



"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowerCAmelCase_   						=   logging.get_logger(__name__)

lowerCAmelCase_   						=   {'vocab_file': 'vocab.txt'}

lowerCAmelCase_   						=   {
    'vocab_file': {
        'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
        'YituTech/conv-bert-medium-small': (
            'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
        ),
        'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
    }
}

lowerCAmelCase_   						=   {
    'YituTech/conv-bert-base': 512,
    'YituTech/conv-bert-medium-small': 512,
    'YituTech/conv-bert-small': 512,
}


lowerCAmelCase_   						=   {
    'YituTech/conv-bert-base': {'do_lower_case': True},
    'YituTech/conv-bert-medium-small': {'do_lower_case': True},
    'YituTech/conv-bert-small': {'do_lower_case': True},
}
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					Union[str, Any]   	=				VOCAB_FILES_NAMES
		lowerCAmelCase			:					str   	=				PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase			:					Any   	=				PRETRAINED_INIT_CONFIGURATION
		lowerCAmelCase			:					List[str]   	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase			:					Dict   	=				ConvBertTokenizer







		def __init__(	self						:  Union[str, Any]			,_snake_case						:  Tuple=None			,_snake_case						:  Dict=None			,_snake_case						:  Union[str, Any]=True			,_snake_case						:  List[str]="[UNK]"			,_snake_case						:  str="[SEP]"			,_snake_case						:  str="[PAD]"			,_snake_case						:  int="[CLS]"			,_snake_case						:  int="[MASK]"			,_snake_case						:  Optional[int]=True			,_snake_case						:  Optional[int]=None			,**_snake_case						:  List[Any]			,)					->		Tuple:






				"""simple docstring"""



				super().__init__(
				    _snake_case			,tokenizer_file=_snake_case			,do_lower_case=_snake_case			,unk_token=_snake_case			,sep_token=_snake_case			,pad_token=_snake_case			,cls_token=_snake_case			,mask_token=_snake_case			,tokenize_chinese_chars=_snake_case			,strip_accents=_snake_case			,**_snake_case			,)

				lowercase__   :    int				=			json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
				if (
				    normalizer_state.get('''lowercase'''			,_snake_case						) != do_lower_case
				    or normalizer_state.get('''strip_accents'''			,_snake_case						) != strip_accents
				    or normalizer_state.get('''handle_chinese_chars'''			,_snake_case						) != tokenize_chinese_chars
				):
						lowercase__   :    int				=			getattr(_snake_case			,normalizer_state.pop('''type'''						)						)
						lowercase__   :    Optional[Any]				=			do_lower_case
						lowercase__   :    List[str]				=			strip_accents
						lowercase__   :    Optional[Any]				=			tokenize_chinese_chars
						lowercase__   :    List[str]				=			normalizer_class(**_snake_case						)

				lowercase__   :    Optional[int]				=			do_lower_case







		def 					UpperCAmelCase    (	self						:  int			,_snake_case						:  Dict			,_snake_case						:  Dict=None						)					->		Tuple:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

				if token_ids_a:
						output += token_ids_a + [self.sep_token_id]

				return output







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  List[int]			,_snake_case						:  Optional[List[int]] = None						)					->		List[int]:






				"""simple docstring"""



				lowercase__   :    Tuple				=			[self.sep_token_id]
				lowercase__   :    Union[str, Any]				=			[self.cls_token_id]
				if token_ids_a is None:
						return len(cls + token_ids_a + sep						) * [0]
				return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]







		def 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  str			,_snake_case						:  Optional[str] = None						)					->		Tuple[str]:






				"""simple docstring"""



				lowercase__   :    str				=			self._tokenizer.model.save(_snake_case			,name=_snake_case						)
				return tuple(_snake_case						)






from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  str						,							A_					:  TransformeraDModel						,							A_					:  AutoencoderKL						,							A_					:  KarrasDiffusionSchedulers						,							A_					:  Optional[Dict[int, str]] = None						,							)   ->	str:

       """simple docstring"""






       super().__init__()
       self.register_modules(transformer=A_						,							vae=A_						,							scheduler=A_  )

       # create a imagenet -> id dictionary for easier use
       lowerCamelCase_		     =    {}
       if idalabel is not None:
             for key, value in idalabel.items():
                   for label in value.split(','  ):
                         lowerCamelCase_		     =    int(A_  )
             lowerCamelCase_		     =    dict(sorted(self.labels.items()  )  )

 def 				a__ (				self					:  Optional[int]						,							A_					:  Union[str, List[str]]  )   ->	List[int]:

