


import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


_snake_case			 =	logging.get_logger(__name__)
class       UpperCAmelCase_   (   __lowerCAmelCase       ):

   '''simple docstring'''




   def __init__(						self		,							__A					):






     """simple docstring"""





     super().__init__()
     lowerCamelCase					:   Any       =							nn.ModuleList(_SCREAMING_SNAKE_CASE					)






   def _snake_case       (						self		,							__A		,							__A		,							__A		,							__A		,							__A		,							__A = None		,							__A = None		,							__A = None		,							__A = None		,							__A = False		,							__A = True		,							):






     """simple docstring"""





     for i, (image, scale, controlnet) in enumerate(zip(_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							self.nets					)					):
       lowerCamelCase       ,      lowerCamelCase					:   Optional[Any]       =							controlnet(
           _SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE		,							)

       # merge samples
       if i == 0:
         lowerCamelCase       ,      lowerCamelCase					:   Optional[int]       =							down_samples, mid_sample
       else:
         lowerCamelCase					:   Optional[Any]       =							[
             samples_prev + samples_curr
             for samples_prev, samples_curr in zip(_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE					)
         ]
         mid_block_res_sample += mid_sample

     return down_block_res_samples, mid_block_res_sample






   def _snake_case       (						self		,							__A		,							__A = True		,							__A = None		,							__A = False		,							__A = None		,							):






     """simple docstring"""





     lowerCamelCase					:   Union[str, Any]       =							0
     lowerCamelCase					:   List[str]       =							save_directory
     for controlnet in self.nets:
       controlnet.save_pretrained(
           _SCREAMING_SNAKE_CASE		,							is_main_process=_SCREAMING_SNAKE_CASE		,							save_function=_SCREAMING_SNAKE_CASE		,							safe_serialization=_SCREAMING_SNAKE_CASE		,							variant=_SCREAMING_SNAKE_CASE		,							)

       idx += 1
       lowerCamelCase					:   Tuple       =							model_path_to_save + F"""_{idx}"""






   @classmethod
   def _snake_case       (						cls		,							__A		,							**__A					):






     """simple docstring"""





     lowerCamelCase					:   Optional[Any]       =							0
     lowerCamelCase					:   Any       =							[]

     # load controlnet and append to list until no controlnet directory exists anymore
     # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
     # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
     lowerCamelCase					:   Tuple       =							pretrained_model_path
     while os.path.isdir(_SCREAMING_SNAKE_CASE					):
       lowerCamelCase					:   Optional[Any]       =							ControlNetModel.from_pretrained(_SCREAMING_SNAKE_CASE		,							**_SCREAMING_SNAKE_CASE					)
       controlnets.append(_SCREAMING_SNAKE_CASE					)

       idx += 1
       lowerCamelCase					:   Dict       =							pretrained_model_path + F"""_{idx}"""

     logger.info(F"""{len(_SCREAMING_SNAKE_CASE					)} controlnets loaded from {pretrained_model_path}."""					)

     if len(_SCREAMING_SNAKE_CASE					) == 0:
       raise ValueError(
           F"""No ControlNets found under {os.path.dirname(_SCREAMING_SNAKE_CASE					)}. Expected at least {pretrained_model_path + '_0'}."""					)

     return cls(_SCREAMING_SNAKE_CASE					)









'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


UpperCamelCase_		:     Union[str, Any]          =		logging.get_logger(__name__)


UpperCamelCase_		:     Optional[Any]          =		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase_		:     Dict          =		{
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase_		:     Tuple          =		{
    '''facebook/bart-base''': 1024,
    '''facebook/bart-large''': 1024,
    '''facebook/bart-large-mnli''': 1024,
    '''facebook/bart-large-cnn''': 1024,
    '''facebook/bart-large-xsum''': 1024,
    '''yjernite/bart_eli5''': 1024,
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       Union[str, Any]       							=		VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_				:       int       							=		PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_				:       Optional[Any]       							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_				:       Any       							=		["""input_ids""", """attention_mask"""]
     SCREAMING_SNAKE_CASE_				:       str       							=		BartTokenizer






