
'''simple docstring'''
import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCAmelCase					:    Union[str, Any]           = False
lowerCAmelCase					:    Optional[int]           = True
lowerCAmelCase					:    Union[str, Any]           = False


if __name__ == "__main__":
						lowerCAmelCase					:    Union[str, Any]           = 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					:    Tuple           = parser.parse_args()

						lowerCAmelCase					:    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',
						}

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

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

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

						lowerCAmelCase					:    List[Any]           = dict(model.config)

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

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

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

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

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






"""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 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     BioGptTokenizer
						_lowerCamelCase:     Tuple   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[int]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							A     = [
							    '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>',
							]
							A     = dict(zip(A_	,range(len(A_   )   )   )   )
							A     = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   Tuple   )  -> int:
							A     = 'lower newer'
							A     = 'lower newer'
							return input_text, output_text


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							A     = BioGptTokenizer(self.vocab_file	,self.merges_file   )

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

							A     = tokens + ['<unk>']
							A     = [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_   )	,A_   )




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Optional[Any]:
							A     = BioGptTokenizer.from_pretrained('microsoft/biogpt'   )

							A     = tokenizer.encode('sequence builders'	,add_special_tokens=A_   )
							A     = tokenizer.encode('multi-sequence build'	,add_special_tokens=A_   )

							A     = tokenizer.build_inputs_with_special_tokens(A_   )
							A     = tokenizer.build_inputs_with_special_tokens(A_	,A_   )

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