

'''simple docstring'''






import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class     lowercase_			(lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Tuple           =    BioGptTokenizer
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   __lowercase													=				[
       '''l''',
       '''o''',
       '''w''',
       '''e''',
       '''r''',
       '''s''',
       '''t''',
       '''i''',
       '''d''',
       '''n''',
       '''w</w>''',
       '''r</w>''',
       '''t</w>''',
       '''lo''',
       '''low''',
       '''er</w>''',
       '''low</w>''',
       '''lowest</w>''',
       '''newer</w>''',
       '''wider</w>''',
       '''<unk>''',
   ]
   __lowercase													=				dict(zip(lowercase__      ,range(len(lowercase__    )    )    )    )
   __lowercase													=				['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

   __lowercase													=				os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['''vocab_file''']    )
   __lowercase													=				os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['''merges_file''']    )
   with open(self.vocab_file      ,'''w'''    ) as fp:
    fp.write(json.dumps(lowercase__    )    )
   with open(self.merges_file      ,'''w'''    ) as fp:
    fp.write('''\n'''.join(lowercase__    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  Optional[Any]    ):
   __lowercase													=				'''lower newer'''
   __lowercase													=				'''lower newer'''
   return input_text, output_text






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				BioGptTokenizer(self.vocab_file      ,self.merges_file    )

   __lowercase													=				'''lower'''
   __lowercase													=				['''low''', '''er</w>''']
   __lowercase													=				tokenizer.tokenize(lowercase__    )
   self.assertListEqual(lowercase__      ,lowercase__    )

   __lowercase													=				tokens + ['''<unk>''']
   __lowercase													=				[1_4, 1_5, 2_0]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__    )      ,lowercase__    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __lowercase													=				BioGptTokenizer.from_pretrained('''microsoft/biogpt'''    )

   __lowercase													=				tokenizer.encode('''sequence builders'''      ,add_special_tokens=lowercase__    )
   __lowercase													=				tokenizer.encode('''multi-sequence build'''      ,add_special_tokens=lowercase__    )

   __lowercase													=				tokenizer.build_inputs_with_special_tokens(lowercase__    )
   __lowercase													=				tokenizer.build_inputs_with_special_tokens(lowercase__      ,lowercase__    )

   self.assertTrue(encoded_sentence == [2] + text    )
   self.assertTrue(encoded_pair == [2] + text + [2] + text_a    )




'''simple docstring'''






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCAmelCase__      		=			False
lowerCAmelCase__      		=			True
lowerCAmelCase__      		=			False


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

 lowerCAmelCase__      		=			parser.parse_args()

 lowerCAmelCase__      		=			{
     '''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''',
 }

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

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

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

 lowerCAmelCase__      		=			dict(model.config)

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

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

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

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

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

