


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

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


if is_sentencepiece_available():
							from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
							snake_case_  :	str							  =							None


snake_case_  :	Optional[Any]							  =							logging.get_logger(__name__)

snake_case_  :	str							  =							{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_  :	Union[str, Any]							  =							{
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case_  :	List[Any]							  =							{
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}

class   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  VOCAB_FILES_NAMES
						UpperCAmelCase									=  PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCAmelCase									=  ["input_ids", "attention_mask"]
						UpperCAmelCase									=  XLMRobertaTokenizer




						def __init__(				self    :					Optional[int] ,			_a    :					Dict=None ,			_a    :					Tuple=None ,			_a    :					str="<s>" ,			_a    :					List[str]="</s>" ,			_a    :					Union[str, Any]="</s>" ,			_a    :					Any="<s>" ,			_a    :					Dict="<unk>" ,			_a    :					List[Any]="<pad>" ,			_a    :					List[str]="<mask>" ,			**_a    :					Any ,			)							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =AddedToken(_a ,			lstrip=_a ,			rstrip=_a     ) if isinstance(_a ,			_a     ) else mask_token

										super().__init__(
										    _a ,			tokenizer_file=_a ,			bos_token=_a ,			eos_token=_a ,			sep_token=_a ,			cls_token=_a ,			unk_token=_a ,			pad_token=_a ,			mask_token=_a ,			**_a ,			)

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




						def 							__UpperCamelCase (				self    :					int ,			_a    :					List[int] ,			_a    :					Optional[List[int]] = None     )							->   List[int]:


										"""simple docstring"""





										if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										_SCREAMING_SNAKE_CASE	     =[self.cls_token_id]
										_SCREAMING_SNAKE_CASE	     =[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep




						def 							__UpperCamelCase (				self    :					str ,			_a    :					List[int] ,			_a    :					Optional[List[int]] = None     )							->   List[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[self.sep_token_id]
										_SCREAMING_SNAKE_CASE	     =[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    :					Any ,			_a    :					str ,			_a    :					Optional[str] = None     )							->   Tuple[str]:


										"""simple docstring"""





										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(_a     ):
														logger.error(f"Vocabulary path ({save_directory}) should be a directory."     )
														return
										_SCREAMING_SNAKE_CASE	     =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     ):
														copyfile(self.vocab_file ,			_a     )

										return (out_vocab_file,)



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =UNetaDModel(**a__)
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.2.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.2.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.3.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.3.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-4]].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-3]].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-2]].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[list(checkpoint.keys())[-1]].data

				_SCREAMING_SNAKE_CASE	     =4

				def set_attention_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

				def set_resnet_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data

								if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

				for i, block in enumerate(new_model_architecture.downsample_blocks):
								_SCREAMING_SNAKE_CASE	     =hasattr(a__		,'''attentions''')
								for j in range(len(block.resnets)):
												set_resnet_weights(block.resnets[j]		,a__		,a__)
												module_index += 1
												if has_attentions:
																set_attention_weights(block.attentions[j]		,a__		,a__)
																module_index += 1

								if hasattr(a__		,'''downsamplers''') and block.downsamplers is not None:
												set_resnet_weights(block.resnet_down		,a__		,a__)
												module_index += 1
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
												module_index += 1

				set_resnet_weights(new_model_architecture.mid_block.resnets[0]		,a__		,a__)
				module_index += 1
				set_attention_weights(new_model_architecture.mid_block.attentions[0]		,a__		,a__)
				module_index += 1
				set_resnet_weights(new_model_architecture.mid_block.resnets[1]		,a__		,a__)
				module_index += 1

				for i, block in enumerate(new_model_architecture.up_blocks):
								_SCREAMING_SNAKE_CASE	     =hasattr(a__		,'''attentions''')
								for j in range(len(block.resnets)):
												set_resnet_weights(block.resnets[j]		,a__		,a__)
												module_index += 1
								if has_attentions:
												set_attention_weights(
												    block.attentions[0]		,a__		,a__)  # why can there only be a single attention layer for up?
												module_index += 1

								if hasattr(a__		,'''resnet_up''') and block.resnet_up is not None:
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
												module_index += 1
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
												module_index += 1
												set_resnet_weights(block.resnet_up		,a__		,a__)
												module_index += 1

				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
				module_index += 1
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data

				return new_model_architecture.state_dict()


if __name__ == "__main__":
							snake_case_  :	Tuple							  =							argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_path''',
							    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin''',
							    type=str,
							    required=False,
							    help='''Path to the checkpoint to convert.''',
							)

							parser.add_argument(
							    '''--config_file''',
							    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/config.json''',
							    type=str,
							    required=False,
							    help='''The config json file corresponding to the architecture.''',
							)

							parser.add_argument(
							    '''--dump_path''',
							    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt''',
							    type=str,
							    required=False,
							    help='''Path to the output model.''',
							)

							snake_case_  :	List[str]							  =							parser.parse_args()

							snake_case_  :	List[Any]							  =							torch.load(args.checkpoint_path, map_location='''cpu''')

							with open(args.config_file) as f:
														snake_case_  :	List[str]							  =							json.loads(f.read())

							snake_case_  :	Optional[int]							  =							convert_ncsnpp_checkpoint(
							    checkpoint,
							    config,
							)

							if "sde" in config:
														del config["sde"]

							snake_case_  :	Union[str, Any]							  =							UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														snake_case_  :	List[Any]							  =							ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

														snake_case_  :	int							  =							ScoreSdeVePipeline(unet=model, scheduler=scheduler)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)