






"""simple docstring"""
from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''EncodecFeatureExtractor'''
	__magic_name__       =							('''T5Tokenizer''', '''T5TokenizerFast''')





	def __init__( self   :					str      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					Optional[Any]		)		->     Dict:
				super().__init__(lowerCAmelCase_      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[int]     	=   self.feature_extractor
				UpperCAmelCase_	:					Dict     	=   False





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					Optional[int]=None      ,      lowerCAmelCase_   :					Any=None      ,      lowerCAmelCase_   :					str=True		)		->     Any:
				return self.tokenizer.get_decoder_prompt_ids(task=lowerCAmelCase_      ,      language=lowerCAmelCase_      ,      no_timestamps=lowerCAmelCase_		)





	def __call__( self   :					Any      ,      *lowerCAmelCase_   :					Dict      ,      **lowerCAmelCase_   :					str		)		->     int:
				# For backward compatibility
				if self._in_target_context_manager:
							return self.current_processor(*lowerCAmelCase_      ,      **lowerCAmelCase_		)

				UpperCAmelCase_	:					List[Any]     	=   kwargs.pop("audio"      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   kwargs.pop("sampling_rate"      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   kwargs.pop("text"      ,      lowerCAmelCase_		)
				if len(lowerCAmelCase_		) > 0:
							UpperCAmelCase_	:					Any     	=   args[0]
							UpperCAmelCase_	:					Optional[Any]     	=   args[1:]

				if audio is None and text is None:
							raise ValueError("You need to specify either an `audio` or `text` input to process."		)

				if text is not None:
							UpperCAmelCase_	:					int     	=   self.tokenizer(lowerCAmelCase_      ,      **lowerCAmelCase_		)

				if audio is not None:
							UpperCAmelCase_	:					Optional[Any]     	=   self.feature_extractor(lowerCAmelCase_      ,      *lowerCAmelCase_      ,      sampling_rate=lowerCAmelCase_      ,      **lowerCAmelCase_		)

				if audio is None:
							return inputs

				elif text is None:
							return audio_inputs

				else:
							UpperCAmelCase_	:					Tuple     	=   audio_inputs["input_values"]
							if "padding_mask" in audio_inputs:
										UpperCAmelCase_	:					int     	=   audio_inputs["padding_mask"]
							return inputs





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      *lowerCAmelCase_   :					Dict      ,      **lowerCAmelCase_   :					Any		)		->     Dict:
				UpperCAmelCase_	:					List[str]     	=   kwargs.pop("audio"      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   kwargs.pop("padding_mask"      ,      lowerCAmelCase_		)

				if len(lowerCAmelCase_		) > 0:
							UpperCAmelCase_	:					int     	=   args[0]
							UpperCAmelCase_	:					Optional[Any]     	=   args[1:]

				if audio_values is not None:
							return self._decode_audio(lowerCAmelCase_      ,      padding_mask=lowerCAmelCase_		)
				else:
							return self.tokenizer.batch_decode(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      *lowerCAmelCase_   :					List[str]      ,      **lowerCAmelCase_   :					List[Any]		)		->     Tuple:
				return self.tokenizer.decode(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					Optional = None		)		->     List[np.ndarray]:
				UpperCAmelCase_	:					Optional[Any]     	=   to_numpy(lowerCAmelCase_		)
				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Any     	=   audio_values.shape

				if padding_mask is None:
							return list(lowerCAmelCase_		)

				UpperCAmelCase_	:					Optional[int]     	=   to_numpy(lowerCAmelCase_		)

				# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
				# token (so that the generated audio values are **not** treated as padded tokens)
				UpperCAmelCase_	:					int     	=   seq_len - padding_mask.shape[-1]
				UpperCAmelCase_	:					str     	=   1 - self.feature_extractor.padding_value
				UpperCAmelCase_	:					Tuple     	=   np.pad(lowerCAmelCase_      ,      ((0, 0), (0, difference))      ,      "constant"      ,      constant_values=lowerCAmelCase_		)

				UpperCAmelCase_	:					int     	=   audio_values.tolist()
				for i in range(lowerCAmelCase_		):
							UpperCAmelCase_	:					Tuple     	=   np.asarray(audio_values[i]		)[
							    padding_mask[i][None, :] != self.feature_extractor.padding_value
							]
							UpperCAmelCase_	:					Any     	=   sliced_audio.reshape(lowerCAmelCase_      ,      -1		)

				return audio_values










'''simple docstring'''

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase__		:     int			=							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__		:     Union[str, Any]			=							parser.parse_args()


lowercase__		:     Union[str, Any]			=							'cpu'
lowercase__		:     Tuple			=							'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

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

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

# optimize with ipex
lowercase__		:     List[str]			=							torch.randn(2, 4, 64, 64)
lowercase__		:     int			=							torch.rand(1) * 9_99
lowercase__		:     Optional[int]			=							torch.randn(2, 77, 7_68)
lowercase__		:     List[str]			=							(sample, timestep, encoder_hidden_status)
try:
   lowercase__		:     Optional[Any]			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
   lowercase__		:     Any			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Tuple			=							ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Any			=							ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
   lowercase__		:     Union[str, Any]			=							ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase__		:     Optional[Any]			=							6_66
lowercase__		:     Tuple			=							torch.Generator(device).manual_seed(seed)
lowercase__		:     Dict			=							{'generator': generator}
if args.steps is not None:
   lowercase__		:     int			=							args.steps

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

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



