




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


lowerCamelCase__			:       List[Any]         =							logging.get_logger(__name__)

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

lowerCamelCase__			:       Tuple         =							{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

lowerCamelCase__			:       Any         =							{
    'junnyu/roformer_chinese_small': 1_536,
    'junnyu/roformer_chinese_base': 1_536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


lowerCamelCase__			:       Optional[int]         =							{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   VOCAB_FILES_NAMES
 lowercase_       =   PRETRAINED_VOCAB_FILES_MAP
 lowercase_       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_       =   PRETRAINED_INIT_CONFIGURATION
 lowercase_       =   RoFormerTokenizer



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

       SCREAMING_SNAKE_CASE_     						=      json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
       if (
           pre_tok_state.get('lowercase'    ,    _lowerCAmelCase						) != do_lower_case
           or pre_tok_state.get('strip_accents'    ,    _lowerCAmelCase						) != strip_accents
       ):
             SCREAMING_SNAKE_CASE_     						=      getattr(_lowerCAmelCase    ,    pre_tok_state.pop('type'						)						)
             SCREAMING_SNAKE_CASE_     						=      do_lower_case
             SCREAMING_SNAKE_CASE_     						=      strip_accents
             SCREAMING_SNAKE_CASE_     						=      pre_tok_class(**_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      do_lower_case



 def __getstate__(							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_     						=      self.__dict__.copy()
       SCREAMING_SNAKE_CASE_     						=      BertPreTokenizer()
       return state



 def __setstate__(							self  :		Tuple    ,    _lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_     						=      d
       SCREAMING_SNAKE_CASE_     						=      self.__dict__['_tokenizer'].get_vocab()
       SCREAMING_SNAKE_CASE_     						=      PreTokenizer.custom(JiebaPreTokenizer(_lowerCAmelCase						)						)



 def 				lowerCAmelCase_  (							self  :		Dict    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		str=None						):
       SCREAMING_SNAKE_CASE_     						=      [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 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		List[int]    ,    _lowerCAmelCase  :		Optional[List[int]] = None						):
       SCREAMING_SNAKE_CASE_     						=      [self.sep_token_id]
       SCREAMING_SNAKE_CASE_     						=      [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 				lowerCAmelCase_  (							self  :		Dict    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		Optional[str] = None						):
       SCREAMING_SNAKE_CASE_     						=      self._tokenizer.model.save(_lowerCAmelCase    ,    name=_lowerCAmelCase						)
       return tuple(_lowerCAmelCase						)




 def 				lowerCAmelCase_  (							self  :		Tuple    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		Union[str, Any]=None    ,    _lowerCAmelCase  :		str=None    ,    _lowerCAmelCase  :		Union[str, Any]=False    ,    **_lowerCAmelCase  :		str    ,    ):
       SCREAMING_SNAKE_CASE_     						=      BertPreTokenizer()
       return super().save_pretrained(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase    ,    **_lowerCAmelCase						)





import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowerCamelCase__			:       List[Any]         =							logging.get_logger(__name__)  # pylint: disable=invalid-name





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 def __init__(							self  :		List[Any]    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Tuple=768						):
       super().__init__(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      proj_size

       SCREAMING_SNAKE_CASE_     						=      CLIPVisionModel(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      PaintByExampleMapper(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      nn.LayerNorm(config.hidden_size						)
       SCREAMING_SNAKE_CASE_     						=      nn.Linear(config.hidden_size    ,    self.proj_size						)

       # uncondition for scaling
       SCREAMING_SNAKE_CASE_     						=      nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)




 def 				lowerCAmelCase_  (							self  :		Any    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		int=False						):
       SCREAMING_SNAKE_CASE_     						=      self.model(pixel_values=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      clip_output.pooler_output
       SCREAMING_SNAKE_CASE_     						=      self.mapper(latent_states[:, None]						)
       SCREAMING_SNAKE_CASE_     						=      self.final_layer_norm(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      self.proj_out(_lowerCAmelCase						)
       if return_uncond_vector:
             return latent_states, self.uncond_vector

       return latent_states





class 	lowerCamelCase_							(				nn.Module ):


 '''simple docstring'''







 def __init__(							self  :		int    ,    _lowerCAmelCase  :		Optional[Any]						):
       super().__init__()
       SCREAMING_SNAKE_CASE_     						=      (config.num_hidden_layers + 1) // 5
       SCREAMING_SNAKE_CASE_     						=      config.hidden_size
       SCREAMING_SNAKE_CASE_     						=      1
       SCREAMING_SNAKE_CASE_     						=      nn.ModuleList(
           [
               BasicTransformerBlock(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase    ,    activation_fn='gelu'    ,    attention_bias=_lowerCAmelCase						)
               for _ in range(_lowerCAmelCase						)
           ]						)




 def 				lowerCAmelCase_  (							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		Optional[Any]						):
       for block in self.blocks:
             SCREAMING_SNAKE_CASE_     						=      block(_lowerCAmelCase						)

       return hidden_states