


import os

import numpy
import onnx




def        snake_case_					(  snake_case      ,							snake_case					)  ->    int:
       lowercase__:       Optional[Any]        =       a.name
       lowercase__:       int        =       b.name

       lowercase__:       Union[str, Any]        =       ''
       lowercase__:       int        =       ''

       lowercase__:       List[str]        =       a == b

       lowercase__:       int        =       name_a
       lowercase__:       Any        =       name_b

       return res




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    Optional[Any]:
       for i, input_name in enumerate(node_proto.input					):
              if input_name == name:
                     node_proto.input.insert(snake_case      ,							snake_case					)
                     node_proto.input.pop(i + 1					)

       if node_proto.op_type == "If":
              _graph_replace_input_with(node_proto.attribute[0].g      ,							snake_case      ,							snake_case					)
              _graph_replace_input_with(node_proto.attribute[1].g      ,							snake_case      ,							snake_case					)
       if node_proto.op_type == "Loop":
              _graph_replace_input_with(node_proto.attribute[0].g      ,							snake_case      ,							snake_case					)




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    List[Any]:
       for n in graph_proto.node:
              _node_replace_input_with(snake_case      ,							snake_case      ,							snake_case					)




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    List[Any]:
       lowercase__:       List[str]        =       list(model.graph.initializer					)
       lowercase__:       Union[str, Any]        =       list(model_without_ext.graph.initializer					)
       for i, ref_i in ind_to_replace:
              assert inits_with_data[i].name == inits[i].name
              assert inits_with_data[ref_i].name == inits[ref_i].name
              assert i > ref_i

              lowercase__:       int        =       inits[i].name
              lowercase__:       Optional[Any]        =       inits[ref_i].name

              model_without_ext.graph.initializer.remove(inits[i]					)

              # for n in model.graph.node:
              _graph_replace_input_with(model_without_ext.graph      ,							snake_case      ,							snake_case					)




def        snake_case_					(  snake_case					)  ->    Tuple:
       lowercase__:       Optional[Any]        =       os.path.dirname(snake_case					)
       lowercase__:       Optional[int]        =       os.path.basename(snake_case					)

       lowercase__:       List[str]        =       onnx.load(os.path.join(snake_case      ,							snake_case					)					)

       lowercase__:       Any        =       list(model.graph.initializer					)

       lowercase__:       Optional[int]        =       set()
       lowercase__:       List[Any]        =       {}
       lowercase__:       Union[str, Any]        =       []

       lowercase__:       str        =       0

       for i in range(len(snake_case					)					):
              if i in dup_set:
                     continue

              for j in range(i + 1      ,							len(snake_case					)					):
                     if j in dup_set:
                            continue
                     if _is_equal_tensor_proto(inits[i]      ,							inits[j]					):
                            dup_set.add(snake_case					)
                            dup_set.add(snake_case					)

                            lowercase__:       Tuple        =       inits[j].data_type
                            lowercase__:       Any        =       numpy.prod(inits[j].dims					)
                            if dtype == 1:
                                   mem_size *= 4
                            elif dtype == 6:
                                   mem_size *= 4
                            elif dtype == 7 or dtype == 11:
                                   mem_size *= 8
                            else:
                                   print('unexpected data type: '      ,							snake_case					)
                            total_reduced_size += mem_size

                            lowercase__:       str        =       inits[i].name
                            lowercase__:       str        =       inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(snake_case					)
                            else:
                                   lowercase__:       Tuple        =       [name_j]
                            ind_to_replace.append((j, i)					)

       print('total reduced size: '      ,							total_reduced_size / 10_24 / 10_24 / 10_24      ,							'GB'					)

       lowercase__:       Dict        =       sorted(snake_case					)
       _remove_dup_initializers_from_model(snake_case      ,							snake_case      ,							snake_case					)

       lowercase__:       str        =       'optimized_' + model_file_name
       lowercase__:       Optional[Any]        =       os.path.join(snake_case      ,							snake_case					)
       onnx.save(snake_case      ,							snake_case					)

       return new_model







from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


__lowerCAmelCase			=							logging.get_logger(__name__)


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset

 from transformers import Trainer





 class   __a      (   __UpperCamelCase				):
  def __init__(       self    ,			lowerCAmelCase__ = 101			) ->    Any:




         '''simple docstring'''
         lowercase__:       Any        =       length
  def __len__(       self			) ->    List[Any]:




         '''simple docstring'''
         return self.length
  def __getitem__(       self    ,			lowerCAmelCase__			) ->    int:




         '''simple docstring'''
         return i





 class   __a      :
  def __call__(       self    ,			lowerCAmelCase__			) ->    List[str]:




         '''simple docstring'''
         return {"input_ids": torch.tensor(lowerCAmelCase__			), "labels": torch.tensor(lowerCAmelCase__			)}





 class   __a      (   nn.Module				):
  def __init__(       self			) ->    Tuple:




         '''simple docstring'''
         super().__init__()
         # Add some (unused) params otherwise DDP will complain.
         lowercase__:       List[str]        =       nn.Linear(120    ,			80			)
  def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=None			) ->    int:




         '''simple docstring'''
         if labels is not None:
                return torch.tensor(0.0    ,			device=input_ids.device			), input_ids
         else:
                return input_ids





class   __a      (   __UpperCamelCase				):
 @require_torch_neuroncore
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       int        =       F'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
        lowercase__:       Tuple        =       self.get_auto_remove_tmp_dir()
        lowercase__:       Optional[int]        =       F'--output_dir {output_dir}'.split()
        lowercase__:       int        =       ['torchrun'] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase__    ,			env=self.get_env()			)
        # successful return here == success - any errors would have caused an error in the sub-call





class   __a      (   __UpperCamelCase				):
 @require_torch_multi_gpu
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       List[str]        =       F'--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
        lowercase__:       Tuple        =       self.get_auto_remove_tmp_dir()
        lowercase__:       List[str]        =       F'--output_dir {output_dir}'.split()
        lowercase__:       int        =       ['torchrun'] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase__    ,			env=self.get_env()			)
        # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
 # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
 #
 # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

 __lowerCAmelCase			=							HfArgumentParser((TrainingArguments,))
 __lowerCAmelCase			=							parser.parse_args_into_dataclasses()[0]

 logger.warning(
     F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
     F'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
 )

 # Essentially, what we want to verify in the distributed case is that we get all samples back,
 # in the right order. (this is crucial for prediction for instance)
 for dataset_length in [1_01, 40, 7]:
  __lowerCAmelCase			=							DummyDataset(dataset_length)




  def        snake_case_					(  snake_case					)  ->    Dict:
         lowercase__:       str        =       list(range(len(snake_case					)					)					)
         lowercase__:       Tuple        =       p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
         if not success and training_args.local_rank == 0:
                logger.warning(
                    'Predictions and/or labels do not match expected results:\n  - predictions: '
                    f'{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'					)
         return {"success": success}

  __lowerCAmelCase			=							Trainer(
      model=DummyModel(),
      args=training_args,
      data_collator=DummyDataCollator(),
      eval_dataset=dataset,
      compute_metrics=compute_metrics,
  )
  __lowerCAmelCase			=							trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  __lowerCAmelCase			=							trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)

  __lowerCAmelCase			=							2

  __lowerCAmelCase			=							trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  __lowerCAmelCase			=							trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)




  __lowerCAmelCase			=							None


