





import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A :   int  			=  logging.get_logger(__name__)



def 	_a						(   UpperCAmelCase					)    ->    List[str]:







   """simple docstring"""
   print('''Loading config file...'''					)

   def flatten_yaml_as_dict(UpperCAmelCase      ,  UpperCAmelCase=""      ,  UpperCAmelCase="."					):
      lowerCamelCase__       :						List[Any]        =							[]
      for k, v in d.items():
         lowerCamelCase__       :						Dict        =							parent_key + sep + k if parent_key else k
         if isinstance(UpperCAmelCase      ,  collections.abc.MutableMapping					):
            items.extend(flatten_yaml_as_dict(UpperCAmelCase      ,  UpperCAmelCase      ,  sep=UpperCAmelCase					).items()					)
         else:
            items.append((new_key, v)					)
      return dict(UpperCAmelCase					)

   lowerCamelCase__       :						Optional[Any]        =							argparse.Namespace()
   with open(UpperCAmelCase      ,  '''r'''					) as yaml_file:
      try:
         lowerCamelCase__       :						Union[str, Any]        =							yaml.load(UpperCAmelCase      ,  Loader=yaml.FullLoader					)

         lowerCamelCase__       :						Tuple        =							flatten_yaml_as_dict(UpperCAmelCase					)
         for k, v in flat_cfg.items():
            setattr(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)
      except yaml.YAMLError as exc:
         logger.error('''Error while loading config file: {}. Error message: {}'''.format(UpperCAmelCase      ,  str(UpperCAmelCase					)					)					)
   return config



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[int]:







   """simple docstring"""
   lowerCamelCase__       :						Any        =							MobileViTVaConfig()

   lowerCamelCase__       :						Optional[int]        =							False

   # dataset
   if task_name.startswith('''imagenet1k_'''					):
      lowerCamelCase__       :						Optional[Any]        =							1000
      if int(task_name.strip().split('''_'''					)[-1]					) == 384:
         lowerCamelCase__       :						Union[str, Any]        =							384
      else:
         lowerCamelCase__       :						int        =							256
      lowerCamelCase__       :						Dict        =							'''imagenet-1k-id2label.json'''
   elif task_name.startswith('''imagenet21k_to_1k_'''					):
      lowerCamelCase__       :						Dict        =							21000
      if int(task_name.strip().split('''_'''					)[-1]					) == 384:
         lowerCamelCase__       :						Union[str, Any]        =							384
      else:
         lowerCamelCase__       :						int        =							256
      lowerCamelCase__       :						Any        =							'''imagenet-22k-id2label.json'''
   elif task_name.startswith('''ade20k_'''					):
      lowerCamelCase__       :						Any        =							151
      lowerCamelCase__       :						List[str]        =							512
      lowerCamelCase__       :						List[str]        =							'''ade20k-id2label.json'''
      lowerCamelCase__       :						List[Any]        =							True
   elif task_name.startswith('''voc_'''					):
      lowerCamelCase__       :						Optional[Any]        =							21
      lowerCamelCase__       :						Optional[Any]        =							512
      lowerCamelCase__       :						List[str]        =							'''pascal-voc-id2label.json'''
      lowerCamelCase__       :						str        =							True

   # orig_config
   lowerCamelCase__       :						Dict        =							load_orig_config_file(UpperCAmelCase					)
   assert getattr(UpperCAmelCase      ,  '''model.classification.name'''      ,  -1					) == "mobilevit_v2", "Invalid model"
   lowerCamelCase__       :						Union[str, Any]        =							getattr(UpperCAmelCase      ,  '''model.classification.mitv2.width_multiplier'''      ,  1.0					)
   assert (
       getattr(UpperCAmelCase      ,  '''model.classification.mitv2.attn_norm_layer'''      ,  -1					) == "layer_norm_2d"
   ), "Norm layers other than layer_norm_2d is not supported"
   lowerCamelCase__       :						Tuple        =							getattr(UpperCAmelCase      ,  '''model.classification.activation.name'''      ,  '''swish'''					)
   # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

   if is_segmentation_model:
      lowerCamelCase__       :						str        =							getattr(UpperCAmelCase      ,  '''model.segmentation.output_stride'''      ,  16					)
      if "_deeplabv3" in task_name:
         lowerCamelCase__       :						List[Any]        =							getattr(UpperCAmelCase      ,  '''model.segmentation.deeplabv3.aspp_rates'''      ,  [12, 24, 36]					)
         lowerCamelCase__       :						Dict        =							getattr(UpperCAmelCase      ,  '''model.segmentation.deeplabv3.aspp_out_channels'''      ,  512					)
         lowerCamelCase__       :						int        =							getattr(UpperCAmelCase      ,  '''model.segmentation.deeplabv3.aspp_dropout'''      ,  0.1					)

