






"""simple docstring"""



import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
						from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments





@require_torch
class 					_UpperCAmelCase       ( unittest.TestCase							):




					def   A  (				self     :						List[str]    ,						A     :						Union[str, Any]   )  ->  int:
							for model_result in results.values():
									for batch_size, sequence_length in zip(model_result['''bs''']    ,						model_result['''ss''']   ):
											lowercase_ : Tuple							       =   model_result['''result'''][batch_size][sequence_length]
											self.assertIsNotNone(A   )




					def   A  (				self     :						int   )  ->  Optional[Any]:
							lowercase_ : Optional[Any]							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : int							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : List[str]							       =   PyTorchBenchmark(A   )
							lowercase_ : Optional[Any]							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )




					def   A  (				self     :						int   )  ->  Optional[Any]:
							lowercase_ : List[Any]							       =   '''sgugger/tiny-distilbert-classification'''
							lowercase_ : Optional[Any]							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						only_pretrain_model=A    ,						)
							lowercase_ : Tuple							       =   PyTorchBenchmark(A   )
							lowercase_ : Optional[int]							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )




					def   A  (				self     :						List[Any]   )  ->  str:
							lowercase_ : str							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : Any							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						torchscript=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : List[Any]							       =   PyTorchBenchmark(A   )
							lowercase_ : Dict							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )




					@unittest.skipIf(torch_device == '''cpu'''    ,						'''Cant do half precision'''   )
					def   A  (				self     :						List[Any]   )  ->  str:
							lowercase_ : int							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : Tuple							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						fpaa=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : Any							       =   PyTorchBenchmark(A   )
							lowercase_ : Dict							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )




					def   A  (				self     :						Union[str, Any]   )  ->  Optional[int]:
							lowercase_ : Union[str, Any]							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : Optional[int]							       =   AutoConfig.from_pretrained(A   )
							# set architectures equal to `None`
							lowercase_ : str							       =   None
							lowercase_ : Tuple							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : Any							       =   PyTorchBenchmark(A    ,						configs=[config]   )
							lowercase_ : List[str]							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )




					def   A  (				self     :						Optional[Any]   )  ->  List[Any]:
							lowercase_ : Optional[Any]							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : str							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : List[str]							       =   PyTorchBenchmark(A   )
							lowercase_ : Dict							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )




					@unittest.skipIf(torch_device == '''cpu'''    ,						'''Can\'t do half precision'''   )
					def   A  (				self     :						Optional[Any]   )  ->  Dict:
							lowercase_ : Optional[Any]							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : str							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						fpaa=A    ,						multi_process=A    ,						)
							lowercase_ : int							       =   PyTorchBenchmark(A   )
							lowercase_ : Optional[int]							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )




					def   A  (				self     :						int   )  ->  Optional[Any]:
							lowercase_ : List[Any]							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : Any							       =   AutoConfig.from_pretrained(A   )
							lowercase_ : Any							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : Optional[Any]							       =   PyTorchBenchmark(A    ,						configs=[config]   )
							lowercase_ : List[Any]							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )




					def   A  (				self     :						Any   )  ->  List[Any]:
							lowercase_ : Union[str, Any]							       =   '''sshleifer/tinier_bart'''
							lowercase_ : Optional[Any]							       =   AutoConfig.from_pretrained(A   )
							lowercase_ : int							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : Any							       =   PyTorchBenchmark(A    ,						configs=[config]   )
							lowercase_ : int							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )




					def   A  (				self     :						List[str]   )  ->  Optional[int]:
							lowercase_ : str							       =   '''sshleifer/tiny-gpt2'''
							lowercase_ : Union[str, Any]							       =   AutoConfig.from_pretrained(A   )
							lowercase_ : List[Any]							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : List[str]							       =   PyTorchBenchmark(A    ,						configs=[config]   )
							lowercase_ : str							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )




					def   A  (				self     :						Optional[Any]   )  ->  int:
							lowercase_ : Optional[Any]							       =   '''sshleifer/tinier_bart'''
							lowercase_ : Optional[Any]							       =   AutoConfig.from_pretrained(A   )
							lowercase_ : Tuple							       =   PyTorchBenchmarkArguments(
							    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=A    ,						)
							lowercase_ : str							       =   PyTorchBenchmark(A    ,						configs=[config]   )
							lowercase_ : str							       =   benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )




					def   A  (				self     :						Union[str, Any]   )  ->  Optional[int]:
							lowercase_ : List[Any]							       =   '''sshleifer/tiny-gpt2'''
							with tempfile.TemporaryDirectory() as tmp_dir:
									lowercase_ : Any							       =   PyTorchBenchmarkArguments(
									    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						save_to_csv=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						inference_time_csv_file=os.path.join(A    ,						'''inf_time.csv'''   )    ,						train_memory_csv_file=os.path.join(A    ,						'''train_mem.csv'''   )    ,						inference_memory_csv_file=os.path.join(A    ,						'''inf_mem.csv'''   )    ,						train_time_csv_file=os.path.join(A    ,						'''train_time.csv'''   )    ,						env_info_csv_file=os.path.join(A    ,						'''env.csv'''   )    ,						multi_process=A    ,						)
									lowercase_ : str							       =   PyTorchBenchmark(A   )
									benchmark.run()
									self.assertTrue(Path(os.path.join(A    ,						'''inf_time.csv'''   )   ).exists()   )
									self.assertTrue(Path(os.path.join(A    ,						'''train_time.csv'''   )   ).exists()   )
									self.assertTrue(Path(os.path.join(A    ,						'''inf_mem.csv'''   )   ).exists()   )
									self.assertTrue(Path(os.path.join(A    ,						'''train_mem.csv'''   )   ).exists()   )
									self.assertTrue(Path(os.path.join(A    ,						'''env.csv'''   )   ).exists()   )






