


'''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


a_  :				Tuple						= 16
a_  :				Tuple						= 32





def __snake_case					(      UpperCAmelCase_						:						Accelerator					,				UpperCAmelCase_						:						int = 16					,				UpperCAmelCase_						:						str = "bert-base-cased"		):
 lowerCamelCase_      =  AutoTokenizer.from_pretrained(UpperCAmelCase_		)
 lowerCamelCase_      =  load_dataset("glue"					,				"mrpc"		)

 def tokenize_function(UpperCAmelCase_						:						List[Any]		):
  # max_length=None => use the model max length (it's actually the default)
  lowerCamelCase_      =  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
 lowerCamelCase_      =  datasets.map(
     UpperCAmelCase_					,				batched=UpperCAmelCase_					,				remove_columns=["idx", "sentence1", "sentence2"]					,				load_from_cache_file=UpperCAmelCase_		)

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

 def collate_fn(UpperCAmelCase_						:						str		):
  # 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(UpperCAmelCase_					,				padding="max_length"					,				max_length=128					,				return_tensors="pt"		)
  return tokenizer.pad(UpperCAmelCase_					,				padding="longest"					,				return_tensors="pt"		)

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

 return train_dataloader, eval_dataloader





def __snake_case					(      UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						Any		):
 # Initialize accelerator
 lowerCamelCase_      =  Accelerator()

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

 set_seed(UpperCAmelCase_		)
 lowerCamelCase_ ,lowerCamelCase_      =  get_dataloaders(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)

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

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

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

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

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

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

   overall_step += 1

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

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

  if best_performance < eval_metric["accuracy"]:
   lowerCamelCase_      =  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(UpperCAmelCase_					,				UpperCAmelCase_		)





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


if __name__ == "__main__":
     main()










"""simple docstring"""





import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np

class 		lowercase					:





      def     _snake_case			(       self )		->     List[Any]:
            torch.manual_seed(0 )
            lowerCAmelCase  						=       TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

            torch.manual_seed(0 )
            lowerCAmelCase  						=       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

            torch.manual_seed(0 )
            lowerCAmelCase  						=       UNetaDConditionModel(
                sample_size=32      ,						layers_per_block=1      ,						block_out_channels=[32, 64]      ,						down_block_types=[
                    """ResnetDownsampleBlock2D""",
                    """SimpleCrossAttnDownBlock2D""",
                ]      ,						mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""      ,						up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]      ,						in_channels=3      ,						out_channels=6      ,						cross_attention_dim=32      ,						encoder_hid_dim=32      ,						attention_head_dim=8      ,						addition_embed_type="""text"""      ,						addition_embed_type_num_heads=2      ,						cross_attention_norm="""group_norm"""      ,						resnet_time_scale_shift="""scale_shift"""      ,						act_fn="""gelu"""      ,						)
            unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

            torch.manual_seed(0 )
            lowerCAmelCase  						=       DDPMScheduler(
                num_train_timesteps=1_000      ,						beta_schedule="""squaredcos_cap_v2"""      ,						beta_start=0.0_001      ,						beta_end=0.02      ,						thresholding=lowercase      ,						dynamic_thresholding_ratio=0.95      ,						sample_max_value=1.0      ,						prediction_type="""epsilon"""      ,						variance_type="""learned_range"""      ,						)

            torch.manual_seed(0 )
            lowerCAmelCase  						=       IFWatermarker()

            return {
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "unet": unet,
                "scheduler": scheduler,
                "watermarker": watermarker,
                "safety_checker": None,
                "feature_extractor": None,
            }





      def     _snake_case			(       self )		->     int:
            torch.manual_seed(0 )
            lowerCAmelCase  						=       TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

            torch.manual_seed(0 )
            lowerCAmelCase  						=       AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

            torch.manual_seed(0 )
            lowerCAmelCase  						=       UNetaDConditionModel(
                sample_size=32      ,						layers_per_block=[1, 2]      ,						block_out_channels=[32, 64]      ,						down_block_types=[
                    """ResnetDownsampleBlock2D""",
                    """SimpleCrossAttnDownBlock2D""",
                ]      ,						mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""      ,						up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]      ,						in_channels=6      ,						out_channels=6      ,						cross_attention_dim=32      ,						encoder_hid_dim=32      ,						attention_head_dim=8      ,						addition_embed_type="""text"""      ,						addition_embed_type_num_heads=2      ,						cross_attention_norm="""group_norm"""      ,						resnet_time_scale_shift="""scale_shift"""      ,						act_fn="""gelu"""      ,						class_embed_type="""timestep"""      ,						mid_block_scale_factor=1.414      ,						time_embedding_act_fn="""gelu"""      ,						time_embedding_dim=32      ,						)
            unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

            torch.manual_seed(0 )
            lowerCAmelCase  						=       DDPMScheduler(
                num_train_timesteps=1_000      ,						beta_schedule="""squaredcos_cap_v2"""      ,						beta_start=0.0_001      ,						beta_end=0.02      ,						thresholding=lowercase      ,						dynamic_thresholding_ratio=0.95      ,						sample_max_value=1.0      ,						prediction_type="""epsilon"""      ,						variance_type="""learned_range"""      ,						)

