





'''simple docstring'''






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__lowerCamelCase						: Tuple		=   False
__lowerCamelCase						: str		=   True
__lowerCamelCase						: Tuple		=   False


if __name__ == "__main__":
      __lowerCamelCase						: Union[str, Any]		=   argparse.ArgumentParser()

      parser.add_argument(
          '--repo_path',
          default=None,
          type=str,
          required=True,
          help='The config json file corresponding to the architecture.',
      )

      parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

      __lowerCamelCase						: Any		=   parser.parse_args()

      __lowerCamelCase						: Optional[Any]		=   {
          'image_size': 'sample_size',
          'num_res_blocks': 'layers_per_block',
          'block_channels': 'block_out_channels',
          'down_blocks': 'down_block_types',
          'up_blocks': 'up_block_types',
          'downscale_freq_shift': 'freq_shift',
          'resnet_num_groups': 'norm_num_groups',
          'resnet_act_fn': 'act_fn',
          'resnet_eps': 'norm_eps',
          'num_head_channels': 'attention_head_dim',
      }

      __lowerCamelCase						: Tuple		=   {
          'time_steps': 'time_proj',
          'mid': 'mid_block',
          'downsample_blocks': 'down_blocks',
          'upsample_blocks': 'up_blocks',
      }

      __lowerCamelCase						: Any		=   '' if has_file(args.repo_path, 'config.json') else 'unet'

      with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
            __lowerCamelCase						: str		=   reader.read()
            __lowerCamelCase						: int		=   json.loads(text)

      if do_only_config:
            for key in config_parameters_to_change.keys():
                  config.pop(key, None)

      if has_file(args.repo_path, 'config.json'):
            __lowerCamelCase						: Optional[int]		=   UNetaDModel(**config)
      else:
            __lowerCamelCase						: List[str]		=   UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
            __lowerCamelCase						: Union[str, Any]		=   class_name(**config)

      if do_only_config:
            model.save_config(os.path.join(args.repo_path, subfolder))

      __lowerCamelCase						: List[Any]		=   dict(model.config)

      if do_only_renaming:
            for key, value in config_parameters_to_change.items():
                  if key in config:
                        __lowerCamelCase						: Any		=   config[key]
                        del config[key]

            __lowerCamelCase						: Tuple		=   [k.replace('UNetRes', '') for k in config['down_block_types']]
            __lowerCamelCase						: List[str]		=   [k.replace('UNetRes', '') for k in config['up_block_types']]

      if do_only_weights:
            __lowerCamelCase						: Any		=   torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

            __lowerCamelCase						: str		=   {}
            for param_key, param_value in state_dict.items():
                  if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
                        continue
                  __lowerCamelCase						: str		=   False
                  for key, new_key in key_parameters_to_change.items():
                        if not has_changed and param_key.split('.')[0] == key:
                              __lowerCamelCase						: Union[str, Any]		=   param_value
                              __lowerCamelCase						: Tuple		=   True
                  if not has_changed:
                        __lowerCamelCase						: Optional[int]		=   param_value

            model.load_state_dict(new_state_dict)
            model.save_pretrained(os.path.join(args.repo_path, subfolder))













'''simple docstring'''






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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_mbart import MBartTokenizer
else:
      __lowerCamelCase						: List[Any]		=   None


__lowerCamelCase						: Optional[int]		=   logging.get_logger(__name__)


__lowerCamelCase						: Tuple		=   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase						: Union[str, Any]		=   {
    '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'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

__lowerCamelCase						: List[str]		=   {
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
__lowerCamelCase						: Optional[int]		=   ['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 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				VOCAB_FILES_NAMES
  lowerCAmelCase_					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase_					=				PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase_					=				["""input_ids""", """attention_mask"""]
  lowerCAmelCase_					=				MBartTokenizer

  lowerCAmelCase_					=				[]
  lowerCAmelCase_					=				[]


  def __init__(				self    :					Optional[int]							,	UpperCamelCase__    :					List[Any]=None							,	UpperCamelCase__    :					Tuple=None							,	UpperCamelCase__    :					List[str]="<s>"							,	UpperCamelCase__    :					Optional[Any]="</s>"							,	UpperCamelCase__    :					Dict="</s>"							,	UpperCamelCase__    :					str="<s>"							,	UpperCamelCase__    :					Tuple="<unk>"							,	UpperCamelCase__    :					Dict="<pad>"							,	UpperCamelCase__    :					str="<mask>"							,	UpperCamelCase__    :					Any=None							,	UpperCamelCase__    :					List[str]=None							,	UpperCamelCase__    :					Dict=None							,	**UpperCamelCase__    :					Any							,	)     ->						List[str]:
    # Mask token behave like a normal word, i.e. include the space before it
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else mask_token

    super().__init__(
        vocab_file=UpperCamelCase__							,	tokenizer_file=UpperCamelCase__							,	bos_token=UpperCamelCase__							,	eos_token=UpperCamelCase__							,	sep_token=UpperCamelCase__							,	cls_token=UpperCamelCase__							,	unk_token=UpperCamelCase__							,	pad_token=UpperCamelCase__							,	mask_token=UpperCamelCase__							,	src_lang=UpperCamelCase__							,	tgt_lang=UpperCamelCase__							,	additional_special_tokens=UpperCamelCase__							,	**UpperCamelCase__							,	)