    # id2label
   lowerCamelCase__       :						List[str]        =							'''huggingface/label-files'''
   lowerCamelCase__       :						Any        =							json.load(open(hf_hub_download(UpperCAmelCase      ,  UpperCAmelCase      ,  repo_type='''dataset'''					)      ,  '''r'''					)					)
   lowerCamelCase__       :						str        =							{int(UpperCAmelCase					): v for k, v in idalabel.items()}
   lowerCamelCase__       :						Any        =							idalabel
   lowerCamelCase__       :						List[str]        =							{v: k for k, v in idalabel.items()}

   return config



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							dct.pop(UpperCAmelCase					)
   lowerCamelCase__       :						str        =							val



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    Optional[Any]:







   """simple docstring"""
   if base_model:
      lowerCamelCase__       :						int        =							''''''
   else:
      lowerCamelCase__       :						Dict        =							'''mobilevitv2.'''

   lowerCamelCase__       :						int        =							[]
   for k in state_dict.keys():
      if k[:8] == "encoder.":
         lowerCamelCase__       :						Any        =							k[8:]
      else:
         lowerCamelCase__       :						int        =							k

      if ".block." in k:
         lowerCamelCase__       :						Tuple        =							k_new.replace('''.block.'''      ,  '''.'''					)
      if ".conv." in k:
         lowerCamelCase__       :						str        =							k_new.replace('''.conv.'''      ,  '''.convolution.'''					)
      if ".norm." in k:
         lowerCamelCase__       :						List[Any]        =							k_new.replace('''.norm.'''      ,  '''.normalization.'''					)

      if "conv_1." in k:
         lowerCamelCase__       :						Optional[Any]        =							k_new.replace('''conv_1.'''      ,  f"{model_prefix}conv_stem."					)
      for i in [1, 2]:
         if f"layer_{i}." in k:
            lowerCamelCase__       :						int        =							k_new.replace(f"layer_{i}."      ,  f"{model_prefix}encoder.layer.{i-1}.layer."					)
      if ".exp_1x1." in k:
         lowerCamelCase__       :						int        =							k_new.replace('''.exp_1x1.'''      ,  '''.expand_1x1.'''					)
      if ".red_1x1." in k:
         lowerCamelCase__       :						Optional[Any]        =							k_new.replace('''.red_1x1.'''      ,  '''.reduce_1x1.'''					)

      for i in [3, 4, 5]:
         if f"layer_{i}.0." in k:
            lowerCamelCase__       :						Union[str, Any]        =							k_new.replace(f"layer_{i}.0."      ,  f"{model_prefix}encoder.layer.{i-1}.downsampling_layer."					)
         if f"layer_{i}.1.local_rep.0." in k:
            lowerCamelCase__       :						Dict        =							k_new.replace(f"layer_{i}.1.local_rep.0."      ,  f"{model_prefix}encoder.layer.{i-1}.conv_kxk."					)
         if f"layer_{i}.1.local_rep.1." in k:
            lowerCamelCase__       :						int        =							k_new.replace(f"layer_{i}.1.local_rep.1."      ,  f"{model_prefix}encoder.layer.{i-1}.conv_1x1."					)

      for i in [3, 4, 5]:
         if i == 3:
            lowerCamelCase__       :						Union[str, Any]        =							[0, 1]
         elif i == 4:
            lowerCamelCase__       :						int        =							[0, 1, 2, 3]
         elif i == 5:
            lowerCamelCase__       :						Any        =							[0, 1, 2]

         for j in j_in:
            if f"layer_{i}.1.global_rep.{j}." in k:
               lowerCamelCase__       :						Tuple        =							k_new.replace(
                   f"layer_{i}.1.global_rep.{j}."      ,  f"{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."					)
         if f"layer_{i}.1.global_rep.{j+1}." in k:
            lowerCamelCase__       :						Tuple        =							k_new.replace(
                f"layer_{i}.1.global_rep.{j+1}."      ,  f"{model_prefix}encoder.layer.{i-1}.layernorm."					)

         if f"layer_{i}.1.conv_proj." in k:
            lowerCamelCase__       :						Union[str, Any]        =							k_new.replace(f"layer_{i}.1.conv_proj."      ,  f"{model_prefix}encoder.layer.{i-1}.conv_projection."					)

