



import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
	import torch

	from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
	import pyanvml.pyanvml as nvml


UpperCAmelCase							=       logging.get_logger(__name__)

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   PyTorchBenchmarkArguments
							UpperCamelCase__					:   PretrainedConfig
							UpperCamelCase__					:   str       =       "PyTorch"


							@property
							def _a   (				self		):
											return torch.__version__


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self._prepare_inference_func(a_	,	a_	,	a_		)
											return self._measure_speed(_inference		)


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self._prepare_inference_func(a_	,	a_	,	a_		)
											return self._measure_memory(_inference		)


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self._prepare_train_func(a_	,	a_	,	a_		)
											return self._measure_speed(_train		)


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self._prepare_train_func(a_	,	a_	,	a_		)
											return self._measure_memory(_train		)


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self.config_dict[model_name]

											if self.args.torchscript:
															a__					   =		True

											a__					   =		(
											    hasattr(a_	,	"""architectures"""		)
											    and isinstance(config.architectures	,	a_		)
											    and len(config.architectures		) > 0
											)
											if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																			a__					   =		config.architectures[0]
																			a__					   =		__import__("""transformers"""	,	fromlist=[model_class]		)
																			a__					   =		getattr(a_	,	a_		)
																			a__					   =		model_cls(a_		)
															except ImportError:
																			raise ImportError(
																			    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																			    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""		)
											else:
															a__					   =		MODEL_MAPPING[config.__class__](a_		)

											model.eval()
											model.to(self.args.device		)

											# encoder-decoder has vocab size saved differently
											a__					   =		config.vocab_size if hasattr(a_	,	"""vocab_size"""		) else config.encoder.vocab_size
											a__					   =		torch.randint(a_	,	(batch_size, sequence_length)	,	dtype=torch.long	,	device=self.args.device		)

											if self.args.fpaa:
															logger.info("""Running training in Mixed Precision..."""		)
															if not self.args.is_gpu:
																			raise ValueError("""Mixed precision is possible only for GPU."""		)
															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

											if self.args.torchscript:
															with torch.no_grad():
																			a__					   =		torch.jit.trace(a_	,	a_		)
											else:
															a__					   =		model

											def encoder_decoder_forward():
															with torch.no_grad():
																			a__					   =		inference_model(a_	,	decoder_input_ids=a_		)
															return outputs

											def encoder_forward():
															with torch.no_grad():
																			a__					   =		inference_model(a_		)
															return outputs

											a__					   =		encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
											return _forward


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self.config_dict[model_name]

											a__					   =		(
											    hasattr(a_	,	"""architectures"""		)
											    and isinstance(config.architectures	,	a_		)
											    and len(config.architectures		) > 0
											)
											if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																			a__					   =		config.architectures[0]
																			a__					   =		__import__("""transformers"""	,	fromlist=[model_class]		)
																			a__					   =		getattr(a_	,	a_		)
																			a__					   =		model_cls(a_		)
															except ImportError:
																			raise ImportError(
																			    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																			    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""		)
											else:
															a__					   =		MODEL_WITH_LM_HEAD_MAPPING[config.__class__](a_		)

											if self.args.torchscript:
															raise NotImplementedError("""Training for torchscript is currently not implemented"""		)
											else:
															a__					   =		model

											model.train()
											model.to(self.args.device		)

											# encoder-decoder has vocab size saved differently
											a__					   =		config.vocab_size if hasattr(a_	,	"""vocab_size"""		) else config.encoder.vocab_size
											a__					   =		torch.randint(a_	,	(batch_size, sequence_length)	,	dtype=torch.long	,	device=self.args.device		)

											if self.args.fpaa:
															logger.info("""Running training in Mixed Precision..."""		)
															if not self.args.is_gpu:
																			raise ValueError("""Mixed precision is possible only for GPU."""		)

															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

											def compute_loss_and_backprob_encoder():
															a__					   =		train_model(a_	,	labels=a_		)[0]
															loss.backward()
															return loss

											def compute_loss_and_backprob_encoder_decoder():
															a__					   =		train_model(a_	,	decoder_input_ids=a_	,	labels=a_		)[0]
															loss.backward()
															return loss

											a__					   =		(
											    compute_loss_and_backprob_encoder_decoder
											    if config.is_encoder_decoder
											    else compute_loss_and_backprob_encoder
											)
											return _train


							def _a   (				self	,	a_		):
											try:
															if self.args.is_tpu or self.args.torchscript:
																			# run additional 10 times to stabilize compilation for tpu and torchscript
																			logger.info("""Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"""		)
																			timeit.repeat(
																			    a_	,	repeat=1	,	number=5	,	)

															# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
															a__					   =		timeit.repeat(
															    a_	,	repeat=self.args.repeat	,	number=10	,	)

															if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
																			import torch_xla.debug.metrics as met

																			self.print_fn(met.metrics_report()		)

															return min(a_		) / 10.0
											except RuntimeError as e:
															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''		)
															return "N/A"







							def _a   (				self	,	a_		):
											try:
															if self.args.trace_memory_line_by_line:
																			a__					   =		start_memory_tracing("""transformers"""		)

															if self.args.is_tpu:
																			# tpu
																			raise NotImplementedError(
																			    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"""
																			    """ `--no-memory` or `args.memory=False`"""		)
															elif self.args.is_gpu:
																			if not is_pyanvml_available():
																							logger.warning(
																							    """py3nvml not installed, we won't log GPU memory usage. """
																							    """Install py3nvml (pip install py3nvml) to log information about GPU."""		)
																							a__					   =		"""N/A"""
																			else:
																							logger.info(
																							    """Measuring total GPU usage on GPU device. Make sure to not have additional processes running"""
																							    """ on the same GPU."""		)
																							# init nvml
																							nvml.nvmlInit()
																							func()
																							a__					   =		nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx		)
																							a__					   =		nvml.nvmlDeviceGetMemoryInfo(a_		)
																							a__					   =		meminfo.used
																							a__					   =		Memory(a_		)
																							# shutdown nvml
																							nvml.nvmlShutdown()
															else:
																			# cpu
																			a__					   =		measure_peak_memory_cpu(a_		)
																			a__					   =		Memory(a_		) if isinstance(a_	,	a_		) else memory_bytes

															if self.args.trace_memory_line_by_line:
																			a__					   =		stop_memory_tracing(a_		)
															else:
																			a__					   =		None

															return memory, summary
											except RuntimeError as e:
															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''		)
															return "N/A", None




"""simple docstring"""





import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger




class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   Tuple  						=      "config.json"


 @register_to_config
 def __init__(     self				:     List[str],   UpperCAmelCase__				:     Dict=2,   UpperCAmelCase__				:     str=5,   UpperCAmelCase__				:     List[Any]=(2, 5),   UpperCAmelCase__				:     Dict="for diffusion",   UpperCAmelCase__				:     Optional[int]=[1, 3],   ):
    pass







class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   List[str]  						=      "config.json"


 @register_to_config
 def __init__(     self				:     Optional[int],   UpperCAmelCase__				:     List[str]=2,   UpperCAmelCase__				:     List[str]=5,   UpperCAmelCase__				:     List[Any]=(2, 5),   UpperCAmelCase__				:     str="for diffusion",   UpperCAmelCase__				:     str=[1, 3],   ):
    pass







class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   str  						=      "config.json"


 @register_to_config
 def __init__(     self				:     Union[str, Any],   UpperCAmelCase__				:     Union[str, Any]=2,   UpperCAmelCase__				:     str=5,   UpperCAmelCase__				:     Optional[int]=(2, 5),   UpperCAmelCase__				:     str="for diffusion",   UpperCAmelCase__				:     str=[1, 3],   UpperCAmelCase__				:     Dict=[1, 3],   ):
    pass







class    _lowerCAmelCase    (      lowercase  ):







 """simple docstring"""







 __UpperCAmelCase      :   List[str]  						=      "config.json"


 @register_to_config
 def __init__(     self				:     Union[str, Any],   UpperCAmelCase__				:     Any=2,   UpperCAmelCase__				:     Union[str, Any]=5,   UpperCAmelCase__				:     Optional[Any]=(2, 5),   UpperCAmelCase__				:     List[str]="for diffusion",   UpperCAmelCase__				:     Optional[Any]=[1, 5],   UpperCAmelCase__				:     Dict=[5, 4],   ):
    pass







class    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 def    _lowercase					(     self				:     Optional[int]      ):
    with self.assertRaises(UpperCAmelCase__      ):
       ConfigMixin.load_config("dummy_path"      )


 def    _lowercase					(     self				:     Optional[int]      ):
    __lowercase								=		SampleObject()
    __lowercase								=		obj.config
    assert config["a"] == 2
    assert config["b"] == 5
    assert config["c"] == (2, 5)
    assert config["d"] == "for diffusion"
    assert config["e"] == [1, 3]

    # init ignore private arguments
    __lowercase								=		SampleObject(_name_or_path="lalala"      )
    __lowercase								=		obj.config
    assert config["a"] == 2
    assert config["b"] == 5
    assert config["c"] == (2, 5)
    assert config["d"] == "for diffusion"
    assert config["e"] == [1, 3]

