

import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

a__												=						[
    '''cross_validation.py''',
    '''gradient_accumulation.py''',
    '''local_sgd.py''',
    '''multi_process_metrics.py''',
    '''memory.py''',
    '''automatic_gradient_accumulation.py''',
    '''fsdp_with_peak_mem_tracking.py''',
    '''deepspeed_with_config_support.py''',
    '''megatron_lm_gpt_pretraining.py''',
]

class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			,				_a			,				_a			,				_a = None			,				_a = None			)       ->       List[Any]:
												_a      :       Union[str, Any]   =						None
												_a      :       Optional[Any]   =						os.path.abspath(os.path.join('''examples'''			,				'''by_feature'''			)			)
												_a      :       Optional[int]   =						os.path.abspath('''examples'''			)
												for item in os.listdir(_a			):
																	if item not in EXCLUDE_EXAMPLES:
																						_a      :       Any   =						os.path.join(_a			,				_a			)
																						if os.path.isfile(_a			) and ".py" in item_path:
																											with self.subTest(
																											    tested_script=_a			,				feature_script=_a			,				tested_section='''main()''' if parser_only else '''training_function()'''			,				):
																																_a      :       Optional[int]   =						compare_against_test(
																																    os.path.join(_a			,				_a			)			,				_a			,				_a			,				_a			)
																																_a      :       Union[str, Any]   =						'''\n'''.join(_a			)
																																if special_strings is not None:
																																					for string in special_strings:
																																										_a      :       Union[str, Any]   =						diff.replace(_a			,				''''''			)
																																self.assertEqual(_a			,				''''''			)


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												self.one_complete_example('''complete_nlp_example.py'''			,				_a			)
												self.one_complete_example('''complete_nlp_example.py'''			,				_a			)




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       Optional[int]   =						os.path.abspath(os.path.join('''examples'''			,				'''cv_example.py'''			)			)
												_a      :       int   =						[
												    ''' ''' * 1_6 + '''{\n\n''',
												    ''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
												    ''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
												    ''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
												    ''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
												    ''' ''' * 1_6 + '''},\n\n''',
												    ''' ''' * 1_6 + '''step=epoch,\n''',
												    ''' ''' * 1_2,
												    ''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
												]
												self.one_complete_example('''complete_cv_example.py'''			,				_a			,				_a			,				_a			)
												self.one_complete_example('''complete_cv_example.py'''			,				_a			,				_a			,				_a			)



@mock.patch.dict(os.environ							,			{"TESTING_MOCKED_DATALOADERS": "1"}  )
class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Any             =       False


							@classmethod
							def 							__lowercase							(     cls			)       ->       List[Any]:
												super().setUpClass()
												_a      :       str   =						tempfile.mkdtemp()
												_a      :       str   =						os.path.join(cls._tmpdir			,				'''default_config.yml'''			)

												write_basic_config(save_location=cls.configPath			)
												_a      :       int   =						['''accelerate''', '''launch''', '''--config_file''', cls.configPath]


							@classmethod
							def 							__lowercase							(     cls			)       ->       Optional[int]:
												super().tearDownClass()
												shutil.rmtree(cls._tmpdir			)


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Union[str, Any]   =						F"""
        examples/by_feature/checkpointing.py
        --checkpointing_steps epoch
        --output_dir {self.tmpdir}
        """.split()
												run_command(self._launch_args + testargs			)
												self.assertTrue(os.path.exists(os.path.join(self.tmpdir			,				'''epoch_0'''			)			)			)


							def 							__lowercase							(     self			)       ->       List[str]:
												_a      :       Union[str, Any]   =						F"""
        examples/by_feature/checkpointing.py
        --checkpointing_steps 1
        --output_dir {self.tmpdir}
        """.split()
												_a      :       List[str]   =						run_command(self._launch_args + testargs			)
												self.assertTrue(os.path.exists(os.path.join(self.tmpdir			,				'''step_2'''			)			)			)


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Dict   =						F"""
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir			,				'epoch_0'			)}
        """.split()
												_a      :       str   =						run_command(self._launch_args + testargs			,				return_stdout=_a			)
												self.assertNotIn('''epoch 0:'''			,				_a			)
												self.assertIn('''epoch 1:'''			,				_a			)