      if "pre_norm_attn.0." in k:
         lowerCamelCase__       :						Dict        =							k_new.replace('''pre_norm_attn.0.'''      ,  '''layernorm_before.'''					)
      if "pre_norm_attn.1." in k:
         lowerCamelCase__       :						List[Any]        =							k_new.replace('''pre_norm_attn.1.'''      ,  '''attention.'''					)
      if "pre_norm_ffn.0." in k:
         lowerCamelCase__       :						List[str]        =							k_new.replace('''pre_norm_ffn.0.'''      ,  '''layernorm_after.'''					)
      if "pre_norm_ffn.1." in k:
         lowerCamelCase__       :						Optional[Any]        =							k_new.replace('''pre_norm_ffn.1.'''      ,  '''ffn.conv1.'''					)
      if "pre_norm_ffn.3." in k:
         lowerCamelCase__       :						List[Any]        =							k_new.replace('''pre_norm_ffn.3.'''      ,  '''ffn.conv2.'''					)

      if "classifier.1." in k:
         lowerCamelCase__       :						Dict        =							k_new.replace('''classifier.1.'''      ,  '''classifier.'''					)

      if "seg_head." in k:
         lowerCamelCase__       :						List[str]        =							k_new.replace('''seg_head.'''      ,  '''segmentation_head.'''					)
      if ".aspp_layer." in k:
         lowerCamelCase__       :						Optional[Any]        =							k_new.replace('''.aspp_layer.'''      ,  '''.'''					)
      if ".aspp_pool." in k:
         lowerCamelCase__       :						str        =							k_new.replace('''.aspp_pool.'''      ,  '''.'''					)

      rename_keys.append((k, k_new)					)
   return rename_keys



def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							[]
   for k in state_dict.keys():
      if k.startswith('''seg_head.aux_head.'''					):
         keys_to_ignore.append(UpperCAmelCase					)
   for k in keys_to_ignore:
      state_dict.pop(UpperCAmelCase      ,  UpperCAmelCase					)



def 	_a						(   )    ->    Union[str, Any]:







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
   # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
   lowerCamelCase__       :						Tuple        =							Image.open(requests.get(UpperCAmelCase      ,  stream=UpperCAmelCase					).raw					)
   return im



@torch.no_grad()
def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    str:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							get_mobilevitva_config(UpperCAmelCase      ,  UpperCAmelCase					)

   # load original state_dict
   lowerCamelCase__       :						Optional[int]        =							torch.load(UpperCAmelCase      ,  map_location='''cpu'''					)

   # load huggingface model
   if task_name.startswith('''ade20k_'''					) or task_name.startswith('''voc_'''					):
      lowerCamelCase__       :						Tuple        =							MobileViTVaForSemanticSegmentation(UpperCAmelCase					).eval()
      lowerCamelCase__       :						Union[str, Any]        =							False
   else:
      lowerCamelCase__       :						Optional[int]        =							MobileViTVaForImageClassification(UpperCAmelCase					).eval()
      lowerCamelCase__       :						Optional[Any]        =							False

   # remove and rename some keys of load the original model
   lowerCamelCase__       :						Dict        =							checkpoint
   remove_unused_keys(UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							create_rename_keys(UpperCAmelCase      ,  base_model=UpperCAmelCase					)
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)

   # load modified state_dict
   model.load_state_dict(UpperCAmelCase					)

   # Check outputs on an image, prepared by MobileViTImageProcessor
   lowerCamelCase__       :						int        =							MobileViTImageProcessor(crop_size=config.image_size      ,  size=config.image_size + 32					)
   lowerCamelCase__       :						Any        =							image_processor(images=prepare_img()      ,  return_tensors='''pt'''					)
   lowerCamelCase__       :						List[Any]        =							model(**UpperCAmelCase					)

   # verify classification model
   if task_name.startswith('''imagenet'''					):
      lowerCamelCase__       :						Optional[int]        =							outputs.logits
      lowerCamelCase__       :						Tuple        =							logits.argmax(-1					).item()
      print('''Predicted class:'''      ,  model.config.idalabel[predicted_class_idx]					)
      if task_name.startswith('''imagenet1k_256'''					) and config.width_multiplier == 1.0:
         # expected_logits for base variant
         lowerCamelCase__       :						Dict        =							torch.tensor([-1.6_3_3_6E0_0, -7.3_2_0_4E-0_2, -5.1_8_8_3E-0_1]					)
         assert torch.allclose(logits[0, :3]      ,  UpperCAmelCase      ,  atol=1E-4					)

