




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


UpperCamelCase__         =      logging.get_logger(__name__)

UpperCamelCase__         =      '▁'

UpperCamelCase__         =      {
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCamelCase__         =      {
    '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',
    },
}

UpperCamelCase__         =      {
    'facebook/m2m100_418M': 1024,
}

# fmt: off
UpperCamelCase__         =      {
    '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       a__   ( snake_case__				):
						_a							:   Tuple =   VOCAB_FILES_NAMES
						_a							:   Tuple =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a							:   Any =   PRETRAINED_VOCAB_FILES_MAP
						_a							:   Optional[int] =   ["""input_ids""", """attention_mask"""]

						_a							:   Dict =   []
						_a							:   Optional[Any] =   []






						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		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	{} if sp_model_kwargs is None else sp_model_kwargs

											__lowerCAmelCase           =	language_codes
											__lowerCAmelCase           =	FAIRSEQ_LANGUAGE_CODES[language_codes]
											__lowerCAmelCase           =	{lang_code: f"""__{lang_code}__""" for lang_code in fairseq_language_code}

											__lowerCAmelCase           =	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		,      )

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

											__lowerCAmelCase           =	len(self.encoder      )

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

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

											__lowerCAmelCase           =	num_madeup_words






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return len(self.encoder      ) + len(self.lang_token_to_id      )






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return self._src_lang






						@src_lang.setter
						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	new_src_lang
											self.set_src_lang_special_tokens(self._src_lang      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											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     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	[]
											__lowerCAmelCase           =	''''''
											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
																					__lowerCAmelCase           =	[]
																else:
																					current_sub_tokens.append(_A      )
											out_string += self.sp_model.decode(_A      )
											return out_string.strip()






						def     __SCREAMING_SNAKE_CASE(       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      )

											__lowerCAmelCase           =	[1] * len(self.prefix_tokens      )
											__lowerCAmelCase           =	[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     __SCREAMING_SNAKE_CASE(       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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	{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      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.__dict__.copy()
											__lowerCAmelCase           =	None
											return state






						def __setstate__(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	d

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

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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = None      ):




											"""simple docstring"""



											__lowerCAmelCase           =	Path(_A      )
											if not save_dir.is_dir():
																raise OSError(f"""{save_directory} should be a directory"""      )
											__lowerCAmelCase           =	save_dir / (
											    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
											)
											__lowerCAmelCase           =	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:
																					__lowerCAmelCase           =	self.sp_model.serialized_model_proto()
																					fi.write(_A      )

											return (str(_A      ), str(_A      ))






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = "en"		,      _A = None		,      _A = "ro"		,      **_A		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	src_lang
											__lowerCAmelCase           =	tgt_lang
											self.set_src_lang_special_tokens(self.src_lang      )
											return super().prepare_seqaseq_batch(_A		,      _A		,      **_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _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"      )
											__lowerCAmelCase           =	src_lang
											__lowerCAmelCase           =	self(_A		,      add_special_tokens=_A		,      **_A      )
											__lowerCAmelCase           =	self.get_lang_id(_A      )
											__lowerCAmelCase           =	tgt_lang_id
											return inputs






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.set_src_lang_special_tokens(self.src_lang      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.set_tgt_lang_special_tokens(self.tgt_lang      )






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_lang_token(_A      )
											__lowerCAmelCase           =	self.lang_token_to_id[lang_token]
											__lowerCAmelCase           =	[self.cur_lang_id]
											__lowerCAmelCase           =	[self.eos_token_id]






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_lang_token(_A      )
											__lowerCAmelCase           =	self.lang_token_to_id[lang_token]
											__lowerCAmelCase           =	[self.cur_lang_id]
											__lowerCAmelCase           =	[self.eos_token_id]






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											return self.lang_code_to_token[lang]






						def     __SCREAMING_SNAKE_CASE(       self		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.get_lang_token(_A      )
											return self.lang_token_to_id[lang_token]







def      _a				(						SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    Dict[str, Any]					):






					"""simple docstring"""




					__lowerCAmelCase           =	sentencepiece.SentencePieceProcessor(**UpperCamelCase__					)
					spm.Load(str(UpperCamelCase__					)					)
					return spm







def      _a				(						SCREAMING_SNAKE_CASE_							:    str					):






					"""simple docstring"""




					with open(UpperCamelCase__				,				"r"					) as f:
										return json.load(UpperCamelCase__					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    str					):






					"""simple docstring"""




					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__":
					UpperCamelCase__         =      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      _a				(						SCREAMING_SNAKE_CASE_							:    List[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)

					UpperCamelCase__         =      parser.parse_args()

					UpperCamelCase__         =      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)




