import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


__lowercase							 =					logging.get_logger(__name__)


__lowercase							 =					{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
__lowercase							 =					{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

__lowercase							 =					{
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}


class 	lowerCamelCase_     (   __SCREAMING_SNAKE_CASE ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      VOCAB_FILES_NAMES
    a__	:						Optional[int]						=      PRETRAINED_VOCAB_FILES_MAP
    a__	:						Dict						=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__	:						Tuple						=      ["""input_ids""", """attention_mask"""]
    a__	:						List[Any]						=      BartTokenizer







    def __init__(	self , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase="replace" , __lowercase="<s>" , __lowercase="</s>" , __lowercase="</s>" , __lowercase="<s>" , __lowercase="<unk>" , __lowercase="<pad>" , __lowercase="<mask>" , __lowercase=False , __lowercase=True , **__lowercase , )       ->	Dict:
        super().__init__(
            __lowercase , __lowercase , tokenizer_file=__lowercase , errors=__lowercase , bos_token=__lowercase , eos_token=__lowercase , sep_token=__lowercase , cls_token=__lowercase , unk_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , add_prefix_space=__lowercase , trim_offsets=__lowercase , **__lowercase , )

        __UpperCamelCase     :List[Any]			  =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
        if pre_tok_state.get('''add_prefix_space''' , __lowercase) != add_prefix_space:
            __UpperCamelCase     :Any			  =		getattr(__lowercase , pre_tok_state.pop('''type'''))
            __UpperCamelCase     :int			  =		add_prefix_space
            __UpperCamelCase     :Optional[Any]			  =		pre_tok_class(**__lowercase)

        __UpperCamelCase     :Tuple			  =		add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        __UpperCamelCase     :Dict			  =		'''post_processor'''
        __UpperCamelCase     :List[Any]			  =		getattr(self.backend_tokenizer , __lowercase , __lowercase)
        if tokenizer_component_instance:
            __UpperCamelCase     :Optional[Any]			  =		json.loads(tokenizer_component_instance.__getstate__())

            # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
            if "sep" in state:
                __UpperCamelCase     :Any			  =		tuple(state['''sep'''])
            if "cls" in state:
                __UpperCamelCase     :List[Any]			  =		tuple(state['''cls'''])

            __UpperCamelCase     :Optional[Any]			  =		False

            if state.get('''add_prefix_space''' , __lowercase) != add_prefix_space:
                __UpperCamelCase     :Any			  =		add_prefix_space
                __UpperCamelCase     :Optional[int]			  =		True

            if state.get('''trim_offsets''' , __lowercase) != trim_offsets:
                __UpperCamelCase     :Tuple			  =		trim_offsets
                __UpperCamelCase     :Optional[int]			  =		True

            if changes_to_apply:
                __UpperCamelCase     :Any			  =		getattr(__lowercase , state.pop('''type'''))
                __UpperCamelCase     :Tuple			  =		component_class(**__lowercase)
                setattr(self.backend_tokenizer , __lowercase , __lowercase)







    @property
    def 			UpperCamelCase__   (	self)       ->	Dict:
        if self._mask_token is None:
            if self.verbose:
                logger.error('''Using mask_token, but it is not set yet.''')
            return None
        return str(self._mask_token)







    @mask_token.setter
    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :Any			  =		AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase) if isinstance(__lowercase , __lowercase) else value
        __UpperCamelCase     :int			  =		value







    def 			UpperCamelCase__   (	self , *__lowercase , **__lowercase)       ->	str:
        __UpperCamelCase     :Dict			  =		kwargs.get('''is_split_into_words''' , __lowercase)

        if is_split_into_words and not self.add_prefix_space:
            raise ValueError(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                '''to use it with pretokenized inputs.''')

        return super()._batch_encode_plus(*__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , *__lowercase , **__lowercase)       ->	Dict:
        __UpperCamelCase     :Optional[int]			  =		kwargs.get('''is_split_into_words''' , __lowercase)

        if is_split_into_words and not self.add_prefix_space:
            raise ValueError(
                f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                '''to use it with pretokenized inputs.''')

        return super()._encode_plus(*__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	str:
        __UpperCamelCase     :int			  =		self._tokenizer.model.save(__lowercase , name=__lowercase)
        return tuple(__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=None)       ->	List[str]:
        __UpperCamelCase     :Dict			  =		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
            return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	List[Any]:
        __UpperCamelCase     :Dict			  =		[self.sep_token_id]
        __UpperCamelCase     :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]







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				False
SCREAMING_SNAKE_CASE							:Any							 =				True
SCREAMING_SNAKE_CASE							:Tuple							 =				False


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple							 =				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.')

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				parser.parse_args()

					SCREAMING_SNAKE_CASE							:Dict							 =				{
					    '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',
					}

					SCREAMING_SNAKE_CASE							:Optional[int]							 =				{
					    'time_steps': 'time_proj',
					    'mid': 'mid_block',
					    'downsample_blocks': 'down_blocks',
					    'upsample_blocks': 'up_blocks',
					}

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

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

					SCREAMING_SNAKE_CASE							:List[str]							 =				dict(model.config)

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

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

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

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

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