


'''simple docstring'''
import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
						import torch






class 		UpperCamelCase_		(       A ):





						"""simple docstring"""




						UpperCAmelCase__						:							Optional[Any]       =  '''char'''
						UpperCAmelCase__						:							int       =  '''bpe'''
						UpperCAmelCase__						:							Any       =  '''wp'''





__magic_name__ : str					=(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class 		UpperCamelCase_		(       A ):





						"""simple docstring"""




						UpperCAmelCase__						:							Dict       =  ['''image_processor''', '''char_tokenizer''']
						UpperCAmelCase__						:							Dict       =  '''ViTImageProcessor'''
						UpperCAmelCase__						:							int       =  '''MgpstrTokenizer'''
						def __init__(			self					:       Union[str, Any]						,					_lowerCamelCase					:       List[Any]=None						,					_lowerCamelCase					:       Any=None						,					**_lowerCamelCase					:       List[str]	)				->       List[str]:
										__magic_name__							=			None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
														    " instead."						,					_lowerCamelCase						,					)
														__magic_name__							=			kwargs.pop("feature_extractor"	)

										__magic_name__							=			image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError("You need to specify an `image_processor`."	)
										if tokenizer is None:
														raise ValueError("You need to specify a `tokenizer`."	)

										__magic_name__							=			tokenizer
										__magic_name__							=			AutoTokenizer.from_pretrained("gpt2"	)
										__magic_name__							=			AutoTokenizer.from_pretrained("bert-base-uncased"	)

										super().__init__(_lowerCamelCase						,					_lowerCamelCase	)
						def __call__(			self					:       Tuple						,					_lowerCamelCase					:       Union[str, Any]=None						,					_lowerCamelCase					:       Any=None						,					_lowerCamelCase					:       Optional[int]=None						,					**_lowerCamelCase					:       List[Any]	)				->       str:
										if images is None and text is None:
														raise ValueError("You need to specify either an `images` or `text` input to process."	)

										if images is not None:
														__magic_name__							=			self.image_processor(_lowerCamelCase						,					return_tensors=_lowerCamelCase						,					**_lowerCamelCase	)
										if text is not None:
														__magic_name__							=			self.char_tokenizer(_lowerCamelCase						,					return_tensors=_lowerCamelCase						,					**_lowerCamelCase	)

										if text is None:
														return inputs
										elif images is None:
														return encodings
										else:
														__magic_name__							=			encodings['input_ids']
														return inputs
						def        __A       (			self					:       List[Any]						,					_lowerCamelCase					:       List[str]	)				->       Optional[Any]:
										__magic_name__							=			sequences
										__magic_name__							=			char_preds.size(0	)

										__magic_name__							=			self._decode_helper(_lowerCamelCase						,					"char"	)
										__magic_name__							=			self._decode_helper(_lowerCamelCase						,					"bpe"	)
										__magic_name__							=			self._decode_helper(_lowerCamelCase						,					"wp"	)

										__magic_name__							=			[]
										__magic_name__							=			[]
										for i in range(_lowerCamelCase	):
														__magic_name__							=			[char_scores[i], bpe_scores[i], wp_scores[i]]
														__magic_name__							=			[char_strs[i], bpe_strs[i], wp_strs[i]]
														__magic_name__							=			scores.index(max(_lowerCamelCase	)	)
														final_strs.append(strs[max_score_index]	)
														final_scores.append(scores[max_score_index]	)

										__magic_name__							=			{}
										__magic_name__							=			final_strs
										__magic_name__							=			final_scores
										__magic_name__							=			char_strs
										__magic_name__							=			bpe_strs
										__magic_name__							=			wp_strs
										return out
						def        __A       (			self					:       Any						,					_lowerCamelCase					:       Optional[int]						,					_lowerCamelCase					:       Any	)				->       Optional[int]:
										if format == DecodeType.CHARACTER:
														__magic_name__							=			self.char_decode
														__magic_name__							=			1
														__magic_name__							=			'[s]'
										elif format == DecodeType.BPE:
														__magic_name__							=			self.bpe_decode
														__magic_name__							=			2
														__magic_name__							=			'#'
										elif format == DecodeType.WORDPIECE:
														__magic_name__							=			self.wp_decode
														__magic_name__							=			1_02
														__magic_name__							=			'[SEP]'
										else:
														raise ValueError(f'Format {format} is not supported.'	)

										__magic_name__							=			[], []
										__magic_name__							=			pred_logits.size(0	)
										__magic_name__							=			pred_logits.size(1	)
										__magic_name__							=			pred_logits.topk(1						,					dim=-1						,					largest=_lowerCamelCase						,					sorted=_lowerCamelCase	)
										__magic_name__							=			preds_index.view(-1						,					_lowerCamelCase	)[:, 1:]
										__magic_name__							=			decoder(_lowerCamelCase	)
										__magic_name__							=			torch.nn.functional.softmax(_lowerCamelCase						,					dim=2	).max(dim=2	)
										__magic_name__							=			preds_max_prob[:, 1:]

										for index in range(_lowerCamelCase	):
														__magic_name__							=			preds_str[index].find(_lowerCamelCase	)
														__magic_name__							=			preds_str[index][:pred_eos]
														__magic_name__							=			preds_index[index].cpu().tolist()
														__magic_name__							=			pred_index.index(_lowerCamelCase	) if eos_token in pred_index else -1
														__magic_name__							=			preds_max_prob[index][: pred_eos_index + 1]
														__magic_name__							=			pred_max_prob.cumprod(dim=0	)[-1] if pred_max_prob.nelement() != 0 else 0.0
														dec_strs.append(_lowerCamelCase	)
														conf_scores.append(_lowerCamelCase	)

										return dec_strs, conf_scores
						def        __A       (			self					:       Any						,					_lowerCamelCase					:       Optional[int]	)				->       Dict:
										__magic_name__							=			[seq.replace(" "						,					""	) for seq in self.char_tokenizer.batch_decode(_lowerCamelCase	)]
										return decode_strs
						def        __A       (			self					:       Union[str, Any]						,					_lowerCamelCase					:       List[str]	)				->       List[Any]:
										return self.bpe_tokenizer.batch_decode(_lowerCamelCase	)

						def        __A       (			self					:       Optional[Any]						,					_lowerCamelCase					:       str	)				->       List[str]:
										__magic_name__							=			[seq.replace(" "						,					""	) for seq in self.wp_tokenizer.batch_decode(_lowerCamelCase	)]
										return decode_strs









"""simple docstring"""






import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase__			=		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__			=		parser.parse_args()


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

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

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

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

# compute
lowercase__			=		666
lowercase__			=		torch.Generator(device).manual_seed(seed)
lowercase__			=		{"""generator""": generator}
if args.steps is not None:
			lowercase__			=		args.steps

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

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