   Path(UpperCAmelCase					).mkdir(exist_ok=UpperCAmelCase					)
   print(f"Saving model {task_name} to {pytorch_dump_folder_path}"					)
   model.save_pretrained(UpperCAmelCase					)
   print(f"Saving image processor to {pytorch_dump_folder_path}"					)
   image_processor.save_pretrained(UpperCAmelCase					)


if __name__ == "__main__":
       _A :   Any  			=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--task',
           default='imagenet1k_256',
           type=str,
           help=(
               'Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '
               '\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            '
           ),
           choices=[
               'imagenet1k_256',
               'imagenet1k_384',
               'imagenet21k_to_1k_256',
               'imagenet21k_to_1k_384',
               'ade20k_deeplabv3',
               'voc_deeplabv3',
           ],
       )

       parser.add_argument(
           '--orig_checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
       )
       parser.add_argument('--orig_config_path', required=True, type=str, help='Path to the original config file.')
       parser.add_argument(
           '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
       )

       _A :   List[Any]  			=  parser.parse_args()
       convert_mobilevitva_checkpoint(
           args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
       )










import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
       import tensorflow as tf
       from tensorflow.python.framework.errors_impl import ResourceExhaustedError

       from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
       import pyanvml.pyanvml as nvml

_A :   Any  			=  logging.get_logger(__name__)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    List[str]:







   """simple docstring"""
   def run_func(UpperCAmelCase					):
      @wraps(UpperCAmelCase					)
      def run_in_eager_mode(*UpperCAmelCase      ,  **UpperCAmelCase					):
         return func(*UpperCAmelCase      ,  **UpperCAmelCase					)

      @wraps(UpperCAmelCase					)
      @tf.function(experimental_compile=UpperCAmelCase					)
      def run_in_graph_mode(*UpperCAmelCase      ,  **UpperCAmelCase					):
         return func(*UpperCAmelCase      ,  **UpperCAmelCase					)

      if do_eager_mode is True:
         if use_xla is not False:
            raise ValueError(
                '''Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'''					)
         return run_in_eager_mode
      else:
         return run_in_graph_mode

   return run_func



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    ["tf.Tensor"]:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							random.Random()
   lowerCamelCase__       :						str        =							[rng.randint(0      ,  vocab_size - 1					) for i in range(batch_size * sequence_length					)]
   return tf.constant(UpperCAmelCase      ,  shape=(batch_size, sequence_length)      ,  dtype=tf.intaa					)




class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  TensorFlowBenchmarkArguments
       _UpperCAmelCase				:  PretrainedConfig
       _UpperCAmelCase				:  str			   =   "TensorFlow"







       @property
       def 						__lowerCamelCase   (				self					:     int      )       ->Optional[int]:
          return tf.__version__







       def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     str		, A					:     int		, A					:     int      )       ->float:
          # initialize GPU on separate process
          lowerCamelCase__       :						Dict        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )
          lowerCamelCase__       :						int        =							self._prepare_inference_func(A		, A		, A      )
          return self._measure_speed(_inference      )







       def 						__lowerCamelCase   (				self					:     str		, A					:     str		, A					:     int		, A					:     int      )       ->float:
          lowerCamelCase__       :						Optional[int]        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )
          lowerCamelCase__       :						List[Any]        =							self._prepare_train_func(A		, A		, A      )
          return self._measure_speed(_train      )







       def 						__lowerCamelCase   (				self					:     int		, A					:     str		, A					:     int		, A					:     int      )       ->[Memory, Optional[MemorySummary]]:
          # initialize GPU on separate process
          if self.args.is_gpu:
             tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]		, A      )
          lowerCamelCase__       :						int        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )
          lowerCamelCase__       :						str        =							self._prepare_inference_func(A		, A		, A      )
          return self._measure_memory(_inference      )







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     str		, A					:     int		, A					:     int      )       ->[Memory, Optional[MemorySummary]]:
          if self.args.is_gpu:
             tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]		, A      )
          lowerCamelCase__       :						List[Any]        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )

          lowerCamelCase__       :						str        =							self._prepare_train_func(A		, A		, A      )
          return self._measure_memory(_train      )







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     str		, A					:     int		, A					:     int      )       ->Callable[[], None]:
          lowerCamelCase__       :						Tuple        =							self.config_dict[model_name]

          if self.args.fpaa:
             raise NotImplementedError('''Mixed precision is currently not supported.'''      )

          lowerCamelCase__       :						Tuple        =							(
              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:
                lowerCamelCase__       :						Any        =							'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                lowerCamelCase__       :						List[Any]        =							__import__('''transformers'''		, fromlist=[model_class]      )
                lowerCamelCase__       :						int        =							getattr(A		, A      )
                lowerCamelCase__       :						int        =							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:
             lowerCamelCase__       :						Union[str, Any]        =							TF_MODEL_MAPPING[config.__class__](A      )