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE="replace"   ,_SCREAMING_SNAKE_CASE="<s>"   ,_SCREAMING_SNAKE_CASE="</s>"   ,_SCREAMING_SNAKE_CASE="</s>"   ,_SCREAMING_SNAKE_CASE="<s>"   ,_SCREAMING_SNAKE_CASE="<unk>"   ,_SCREAMING_SNAKE_CASE="<pad>"   ,_SCREAMING_SNAKE_CASE="<mask>"   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True   ,**_SCREAMING_SNAKE_CASE   ,)				->     List[Any]:
          super().__init__(
              _SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,tokenizer_file=_SCREAMING_SNAKE_CASE   ,errors=_SCREAMING_SNAKE_CASE   ,bos_token=_SCREAMING_SNAKE_CASE   ,eos_token=_SCREAMING_SNAKE_CASE   ,sep_token=_SCREAMING_SNAKE_CASE   ,cls_token=_SCREAMING_SNAKE_CASE   ,unk_token=_SCREAMING_SNAKE_CASE   ,pad_token=_SCREAMING_SNAKE_CASE   ,mask_token=_SCREAMING_SNAKE_CASE   ,add_prefix_space=_SCREAMING_SNAKE_CASE   ,trim_offsets=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE   ,)

          _snake_case				= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
          if pre_tok_state.get("add_prefix_space"   ,_SCREAMING_SNAKE_CASE       ) != add_prefix_space:
               _snake_case				= getattr(_SCREAMING_SNAKE_CASE   ,pre_tok_state.pop("type"       )       )
               _snake_case				= add_prefix_space
               _snake_case				= pre_tok_class(**_SCREAMING_SNAKE_CASE       )

          _snake_case				= add_prefix_space

          # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
          _snake_case				= "post_processor"
          _snake_case				= getattr(self.backend_tokenizer   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
          if tokenizer_component_instance:
               _snake_case				= json.loads(tokenizer_component_instance.__getstate__()       )

               # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
               if "sep" in state:
                    _snake_case				= tuple(state["sep"]       )
               if "cls" in state:
                    _snake_case				= tuple(state["cls"]       )

               _snake_case				= False

               if state.get("add_prefix_space"   ,_SCREAMING_SNAKE_CASE       ) != add_prefix_space:
                    _snake_case				= add_prefix_space
                    _snake_case				= True

               if state.get("trim_offsets"   ,_SCREAMING_SNAKE_CASE       ) != trim_offsets:
                    _snake_case				= trim_offsets
                    _snake_case				= True

               if changes_to_apply:
                    _snake_case				= getattr(_SCREAMING_SNAKE_CASE   ,state.pop("type"       )       )
                    _snake_case				= component_class(**_SCREAMING_SNAKE_CASE       )
                    setattr(self.backend_tokenizer   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )






     @property
     def 							_lowercase      (					self       )				->     str:
          if self._mask_token is None:
               if self.verbose:
                    logger.error("Using mask_token, but it is not set yet."       )
               return None
          return str(self._mask_token       )






     @mask_token.setter
     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     str:
          _snake_case				= AddedToken(_SCREAMING_SNAKE_CASE   ,lstrip=_SCREAMING_SNAKE_CASE   ,rstrip=_SCREAMING_SNAKE_CASE       ) if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ) else value
          _snake_case				= value






     def 							_lowercase      (					self   ,*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     BatchEncoding:
          _snake_case				= kwargs.get("is_split_into_words"   ,_SCREAMING_SNAKE_CASE       )

          if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                   "to use it with pretokenized inputs."       )

          return super()._batch_encode_plus(*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     BatchEncoding:
          _snake_case				= kwargs.get("is_split_into_words"   ,_SCREAMING_SNAKE_CASE       )

          if is_split_into_words and not self.add_prefix_space:
               raise ValueError(
                   f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                   "to use it with pretokenized inputs."       )

          return super()._encode_plus(*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     Tuple[str]:
          _snake_case				= self._tokenizer.model.save(_SCREAMING_SNAKE_CASE   ,name=_SCREAMING_SNAKE_CASE       )
          return tuple(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=None       )				->     Tuple:
          _snake_case				= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
               return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     List[int]:
          _snake_case				= [self.sep_token_id]
          _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 + sep + token_ids_a + sep       ) * [0]


