



import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		"▁"

_lowercase						  =		{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

_lowercase						  =		{
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

_lowercase						  =		{
    "facebook/m2m100_418M": 1024,
}

# fmt: off
_lowercase						  =		{
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				VOCAB_FILES_NAMES
     UpperCamelCase__		    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase__		    =				PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase__		    =				['''input_ids''', '''attention_mask''']

     UpperCamelCase__		    =				[]
     UpperCamelCase__		    =				[]




     def __init__(				self ,						a__ ,						a__ ,						a__=None ,						a__=None ,						a__="<s>" ,						a__="</s>" ,						a__="</s>" ,						a__="<pad>" ,						a__="<unk>" ,						a__="m2m100" ,						a__ = None ,						a__=8 ,						**a__ ,						):
         A__		 =					{} if sp_model_kwargs is None else sp_model_kwargs

         A__		 =					language_codes
         A__		 =					FAIRSEQ_LANGUAGE_CODES[language_codes]
         A__		 =					{lang_code: F"__{lang_code}__" for lang_code in fairseq_language_code}

         A__		 =					kwargs.get('''additional_special_tokens''' ,						[])
         kwargs["additional_special_tokens"] += [
             self.get_lang_token(a__)
             for lang_code in fairseq_language_code
             if self.get_lang_token(a__) not in kwargs["additional_special_tokens"]
         ]

         super().__init__(
             src_lang=a__ ,						tgt_lang=a__ ,						bos_token=a__ ,						eos_token=a__ ,						sep_token=a__ ,						unk_token=a__ ,						pad_token=a__ ,						language_codes=a__ ,						sp_model_kwargs=self.sp_model_kwargs ,						num_madeup_words=a__ ,						**a__ ,						)

         A__		 =					vocab_file
         A__		 =					load_json(a__)
         A__		 =					{v: k for k, v in self.encoder.items()}
         A__		 =					spm_file
         A__		 =					load_spm(a__ ,						self.sp_model_kwargs)

         A__		 =					len(self.encoder)

         A__		 =					{
             self.get_lang_token(a__): self.encoder_size + i for i, lang_code in enumerate(a__)
         }
         A__		 =					{lang_code: self.encoder_size + i for i, lang_code in enumerate(a__)}
         A__		 =					{v: k for k, v in self.lang_token_to_id.items()}

         A__		 =					src_lang if src_lang is not None else '''en'''
         A__		 =					tgt_lang
         A__		 =					self.get_lang_id(self._src_lang)
         self.set_src_lang_special_tokens(self._src_lang)

         A__		 =					num_madeup_words




     @property
     def 		snake_case_		(				self):
         return len(self.encoder) + len(self.lang_token_to_id)




     @property
     def 		snake_case_		(				self):
         return self._src_lang




     @src_lang.setter
     def 		snake_case_		(				self ,						a__):
         A__		 =					new_src_lang
         self.set_src_lang_special_tokens(self._src_lang)




     def 		snake_case_		(				self ,						a__):
         return self.sp_model.encode(a__ ,						out_type=a__)




     def 		snake_case_		(				self ,						a__):
         if token in self.lang_token_to_id:
             return self.lang_token_to_id[token]
         return self.encoder.get(a__ ,						self.encoder[self.unk_token])




     def 		snake_case_		(				self ,						a__):
         if index in self.id_to_lang_token:
             return self.id_to_lang_token[index]
         return self.decoder.get(a__ ,						self.unk_token)




     def 		snake_case_		(				self ,						a__):
         A__		 =					[]
         A__		 =					''''''
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 out_string += self.sp_model.decode(a__) + token
                 A__		 =					[]
             else:
                 current_sub_tokens.append(a__)
         out_string += self.sp_model.decode(a__)
         return out_string.strip()




     def 		snake_case_		(				self ,						a__ ,						a__ = None ,						a__ = False):

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

         A__		 =					[1] * len(self.prefix_tokens)
         A__		 =					[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 		snake_case_		(				self ,						a__ ,						a__ = None):
         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 		snake_case_		(				self):
         A__		 =					{self.convert_ids_to_tokens(a__): i for i in range(self.vocab_size)}
         vocab.update(self.added_tokens_encoder)
         return vocab




     def __getstate__(				self):
         A__		 =					self.__dict__.copy()
         A__		 =					None
         return state




     def __setstate__(				self ,						a__):
         A__		 =					d

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

         A__		 =					load_spm(self.spm_file ,						self.sp_model_kwargs)




