




'''simple docstring'''



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase_    	=				logging.get_logger(__name__)

UpperCamelCase_    	=				"▁"

UpperCamelCase_    	=				{"vocab_file": "sentencepiece.bpe.model"}

UpperCamelCase_    	=				{
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

UpperCamelCase_    	=				{
    "facebook/mbart-large-en-ro": 1_0_2_4,
    "facebook/mbart-large-cc25": 1_0_2_4,
}

# fmt: off
UpperCamelCase_    	=				["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				List[str]        =						VOCAB_FILES_NAMES
       A						:				str        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A						:				List[str]        =						PRETRAINED_VOCAB_FILES_MAP
       A						:				Dict        =						['''input_ids''', '''attention_mask''']

       A						:				List[int]        =						[]
       A						:				List[int]        =						[]







       def __init__(      self,							A,							A="<s>",							A="</s>",							A="</s>",							A="<s>",							A="<unk>",							A="<pad>",							A="<mask>",							A=None,							A=None,							A=None,							A = None,							A=None,							**A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						AddedToken(A,							lstrip=A,							rstrip=A  ) if isinstance(A,							A  ) else mask_token

             SCREAMING_SNAKE_CASE   :      Tuple    					=						{} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 bos_token=A,							eos_token=A,							unk_token=A,							sep_token=A,							cls_token=A,							pad_token=A,							mask_token=A,							tokenizer_file=A,							src_lang=A,							tgt_lang=A,							additional_special_tokens=A,							sp_model_kwargs=self.sp_model_kwargs,							**A,							)

             SCREAMING_SNAKE_CASE   :      int    					=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
             self.sp_model.Load(str(A  )  )
             SCREAMING_SNAKE_CASE   :      str    					=						vocab_file

             # Original fairseq vocab and spm vocab must be "aligned":
             # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
             # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
             # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
             # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

             # Mimic fairseq token-to-id alignment for the first 4 token
             SCREAMING_SNAKE_CASE   :      str    					=						{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

             # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						1

             SCREAMING_SNAKE_CASE   :      Dict    					=						len(self.sp_model  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{
                 code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(A  )
             }
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{v: k for k, v in self.lang_code_to_id.items()}
             SCREAMING_SNAKE_CASE   :      int    					=						len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset

             self.fairseq_tokens_to_ids.update(self.lang_code_to_id  )
             SCREAMING_SNAKE_CASE   :      str    					=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
             SCREAMING_SNAKE_CASE   :      Tuple    					=						list(self.lang_code_to_id.keys()  )

             if additional_special_tokens is not None:
                   # Only add those special tokens if they are not already there.
                   self._additional_special_tokens.extend(
                       [t for t in additional_special_tokens if t not in self._additional_special_tokens]  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						src_lang if src_lang is not None else 'en_XX'
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.lang_code_to_id[self._src_lang]
             SCREAMING_SNAKE_CASE   :      Tuple    					=						tgt_lang
             self.set_src_lang_special_tokens(self._src_lang  )







       def __getstate__(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.__dict__.copy()
             SCREAMING_SNAKE_CASE   :      str    					=						None
             SCREAMING_SNAKE_CASE   :      str    					=						self.sp_model.serialized_model_proto()
             return state







       def __setstate__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						d

             # for backward compatibility
             if not hasattr(self,							'sp_model_kwargs'  ):
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						{}

             SCREAMING_SNAKE_CASE   :      Tuple    					=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
             self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )







       @property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset + 1  # Plus 1 for the mask token







       @property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return self._src_lang







       @src_lang.setter
       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						new_src_lang
             self.set_src_lang_special_tokens(self._src_lang  )







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







             '''simple docstring'''







             if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=A,							token_ids_a=A,							already_has_special_tokens=A  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[1] * len(self.prefix_tokens  )
             SCREAMING_SNAKE_CASE   :      int    					=						[1] * len(self.suffix_tokens  )
             if token_ids_a is None:
                   return prefix_ones + ([0] * len(A  )) + suffix_ones
             return prefix_ones + ([0] * len(A  )) + ([0] * len(A  )) + suffix_ones