       """simple docstring"""






       if not isinstance(A_						,							A_  ):
             lowerCamelCase_		     =    list(A_  )

       for l in label:
             if l not in self.labels:
                   raise ValueError(
                       f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""  )

       return [self.labels[l] for l in label]





 @torch.no_grad()
 def __call__(				self					:  Any						,							A_					:  List[int]						,							A_					:  float = 4.0						,							A_					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None						,							A_					:  int = 50						,							A_					:  Optional[str] = "pil"						,							A_					:  bool = True						,							)   ->	Union[ImagePipelineOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_		     =    len(A_  )
       lowerCamelCase_		     =    self.transformer.config.sample_size
       lowerCamelCase_		     =    self.transformer.config.in_channels

       lowerCamelCase_		     =    randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)						,							generator=A_						,							device=self.device						,							dtype=self.transformer.dtype						,							)
       lowerCamelCase_		     =    torch.cat([latents] * 2  ) if guidance_scale > 1 else latents

       lowerCamelCase_		     =    torch.tensor(A_						,							device=self.device  ).reshape(-1  )
       lowerCamelCase_		     =    torch.tensor([1000] * batch_size						,							device=self.device  )
       lowerCamelCase_		     =    torch.cat([class_labels, class_null]						,							0  ) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(A_  )

       for t in self.progress_bar(self.scheduler.timesteps  ):
             if guidance_scale > 1:
                   lowerCamelCase_		     =    latent_model_input[: len(A_  ) // 2]
                   lowerCamelCase_		     =    torch.cat([half, half]						,							dim=0  )
             lowerCamelCase_		     =    self.scheduler.scale_model_input(A_						,							A_  )

             lowerCamelCase_		     =    t
             if not torch.is_tensor(A_  ):
                   # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                   # This would be a good case for the `match` statement (Python 3.10+)
                   lowerCamelCase_		     =    latent_model_input.device.type == 'mps'
                   if isinstance(A_						,							A_  ):
                         lowerCamelCase_		     =    torch.floataa if is_mps else torch.floataa
                   else:
                         lowerCamelCase_		     =    torch.intaa if is_mps else torch.intaa
                   lowerCamelCase_		     =    torch.tensor([timesteps]						,							dtype=A_						,							device=latent_model_input.device  )
             elif len(timesteps.shape  ) == 0:
                   lowerCamelCase_		     =    timesteps[None].to(latent_model_input.device  )
             # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
             lowerCamelCase_		     =    timesteps.expand(latent_model_input.shape[0]  )
             # predict noise model_output
             lowerCamelCase_		     =    self.transformer(
                 A_						,							timestep=A_						,							class_labels=A_  ).sample

             # perform guidance
             if guidance_scale > 1:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                   lowerCamelCase_     ,				lowerCamelCase_		     =    torch.split(A_						,							len(A_  ) // 2						,							dim=0  )

                   lowerCamelCase_		     =    uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                   lowerCamelCase_		     =    torch.cat([half_eps, half_eps]						,							dim=0  )

                   lowerCamelCase_		     =    torch.cat([eps, rest]						,							dim=1  )

             # learned sigma
             if self.transformer.config.out_channels // 2 == latent_channels:
                   lowerCamelCase_     ,				lowerCamelCase_		     =    torch.split(A_						,							A_						,							dim=1  )
             else:
                   lowerCamelCase_		     =    noise_pred

             # compute previous image: x_t -> x_t-1
             lowerCamelCase_		     =    self.scheduler.step(A_						,							A_						,							A_  ).prev_sample

       if guidance_scale > 1:
             lowerCamelCase_     ,				lowerCamelCase_		     =    latent_model_input.chunk(2						,							dim=0  )
       else:
             lowerCamelCase_		     =    latent_model_input

       lowerCamelCase_		     =    1 / self.vae.config.scaling_factor * latents
       lowerCamelCase_		     =    self.vae.decode(A_  ).sample

       lowerCamelCase_		     =    (samples / 2 + 0.5).clamp(0						,							1  )

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

       if output_type == "pil":
             lowerCamelCase_		     =    self.numpy_to_pil(A_  )

       if not return_dict:
             return (samples,)

       return ImagePipelineOutput(images=A_  )



