


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 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  PyTorchBenchmarkArguments
       lowerCamelCase__:  PretrainedConfig
       lowerCamelCase__:  str				     =  "PyTorch"

       @property
       def     UpperCAmelCase( self   :  str     ):
            return torch.__version__

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  Tuple				      =     self._prepare_inference_func(lowerCamelCase_   ,				lowerCamelCase_   ,				lowerCamelCase_     )
            return self._measure_speed(_inference     )

       def     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  List[str]				      =     self._prepare_inference_func(lowerCamelCase_   ,				lowerCamelCase_   ,				lowerCamelCase_     )
            return self._measure_memory(_inference     )

       def     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  str				      =     self._prepare_train_func(lowerCamelCase_   ,				lowerCamelCase_   ,				lowerCamelCase_     )
            return self._measure_speed(_train     )

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  List[Any]				      =     self._prepare_train_func(lowerCamelCase_   ,				lowerCamelCase_   ,				lowerCamelCase_     )
            return self._measure_memory(_train     )

       def     UpperCAmelCase( self   :  Union[str, Any]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  Union[str, Any]				      =     self.config_dict[model_name]

            if self.args.torchscript:
                 a_     :  Optional[Any]				      =     True

            a_     :  Any				      =     (
                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_     :  Union[str, Any]				      =     config.architectures[0]
                      a_     :  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_     :  str				      =     MODEL_MAPPING[config.__class__](lowerCamelCase_     )

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

            # encoder-decoder has vocab size saved differently
            a_     :  Union[str, Any]				      =     config.vocab_size if hasattr(lowerCamelCase_   ,				"""vocab_size"""     ) else config.encoder.vocab_size
            a_     :  Dict				      =     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_     :  Optional[Any]				      =     model

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

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

            a_     :  Union[str, Any]				      =     encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
            return _forward

       def     UpperCAmelCase( self   :  int   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  Optional[int]				      =     self.config_dict[model_name]

            a_     :  str				      =     (
                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_     :  Any				      =     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_     :  Union[str, Any]				      =     MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase_     )

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

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

            # encoder-decoder has vocab size saved differently
            a_     :  Any				      =     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_     :  Dict				      =     train_model(lowerCamelCase_   ,				labels=lowerCamelCase_     )[0]
                 loss.backward()
                 return loss

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

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

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  Optional[int]     ):
            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_     :  List[str]				      =     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     UpperCAmelCase( self   :  Tuple   ,				lowerCamelCase_   :  Callable[[], None]     ):
            try:
                 if self.args.trace_memory_line_by_line:
                      a_     :  Dict				      =     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_     :  str				      =     """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_     :  List[Any]				      =     nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx     )
                           a_     :  Tuple				      =     nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase_     )
                           a_     :  Any				      =     meminfo.used
                           a_     :  Any				      =     Memory(lowerCamelCase_     )
                           # shutdown nvml
                           nvml.nvmlShutdown()
                 else:
                      # cpu
                      a_     :  Union[str, Any]				      =     measure_peak_memory_cpu(lowerCamelCase_     )
                      a_     :  Optional[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_     :  Any				      =     None

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







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


__lowerCamelCase   	=  {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase   	=  [
           '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TvltModel''',
           '''TvltForPreTraining''',
           '''TvltForAudioVisualClassification''',
           '''TvltPreTrainedModel''',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase   	=  ['''TvltImageProcessor''']


if TYPE_CHECKING:
       from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
       from .processing_tvlt import TvltProcessor
       from .feature_extraction_tvlt import TvltFeatureExtractor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tvlt import (
                  TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TvltForAudioVisualClassification,
                  TvltForPreTraining,
                  TvltModel,
                  TvltPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_tvlt import TvltImageProcessor


else:
       import sys

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