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







             '''simple docstring'''







             if token_ids_a is None:
                   return self.prefix_tokens + token_ids_a + self.suffix_tokens
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







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







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[self.sep_token_id]
             SCREAMING_SNAKE_CASE   :      Tuple    					=						[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]







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							**A  ):







             '''simple docstring'''







             if src_lang is None or tgt_lang is None:
                   raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'  )
             SCREAMING_SNAKE_CASE   :      int    					=						src_lang
             SCREAMING_SNAKE_CASE   :      List[str]    					=						self(A,							add_special_tokens=A,							return_tensors=A,							**A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						self.convert_tokens_to_ids(A  )
             SCREAMING_SNAKE_CASE   :      int    					=						tgt_lang_id
             return inputs







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{self.convert_ids_to_tokens(A  ): i for i in range(self.vocab_size  )}
             vocab.update(self.added_tokens_encoder  )
             return vocab







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             return self.sp_model.encode(A,							out_type=A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if token in self.fairseq_tokens_to_ids:
                   return self.fairseq_tokens_to_ids[token]
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.sp_model.PieceToId(A  )

             # Need to return unknown token if the SP model returned 0
             return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if index in self.fairseq_ids_to_tokens:
                   return self.fairseq_ids_to_tokens[index]
             return self.sp_model.IdToPiece(index - self.fairseq_offset  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						''.join(A  ).replace(A,							' '  ).strip()
             return out_string







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







             '''simple docstring'''







             if not os.path.isdir(A  ):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
                   return
             SCREAMING_SNAKE_CASE   :      Dict    					=						os.path.join(
                 A,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

             if os.path.abspath(self.vocab_file  ) != os.path.abspath(A  ) and os.path.isfile(self.vocab_file  ):
                   copyfile(self.vocab_file,							A  )
             elif not os.path.isfile(self.vocab_file  ):
                   with open(A,							'wb'  ) as fi:
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						self.sp_model.serialized_model_proto()
                         fi.write(A  )

             return (out_vocab_file,)







       def        UpperCamelCase_							(      self,							A,							A = "en_XX",							A = None,							A = "ro_RO",							**A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						src_lang
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tgt_lang
             return super().prepare_seqaseq_batch(A,							A,							**A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return self.set_src_lang_special_tokens(self.src_lang  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return self.set_tgt_lang_special_tokens(self.tgt_lang  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						self.lang_code_to_id[src_lang]
             SCREAMING_SNAKE_CASE   :      int    					=						[]
             SCREAMING_SNAKE_CASE   :      int    					=						[self.eos_token_id, self.cur_lang_code]







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						self.lang_code_to_id[lang]
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[]
             SCREAMING_SNAKE_CASE   :      Dict    					=						[self.eos_token_id, self.cur_lang_code]








'''simple docstring'''



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


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             super().__init__()
             SCREAMING_SNAKE_CASE   :      Dict    					=						nn.ModuleList(A  )







       def        UpperCamelCase_							(      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(A,							A,							self.nets  )  ):
                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						controlnet(
                       A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							)

                   # merge samples
                   if i == 0:
                         SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						down_samples, mid_sample
                   else:
                         SCREAMING_SNAKE_CASE   :      str    					=						[
                             samples_prev + samples_curr
                             for samples_prev, samples_curr in zip(A,							A  )
                         ]
                         mid_block_res_sample += mid_sample

             return down_block_res_samples, mid_block_res_sample







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







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						0
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						save_directory
             for controlnet in self.nets:
                   controlnet.save_pretrained(
                       A,							is_main_process=A,							save_function=A,							safe_serialization=A,							variant=A,							)

                   idx += 1
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						model_path_to_save + F"_{idx}"







       @classmethod
       def        UpperCamelCase_							(      cls,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						0
             SCREAMING_SNAKE_CASE   :      List[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`, ...
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						pretrained_model_path
             while os.path.isdir(A  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						ControlNetModel.from_pretrained(A,							**A  )
                   controlnets.append(A  )

                   idx += 1
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pretrained_model_path + F"_{idx}"

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

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

             return cls(A  )