            torch.manual_seed(0 )
            lowerCAmelCase  						=       DDPMScheduler(
                num_train_timesteps=1_000      ,						beta_schedule="""squaredcos_cap_v2"""      ,						beta_start=0.0_001      ,						beta_end=0.02      ,						)

            torch.manual_seed(0 )
            lowerCAmelCase  						=       IFWatermarker()

            return {
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "unet": unet,
                "scheduler": scheduler,
                "image_noising_scheduler": image_noising_scheduler,
                "watermarker": watermarker,
                "safety_checker": None,
                "feature_extractor": None,
            }





      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       self.get_dummy_components()
            lowerCAmelCase  						=       self.pipeline_class(**lowercase )
            pipe.to(lowercase )
            pipe.set_progress_bar_config(disable=lowercase )

            lowerCAmelCase  						=       self.get_dummy_inputs(lowercase )

            lowerCAmelCase  						=       inputs["""prompt"""]
            lowerCAmelCase  						=       inputs["""generator"""]
            lowerCAmelCase  						=       inputs["""num_inference_steps"""]
            lowerCAmelCase  						=       inputs["""output_type"""]

            if "image" in inputs:
                  lowerCAmelCase  						=       inputs["""image"""]
            else:
                  lowerCAmelCase  						=       None

            if "mask_image" in inputs:
                  lowerCAmelCase  						=       inputs["""mask_image"""]
            else:
                  lowerCAmelCase  						=       None

            if "original_image" in inputs:
                  lowerCAmelCase  						=       inputs["""original_image"""]
            else:
                  lowerCAmelCase  						=       None

            lowerCAmelCase						,		lowerCAmelCase  						=       pipe.encode_prompt(lowercase )

            # inputs with prompt converted to embeddings
            lowerCAmelCase  						=       {
                """prompt_embeds""": prompt_embeds,
                """negative_prompt_embeds""": negative_prompt_embeds,
                """generator""": generator,
                """num_inference_steps""": num_inference_steps,
                """output_type""": output_type,
            }

            if image is not None:
                  lowerCAmelCase  						=       image

            if mask_image is not None:
                  lowerCAmelCase  						=       mask_image

            if original_image is not None:
                  lowerCAmelCase  						=       original_image

            # set all optional components to None
            for optional_component in pipe._optional_components:
                  setattr(lowercase      ,						lowercase      ,						lowercase )

            lowerCAmelCase  						=       pipe(**lowercase )[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(lowercase )
                  lowerCAmelCase  						=       self.pipeline_class.from_pretrained(lowercase )
                  pipe_loaded.to(lowercase )
                  pipe_loaded.set_progress_bar_config(disable=lowercase )

            pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

            for optional_component in pipe._optional_components:
                  self.assertTrue(
                      getattr(lowercase      ,						lowercase ) is None      ,						f'`{optional_component}` did not stay set to None after loading.'      ,						)

            lowerCAmelCase  						=       self.get_dummy_inputs(lowercase )

            lowerCAmelCase  						=       inputs["""generator"""]
            lowerCAmelCase  						=       inputs["""num_inference_steps"""]
            lowerCAmelCase  						=       inputs["""output_type"""]

            # inputs with prompt converted to embeddings
            lowerCAmelCase  						=       {
                """prompt_embeds""": prompt_embeds,
                """negative_prompt_embeds""": negative_prompt_embeds,
                """generator""": generator,
                """num_inference_steps""": num_inference_steps,
                """output_type""": output_type,
            }

            if image is not None:
                  lowerCAmelCase  						=       image

            if mask_image is not None:
                  lowerCAmelCase  						=       mask_image

            if original_image is not None:
                  lowerCAmelCase  						=       original_image

            lowerCAmelCase  						=       pipe_loaded(**lowercase )[0]

            lowerCAmelCase  						=       np.abs(to_np(lowercase ) - to_np(lowercase ) ).max()
            self.assertLess(lowercase      ,						1e-4 )





      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       self.get_dummy_components()
            lowerCAmelCase  						=       self.pipeline_class(**lowercase )
            pipe.to(lowercase )
            pipe.set_progress_bar_config(disable=lowercase )

            lowerCAmelCase  						=       self.get_dummy_inputs(lowercase )
            lowerCAmelCase  						=       pipe(**lowercase )[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(lowercase )
                  lowerCAmelCase  						=       self.pipeline_class.from_pretrained(lowercase )
                  pipe_loaded.to(lowercase )
                  pipe_loaded.set_progress_bar_config(disable=lowercase )

            pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

            lowerCAmelCase  						=       self.get_dummy_inputs(lowercase )
            lowerCAmelCase  						=       pipe_loaded(**lowercase )[0]

            lowerCAmelCase  						=       np.abs(to_np(lowercase ) - to_np(lowercase ) ).max()
            self.assertLess(lowercase      ,						1e-4 )






