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


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def __init__(     self						:						int   ,   lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.ModuleList(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						List[str] = None   ,   lowerCamelCase_						:						Any = None   ,   lowerCamelCase_						:						Optional[int] = None   ,   lowerCamelCase_						:						Optional[Any] = None   ,   lowerCamelCase_						:						List[Any] = False   ,   lowerCamelCase_						:						int = True   ,   ):





    """simple docstring"""


    for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase_   ,   lowerCamelCase_   ,   self.nets				)				):
     UpperCamelCase			  =      controlnet(
         lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   )

     # merge samples
     if i == 0:
      UpperCamelCase			  =      down_samples, mid_sample
     else:
      UpperCamelCase			  =      [
          samples_prev + samples_curr
          for samples_prev, samples_curr in zip(lowerCamelCase_   ,   lowerCamelCase_				)
      ]
      mid_block_res_sample += mid_sample

    return down_block_res_samples, mid_block_res_sample







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Optional[int] = True   ,   lowerCamelCase_						:						List[Any] = None   ,   lowerCamelCase_						:						Dict = False   ,   lowerCamelCase_						:						Any = None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      0
    UpperCamelCase			  =      save_directory
    for controlnet in self.nets:
     controlnet.save_pretrained(
         lowerCamelCase_   ,   is_main_process=lowerCamelCase_   ,   save_function=lowerCamelCase_   ,   safe_serialization=lowerCamelCase_   ,   variant=lowerCamelCase_   ,   )

     idx += 1
     UpperCamelCase			  =      model_path_to_save + f"""_{idx}"""







   @classmethod
   def 						lowerCamelCase_	(     cls						:						List[str]   ,   lowerCamelCase_						:						Optional[int]   ,   **lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      0
    UpperCamelCase			  =      []

    # 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`, ...
    UpperCamelCase			  =      pretrained_model_path
    while os.path.isdir(lowerCamelCase_				):
     UpperCamelCase			  =      ControlNetModel.from_pretrained(lowerCamelCase_   ,   **lowerCamelCase_				)
     controlnets.append(lowerCamelCase_				)

     idx += 1
     UpperCamelCase			  =      pretrained_model_path + f"""_{idx}"""

    logger.info(f"""{len(lowerCamelCase_				)} controlnets loaded from {pretrained_model_path}."""				)

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

    return cls(lowerCamelCase_				)






'''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_distilbert import DistilBertTokenizer


UpperCamelCase_    	=				logging.get_logger(__name__)

UpperCamelCase_    	=				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase_    	=				{
    "vocab_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-german-cased": (
            "https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
        ),
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase_    	=				{
    "distilbert-base-uncased": 5_1_2,
    "distilbert-base-uncased-distilled-squad": 5_1_2,
    "distilbert-base-cased": 5_1_2,
    "distilbert-base-cased-distilled-squad": 5_1_2,
    "distilbert-base-german-cased": 5_1_2,
    "distilbert-base-multilingual-cased": 5_1_2,
}


UpperCamelCase_    	=				{
    "distilbert-base-uncased": {"do_lower_case": True},
    "distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
    "distilbert-base-cased": {"do_lower_case": False},
    "distilbert-base-cased-distilled-squad": {"do_lower_case": False},
    "distilbert-base-german-cased": {"do_lower_case": False},
    "distilbert-base-multilingual-cased": {"do_lower_case": False},
}






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				List[Any]        =						VOCAB_FILES_NAMES
       A						:				Dict        =						PRETRAINED_VOCAB_FILES_MAP
       A						:				Optional[int]        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A						:				Optional[Any]        =						PRETRAINED_INIT_CONFIGURATION
       A						:				Optional[int]        =						['''input_ids''', '''attention_mask''']
       A						:				List[Any]        =						DistilBertTokenizer







       def __init__(      self,							A=None,							A=None,							A=True,							A="[UNK]",							A="[SEP]",							A="[PAD]",							A="[CLS]",							A="[MASK]",							A=True,							A=None,							**A,							):







             '''simple docstring'''







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

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

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						do_lower_case







       def        UpperCamelCase_							(      self,							A,							A=None  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      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,							A,							A = None  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						[self.sep_token_id]
             SCREAMING_SNAKE_CASE   :      str    					=						[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,							A,							A = None  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self._tokenizer.model.save(A,							name=A  )
             return tuple(A  )


