



"""simple docstring"""

import torch

from transformers import AutoModel
class     lowerCAmelCase__						(	torch.nn.Module ):


	'''simple docstring'''







	def __init__(      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       List[Any]="sayef/fsner-bert-base-uncased"    )						->	List[Any]:





					"""simple docstring"""


					super(_SCREAMING_SNAKE_CASE     ,       self    ).__init__()

					SCREAMING_SNAKE_CASE						:							int							=     AutoModel.from_pretrained(_SCREAMING_SNAKE_CASE     ,       return_dict=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							int							=     torch.nn.CosineSimilarity(3     ,       1E-0_8    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     torch.nn.Softmax(dim=1    )





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       **_SCREAMING_SNAKE_CASE				:       Any    )						->	Union[str, Any]:





					"""simple docstring"""


					return self.bert(**_SCREAMING_SNAKE_CASE    ).last_hidden_state





	def 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       Any    )						->	int:





					"""simple docstring"""


					return token_embeddings.sum(2     ,       keepdim=_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=1    )						->	str:





					"""simple docstring"""


					return self.softmax(T * self.cos(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    )







	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]    )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     W_supports['sizes'].tolist()
					SCREAMING_SNAKE_CASE						:							List[str]							=     W_supports['start_token_id'].item()
					SCREAMING_SNAKE_CASE						:							Tuple							=     W_supports['end_token_id'].item()

					del W_supports["sizes"]
					del W_supports["start_token_id"]
					del W_supports["end_token_id"]

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.BERT(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     self.BERT(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							int							=     None
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     None

					SCREAMING_SNAKE_CASE						:							Dict							=     W_supports['input_ids'] == start_token_id
					SCREAMING_SNAKE_CASE						:							List[Any]							=     W_supports['input_ids'] == end_token_id

					for i, size in enumerate(_SCREAMING_SNAKE_CASE    ):
									if i == 0:
													SCREAMING_SNAKE_CASE						:							Dict							=     0
									else:
													SCREAMING_SNAKE_CASE						:							Dict							=     support_sizes[i - 1]

									SCREAMING_SNAKE_CASE						:							Dict							=     S[s : s + size][start_token_masks[s : s + size]]
									SCREAMING_SNAKE_CASE						:							Dict							=     S[s : s + size][end_token_masks[s : s + size]]

									SCREAMING_SNAKE_CASE						:							Optional[int]							=     torch.matmul(q[i]     ,       s_start.T    ).sum(1    ).softmax(0    )
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     torch.matmul(q[i]     ,       s_end.T    ).sum(1    ).softmax(0    )

									if p_starts is not None:
													SCREAMING_SNAKE_CASE						:							Any							=     torch.vstack((p_starts, p_start)    )
													SCREAMING_SNAKE_CASE						:							int							=     torch.vstack((p_ends, p_end)    )
									else:
													SCREAMING_SNAKE_CASE						:							Tuple							=     p_start
													SCREAMING_SNAKE_CASE						:							Tuple							=     p_end

					return p_starts, p_ends










"""simple docstring"""

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A_						:    List[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')
A_						:    Tuple				=      parser.parse_args()


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

A_						:    List[str]				=      'path-to-your-trained-model'
A_						:    Any				=      StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
							A_						:    Tuple				=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
A_						:    Tuple				=      pipe.to(device)

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

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

# compute
A_						:    Union[str, Any]				=      666
A_						:    Optional[Any]				=      torch.Generator(device).manual_seed(seed)
A_						:    Union[str, Any]				=      {'generator': generator}
if args.steps is not None:
							A_						:    int				=      args.steps

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

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





