



import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


lowerCAmelCase_           = logging.get_logger(__name__)  # pylint: disable=invalid-name
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def __init__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       WhisperForConditionalGeneration						,_SCREAMING_SNAKE_CASE		:       WhisperProcessor						,_SCREAMING_SNAKE_CASE		:       AutoencoderKL						,_SCREAMING_SNAKE_CASE		:       CLIPTextModel						,_SCREAMING_SNAKE_CASE		:       CLIPTokenizer						,_SCREAMING_SNAKE_CASE		:       UNetaDConditionModel						,_SCREAMING_SNAKE_CASE		:       Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]						,_SCREAMING_SNAKE_CASE		:       StableDiffusionSafetyChecker						,_SCREAMING_SNAKE_CASE		:       CLIPImageProcessor						,)    ->   Any:


						'''simple docstring'''
						super().__init__()

						if safety_checker is None:
										logger.warning(
										    f'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
										    ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
										    ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
										    ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
										    ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
										    ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'		)

						self.register_modules(
						    speech_model=UpperCAmelCase_						,speech_processor=UpperCAmelCase_						,vae=UpperCAmelCase_						,text_encoder=UpperCAmelCase_						,tokenizer=UpperCAmelCase_						,unet=UpperCAmelCase_						,scheduler=UpperCAmelCase_						,feature_extractor=UpperCAmelCase_						,)
		def       A(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Union[str, int]] = "auto"		)    ->   Dict:


						'''simple docstring'''



						if slice_size == "auto":
										A	 =      self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(UpperCAmelCase_		)
		def       A(  self		:       str		)    ->   List[Any]:


						'''simple docstring'''
						self.enable_attention_slicing(UpperCAmelCase_		)







		@torch.no_grad()
		def __call__(  self		:       str						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       Any=1_6_0_0_0						,_SCREAMING_SNAKE_CASE		:       int = 5_1_2						,_SCREAMING_SNAKE_CASE		:       int = 5_1_2						,_SCREAMING_SNAKE_CASE		:       int = 5_0						,_SCREAMING_SNAKE_CASE		:       float = 7.5						,_SCREAMING_SNAKE_CASE		:       Optional[Union[str, List[str]]] = None						,_SCREAMING_SNAKE_CASE		:       Optional[int] = 1						,_SCREAMING_SNAKE_CASE		:       float = 0.0						,_SCREAMING_SNAKE_CASE		:       Optional[torch.Generator] = None						,_SCREAMING_SNAKE_CASE		:       Optional[torch.FloatTensor] = None						,_SCREAMING_SNAKE_CASE		:       Optional[str] = "pil"						,_SCREAMING_SNAKE_CASE		:       bool = True						,_SCREAMING_SNAKE_CASE		:       Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,_SCREAMING_SNAKE_CASE		:       int = 1						,**_SCREAMING_SNAKE_CASE		:       int						,)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      self.speech_processor.feature_extractor(
						    UpperCAmelCase_						,return_tensors='pt'						,sampling_rate=UpperCAmelCase_		).input_features.to(self.device		)
						A	 =      self.speech_model.generate(UpperCAmelCase_						,max_length=4_8_0_0_0_0		)

						A	 =      self.speech_processor.tokenizer.batch_decode(UpperCAmelCase_						,skip_special_tokens=UpperCAmelCase_						,normalize=UpperCAmelCase_		)[
						    0
						]

						if isinstance(UpperCAmelCase_						,UpperCAmelCase_		):
										A	 =      1
						elif isinstance(UpperCAmelCase_						,UpperCAmelCase_		):
										A	 =      len(UpperCAmelCase_		)
						else:
										raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase_		)}'		)

						if height % 8 != 0 or width % 8 != 0:
										raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.'		)

						if (callback_steps is None) or (
						    callback_steps is not None and (not isinstance(UpperCAmelCase_						,UpperCAmelCase_		) or callback_steps <= 0)
						):
										raise ValueError(
										    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
										    f' {type(UpperCAmelCase_		)}.'		)

						# get prompt text embeddings
						A	 =      self.tokenizer(
						    UpperCAmelCase_						,padding='max_length'						,max_length=self.tokenizer.model_max_length						,return_tensors='pt'						,)
						A	 =      text_inputs.input_ids

						if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
										A	 =      self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]		)
										logger.warning(
										    'The following part of your input was truncated because CLIP can only handle sequences up to'
										    f' {self.tokenizer.model_max_length} tokens: {removed_text}'		)
										A	 =      text_input_ids[:, : self.tokenizer.model_max_length]
						A	 =      self.text_encoder(text_input_ids.to(self.device		)		)[0]

						# duplicate text embeddings for each generation per prompt, using mps friendly method
						A ,		A ,		A	 =      text_embeddings.shape
						A	 =      text_embeddings.repeat(1						,UpperCAmelCase_						,1		)
						A	 =      text_embeddings.view(bs_embed * num_images_per_prompt						,UpperCAmelCase_						,-1		)

						# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
						# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
						# corresponds to doing no classifier free guidance.
						A	 =      guidance_scale > 1.0
						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance:
										A	 =      4_2
										if negative_prompt is None:
														A	 =      [''] * batch_size
										elif type(UpperCAmelCase_		) is not type(UpperCAmelCase_		):
														raise TypeError(
														    f'`negative_prompt` should be the same type to `prompt`, but got {type(UpperCAmelCase_		)} !='
														    f' {type(UpperCAmelCase_		)}.'		)
										elif isinstance(UpperCAmelCase_						,UpperCAmelCase_		):
														A	 =      [negative_prompt]
										elif batch_size != len(UpperCAmelCase_		):
														raise ValueError(
														    f'`negative_prompt`: {negative_prompt} has batch size {len(UpperCAmelCase_		)}, but `prompt`:'
														    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
														    ' the batch size of `prompt`.'		)
										else:
														A	 =      negative_prompt

										A	 =      text_input_ids.shape[-1]
										A	 =      self.tokenizer(
										    UpperCAmelCase_						,padding='max_length'						,max_length=UpperCAmelCase_						,truncation=UpperCAmelCase_						,return_tensors='pt'						,)
										A	 =      self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

										# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
										A	 =      uncond_embeddings.shape[1]
										A	 =      uncond_embeddings.repeat(1						,UpperCAmelCase_						,1		)
										A	 =      uncond_embeddings.view(batch_size * num_images_per_prompt						,UpperCAmelCase_						,-1		)

										# For classifier free guidance, we need to do two forward passes.
										# Here we concatenate the unconditional and text embeddings into a single batch
										# to avoid doing two forward passes
										A	 =      torch.cat([uncond_embeddings, text_embeddings]		)

						# get the initial random noise unless the user supplied it

						# Unlike in other pipelines, latents need to be generated in the target device
						# for 1-to-1 results reproducibility with the CompVis implementation.
						# However this currently doesn't work in `mps`.
						A	 =      (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
						A	 =      text_embeddings.dtype
						if latents is None:
										if self.device.type == "mps":
														# randn does not exist on mps
														A	 =      torch.randn(UpperCAmelCase_						,generator=UpperCAmelCase_						,device='cpu'						,dtype=UpperCAmelCase_		).to(
														    self.device		)
										else:
														A	 =      torch.randn(UpperCAmelCase_						,generator=UpperCAmelCase_						,device=self.device						,dtype=UpperCAmelCase_		)
						else:
										if latents.shape != latents_shape:
														raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'		)
										A	 =      latents.to(self.device		)

						# set timesteps
						self.scheduler.set_timesteps(UpperCAmelCase_		)

						# Some schedulers like PNDM have timesteps as arrays
						# It's more optimized to move all timesteps to correct device beforehand
						A	 =      self.scheduler.timesteps.to(self.device		)

						# scale the initial noise by the standard deviation required by the scheduler
						A	 =      latents * self.scheduler.init_noise_sigma

						# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
						# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
						# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
						# and should be between [0, 1]
						A	 =      'eta' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
						A	 =      {}
						if accepts_eta:
										A	 =      eta

						for i, t in enumerate(self.progress_bar(UpperCAmelCase_		)		):
										# expand the latents if we are doing classifier free guidance
										A	 =      torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
										A	 =      self.scheduler.scale_model_input(UpperCAmelCase_						,UpperCAmelCase_		)

										# predict the noise residual
										A	 =      self.unet(UpperCAmelCase_						,UpperCAmelCase_						,encoder_hidden_states=UpperCAmelCase_		).sample

										# perform guidance
										if do_classifier_free_guidance:
														A ,		A	 =      noise_pred.chunk(2		)
														A	 =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

										# compute the previous noisy sample x_t -> x_t-1
										A	 =      self.scheduler.step(UpperCAmelCase_						,UpperCAmelCase_						,UpperCAmelCase_						,**UpperCAmelCase_		).prev_sample

										# call the callback, if provided
										if callback is not None and i % callback_steps == 0:
														callback(UpperCAmelCase_						,UpperCAmelCase_						,UpperCAmelCase_		)

						A	 =      1 / 0.18215 * latents
						A	 =      self.vae.decode(UpperCAmelCase_		).sample

						A	 =      (image / 2 + 0.5).clamp(0						,1		)

						# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
						A	 =      image.cpu().permute(0						,2						,3						,1		).float().numpy()

						if output_type == "pil":
										A	 =      self.numpy_to_pil(UpperCAmelCase_		)

						if not return_dict:
										return image

						return StableDiffusionPipelineOutput(images=UpperCAmelCase_						,nsfw_content_detected=UpperCAmelCase_		)













import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def      snake_case		(							UpperCAmelCase	:       List[str]   ):
				# This defines a "chinese character" as anything in the CJK Unicode block:
				#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
				#
				# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
				# despite its name. The modern Korean Hangul alphabet is a different block,
				# as is Japanese Hiragana and Katakana. Those alphabets are used to write
				# space-separated words, so they are not treated specially and handled
				# like the all of the other languages.
				if (
				    (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
				    or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
				    or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
				    or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
				    or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
				    or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
				    or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
				    or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
				):  #
								return True

				return False







def      snake_case		(							UpperCAmelCase	:       str   ):
				# word like '180' or '身高' or '神'
				for char in word:
								A	 =      ord(UpperCAmelCase   )
								if not _is_chinese_char(UpperCAmelCase   ):
												return 0
				return 1







def      snake_case		(							UpperCAmelCase	:       List[str]   ):
				A	 =      set()

				for token in tokens:
								A	 =      len(UpperCAmelCase   ) > 1 and is_chinese(UpperCAmelCase   )
								if chinese_word:
												word_set.add(UpperCAmelCase   )
				A	 =      list(UpperCAmelCase   )
				return word_list







def      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       set()   ):
				if not chinese_word_set:
								return bert_tokens
				A	 =      max([len(UpperCAmelCase   ) for w in chinese_word_set]   )

				A	 =      bert_tokens
				A ,		A	 =      0, len(UpperCAmelCase   )
				while start < end:
								A	 =      True
								if is_chinese(bert_word[start]   ):
												A	 =      min(end - start,						UpperCAmelCase   )
												for i in range(UpperCAmelCase,						1,						-1   ):
																A	 =      ''.join(bert_word[start : start + i]   )
																if whole_word in chinese_word_set:
																				for j in range(start + 1,						start + i   ):
																								A	 =      '##' + bert_word[j]
																				A	 =      start + i
																				A	 =      False
																				break
								if single_word:
												start += 1
				return bert_word







def      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       LTP,						UpperCAmelCase	:       BertTokenizer   ):
				A	 =      []

				for i in range(0,						len(UpperCAmelCase   ),						1_00   ):
								A	 =      ltp_tokenizer.seg(lines[i : i + 1_00]   )[0]
								A	 =      [get_chinese_word(UpperCAmelCase   ) for r in res]
								ltp_res.extend(UpperCAmelCase   )
				assert len(UpperCAmelCase   ) == len(UpperCAmelCase   )

				A	 =      []
				for i in range(0,						len(UpperCAmelCase   ),						1_00   ):
								A	 =      bert_tokenizer(lines[i : i + 1_00],						add_special_tokens=UpperCAmelCase,						truncation=UpperCAmelCase,						max_length=5_12   )
								bert_res.extend(res['input_ids']   )
				assert len(UpperCAmelCase   ) == len(UpperCAmelCase   )

				A	 =      []
				for input_ids, chinese_word in zip(UpperCAmelCase,						UpperCAmelCase   ):
								A	 =      []
								for id in input_ids:
												A	 =      bert_tokenizer._convert_id_to_token(UpperCAmelCase   )
												input_tokens.append(UpperCAmelCase   )
								A	 =      add_sub_symbol(UpperCAmelCase,						UpperCAmelCase   )
								A	 =      []
								# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
								for i, token in enumerate(UpperCAmelCase   ):
												if token[:2] == "##":
																A	 =      token[2:]
																# save chinese tokens' pos
																if len(UpperCAmelCase   ) == 1 and _is_chinese_char(ord(UpperCAmelCase   )   ):
																				ref_id.append(UpperCAmelCase   )
								ref_ids.append(UpperCAmelCase   )

				assert len(UpperCAmelCase   ) == len(UpperCAmelCase   )

				return ref_ids







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
				# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
				with open(args.file_name,						'r',						encoding='utf-8'   ) as f:
								A	 =      f.readlines()
				A	 =      [line.strip() for line in data if len(UpperCAmelCase   ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
				A	 =      LTP(args.ltp   )  # faster in GPU device
				A	 =      BertTokenizer.from_pretrained(args.bert   )

				A	 =      prepare_ref(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase   )

				with open(args.save_path,						'w',						encoding='utf-8'   ) as f:
								A	 =      [json.dumps(UpperCAmelCase   ) + '\n' for ref in ref_ids]
								f.writelines(UpperCAmelCase   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser(description='prepare_chinese_ref')
					parser.add_argument(
					    '--file_name',
					    type=str,
					    default='./resources/chinese-demo.txt',
					    help='file need process, same as training data in lm',
					)
					parser.add_argument(
					    '--ltp', type=str, default='./resources/ltp', help='resources for LTP tokenizer, usually a path'
					)
					parser.add_argument('--bert', type=str, default='./resources/robert', help='resources for Bert tokenizer')
					parser.add_argument('--save_path', type=str, default='./resources/ref.txt', help='path to save res')

					lowerCAmelCase_           = parser.parse_args()
					main(args)