					def   A  (				self     :						str   )  ->  Tuple:
							lowercase_ : str							       =   '''sshleifer/tiny-gpt2'''

							def _check_summary_is_not_empty(A     :						List[Any]   ):
									self.assertTrue(hasattr(A    ,						'''sequential'''   )   )
									self.assertTrue(hasattr(A    ,						'''cumulative'''   )   )
									self.assertTrue(hasattr(A    ,						'''current'''   )   )
									self.assertTrue(hasattr(A    ,						'''total'''   )   )

							with tempfile.TemporaryDirectory() as tmp_dir:
									lowercase_ : Any							       =   PyTorchBenchmarkArguments(
									    models=[MODEL_ID]    ,						training=A    ,						inference=A    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						log_filename=os.path.join(A    ,						'''log.txt'''   )    ,						log_print=A    ,						trace_memory_line_by_line=A    ,						multi_process=A    ,						)
									lowercase_ : Optional[int]							       =   PyTorchBenchmark(A   )
									lowercase_ : Dict							       =   benchmark.run()
									_check_summary_is_not_empty(result.inference_summary   )
									_check_summary_is_not_empty(result.train_summary   )
									self.assertTrue(Path(os.path.join(A    ,						'''log.txt'''   )   ).exists()   )





"""simple docstring"""




import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=False  ) ->				Tuple:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       if display:
              print(yaml.dump(OmegaConf.to_container(__snake_case  )  )  )
       return config


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=None     , __snake_case				:       Union[str, Any]=None  ) ->				Tuple:
       if conf_path is None:
              lowercase    :				List[Any]       		=							"./model_checkpoints/vqgan_only.yaml"
       lowercase    :				Tuple       		=							load_config(__snake_case     , display=__snake_case  )
       lowercase    :				List[Any]       		=							VQModel(**config.model.params  )
       if ckpt_path is None:
              lowercase    :				List[str]       		=							"./model_checkpoints/vqgan_only.pt"
       lowercase    :				Optional[int]       		=							torch.load(__snake_case     , map_location=__snake_case  )
       if ".ckpt" in ckpt_path:
              lowercase    :				str       		=							sd["state_dict"]
       model.load_state_dict(__snake_case     , strict=__snake_case  )
       model.to(__snake_case  )
       del sd
       return model


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       Union[str, Any]  ) ->				int:
       lowercase		,    lowercase		,    lowercase    :				List[Any]       		=							model.encode(__snake_case  )
       print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""  )
       lowercase    :				str       		=							model.decode(__snake_case  )
       return xrec


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[int]=False  ) ->				int:
       lowercase		,    lowercase    :				Union[str, Any]       		=							string.rsplit("."     , 1  )
       if reload:
              lowercase    :				Any       		=							importlib.import_module(__snake_case  )
              importlib.reload(__snake_case  )
       return getattr(importlib.import_module(__snake_case     , package=__snake_case  )     , cls  )


def   __magic_name__	(      __snake_case				:       str  ) ->				List[str]:
       if "target" not in config:
              raise KeyError("Expected key `target` to instantiate."  )
       return get_obj_from_str(config["target"]  )(**config.get("params"     , {}  )  )


def   __magic_name__	(      __snake_case				:       Any     , __snake_case				:       int     , __snake_case				:       List[Any]=True     , __snake_case				:       Dict=True  ) ->				str:
       lowercase    :				Optional[int]       		=							instantiate_from_config(__snake_case  )
       if sd is not None:
              model.load_state_dict(__snake_case  )
       if gpu:
              model.cuda()
       if eval_mode:
              model.eval()
       return {"model": model}


def   __magic_name__	(      __snake_case				:       Optional[int]     , __snake_case				:       Any     , __snake_case				:       Optional[int]     , __snake_case				:       List[str]  ) ->				Any:
       # load the specified checkpoint
       if ckpt:
              lowercase    :				Dict       		=							torch.load(__snake_case     , map_location="cpu"  )
              lowercase    :				List[Any]       		=							pl_sd["global_step"]
              print(f"""loaded model from global step {global_step}."""  )
       else:
              lowercase    :				int       		=							{"state_dict": None}
              lowercase    :				Optional[Any]       		=							None
       lowercase    :				List[Any]       		=							load_model_from_config(config.model     , pl_sd["state_dict"]     , gpu=__snake_case     , eval_mode=__snake_case  )["model"]
       return model, global_step


