

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


lowercase			:				Optional[Any]   =     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 					__lowercase  (						_UpperCamelCase				):





									"""simple docstring"""

									def __init__(					self    ,						__UpperCAmelCase = 1_01						)		->       List[Any]:
																A      :					Tuple      		=					length




									def __len__(					self						)		->       Optional[Any]:
																return self.length






									def __getitem__(					self    ,						__UpperCAmelCase						)		->       Tuple:
																return i







		class 					__lowercase  :





									"""simple docstring"""

									def __call__(					self    ,						__UpperCAmelCase						)		->       int:
																return {"input_ids": torch.tensor(__UpperCAmelCase						), "labels": torch.tensor(__UpperCAmelCase						)}







		class 					__lowercase  (						nn.Module				):





									"""simple docstring"""

									def __init__(					self						)		->       Union[str, Any]:
																super().__init__()
																# Add some (unused) params otherwise DDP will complain.
																A      :					Tuple      		=					nn.Linear(1_20    ,						80						)






									def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=None						)		->       Any:
																if labels is not None:
																							return torch.tensor(0.0    ,						device=input_ids.device						), input_ids
																else:
																							return input_ids







class 					__lowercase  (						_UpperCamelCase				):





							"""simple docstring"""

							@require_torch_neuroncore
							def       snake_case			(					self						)		->       Optional[int]:
														A      :					Any      		=					f'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
														A      :					Optional[int]      		=					self.get_auto_remove_tmp_dir()
														A      :					str      		=					f'--output_dir {output_dir}'.split()
														A      :					Optional[Any]      		=					["torchrun"] + distributed_args + args
														execute_subprocess_async(__UpperCAmelCase    ,						env=self.get_env()						)
														# successful return here == success - any errors would have caused an error in the sub-call







class 					__lowercase  (						_UpperCamelCase				):





							"""simple docstring"""

							@require_torch_multi_gpu
							def       snake_case			(					self						)		->       List[Any]:
														A      :					Any      		=					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()
														A      :					Union[str, Any]      		=					self.get_auto_remove_tmp_dir()
														A      :					Any      		=					f'--output_dir {output_dir}'.split()
														A      :					Any      		=					["torchrun"] + distributed_args + args
														execute_subprocess_async(__UpperCAmelCase    ,						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

		lowercase			:				Optional[int]   =     HfArgumentParser((TrainingArguments,))
		lowercase			:				int   =     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]:
				lowercase			:				Tuple   =     DummyDataset(dataset_length)






				def        snake_case__		(    lowerCamelCase_		):
											A      :					Optional[int]      		=					list(range(len(SCREAMING_SNAKE_CASE__		)		)		)
											A      :					Any      		=					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}

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

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

				lowercase			:				str   =     2

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

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

				lowercase			:				str   =     None











"""simple docstring"""




from manim import *






class       UpperCAmelCase_    (							_UpperCamelCase		):



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      str														=       Rectangle(height=0.5	,      width=0.5		)
           _UpperCAmelCase     :      List[Any]														=       Rectangle(height=0.25	,      width=0.25		)
           _UpperCAmelCase     :      Tuple														=       Rectangle(height=0.46	,      width=0.46		).set_stroke(width=0		)

           _UpperCAmelCase     :      List[Any]														=       [mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      List[str]														=       [mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      int														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Dict														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      int														=       VGroup(A	,      A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      str														=       Text("CPU"	,      font_size=2_4		)
           _UpperCAmelCase     :      Union[str, Any]														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           cpu.move_to([-2.5, -0.5, 0]		)
           self.add(A		)

           _UpperCAmelCase     :      Union[str, Any]														=       [mem.copy() for i in range(4		)]
           _UpperCAmelCase     :      Optional[int]														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Any														=       Text("GPU"	,      font_size=2_4		)
           _UpperCAmelCase     :      Dict														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           gpu.move_to([-1, -1, 0]		)
           self.add(A		)

           _UpperCAmelCase     :      Tuple														=       [mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      str														=       VGroup(*A		).arrange(A	,      buff=0		)

