREAMING_SNAKE_CASE			,						)
      parser.add_argument(
          """--output_dir"""			,						type=_SCREAMING_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=_SCREAMING_SNAKE_CASE			,						default=_SCREAMING_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=_SCREAMING_SNAKE_CASE			,						default=3			,						help="""Number of train epochs."""			,						)
      SCREAMING_SNAKE_CASE		=						parser.parse_args()
      SCREAMING_SNAKE_CASE		=						{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
      training_function(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      main()









import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import 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
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 		UpperCamelCase__			(				lowerCAmelCase_							,				lowerCAmelCase_							,				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     str		     =      AltDiffusionPipeline
     __snake_case   :     int		     =      TEXT_TO_IMAGE_PARAMS
     __snake_case   :     Dict		     =      TEXT_TO_IMAGE_BATCH_PARAMS
     __snake_case   :     Tuple		     =      TEXT_TO_IMAGE_IMAGE_PARAMS
     __snake_case   :     Optional[int]		     =      TEXT_TO_IMAGE_IMAGE_PARAMS






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Optional[Any]:




           '''simple docstring'''


           torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE		=						UNetaDConditionModel(
               block_out_channels=(32, 64)						,layers_per_block=2						,sample_size=32						,in_channels=4						,out_channels=4						,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,cross_attention_dim=32						,)
           SCREAMING_SNAKE_CASE		=						DDIMScheduler(
               beta_start=0.00085						,beta_end=0.012						,beta_schedule="""scaled_linear"""						,clip_sample=lowerCamelCase__						,set_alpha_to_one=lowerCamelCase__						,)
           torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE		=						AutoencoderKL(
               block_out_channels=[32, 64]						,in_channels=3						,out_channels=3						,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,latent_channels=4						,)

           # TODO: address the non-deterministic text encoder (fails for save-load tests)
           # torch.manual_seed(0)
           # text_encoder_config = RobertaSeriesConfig(
           #     hidden_size=32,
           #     project_dim=32,
           #     intermediate_size=37,
           #     layer_norm_eps=1e-05,
           #     num_attention_heads=4,
           #     num_hidden_layers=5,
           #     vocab_size=5002,
           # )
           # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

           torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE		=						CLIPTextConfig(
               bos_token_id=0						,eos_token_id=2						,hidden_size=32						,projection_dim=32						,intermediate_size=37						,layer_norm_eps=1e-0_5						,num_attention_heads=4						,num_hidden_layers=5						,pad_token_id=1						,vocab_size=5002						,)
           SCREAMING_SNAKE_CASE		=						CLIPTextModel(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""	)
           SCREAMING_SNAKE_CASE		=						77

           SCREAMING_SNAKE_CASE		=						{
               """unet""": unet,
               """scheduler""": scheduler,
               """vae""": vae,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
               """safety_checker""": None,
               """feature_extractor""": None,
           }
           return components






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   int=0	)					->		Any:




           '''simple docstring'''


           if str(lowerCamelCase__	).startswith("""mps"""	):
                 SCREAMING_SNAKE_CASE		=						torch.manual_seed(lowerCamelCase__	)
           else:
                 SCREAMING_SNAKE_CASE		=						torch.Generator(device=lowerCamelCase__	).manual_seed(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						{
               """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     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		List[str]:




           '''simple docstring'''


           super().test_attention_slicing_forward_pass(expected_max_diff=3e-3	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Tuple:




           '''simple docstring'''


           super().test_inference_batch_single_identical(expected_max_diff=3e-3	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		int:




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE		=						RobertaSeriesConfig(
               hidden_size=32						,project_dim=32						,intermediate_size=37						,layer_norm_eps=1e-0_5						,num_attention_heads=4						,num_hidden_layers=5						,vocab_size=5002						,)
           # TODO: remove after fixing the non-deterministic text encoder
           SCREAMING_SNAKE_CASE		=						RobertaSeriesModelWithTransformation(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						text_encoder

           SCREAMING_SNAKE_CASE		=						AltDiffusionPipeline(**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						alt_pipe.to(lowerCamelCase__	)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.get_dummy_inputs(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						"""A photo of an astronaut"""
           SCREAMING_SNAKE_CASE		=						alt_pipe(**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						output.images
           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           SCREAMING_SNAKE_CASE		=						np.array(
               [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		str:




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						PNDMScheduler(skip_prk_steps=lowerCamelCase__	)
           torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE		=						RobertaSeriesConfig(
               hidden_size=32						,project_dim=32						,intermediate_size=37						,layer_norm_eps=1e-0_5						,num_attention_heads=4						,num_hidden_layers=5						,vocab_size=5002						,)
           # TODO: remove after fixing the non-deterministic text encoder
           SCREAMING_SNAKE_CASE		=						RobertaSeriesModelWithTransformation(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						text_encoder
           SCREAMING_SNAKE_CASE		=						AltDiffusionPipeline(**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						alt_pipe.to(lowerCamelCase__	)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						self.get_dummy_inputs(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						alt_pipe(**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						output.images
           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           SCREAMING_SNAKE_CASE		=						np.array(
               [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]	)

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

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



     '''simple docstring'''

     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		List[Any]:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""						,safety_checker=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						alt_pipe.to(lowerCamelCase__	)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						"""A painting of a squirrel eating a burger"""
           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0	)
           SCREAMING_SNAKE_CASE		=						alt_pipe([prompt]						,generator=lowerCamelCase__						,guidance_scale=6.0						,num_inference_steps=20						,output_type="""np"""	)

           SCREAMING_SNAKE_CASE		=						output.images

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

           assert image.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""						,subfolder="""scheduler"""	)

           SCREAMING_SNAKE_CASE		=						AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""						,scheduler=lowerCamelCase__						,safety_checker=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						alt_pipe.to(lowerCamelCase__	)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						"""A painting of a squirrel eating a burger"""
           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0	)

           SCREAMING_SNAKE_CASE		=						alt_pipe([prompt]						,generator=lowerCamelCase__						,num_inference_steps=2						,output_type="""numpy"""	)
           SCREAMING_SNAKE_CASE		=						output.images

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

           assert image.shape == (1, 512, 512, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]	)

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


