


from manim import *





class   __a      (   __UpperCamelCase				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       Dict        =       Rectangle(height=0.5    ,			width=0.5			)
        lowercase__:       str        =       Rectangle(height=0.4_6    ,			width=0.4_6			).set_stroke(width=0			)

        lowercase__:       Optional[Any]        =       [mem.copy() for i in range(6			)]
        lowercase__:       str        =       [mem.copy() for i in range(6			)]
        lowercase__:       Dict        =       VGroup(*lowerCAmelCase__			).arrange(lowerCAmelCase__    ,			buff=0			)
        lowercase__:       Optional[int]        =       VGroup(*lowerCAmelCase__			).arrange(lowerCAmelCase__    ,			buff=0			)
        lowercase__:       Optional[Any]        =       VGroup(lowerCAmelCase__    ,			lowerCAmelCase__			).arrange(lowerCAmelCase__    ,			buff=0			)
        lowercase__:       Optional[Any]        =       Text('CPU'    ,			font_size=24			)
        lowercase__:       List[str]        =       Group(lowerCAmelCase__    ,			lowerCAmelCase__			).arrange(lowerCAmelCase__    ,			buff=0.5    ,			aligned_edge=lowerCAmelCase__			)
        cpu.move_to([-2.5, -0.5, 0]			)
        self.add(lowerCAmelCase__			)

        lowercase__:       Dict        =       [mem.copy() for i in range(1			)]
        lowercase__:       List[Any]        =       VGroup(*lowerCAmelCase__			).arrange(lowerCAmelCase__    ,			buff=0			)
        lowercase__:       int        =       Text('GPU'    ,			font_size=24			)
        lowercase__:       Dict        =       Group(lowerCAmelCase__    ,			lowerCAmelCase__			).arrange(lowerCAmelCase__    ,			buff=0.5    ,			aligned_edge=lowerCAmelCase__			)
        gpu.align_to(lowerCAmelCase__    ,			lowerCAmelCase__			)
        gpu.set_x(gpu.get_x() - 1			)

        self.add(lowerCAmelCase__			)

        lowercase__:       List[str]        =       [mem.copy() for i in range(6			)]
        lowercase__:       Dict        =       VGroup(*lowerCAmelCase__			).arrange(lowerCAmelCase__    ,			buff=0			)

        lowercase__:       Union[str, Any]        =       Text('Model'    ,			font_size=24			)
        lowercase__:       Any        =       Group(lowerCAmelCase__    ,			lowerCAmelCase__			).arrange(lowerCAmelCase__    ,			buff=0.5    ,			aligned_edge=lowerCAmelCase__			)
        model.move_to([3, -1.0, 0]			)

        self.play(
            Create(lowerCAmelCase__    ,			run_time=1			)    ,			Create(lowerCAmelCase__    ,			run_time=1			)    ,			Create(lowerCAmelCase__    ,			run_time=1			)    ,			)

        lowercase__:       int        =       MarkupText(
            F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'    ,			font_size=24    ,			)

        lowercase__:       List[Any]        =       Square(side_length=2.2			)
        key.move_to([-5, 2, 0]			)

        lowercase__:       Optional[int]        =       MarkupText(
            F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'    ,			font_size=18    ,			)

        key_text.move_to([-5, 2.4, 0]			)

        step_a.move_to([2, 2, 0]			)
        self.play(Write(lowerCAmelCase__    ,			run_time=2.5			)    ,			Write(lowerCAmelCase__			)    ,			Write(lowerCAmelCase__			)			)

        self.add(lowerCAmelCase__			)

        lowercase__:       Dict        =       []
        lowercase__:       Optional[Any]        =       []
        lowercase__:       List[Any]        =       []
        for i, rect in enumerate(lowerCAmelCase__			):
               lowercase__:       Union[str, Any]        =       Rectangle(height=0.4_6    ,			width=0.4_6			).set_stroke(width=0.0			).set_fill(lowerCAmelCase__    ,			opacity=0.7			)
               cpu_target.move_to(lowerCAmelCase__			)
               cpu_target.generate_target()
               lowercase__:       List[Any]        =       0.4_6 / 4
               lowercase__:       Optional[int]        =       0.4_6 / 3

               if i == 0:
                      cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT			)    ,			buff=0.0_2    ,			direction=lowerCAmelCase__			)
                      cpu_target.target.set_x(cpu_target.target.get_x() + 0.1			)
               elif i == 3:
                      cpu_target.target.next_to(cpu_targs[0].target    ,			direction=lowerCAmelCase__    ,			buff=0.0			)
               else:
                      cpu_target.target.next_to(cpu_targs[i - 1].target    ,			direction=lowerCAmelCase__    ,			buff=0.0			)
               cpu_targs.append(lowerCAmelCase__			)

               first_animations.append(rect.animate(run_time=0.5			).set_stroke(lowerCAmelCase__			)			)
               second_animations.append(MoveToTarget(lowerCAmelCase__    ,			run_time=1.5			)			)

        self.play(*lowerCAmelCase__			)
        self.play(*lowerCAmelCase__			)

        self.wait()






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