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Optional[Any]   =						F"""
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir			,				'step_2'			)}
        """.split()
												_a      :       Optional[int]   =						run_command(self._launch_args + testargs			,				return_stdout=_a			)
												if torch.cuda.is_available():
																	_a      :       List[Any]   =						torch.cuda.device_count()
												else:
																	_a      :       Tuple   =						1
												if num_processes > 1:
																	self.assertNotIn('''epoch 0:'''			,				_a			)
																	self.assertIn('''epoch 1:'''			,				_a			)
												else:
																	self.assertIn('''epoch 0:'''			,				_a			)
																	self.assertIn('''epoch 1:'''			,				_a			)


							@slow
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       List[str]   =						'''
        examples/by_feature/cross_validation.py
        --num_folds 2
        '''.split()
												with mock.patch.dict(os.environ			,				{'''TESTING_MOCKED_DATALOADERS''': '''0'''}			):
																	_a      :       Tuple   =						run_command(self._launch_args + testargs			,				return_stdout=_a			)
																	_a      :       int   =						re.findall('''({.+})'''			,				_a			)
																	_a      :       int   =						[r for r in results if '''accuracy''' in r][-1]
																	_a      :       Optional[Any]   =						ast.literal_eval(_a			)
																	self.assertGreaterEqual(results['''accuracy''']			,				0.75			)


							def 							__lowercase							(     self			)       ->       str:
												_a      :       Optional[int]   =						['''examples/by_feature/multi_process_metrics.py''']
												run_command(self._launch_args + testargs			)


							@require_trackers
							@mock.patch.dict(os.environ			,				{'''WANDB_MODE''': '''offline'''}			)
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												with tempfile.TemporaryDirectory() as tmpdir:
																	_a      :       str   =						F"""
            examples/by_feature/tracking.py
            --with_tracking
            --project_dir {tmpdir}
            """.split()
																	run_command(self._launch_args + testargs			)
																	self.assertTrue(os.path.exists(os.path.join(_a			,				'''tracking'''			)			)			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       List[str]   =						['''examples/by_feature/gradient_accumulation.py''']
												run_command(self._launch_args + testargs			)




							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       Union[str, Any]   =						['''examples/by_feature/local_sgd.py''']
												run_command(self._launch_args + testargs			)






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser()

			parser.add_argument(
			    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
			)
			parser.add_argument(
			    '''--original_config_file''',
			    type=str,
			    required=True,
			    help='''The YAML config file corresponding to the original architecture.''',
			)
			parser.add_argument(
			    '''--num_in_channels''',
			    default=None,
			    type=int,
			    help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
			)
			parser.add_argument(
			    '''--image_size''',
			    default=512,
			    type=int,
			    help=(
			        '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
			        ''' Base. Use 768 for Stable Diffusion v2.'''
			    ),
			)
			parser.add_argument(
			    '''--extract_ema''',
			    action='''store_true''',
			    help=(
			        '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
			        ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
			        ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
			    ),
			)
			parser.add_argument(
			    '''--upcast_attention''',
			    action='''store_true''',
			    help=(
			        '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
			        ''' diffusion 2.1.'''
			    ),
			)
			parser.add_argument(
			    '''--from_safetensors''',
			    action='''store_true''',
			    help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
			)
			parser.add_argument(
			    '''--to_safetensors''',
			    action='''store_true''',
			    help='''Whether to store pipeline in safetensors format or not.''',
			)
			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
			parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')



			def   __UpperCAmelCase					(      __a :     Any      )     ->	List[Any]:

								"""simple docstring"""

								if string == "True":
													return True
								elif string == "False":
													return False
								else:
													raise ValueError(F"""could not parse string as bool {string}"""      )

			parser.add_argument(
			    '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
			)

			parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

			a__												=						parser.parse_args()

			a__												=						download_controlnet_from_original_ckpt(
			    checkpoint_path=args.checkpoint_path,
			    original_config_file=args.original_config_file,
			    image_size=args.image_size,
			    extract_ema=args.extract_ema,
			    num_in_channels=args.num_in_channels,
			    upcast_attention=args.upcast_attention,
			    from_safetensors=args.from_safetensors,
			    device=args.device,
			    use_linear_projection=args.use_linear_projection,
			    cross_attention_dim=args.cross_attention_dim,
			)







			controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



