
"""simple docstring"""



import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer







def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					List[Any]=5						):






							"""simple docstring"""



							assert masked_input.count('<mask>'						) == 1
							_lowerCAmelCase  	=			torch.tensor(tokenizer.encode(__UpperCamelCase     ,  add_special_tokens=__UpperCamelCase						)						).unsqueeze(0						)  # Batch size 1
							_lowerCAmelCase  	=			model(__UpperCamelCase						)[0]  # The last hidden-state is the first element of the output tuple
							_lowerCAmelCase  	=			(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
							_lowerCAmelCase  	=			logits[0, masked_index, :]
							_lowerCAmelCase  	=			logits.softmax(dim=0						)
							_lowerCAmelCase	,		_lowerCAmelCase  	=			prob.topk(k=__UpperCamelCase     ,  dim=0						)
							_lowerCAmelCase  	=			' '.join(
							    [tokenizer.convert_ids_to_tokens(indices[i].item()						) for i in range(len(__UpperCamelCase						)						)]						)
							_lowerCAmelCase  	=			tokenizer.mask_token
							_lowerCAmelCase  	=			[]
							for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' '						)						):
														_lowerCAmelCase  	=			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


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

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

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








"""simple docstring"""



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_snake_case          =						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''')
_snake_case          =						parser.parse_args()


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

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

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

# optimize with ipex
_snake_case          =						torch.randn(2, 4, 6_4, 6_4)
_snake_case          =						torch.rand(1) * 9_9_9
_snake_case          =						torch.randn(2, 7_7, 7_6_8)
_snake_case          =						(sample, timestep, encoder_hidden_status)
try:
	_snake_case          =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
	_snake_case          =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case          =						ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case          =						ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
	_snake_case          =						ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_snake_case          =						6_6_6
_snake_case          =						torch.Generator(device).manual_seed(seed)
_snake_case          =						{'''generator''': generator}
if args.steps is not None:
	_snake_case          =						args.steps

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

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






