


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


lowerCAmelCase__   	=				logging.get_logger(__name__)







class   lowerCAmelCase__ (       a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				42
     __SCREAMING_SNAKE_CASE						    =				42
     __SCREAMING_SNAKE_CASE						    =				"PyTorch"


     @property
     def 				_lowerCamelCase				(		self)    ->       Tuple:
            return torch.__version__


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)    ->       float:
            _A   : List[str]       					=				self._prepare_inference_func(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
            return self._measure_speed(_inference)


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)    ->       [Memory, Optional[MemorySummary]]:
            _A   : List[Any]       					=				self._prepare_inference_func(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
            return self._measure_memory(_inference)


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)    ->       float:
            _A   : Union[str, Any]       					=				self._prepare_train_func(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
            return self._measure_speed(_train)


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)    ->       [Memory, Optional[MemorySummary]]:
            _A   : Union[str, Any]       					=				self._prepare_train_func(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
            return self._measure_memory(_train)


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Callable[[], None]:
            _A   : Union[str, Any]       					=				self.config_dict[model_name]

            if self.args.torchscript:
                   _A   : Tuple       					=				True

            _A   : Tuple       					=				(
                hasattr(__lowerCamelCase				,		"architectures")
                and isinstance(config.architectures				,		__lowerCamelCase)
                and len(config.architectures) > 0
            )
            if not self.args.only_pretrain_model and has_model_class_in_config:
                   try:
                          _A   : Optional[int]       					=				config.architectures[0]
                          _A   : List[Any]       					=				__import__("transformers"				,		fromlist=[model_class])
                          _A   : List[str]       					=				getattr(__lowerCamelCase				,		__lowerCamelCase)
                          _A   : Dict       					=				model_cls(__lowerCamelCase)
                   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   : Any       					=				MODEL_MAPPING[config.__class__](__lowerCamelCase)

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

            # encoder-decoder has vocab size saved differently
            _A   : Tuple       					=				config.vocab_size if hasattr(__lowerCamelCase				,		"vocab_size") else config.encoder.vocab_size
            _A   : Optional[int]       					=				torch.randint(__lowerCamelCase				,		(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   : Optional[int]       					=				torch.jit.trace(__lowerCamelCase				,		__lowerCamelCase)
            else:
                   _A   : List[Any]       					=				model

            def encoder_decoder_forward():
                   with torch.no_grad():
                          _A   : Tuple       					=				inference_model(__lowerCamelCase				,		decoder_input_ids=__lowerCamelCase)
                   return outputs

            def encoder_forward():
                   with torch.no_grad():
                          _A   : Tuple       					=				inference_model(__lowerCamelCase)
                   return outputs

            _A   : List[str]       					=				encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
            return _forward


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Callable[[], None]:
            _A   : Dict       					=				self.config_dict[model_name]

            _A   : Tuple       					=				(
                hasattr(__lowerCamelCase				,		"architectures")
                and isinstance(config.architectures				,		__lowerCamelCase)
                and len(config.architectures) > 0
            )
            if not self.args.only_pretrain_model and has_model_class_in_config:
                   try:
                          _A   : List[Any]       					=				config.architectures[0]
                          _A   : Optional[int]       					=				__import__("transformers"				,		fromlist=[model_class])
                          _A   : List[Any]       					=				getattr(__lowerCamelCase				,		__lowerCamelCase)
                          _A   : Optional[int]       					=				model_cls(__lowerCamelCase)
                   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   : Optional[Any]       					=				MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__lowerCamelCase)

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

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

            # encoder-decoder has vocab size saved differently
            _A   : str       					=				config.vocab_size if hasattr(__lowerCamelCase				,		"vocab_size") else config.encoder.vocab_size
            _A   : Tuple       					=				torch.randint(__lowerCamelCase				,		(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   : Any       					=				train_model(__lowerCamelCase				,		labels=__lowerCamelCase)[0]
                   loss.backward()
                   return loss

            def compute_loss_and_backprob_encoder_decoder():
                   _A   : str       					=				train_model(__lowerCamelCase				,		decoder_input_ids=__lowerCamelCase				,		labels=__lowerCamelCase)[0]
                   loss.backward()
                   return loss

            _A   : List[Any]       					=				(
                compute_loss_and_backprob_encoder_decoder
                if config.is_encoder_decoder
                else compute_loss_and_backprob_encoder
            )
            return _train


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       float:
            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(
                              __lowerCamelCase				,		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   : Any       					=				timeit.repeat(
                       __lowerCamelCase				,		repeat=self.args.repeat				,		number=1_0				,		)

                   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(__lowerCamelCase) / 1_0.0
            except RuntimeError as e:
                   self.print_fn(F"Doesn't fit on GPU. {e}")
                   return "N/A"


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       [Memory, MemorySummary]:
            try:
                   if self.args.trace_memory_line_by_line:
                          _A   : Any       					=				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   : Any       					=				"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   : Optional[Any]       					=				nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
                                 _A   : int       					=				nvml.nvmlDeviceGetMemoryInfo(__lowerCamelCase)
                                 _A   : List[str]       					=				meminfo.used
                                 _A   : List[Any]       					=				Memory(__lowerCamelCase)
                                 # shutdown nvml
                                 nvml.nvmlShutdown()
                   else:
                          # cpu
                          _A   : Tuple       					=				measure_peak_memory_cpu(__lowerCamelCase)
                          _A   : Union[str, Any]       					=				Memory(__lowerCamelCase) if isinstance(__lowerCamelCase				,		__lowerCamelCase) else memory_bytes

                   if self.args.trace_memory_line_by_line:
                          _A   : Dict       					=				stop_memory_tracing(__lowerCamelCase)
                   else:
                          _A   : List[Any]       					=				None

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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase__   	=				{
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				['SegformerFeatureExtractor']
    lowerCAmelCase__   	=				['SegformerImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				[
        'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'SegformerDecodeHead',
        'SegformerForImageClassification',
        'SegformerForSemanticSegmentation',
        'SegformerLayer',
        'SegformerModel',
        'SegformerPreTrainedModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				[
        'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFSegformerDecodeHead',
        'TFSegformerForImageClassification',
        'TFSegformerForSemanticSegmentation',
        'TFSegformerModel',
        'TFSegformerPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_segformer import SegformerFeatureExtractor
        from .image_processing_segformer import SegformerImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_segformer import (
            SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            SegformerDecodeHead,
            SegformerForImageClassification,
            SegformerForSemanticSegmentation,
            SegformerLayer,
            SegformerModel,
            SegformerPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_segformer import (
            TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFSegformerDecodeHead,
            TFSegformerForImageClassification,
            TFSegformerForSemanticSegmentation,
            TFSegformerModel,
            TFSegformerPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