           _UpperCAmelCase     :      Any														=       Text("Model"	,      font_size=2_4		)
           _UpperCAmelCase     :      Any														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           model.move_to([3, -1.0, 0]		)
           self.add(A		)

           _UpperCAmelCase     :      List[str]														=       []
           _UpperCAmelCase     :      Optional[int]														=       []
           _UpperCAmelCase     :      str														=       []

           for i, rect in enumerate(A		):
                rect.set_stroke(A		)

                _UpperCAmelCase     :      Dict														=       Rectangle(height=0.46 / 4	,      width=0.46 / 3		).set_stroke(width=0.0		).set_fill(A	,      opacity=0.7		)

                if i == 0:
                     cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)	,      buff=0.02	,      direction=A		)
                     cpu_target.set_x(cpu_target.get_x() + 0.1		)
                elif i == 3:
                     cpu_target.next_to(model_cpu_arr[0]	,      direction=A	,      buff=0.0		)
                else:
                     cpu_target.next_to(model_cpu_arr[i - 1]	,      direction=A	,      buff=0.0		)
                self.add(A		)
                model_cpu_arr.append(A		)

           self.add(*A	,      *A	,      *A		)

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

           _UpperCAmelCase     :      List[str]														=       Text("Loaded Checkpoint"	,      font_size=2_4		)
           _UpperCAmelCase     :      Optional[int]														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           checkpoint.move_to([3, 0.5, 0]		)

           self.add(A		)

           _UpperCAmelCase     :      Union[str, Any]														=       []
           _UpperCAmelCase     :      Union[str, Any]														=       []

           for i, rect in enumerate(A		):
                _UpperCAmelCase     :      str														=       fill.copy().set_fill(A	,      opacity=0.7		)
                target.move_to(A		)
                ckpt_arr.append(A		)

                _UpperCAmelCase     :      Dict														=       target.copy()
                if i < 5:
                     cpu_target.move_to(cpu_left_col_base[i + 1]		)
                else:
                     cpu_target.move_to(cpu_right_col_base[i - 5]		)
                ckpt_cpu_arr.append(A		)
           self.add(*A	,      *A		)

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

           _UpperCAmelCase     :      List[Any]														=       MarkupText(
               f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'	,      font_size=1_8	,      )

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

           self.add(A	,      A		)

           _UpperCAmelCase     :      Tuple														=       MarkupText(
               f'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'	,      font_size=1_8	,      )

           blue_text.next_to(A	,      DOWN * 2.4	,      aligned_edge=key_text.get_left()		)
           self.add(A		)

           _UpperCAmelCase     :      Any														=       MarkupText(
               f'Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.'	,      font_size=2_4	,      )
           step_a.move_to([2, 2, 0]		)

           _UpperCAmelCase     :      int														=       [meta_mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      Optional[int]														=       [meta_mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      List[str]														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Optional[int]														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      List[Any]														=       VGroup(A	,      A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Union[str, Any]														=       Text("Disk"	,      font_size=2_4		)
           _UpperCAmelCase     :      int														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           disk.move_to([-4.0, -1.25, 0]		)
           self.play(Write(A	,      run_time=3		)	,      Write(A	,      run_time=1		)	,      Create(A	,      run_time=1		)		)

           _UpperCAmelCase     :      List[Any]														=       []
           for i, rect in enumerate(A		):
                _UpperCAmelCase     :      Any														=       rect.copy()
                target.generate_target()
                target.target.move_to(disk_left_col_base[i]		).scale(0.5		)
                animations.append(MoveToTarget(A	,      run_time=1.5		)		)
           self.play(*A		)

           self.play(FadeOut(A		)		)

           _UpperCAmelCase     :      int														=       MarkupText(f'Then, the checkpoint is removed from memory\nthrough garbage collection.'	,      font_size=2_4		)
           step_a.move_to([2, 2, 0]		)

           self.play(Write(A	,      run_time=3		)		)

           self.play(
               FadeOut(A	,      A	,      *A	,      *A		)	,      )

           self.wait()