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         A__		 =					Path(a__)
         if not save_dir.is_dir():
             raise OSError(F"{save_directory} should be a directory")
         A__		 =					save_dir / (
             (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
         )
         A__		 =					save_dir / (
             (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
         )

         save_json(self.encoder ,						a__)

         if os.path.abspath(self.spm_file) != os.path.abspath(a__) and os.path.isfile(self.spm_file):
             copyfile(self.spm_file ,						a__)
         elif not os.path.isfile(self.spm_file):
             with open(a__ ,						'''wb''') as fi:
                 A__		 =					self.sp_model.serialized_model_proto()
                 fi.write(a__)

         return (str(a__), str(a__))




     def 		snake_case_		(				self ,						a__ ,						a__ = "en" ,						a__ = None ,						a__ = "ro" ,						**a__ ,						):
         A__		 =					src_lang
         A__		 =					tgt_lang
         self.set_src_lang_special_tokens(self.src_lang)
         return super().prepare_seqaseq_batch(a__ ,						a__ ,						**a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						**a__):
         if src_lang is None or tgt_lang is None:
             raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''')
         A__		 =					src_lang
         A__		 =					self(a__ ,						add_special_tokens=a__ ,						**a__)
         A__		 =					self.get_lang_id(a__)
         A__		 =					tgt_lang_id
         return inputs




     def 		snake_case_		(				self):
         self.set_src_lang_special_tokens(self.src_lang)




     def 		snake_case_		(				self):
         self.set_tgt_lang_special_tokens(self.tgt_lang)




     def 		snake_case_		(				self ,						a__):
         A__		 =					self.get_lang_token(a__)
         A__		 =					self.lang_token_to_id[lang_token]
         A__		 =					[self.cur_lang_id]
         A__		 =					[self.eos_token_id]




     def 		snake_case_		(				self ,						a__):
         A__		 =					self.get_lang_token(a__)
         A__		 =					self.lang_token_to_id[lang_token]
         A__		 =					[self.cur_lang_id]
         A__		 =					[self.eos_token_id]




     def 		snake_case_		(				self ,						a__):
         return self.lang_code_to_token[lang]




     def 		snake_case_		(				self ,						a__):
         A__		 =					self.get_lang_token(a__)
         return self.lang_token_to_id[lang_token]







def 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       Dict[str, Any]	)->  sentencepiece.SentencePieceProcessor:
    A__		 =					sentencepiece.SentencePieceProcessor(**UpperCamelCase_	)
    spm.Load(str(UpperCamelCase_	)	)
    return spm



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str	)->  Union[Dict, List]:
    with open(UpperCamelCase_ ,       '''r'''	) as f:
        return json.load(UpperCamelCase_	)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Tuple ,       UpperCamelCase_				:       str	)->  None:
    with open(UpperCamelCase_ ,       '''w'''	) as f:
        json.dump(UpperCamelCase_ ,       UpperCamelCase_ ,       indent=2	)





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
   _lowercase						  =		argparse.ArgumentParser()

   parser.add_argument(
       "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
   )
   parser.add_argument(
       "--original_config_file",
       type=str,
       required=True,
       help="The YAML config file corresponding to the original architecture.",
   )
   parser.add_argument(
       "--num_in_channels",
       default=None,
       type=int,
       help="The number of input channels. If `None` number of input channels will be automatically inferred.",
   )
   parser.add_argument(
       "--image_size",
       default=512,
       type=int,
       help=(
           "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
           " Base. Use 768 for Stable Diffusion v2."
       ),
   )
   parser.add_argument(
       "--extract_ema",
       action="store_true",
       help=(
           "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
           " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
           " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
       ),
   )
   parser.add_argument(
       "--upcast_attention",
       action="store_true",
       help=(
           "Whether the attention computation should always be upcasted. This is necessary when running stable"
           " diffusion 2.1."
       ),
   )
   parser.add_argument(
       "--from_safetensors",
       action="store_true",
       help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
   )
   parser.add_argument(
       "--to_safetensors",
       action="store_true",
       help="Whether to store pipeline in safetensors format or not.",
   )
   parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
   parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")



   def 		lowerCAmelCase__    (					UpperCamelCase_				:       Any	)->  Union[str, Any]:
       if string == "True":
           return True
       elif string == "False":
           return False
       else:
           raise ValueError(f"could not parse string as bool {string}"	)

   parser.add_argument(
       "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
   )

   parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

   _lowercase						  =		parser.parse_args()

   _lowercase						  =		download_controlnet_from_original_ckpt(
       checkpoint_path=args.checkpoint_path,
       original_config_file=args.original_config_file,
       image_size=args.image_size,
       extract_ema=args.extract_ema,
       num_in_channels=args.num_in_channels,
       upcast_attention=args.upcast_attention,
       from_safetensors=args.from_safetensors,
       device=args.device,
       use_linear_projection=args.use_linear_projection,
       cross_attention_dim=args.cross_attention_dim,
   )



   controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