    # can override default
    __lowercase								=		SampleObject(c=6      )
    __lowercase								=		obj.config
    assert config["a"] == 2
    assert config["b"] == 5
    assert config["c"] == 6
    assert config["d"] == "for diffusion"
    assert config["e"] == [1, 3]

    # can use positional arguments.
    __lowercase								=		SampleObject(1,   c=6      )
    __lowercase								=		obj.config
    assert config["a"] == 1
    assert config["b"] == 5
    assert config["c"] == 6
    assert config["d"] == "for diffusion"
    assert config["e"] == [1, 3]


 def    _lowercase					(     self				:     Tuple      ):
    __lowercase								=		SampleObject()
    __lowercase								=		obj.config

    assert config["a"] == 2
    assert config["b"] == 5
    assert config["c"] == (2, 5)
    assert config["d"] == "for diffusion"
    assert config["e"] == [1, 3]

    with tempfile.TemporaryDirectory() as tmpdirname:
       obj.save_config(UpperCAmelCase__      )
       __lowercase								=		SampleObject.from_config(SampleObject.load_config(UpperCAmelCase__      )      )
       __lowercase								=		new_obj.config

    # unfreeze configs
    __lowercase								=		dict(UpperCAmelCase__      )
    __lowercase								=		dict(UpperCAmelCase__      )

    assert config.pop("c"      ) == (2, 5)  # instantiated as tuple
    assert new_config.pop("c"      ) == [2, 5]  # saved & loaded as list because of json
    config.pop("_use_default_values"      )
    assert config == new_config


 def    _lowercase					(     self				:     Any      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		DDIMScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler"      )

    assert ddim.__class__ == DDIMScheduler
    # no warning should be thrown
    assert cap_logger.out == ""


 def    _lowercase					(     self				:     int      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		EulerDiscreteScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler"      )

    assert euler.__class__ == EulerDiscreteScheduler
    # no warning should be thrown
    assert cap_logger.out == ""


 def    _lowercase					(     self				:     Optional[Any]      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		EulerAncestralDiscreteScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler"      )

    assert euler.__class__ == EulerAncestralDiscreteScheduler
    # no warning should be thrown
    assert cap_logger.out == ""


 def    _lowercase					(     self				:     str      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		PNDMScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler"      )

    assert pndm.__class__ == PNDMScheduler
    # no warning should be thrown
    assert cap_logger.out == ""


 def    _lowercase					(     self				:     Optional[Any]      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		DDPMScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler",   prediction_type="sample",   beta_end=8,   )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger_a:
       __lowercase								=		DDPMScheduler.from_pretrained("google/ddpm-celebahq-256",   beta_start=8_8      )

    assert ddpm.__class__ == DDPMScheduler
    assert ddpm.config.prediction_type == "sample"
    assert ddpm.config.beta_end == 8
    assert ddpm_a.config.beta_start == 8_8

    # no warning should be thrown
    assert cap_logger.out == ""
    assert cap_logger_a.out == ""


 def    _lowercase					(     self				:     List[Any]      ):
    __lowercase								=		logging.get_logger("diffusers.configuration_utils"      )
    # 30 for warning
    logger.setLevel(3_0      )

    with CaptureLogger(UpperCAmelCase__      ) as cap_logger:
       __lowercase								=		DPMSolverMultistepScheduler.from_pretrained(
           "hf-internal-testing/tiny-stable-diffusion-torch",   subfolder="scheduler"      )

    assert dpm.__class__ == DPMSolverMultistepScheduler
    # no warning should be thrown
    assert cap_logger.out == ""



 def    _lowercase					(     self				:     str      ):
    # let's first save a config that should be in the form
    #    a=2,
    #    b=5,
    #    c=(2, 5),
    #    d="for diffusion",
    #    e=[1, 3],

    __lowercase								=		SampleObject()

    __lowercase								=		{k: v for k, v in config.config.items() if not k.startswith("_"      )}

    # make sure that default config has all keys in `_use_default_values`
    assert set(config_dict.keys()      ) == set(config.config._use_default_values      )

    with tempfile.TemporaryDirectory() as tmpdirname:
       config.save_config(UpperCAmelCase__      )

       # now loading it with SampleObject2 should put f into `_use_default_values`
       __lowercase								=		SampleObjecta.from_config(UpperCAmelCase__      )

       assert "f" in config._use_default_values
       assert config.f == [1, 3]

    # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
    # **BECAUSE** it is part of `config._use_default_values`
    __lowercase								=		SampleObjecta.from_config(config.config      )
    assert new_config.f == [5, 4]

    config.config._use_default_values.pop()
    __lowercase								=		SampleObjecta.from_config(config.config      )
    assert new_config_a.f == [1, 3]

    # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
    assert new_config_a.e == [1, 3]




