
import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


_A						      =				"sshleifer/mar_enro_6_3_student"







class   lowerCamelCase	(						A_						):





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Union[str, Any]:
         super().setUp()

         snake_case				  =      cached_path(
             "https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"       ,					extract_compressed_file=_A       ,					)
         snake_case				  =      f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'





     @slow
     @require_torch_gpu
     def 	UpperCAmelCase(self   :					List[str]     )		->		int:
         MarianMTModel.from_pretrained(_A     )





     @slow
     @require_torch_gpu
     def 	UpperCAmelCase(self   :					Tuple     )		->		Tuple:
         snake_case				  =      {
             "$MAX_LEN": 6_4,
             "$BS": 6_4,
             "$GAS": 1,
             "$ENRO_DIR": self.data_dir,
             "facebook/mbart-large-cc25": MARIAN_MODEL,
             # "val_check_interval=0.25": "val_check_interval=1.0",
             "--learning_rate=3e-5": "--learning_rate 3e-4",
             "--num_train_epochs 6": "--num_train_epochs 1",
         }

         # Clean up bash script
         snake_case				  =      (self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py"     )[1].strip()
         snake_case				  =      bash_script.replace("\\\n"       ,					""     ).strip().replace("\"$@\""       ,					""     )
         for k, v in env_vars_to_replace.items():
             snake_case				  =      bash_script.replace(_A       ,					str(_A     )     )
         snake_case				  =      self.get_auto_remove_tmp_dir()

         # bash_script = bash_script.replace("--fp16 ", "")
         snake_case				  =      f'\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        '.split()
         # XXX: args.gpus > 1 : handle multi_gpu in the future

         snake_case				  =      ["finetune.py"] + bash_script.split() + args
         with patch.object(_A       ,					"argv"       ,					_A     ):
             snake_case				  =      argparse.ArgumentParser()
             snake_case				  =      pl.Trainer.add_argparse_args(_A     )
             snake_case				  =      SummarizationModule.add_model_specific_args(_A       ,					os.getcwd()     )
             snake_case				  =      parser.parse_args()
             snake_case				  =      main(_A     )

         # Check metrics
         snake_case				  =      load_json(model.metrics_save_path     )
         snake_case				  =      metrics["val"][0]
         snake_case				  =      metrics["val"][-1]
         self.assertEqual(len(metrics["val"]     )       ,					(args.max_epochs / args.val_check_interval)     )
         assert isinstance(last_step_stats[f'val_avg_{model.val_metric}']       ,					_A     )

         self.assertGreater(last_step_stats["val_avg_gen_time"]       ,					0.01     )
         # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
         self.assertLessEqual(last_step_stats["val_avg_gen_time"]       ,					1.0     )

         # test learning requirements:

         # 1. BLEU improves over the course of training by more than 2 pts
         self.assertGreater(last_step_stats["val_avg_bleu"] - first_step_stats["val_avg_bleu"]       ,					2     )

         # 2. BLEU finishes above 17
         self.assertGreater(last_step_stats["val_avg_bleu"]       ,					1_7     )

         # 3. test BLEU and val BLEU within ~1.1 pt.
         self.assertLess(abs(metrics["val"][-1]["val_avg_bleu"] - metrics["test"][-1]["test_avg_bleu"]     )       ,					1.1     )

         # check lightning ckpt can be loaded and has a reasonable statedict
         snake_case				  =      os.listdir(_A     )
         snake_case				  =      [x for x in contents if x.endswith(".ckpt"     )][0]
         snake_case				  =      os.path.join(args.output_dir       ,					_A     )
         snake_case				  =      torch.load(_A       ,					map_location="cpu"     )
         snake_case				  =      "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
         assert expected_key in ckpt["state_dict"]
         assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

         # TODO: turn on args.do_predict when PL bug fixed.
         if args.do_predict:
             snake_case				  =      {os.path.basename(_A     ) for p in contents}
             assert "test_generations.txt" in contents
             assert "test_results.txt" in contents
             # assert len(metrics["val"]) ==  desired_n_evals
             assert len(metrics["test"]     ) == 1







class   lowerCamelCase	(						A_						):





     @timeout_decorator.timeout(6_0_0     )
     @slow
     @require_torch_gpu
     def 	UpperCAmelCase(self   :					int     )		->		List[str]:
         snake_case				  =      f'{self.test_file_dir_str}/test_data/wmt_en_ro'
         snake_case				  =      {
             "--fp16_opt_level=O1": "",
             "$MAX_LEN": 1_2_8,
             "$BS": 1_6,
             "$GAS": 1,
             "$ENRO_DIR": data_dir,
             "$m": "sshleifer/student_marian_en_ro_6_1",
             "val_check_interval=0.25": "val_check_interval=1.0",
         }

