



"""simple docstring"""





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel




def 	UpperCAmelCase     (		A	:						Dict     ,				A	:						str     ,				A	:						Union[str, Any]       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		OmegaConf.load(A       )
		_UpperCAmelCase							 =		torch.load(A     ,				map_location='cpu'       )['model']
		_UpperCAmelCase							 =		list(state_dict.keys()       )

		# extract state_dict for VQVAE
		_UpperCAmelCase							 =		{}
		_UpperCAmelCase							 =		'first_stage_model.'
		for key in keys:
				if key.startswith(A       ):
						_UpperCAmelCase							 =		state_dict[key]

    # extract state_dict for UNetLDM
		_UpperCAmelCase							 =		{}
		_UpperCAmelCase							 =		'model.diffusion_model.'
		for key in keys:
				if key.startswith(A       ):
						_UpperCAmelCase							 =		state_dict[key]

		_UpperCAmelCase							 =		config.model.params.first_stage_config.params
		_UpperCAmelCase							 =		config.model.params.unet_config.params

		_UpperCAmelCase							 =		VQModel(**A       ).eval()
		vqvae.load_state_dict(A       )

		_UpperCAmelCase							 =		UNetLDMModel(**A       ).eval()
		unet.load_state_dict(A       )

		_UpperCAmelCase							 =		DDIMScheduler(
		    timesteps=config.model.params.timesteps     ,				beta_schedule='scaled_linear'     ,				beta_start=config.model.params.linear_start     ,				beta_end=config.model.params.linear_end     ,				clip_sample=A     ,				)

		_UpperCAmelCase							 =		LDMPipeline(A     ,				A     ,				A       )
		pipeline.save_pretrained(A       )


if __name__ == "__main__":
					lowercase  							=					argparse.ArgumentParser()
					parser.add_argument('''--checkpoint_path''', type=str, required=True)
					parser.add_argument('''--config_path''', type=str, required=True)
					parser.add_argument('''--output_path''', type=str, required=True)
					lowercase  							=					parser.parse_args()

					convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)







"""simple docstring"""





import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
lowercase  							=					logging.getLogger()




def 	UpperCAmelCase     (		A	:						Path     ,				A	:						list       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		'\n'.join(A       )
		Path(A       ).open('w'       ).writelines(A       )


lowercase  							=					'''patrickvonplaten/t5-tiny-random'''
lowercase  							=					'''sshleifer/bart-tiny-random'''
lowercase  							=					'''sshleifer/tiny-mbart'''

lowercase  							=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class 			lowercase__			(      A    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self	,       snake_case						)			->							str:
									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						) / 'utest_input.source'
									_UpperCAmelCase							 =		input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()
									_UpperCAmelCase							 =		[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
									_dump_articles(snake_case	,       snake_case						)

									_UpperCAmelCase							 =		str(Path(self.get_auto_remove_tmp_dir()						) / 'scores.json'						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		f'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

									with patch.object(snake_case	,       'argv'	,       snake_case						):
											run_generate()
											assert Path(snake_case						).exists()
											# os.remove(Path(output_file_name))






							def        lowerCamelCase_   (  self						)			->							str:
									self.run_eval_tester(snake_case						)






							@parameterized.expand([BART_TINY, MBART_TINY]						)
							@slow
							def        lowerCamelCase_   (  self	,       snake_case						)			->							List[Any]:
									self.run_eval_tester(snake_case						)






							@parameterized.expand([T5_TINY, MBART_TINY]						)
							@slow
							def        lowerCamelCase_   (  self	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						) / 'utest_input.source'
									_UpperCAmelCase							 =		input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()

									_UpperCAmelCase							 =		{
									    'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
									    'de': [
									        'Maschinelles Lernen ist großartig, oder?',
									        'Ich esse gerne Bananen',
									        'Morgen ist wieder ein toller Tag!',
									    ],
									}

									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						)
									_UpperCAmelCase							 =		str(tmp_dir / 'scores.json'						)
									_UpperCAmelCase							 =		str(tmp_dir / 'val.target'						)
									_dump_articles(snake_case	,       text['en']						)
									_dump_articles(snake_case	,       text['de']						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		f'\n            run_eval_search.py\n            {model}\n            {str(snake_case						)}\n            {str(snake_case						)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
									testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']						)

									with patch.object(snake_case	,       'argv'	,       snake_case						):
											with CaptureStdout() as cs:
													run_search()
											_UpperCAmelCase							 =		[' num_beams | length_penalty', model, 'Best score args']
											_UpperCAmelCase							 =		['Info']
											if "translation" in task:
													expected_strings.append('bleu'						)
											else:
													expected_strings.extend(snake_case						)
											for w in expected_strings:
													assert w in cs.out
											for w in un_expected_strings:
													assert w not in cs.out
											assert Path(snake_case						).exists()
											os.remove(Path(snake_case						)						)


