




from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
  import datasets


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    @slow
    @require_torch
    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->List[Any]:





           '''simple docstring'''

           A__       =    EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''					, '''prajjwal1/bert-tiny''')
           A__       =    BertTokenizer.from_pretrained('''bert-base-uncased''')

           A__       =    bertabert.config.encoder.vocab_size
           A__       =    tokenizer.sep_token_id
           A__       =    tokenizer.cls_token_id
           A__       =    128

           A__       =    datasets.load_dataset('''cnn_dailymail'''					, '''3.0.0'''					, split='''train[:1%]''')
           A__       =    datasets.load_dataset('''cnn_dailymail'''					, '''3.0.0'''					, split='''validation[:1%]''')

           A__       =    train_dataset.select(range(32))
           A__       =    val_dataset.select(range(16))

           A__       =    4

           def _map_to_encoder_decoder_inputs(UpperCAmelCase__      :	List[str]):
                  # Tokenizer will automatically set [BOS] <text> [EOS]
                  A__       =    tokenizer(batch['''article''']					, padding='''max_length'''					, truncation=UpperCAmelCase__					, max_length=512)
                  A__       =    tokenizer(batch['''highlights''']					, padding='''max_length'''					, truncation=UpperCAmelCase__					, max_length=128)
                  A__       =    inputs.input_ids
                  A__       =    inputs.attention_mask

                  A__       =    outputs.input_ids
                  A__       =    outputs.input_ids.copy()
                  A__       =    [
                      [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
                  ]
                  A__       =    outputs.attention_mask

                  assert all(len(UpperCAmelCase__) == 512 for x in inputs.input_ids)
                  assert all(len(UpperCAmelCase__) == 128 for x in outputs.input_ids)

                  return batch

           def _compute_metrics(UpperCAmelCase__      :	Tuple):
                  A__       =    pred.label_ids
                  A__       =    pred.predictions

                  # all unnecessary tokens are removed
                  A__       =    tokenizer.batch_decode(UpperCAmelCase__					, skip_special_tokens=UpperCAmelCase__)
                  A__       =    tokenizer.batch_decode(UpperCAmelCase__					, skip_special_tokens=UpperCAmelCase__)

                  A__       =    sum([int(pred_str[i] == label_str[i]) for i in range(len(UpperCAmelCase__))]) / len(UpperCAmelCase__)

                  return {"accuracy": accuracy}

           # map train dataset
           A__       =    train_dataset.map(
               _map_to_encoder_decoder_inputs					, batched=UpperCAmelCase__					, batch_size=UpperCAmelCase__					, remove_columns=['''article''', '''highlights''']					, )
           train_dataset.set_format(
               type='''torch'''					, columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']					, )

           # same for validation dataset
           A__       =    val_dataset.map(
               _map_to_encoder_decoder_inputs					, batched=UpperCAmelCase__					, batch_size=UpperCAmelCase__					, remove_columns=['''article''', '''highlights''']					, )
           val_dataset.set_format(
               type='''torch'''					, columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']					, )

           A__       =    self.get_auto_remove_tmp_dir()

           A__       =    SeqaSeqTrainingArguments(
               output_dir=UpperCAmelCase__					, per_device_train_batch_size=UpperCAmelCase__					, per_device_eval_batch_size=UpperCAmelCase__					, predict_with_generate=UpperCAmelCase__					, evaluation_strategy='''steps'''					, do_train=UpperCAmelCase__					, do_eval=UpperCAmelCase__					, warmup_steps=0					, eval_steps=2					, logging_steps=2					, )

           # instantiate trainer
           A__       =    SeqaSeqTrainer(
               model=UpperCAmelCase__					, args=UpperCAmelCase__					, compute_metrics=_compute_metrics					, train_dataset=UpperCAmelCase__					, eval_dataset=UpperCAmelCase__					, tokenizer=UpperCAmelCase__					, )

           # start training
           trainer.train()




'''simple docstring'''







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase    :							Optional[Any]      =					argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not")
parser.add_argument("--steps", default=None, type=int, help="Num inference steps")
lowercase    :							Tuple      =					parser.parse_args()


lowercase    :							Optional[int]      =					"cpu"
lowercase    :							Optional[Any]      =					"a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

lowercase    :							Optional[int]      =					"path-to-your-trained-model"
lowercase    :							List[str]      =					StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
					lowercase    :							str      =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase    :							Dict      =					pipe.to(device)

# to channels last
lowercase    :							Optional[Any]      =					pipe.unet.to(memory_format=torch.channels_last)
lowercase    :							int      =					pipe.vae.to(memory_format=torch.channels_last)
lowercase    :							Optional[Any]      =					pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
					lowercase    :							Optional[int]      =					pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowercase    :							Any      =					torch.randn(2, 4, 64, 64)
lowercase    :							Optional[int]      =					torch.rand(1) * 999
lowercase    :							Optional[Any]      =					torch.randn(2, 77, 768)
lowercase    :							Optional[Any]      =					(sample, timestep, encoder_hidden_status)
try:
					lowercase    :							List[Any]      =					ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
					lowercase    :							List[str]      =					ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase    :							Tuple      =					ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase    :							Optional[Any]      =					ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
					lowercase    :							Tuple      =					ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase    :							List[str]      =					666
lowercase    :							Tuple      =					torch.Generator(device).manual_seed(seed)
lowercase    :							Union[str, Any]      =					{"generator": generator}
if args.steps is not None:
					lowercase    :							Dict      =					args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
					lowercase    :							List[str]      =					pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("generated.png")

