




import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_lowerCamelCase      			=						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')
_lowerCamelCase      			=						parser.parse_args()


_lowerCamelCase      			=						'cpu'
_lowerCamelCase      			=						'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

_lowerCamelCase      			=						'path-to-your-trained-model'
_lowerCamelCase      			=						StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
 _lowerCamelCase      			=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_lowerCamelCase      			=						pipe.to(device)

# to channels last
_lowerCamelCase      			=						pipe.unet.to(memory_format=torch.channels_last)
_lowerCamelCase      			=						pipe.vae.to(memory_format=torch.channels_last)
_lowerCamelCase      			=						pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
 _lowerCamelCase      			=						pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
_lowerCamelCase      			=						torch.randn(2, 4, 64, 64)
_lowerCamelCase      			=						torch.rand(1) * 9_99
_lowerCamelCase      			=						torch.randn(2, 77, 7_68)
_lowerCamelCase      			=						(sample, timestep, encoder_hidden_status)
try:
 _lowerCamelCase      			=						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
 _lowerCamelCase      			=						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_lowerCamelCase      			=						ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_lowerCamelCase      			=						ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
 _lowerCamelCase      			=						ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_lowerCamelCase      			=						6_66
_lowerCamelCase      			=						torch.Generator(device).manual_seed(seed)
_lowerCamelCase      			=						{'generator': generator}
if args.steps is not None:
 _lowerCamelCase      			=						args.steps

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

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











import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						List[str]=5				)  ->							Dict:
    # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
    assert masked_input.count('''<mask>'''				) == 1
    UpperCAmelCase_										=					torch.tensor(tokenizer.encode(__UpperCamelCase	,					add_special_tokens=__UpperCamelCase				)				).unsqueeze(0				)  # Batch size 1
    UpperCAmelCase_										=					model(__UpperCamelCase				)[0]  # The last hidden-state is the first element of the output tuple
    UpperCAmelCase_										=					(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
    UpperCAmelCase_										=					logits[0, masked_index, :]
    UpperCAmelCase_										=					logits.softmax(dim=0				)
    UpperCAmelCase_			,							UpperCAmelCase_										=					prob.topk(k=__UpperCamelCase	,					dim=0				)
    UpperCAmelCase_										=					''' '''.join(
        [tokenizer.convert_ids_to_tokens(indices[i].item()				) for i in range(len(__UpperCamelCase				)				)]				)
    UpperCAmelCase_										=					tokenizer.mask_token
    UpperCAmelCase_										=					[]
    for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''				)				):
        UpperCAmelCase_										=					predicted_token_bpe.replace('''\u2581'''	,					''' '''				)
        if " {0}".format(__UpperCamelCase				) in masked_input:
            topk_filled_outputs.append(
                (
                    masked_input.replace(''' {0}'''.format(__UpperCamelCase				)	,					__UpperCamelCase				),
                    values[index].item(),
                    predicted_token,
                )				)
        else:
            topk_filled_outputs.append(
                (
                    masked_input.replace(__UpperCamelCase	,					__UpperCamelCase				),
                    values[index].item(),
                    predicted_token,
                )				)
    return topk_filled_outputs


_lowerCamelCase      			=						CamembertTokenizer.from_pretrained('camembert-base')
_lowerCamelCase      			=						CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

_lowerCamelCase      			=						'Le camembert est <mask> :)'


print(fill_mask(masked_input, model, tokenizer, topk=3))





