



'''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_retribert import RetriBertTokenizer


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCAmelCase									=   {
    '''vocab_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

__lowerCAmelCase									=   {
    '''yjernite/retribert-base-uncased''': 512,
}


__lowerCAmelCase									=   {
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Optional[Any]   				=					VOCAB_FILES_NAMES
							lowerCAmelCase       :    Optional[int]   				=					PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase       :    Optional[Any]   				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase       :    Any   				=					PRETRAINED_INIT_CONFIGURATION
							lowerCAmelCase       :    List[str]   				=					RetriBertTokenizer
							lowerCAmelCase       :    Tuple   				=					['input_ids', 'attention_mask']







							def __init__(							self					:       str	,_UpperCAmelCase					:       List[Any]=None	,_UpperCAmelCase					:       Optional[Any]=None	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       Union[str, Any]="[UNK]"	,_UpperCAmelCase					:       List[Any]="[SEP]"	,_UpperCAmelCase					:       str="[PAD]"	,_UpperCAmelCase					:       int="[CLS]"	,_UpperCAmelCase					:       str="[MASK]"	,_UpperCAmelCase					:       int=True	,_UpperCAmelCase					:       Tuple=None	,**_UpperCAmelCase					:       str	,):
									super().__init__(
									    _UpperCAmelCase	,tokenizer_file=_UpperCAmelCase	,do_lower_case=_UpperCAmelCase	,unk_token=_UpperCAmelCase	,sep_token=_UpperCAmelCase	,pad_token=_UpperCAmelCase	,cls_token=_UpperCAmelCase	,mask_token=_UpperCAmelCase	,tokenize_chinese_chars=_UpperCAmelCase	,strip_accents=_UpperCAmelCase	,**_UpperCAmelCase	,)

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

									_a     :      Dict        =							do_lower_case







							def 							__lowercase			(							self					:       Tuple	,_UpperCAmelCase					:       Any	,_UpperCAmelCase					:       Union[str, Any]=None		):
									_a     :      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 							__lowercase			(							self					:       List[str]	,_UpperCAmelCase					:       List[int]	,_UpperCAmelCase					:       Optional[List[int]] = None		):
									_a     :      Optional[int]        =							[self.sep_token_id]
									_a     :      Optional[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 							__lowercase			(							self					:       List[Any]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Optional[str] = None		):
									_a     :      Tuple        =							self._tokenizer.model.save(_UpperCAmelCase	,name=_UpperCAmelCase		)
									return tuple(_UpperCAmelCase		)











"""simple docstring"""




import torch

from diffusers import DiffusionPipeline

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        super().__init__()

        self.register_modules(unet=snake_case_					,      scheduler=snake_case_  )

      def __call__(     self  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  torch.randn(
            (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      )
        A_  :       List[str]										=  1

        A_  :       List[str]										=  self.unet(snake_case_					,      snake_case_  ).sample
        A_  :       Optional[int]										=  self.scheduler.step(snake_case_					,      snake_case_					,      snake_case_  ).prev_sample

        A_  :       List[Any]										=  scheduler_output - scheduler_output + torch.ones_like(snake_case_  )

        return result