    _UpperCamelCase       			=vocab_file
    _UpperCamelCase       			=False if not self.vocab_file else True

    _UpperCamelCase       			=FAIRSEQ_LANGUAGE_CODES.copy()

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

    self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}  )
    _UpperCamelCase       			={
        lang_code: self.convert_tokens_to_ids(UpperCamelCase__  ) for lang_code in FAIRSEQ_LANGUAGE_CODES
    }

    _UpperCamelCase       			=src_lang if src_lang is not None else '''en_XX'''
    _UpperCamelCase       			=self.convert_tokens_to_ids(self._src_lang  )
    _UpperCamelCase       			=tgt_lang
    self.set_src_lang_special_tokens(self._src_lang  )


  @property
  def   UpperCamelCase__      (				self    :					str  )     ->						str:
    return self._src_lang


  @src_lang.setter
  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					str  )     ->						None:
    _UpperCamelCase       			=new_src_lang
    self.set_src_lang_special_tokens(self._src_lang  )


  def   UpperCamelCase__      (				self    :					Dict							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						List[int]:
    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    :					Dict							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						List[int]:
    _UpperCamelCase       			=[self.sep_token_id]
    _UpperCamelCase       			=[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    :					str							,	UpperCamelCase__    :					Optional[int]							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[str]							,	UpperCamelCase__    :					Optional[str]							,	**UpperCamelCase__    :					List[str]  )     ->						str:
    if src_lang is None or tgt_lang is None:
      raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''  )
    _UpperCamelCase       			=src_lang
    _UpperCamelCase       			=self(UpperCamelCase__							,	add_special_tokens=UpperCamelCase__							,	return_tensors=UpperCamelCase__							,	**UpperCamelCase__  )
    _UpperCamelCase       			=self.convert_tokens_to_ids(UpperCamelCase__  )
    _UpperCamelCase       			=tgt_lang_id
    return inputs


  def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					List[str]							,	UpperCamelCase__    :					str = "en_XX"							,	UpperCamelCase__    :					Optional[List[str]] = None							,	UpperCamelCase__    :					str = "ro_RO"							,	**UpperCamelCase__    :					Optional[int]							,	)     ->						BatchEncoding:
    _UpperCamelCase       			=src_lang
    _UpperCamelCase       			=tgt_lang
    return super().prepare_seqaseq_batch(UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Tuple  )     ->						List[str]:
    return self.set_src_lang_special_tokens(self.src_lang  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						List[str]:
    return self.set_tgt_lang_special_tokens(self.tgt_lang  )


  def   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					str  )     ->						None:
    _UpperCamelCase       			=self.convert_tokens_to_ids(UpperCamelCase__  )
    _UpperCamelCase       			=[]
    _UpperCamelCase       			=[self.eos_token_id, self.cur_lang_code]

    _UpperCamelCase       			=self.convert_ids_to_tokens(self.prefix_tokens  )
    _UpperCamelCase       			=self.convert_ids_to_tokens(self.suffix_tokens  )

    _UpperCamelCase       			=processors.TemplateProcessing(
        single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str							,	pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str							,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,	self.prefix_tokens + self.suffix_tokens  )  )							,	)


  def   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					str  )     ->						None:
    _UpperCamelCase       			=self.convert_tokens_to_ids(UpperCamelCase__  )
    _UpperCamelCase       			=[]
    _UpperCamelCase       			=[self.eos_token_id, self.cur_lang_code]

    _UpperCamelCase       			=self.convert_ids_to_tokens(self.prefix_tokens  )
    _UpperCamelCase       			=self.convert_ids_to_tokens(self.suffix_tokens  )

    _UpperCamelCase       			=processors.TemplateProcessing(
        single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str							,	pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str							,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,	self.prefix_tokens + self.suffix_tokens  )  )							,	)




  def   UpperCamelCase__      (				self    :					Optional[Any]							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[str] = None  )     ->						Tuple[str]:
    if not self.can_save_slow_tokenizer:
      raise ValueError(
          '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
          '''tokenizer.'''  )

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

    if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCamelCase__  ):
      copyfile(self.vocab_file							,	UpperCamelCase__  )

    return (out_vocab_file,)






