


"""simple docstring"""






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCAmelCase    :     int   						=		False
UpperCAmelCase    :     int   						=		True
UpperCAmelCase    :     List[Any]   						=		False


if __name__ == "__main__":
							UpperCAmelCase    :     List[str]   						=		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.')

							UpperCAmelCase    :     Any   						=		parser.parse_args()

							UpperCAmelCase    :     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',
							}

							UpperCAmelCase    :     int   						=		{
							    'time_steps': 'time_proj',
							    'mid': 'mid_block',
							    'downsample_blocks': 'down_blocks',
							    'upsample_blocks': 'up_blocks',
							}

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

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

							UpperCAmelCase    :     Dict   						=		dict(model.config)

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

														UpperCAmelCase    :     List[Any]   						=		[k.replace('UNetRes', '') for k in config['down_block_types']]
														UpperCAmelCase    :     List[Any]   						=		[k.replace('UNetRes', '') for k in config['up_block_types']]

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

														UpperCAmelCase    :     Tuple   						=		{}
														for param_key, param_value in state_dict.items():
																					if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
																												continue
																					UpperCAmelCase    :     Any   						=		False
																					for key, new_key in key_parameters_to_change.items():
																												if not has_changed and param_key.split('.')[0] == key:
																																			UpperCAmelCase    :     Optional[Any]   						=		param_value
																																			UpperCAmelCase    :     Dict   						=		True
																					if not has_changed:
																												UpperCAmelCase    :     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 ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_barthez import BarthezTokenizer
else:
							UpperCAmelCase    :     Union[str, Any]   						=		None

UpperCAmelCase    :     Union[str, Any]   						=		logging.get_logger(__name__)

UpperCAmelCase    :     Union[str, Any]   						=		{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase    :     List[str]   						=		{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

UpperCAmelCase    :     Tuple   						=		{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

UpperCAmelCase    :     str   						=		'▁'





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							VOCAB_FILES_NAMES
					__a	       =							PRETRAINED_VOCAB_FILES_MAP
					__a	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a	       =							["""input_ids""", """attention_mask"""]
					__a	       =							BarthezTokenizer






					def __init__(       self    : Optional[int] ,       UpperCamelCase    : List[str]=None ,       UpperCamelCase    : List[str]=None ,       UpperCamelCase    : Union[str, Any]="<s>" ,       UpperCamelCase    : Any="</s>" ,       UpperCamelCase    : Tuple="</s>" ,       UpperCamelCase    : Tuple="<s>" ,       UpperCamelCase    : int="<unk>" ,       UpperCamelCase    : List[str]="<pad>" ,       UpperCamelCase    : int="<mask>" ,       **UpperCamelCase    : Any ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					AddedToken(UpperCamelCase ,       lstrip=UpperCamelCase ,       rstrip=UpperCamelCase  ) if isinstance(UpperCamelCase ,       UpperCamelCase  ) else mask_token

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

												__UpperCAmelCase   :						List[Any]      							=					vocab_file
												__UpperCAmelCase   :						Tuple      							=					False if not self.vocab_file else True






					def lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : List[int] ,       UpperCamelCase    : Optional[List[int]] = None  ):
												'''simple docstring'''






												if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												__UpperCAmelCase   :						List[Any]      							=					[self.cls_token_id]
												__UpperCAmelCase   :						Tuple      							=					[self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep






					def lowerCamelCase__							(       self    : Union[str, Any] ,       UpperCamelCase    : List[int] ,       UpperCamelCase    : Optional[List[int]] = None  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					[self.sep_token_id]
												__UpperCAmelCase   :						Union[str, Any]      							=					[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 lowerCamelCase__							(       self    : Tuple ,       UpperCamelCase    : str ,       UpperCamelCase    : Optional[str] = None  ):
												'''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(UpperCamelCase  ):
																			logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
																			return
												__UpperCAmelCase   :						List[Any]      							=					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,)