         # Clean up bash script
         snake_case				  =      (
             (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py"     )[1].strip()
         )
         snake_case				  =      bash_script.replace("\\\n"       ,					""     ).strip().replace("\"$@\""       ,					""     )
         snake_case				  =      bash_script.replace("--fp16 "       ,					" "     )

         for k, v in env_vars_to_replace.items():
             snake_case				  =      bash_script.replace(_A       ,					str(_A     )     )
         snake_case				  =      self.get_auto_remove_tmp_dir()
         snake_case				  =      bash_script.replace("--fp16"       ,					""     )
         snake_case				  =      6
         snake_case				  =      (
             ["distillation.py"]
             + bash_script.split()
             + [
                 f'--output_dir={output_dir}',
                 "--gpus=1",
                 "--learning_rate=1e-3",
                 f'--num_train_epochs={epochs}',
                 "--warmup_steps=10",
                 "--val_check_interval=1.0",
                 "--do_predict",
             ]
         )
         with patch.object(_A       ,					"argv"       ,					_A     ):
             snake_case				  =      argparse.ArgumentParser()
             snake_case				  =      pl.Trainer.add_argparse_args(_A     )
             snake_case				  =      SummarizationDistiller.add_model_specific_args(_A       ,					os.getcwd()     )
             snake_case				  =      parser.parse_args()
             # assert args.gpus == gpus THIS BREAKS for multi_gpu

             snake_case				  =      distill_main(_A     )

         # Check metrics
         snake_case				  =      load_json(model.metrics_save_path     )
         snake_case				  =      metrics["val"][0]
         snake_case				  =      metrics["val"][-1]
         assert len(metrics["val"]     ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

         assert last_step_stats["val_avg_gen_time"] >= 0.01

         assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
         assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
         assert isinstance(last_step_stats[f'val_avg_{model.val_metric}']       ,					_A     )

         # check lightning ckpt can be loaded and has a reasonable statedict
         snake_case				  =      os.listdir(_A     )
         snake_case				  =      [x for x in contents if x.endswith(".ckpt"     )][0]
         snake_case				  =      os.path.join(args.output_dir       ,					_A     )
         snake_case				  =      torch.load(_A       ,					map_location="cpu"     )
         snake_case				  =      "model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
         assert expected_key in ckpt["state_dict"]
         assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

         # TODO: turn on args.do_predict when PL bug fixed.
         if args.do_predict:
             snake_case				  =      {os.path.basename(_A     ) for p in contents}
             assert "test_generations.txt" in contents
             assert "test_results.txt" in contents
             # assert len(metrics["val"]) ==  desired_n_evals
             assert len(metrics["test"]     ) == 1



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class   lowerCamelCase	(						unittest.TestCase						):





     def 	UpperCAmelCase(self   :					Dict     )		->		Dict:
         snake_case				  =      inspect.getfile(accelerate.test_utils     )
         snake_case				  =      os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_script.py"]     )
         snake_case				  =      os.path.sep.join(
             mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_distributed_data_loop.py"]     )
         snake_case				  =      os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ["scripts", "test_ops.py"]     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					List[Any]     )		->		int:
         print(f'Found {torch.cuda.device_count()} devices.'     )
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
         with patch_environment(omp_num_threads=1     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					Any     )		->		Optional[Any]:
         print(f'Found {torch.cuda.device_count()} devices.'     )
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', self.operation_file_path]
         print(f'Command: {cmd}'     )
         with patch_environment(omp_num_threads=1     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Dict:
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__     )]
         with patch_environment(omp_num_threads=1     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )





     @require_multi_gpu
     def 	UpperCAmelCase(self   :					List[str]     )		->		Dict:
         print(f'Found {torch.cuda.device_count()} devices, using 2 devices only'     )
         snake_case				  =      ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', self.data_loop_file_path]
         with patch_environment(omp_num_threads=1       ,					cuda_visible_devices="0,1"     ):
             execute_subprocess_async(_A       ,					env=os.environ.copy()     )


if __name__ == "__main__":
   _A						      =				Accelerator()
   _A						      =				(accelerator.state.process_index + 2, 10)
   _A						      =				torch.randint(0, 10, shape).to(accelerator.device)

   _A						      =				""

   _A						      =				accelerator.pad_across_processes(tensor)
   if tensora.shape[0] != accelerator.state.num_processes + 1:
      error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
   if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
      error_msg += "Tensors have different values."
   if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
      error_msg += "Padding was not done with the right value (0)."

   _A						      =				accelerator.pad_across_processes(tensor, pad_first=True)
   if tensora.shape[0] != accelerator.state.num_processes + 1:
      error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
   _A						      =				accelerator.state.num_processes - accelerator.state.process_index - 1
   if not torch.equal(tensora[index:], tensor):
      error_msg += "Tensors have different values."
   if not torch.all(tensora[:index] == 0):
      error_msg += "Padding was not done with the right value (0)."

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)

