
"""simple docstring"""



import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCAmelCase__          = False
UpperCAmelCase__          = True
UpperCAmelCase__          = False


if __name__ == "__main__":
 UpperCAmelCase__          = 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__          = parser.parse_args()

 UpperCAmelCase__          = {
     '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__          = {
     'time_steps': 'time_proj',
     'mid': 'mid_block',
     'downsample_blocks': 'down_blocks',
     'upsample_blocks': 'up_blocks',
 }

 UpperCAmelCase__          = '' 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__          = reader.read()
  UpperCAmelCase__          = 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__          = UNetaDModel(**config)
 else:
  UpperCAmelCase__          = UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
  UpperCAmelCase__          = class_name(**config)

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

 UpperCAmelCase__          = dict(model.config)

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

  UpperCAmelCase__          = [k.replace('UNetRes', '') for k in config['down_block_types']]
  UpperCAmelCase__          = [k.replace('UNetRes', '') for k in config['up_block_types']]

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

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

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










"""simple docstring"""
import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_rjieba
@require_tokenizers
class 					_UpperCAmelCase   (	lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							RoFormerTokenizer
					UpperCamelCase      =							RoFormerTokenizerFast
					UpperCamelCase      =							True
					UpperCamelCase      =							True


					def 	lowerCamelCase       (					self			:List[str]		):
												super().setUp()


					def 	lowerCamelCase       (					self			:int     ,       **__UpperCamelCase			:List[Any]		):
												return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Tuple     ,       **__UpperCamelCase			:Optional[int]		):
												return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												A		=  "永和服装饰品有限公司,今天天气非常好"
												A		=  "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
												return input_text, output_text


					def 	lowerCamelCase       (					self			:int		):
												A		=  self.get_tokenizer()
												A,	A		=  self.get_chinese_input_output_texts()
												A		=  tokenizer.tokenize(__UpperCamelCase		)

												self.assertListEqual(__UpperCamelCase     ,       output_text.split()		)

												A		=  tokens + [tokenizer.unk_token]
												A		=  [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:str		):
												A		=  self.get_rust_tokenizer()
												A,	A		=  self.get_chinese_input_output_texts()
												A		=  tokenizer.tokenize(__UpperCamelCase		)
												self.assertListEqual(__UpperCamelCase     ,       output_text.split()		)
												A		=  tokens + [tokenizer.unk_token]
												A		=  [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												pass


					def 	lowerCamelCase       (					self			:Tuple		):
												pass


					def 	lowerCamelCase       (					self			:List[str]		):
												pass


