
# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''


   def __init__(					self     :							List[Any]			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Optional[Any]     ):

      """simple docstring"""
      super().__init__()
      self.register_modules(unet=lowerCAmelCase_			,			scheduler=lowerCAmelCase_     )


   @torch.no_grad()
   def __call__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							int = 1			,			lowerCAmelCase_     :							Optional[torch.Generator] = None			,			lowerCAmelCase_     :							int = 5_0			,			lowerCAmelCase_     :							Optional[str] = "pil"			,			lowerCAmelCase_     :							bool = True			,			**lowerCAmelCase_     :							Any			,			):

      """simple docstring"""
      _A:  Optional[Any]					=       torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,			generator=lowerCAmelCase_			,			)
      _A:  int					=       image.to(self.device     )

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

      for t in self.progress_bar(self.scheduler.timesteps     ):
         # 1. predict noise model_output
         _A:  str					=       self.unet(lowerCAmelCase_			,			lowerCAmelCase_     ).sample

         # 2. predict previous mean of image x_t-1 and add variance depending on eta
         # eta corresponds to η in paper and should be between [0, 1]
         # do x_t -> x_t-1
         _A:  Any					=       self.scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_     ).prev_sample

      _A:  Union[str, Any]					=       (image / 2 + 0.5).clamp(0			,			1     )
      _A:  Optional[Any]					=       image.cpu().permute(0			,			2			,			3			,			1     ).numpy()
      if output_type == "pil":
         _A:  Optional[Any]					=       self.numpy_to_pil(lowerCAmelCase_     )

      if not return_dict:
         return (image,), "This is a local test"

      return ImagePipelineOutput(images=lowerCAmelCase_     ), "This is a local test"





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


UpperCAmelCase__		:     Optional[Any]     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     Dict     			=      {'vocab_file': 'vocab.txt'}

UpperCAmelCase__		:     List[Any]     			=      {
    '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',
    }
}

UpperCAmelCase__		:     Union[str, Any]     			=      {
    'YituTech/conv-bert-base': 512,
    'YituTech/conv-bert-medium-small': 512,
    'YituTech/conv-bert-small': 512,
}


UpperCAmelCase__		:     Dict     			=      {
    '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 UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Any 				=    VOCAB_FILES_NAMES
   __UpperCamelCase		:    Tuple 				=    PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase		:    str 				=    PRETRAINED_INIT_CONFIGURATION
   __UpperCamelCase		:    int 				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase		:    int 				=    ConvBertTokenizer


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

      """simple docstring"""
      super().__init__(
          lowerCAmelCase_			,			tokenizer_file=lowerCAmelCase_			,			do_lower_case=lowerCAmelCase_			,			unk_token=lowerCAmelCase_			,			sep_token=lowerCAmelCase_			,			pad_token=lowerCAmelCase_			,			cls_token=lowerCAmelCase_			,			mask_token=lowerCAmelCase_			,			tokenize_chinese_chars=lowerCAmelCase_			,			strip_accents=lowerCAmelCase_			,			**lowerCAmelCase_			,			)

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

      _A:  Optional[Any]					=       do_lower_case


   def  __magic_name__				(					self     :							str			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Union[str, Any]=None     ):

      """simple docstring"""
      _A:  Dict					=       [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  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							List[int]			,			lowerCAmelCase_     :							Optional[List[int]] = None     ):

      """simple docstring"""
      _A:  Any					=       [self.sep_token_id]
      _A:  int					=       [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  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Optional[str] = None     ):

      """simple docstring"""
      _A:  str					=       self._tokenizer.model.save(lowerCAmelCase_			,			name=lowerCAmelCase_     )
      return tuple(lowerCAmelCase_     )



