




"""simple docstring"""







import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 				snake_case_			(					lowerCamelCase_	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       PhobertTokenizer
   A_				      =       False







   def   UpperCAmelCase__			(		self)				->   Dict:
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       UpperCamelCase              =	['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
       UpperCamelCase              =	dict(zip(lowerCamelCase_       ,					range(len(lowerCamelCase_))))
       UpperCamelCase              =	['''#version: 0.2''', '''l à</w>''']
       UpperCamelCase              =	{'''unk_token''': '''<unk>'''}

       UpperCamelCase              =	os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES['''vocab_file'''])
       UpperCamelCase              =	os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES['''merges_file'''])

       with open(self.vocab_file       ,					'''w'''       ,					encoding='''utf-8''') as fp:
           for token in vocab_tokens:
               fp.write(F'{token} {vocab_tokens[token]}\n')
       with open(self.merges_file       ,					'''w'''       ,					encoding='''utf-8''') as fp:
           fp.write('''\n'''.join(lowerCamelCase_))







   def   UpperCAmelCase__			(		self       ,					**lowerCamelCase_)				->   Any:
       kwargs.update(self.special_tokens_map)
       return PhobertTokenizer.from_pretrained(self.tmpdirname       ,					**lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   Union[str, Any]:
       UpperCamelCase              =	'''Tôi là VinAI Research'''
       UpperCamelCase              =	'''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
       return input_text, output_text







   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	PhobertTokenizer(self.vocab_file       ,					self.merges_file       ,					**self.special_tokens_map)
       UpperCamelCase              =	'''Tôi là VinAI Research'''
       UpperCamelCase              =	'''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
       UpperCamelCase              =	tokenizer.tokenize(lowerCamelCase_)
       print(lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_       ,					lowerCamelCase_)

       UpperCamelCase              =	tokens + [tokenizer.unk_token]

       UpperCamelCase              =	[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_)       ,					lowerCamelCase_)


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase_   							=  False
lowercase_   							=  True
lowercase_   							=  False


if __name__ == "__main__":
  lowercase_   							=  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.')

  lowercase_   							=  parser.parse_args()

  lowercase_   							=  {
      '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',
  }

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

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

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

  lowercase_   							=  dict(model.config)

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

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

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

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

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