          # encoder-decoder has vocab size saved differently
          lowerCamelCase__       :						Tuple        =							config.vocab_size if hasattr(A		, '''vocab_size'''      ) else config.encoder.vocab_size
          lowerCamelCase__       :						Optional[Any]        =							random_input_ids(A		, A		, A      )

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_decoder_forward():
             return model(A		, decoder_input_ids=A		, training=A      )

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_forward():
             return model(A		, training=A      )

          lowerCamelCase__       :						int        =							encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

          return _inference







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     str		, A					:     int		, A					:     int      )       ->Callable[[], None]:
          lowerCamelCase__       :						Tuple        =							self.config_dict[model_name]

          if self.args.eager_mode is not False:
             raise ValueError('''Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'''      )

          if self.args.fpaa:
             raise NotImplementedError('''Mixed precision is currently not supported.'''      )

          lowerCamelCase__       :						Optional[int]        =							(
              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:
                lowerCamelCase__       :						Any        =							'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                lowerCamelCase__       :						List[str]        =							__import__('''transformers'''		, fromlist=[model_class]      )
                lowerCamelCase__       :						Optional[int]        =							getattr(A		, A      )
                lowerCamelCase__       :						Optional[Any]        =							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:
             lowerCamelCase__       :						List[str]        =							TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A      )

          # encoder-decoder has vocab size saved differently
          lowerCamelCase__       :						Optional[int]        =							config.vocab_size if hasattr(A		, '''vocab_size'''      ) else config.encoder.vocab_size
          lowerCamelCase__       :						Dict        =							random_input_ids(A		, A		, A      )

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_decoder_train():
             lowerCamelCase__       :						int        =							model(A		, decoder_input_ids=A		, labels=A		, training=A      )[0]
             lowerCamelCase__       :						List[Any]        =							tf.gradients(A		, model.trainable_variables      )
             return gradients

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_train():
             lowerCamelCase__       :						Optional[int]        =							model(A		, labels=A		, training=A      )[0]
             lowerCamelCase__       :						List[str]        =							tf.gradients(A		, model.trainable_variables      )
             return gradients

          lowerCamelCase__       :						Tuple        =							encoder_decoder_train if config.is_encoder_decoder else encoder_train

          return _train







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Any      )       ->float:
          with self.args.strategy.scope():
             try:
                if self.args.is_tpu or self.args.use_xla:
                   # run additional 10 times to stabilize compilation for tpu
                   logger.info('''Do inference on TPU. 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
                lowerCamelCase__       :						Optional[Any]        =							timeit.repeat(
                    A		, repeat=self.args.repeat		, number=1_0		, )

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







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     Callable[[], None]      )       ->[Memory, MemorySummary]:
          logger.info(
              '''Note that TensorFlow allocates more memory than '''
              '''it might need to speed up computation. '''
              '''The memory reported here corresponds to the memory '''
              '''reported by `nvidia-smi`, which can vary depending '''
              '''on total available memory on the GPU that is used.'''      )
          with self.args.strategy.scope():
             try:
                if self.args.trace_memory_line_by_line:
                   if not self.args.eager_mode:
                      raise ValueError(
                          '''`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'''
                          ''' consumption line by line.'''      )
                   lowerCamelCase__       :						Union[str, 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 `args.memory=False`'''      )
                elif self.args.is_gpu:
                   # 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.'''      )
                      lowerCamelCase__       :						Union[str, 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()
                      lowerCamelCase__       :						Any        =							nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
                      lowerCamelCase__       :						Optional[int]        =							nvml.nvmlDeviceGetMemoryInfo(A      )
                      lowerCamelCase__       :						List[Any]        =							meminfo.used
                      lowerCamelCase__       :						Union[str, Any]        =							Memory(A      )
                      # shutdown nvml
                      nvml.nvmlShutdown()
                else:
                   # cpu
                   if self.args.trace_memory_line_by_line:
                      logger.info(
                          '''When enabling line by line tracing, the max peak memory for CPU is inaccurate in'''
                          ''' TensorFlow.'''      )
                      lowerCamelCase__       :						Tuple        =							None
                   else:
                      lowerCamelCase__       :						Dict        =							measure_peak_memory_cpu(A      )
                      lowerCamelCase__       :						Optional[Any]        =							Memory(A      ) if isinstance(A		, A      ) else memory_bytes
                if self.args.trace_memory_line_by_line:
                   lowerCamelCase__       :						Union[str, Any]        =							stop_memory_tracing(A      )
                   if memory is None:
                      lowerCamelCase__       :						Dict        =							summary.total
                else:
                   lowerCamelCase__       :						Optional[int]        =							None

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



