


"""simple docstring"""


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


UpperCamelCase    :					List[str]							   =   'sshleifer/mar_enro_6_3_student'



class       lowerCamelCase__    ( snake_case_      ):







						def __a    (				self :     int		):
								super().setUp()

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







						@slow
						@require_torch_gpu
						def __a    (				self :     Union[str, Any]		):
								MarianMTModel.from_pretrained(lowercase__		)







						@slow
						@require_torch_gpu
						def __a    (				self :     Tuple		):
								A       							=			{
								    """$MAX_LEN""": 64,
								    """$BS""": 64,
								    """$GAS""": 1,
								    """$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
								A       							=			(self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split('finetune.py'		)[1].strip()
								A       							=			bash_script.replace('\\\n'      ,      ''		).strip().replace('\"$@\"'      ,      ''		)
								for k, v in env_vars_to_replace.items():
										A       							=			bash_script.replace(lowercase__      ,      str(lowercase__		)		)
								A       							=			self.get_auto_remove_tmp_dir()

								# bash_script = bash_script.replace("--fp16 ", "")
								A       							=			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

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

								# Check metrics
								A       							=			load_json(model.metrics_save_path		)
								A       							=			metrics["""val"""][0]
								A       							=			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}']      ,      lowercase__		)

								self.assertGreater(last_step_stats['val_avg_gen_time']      ,      0.0_1		)
								# 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']      ,      17		)

								# 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
								A       							=			os.listdir(lowercase__		)
								A       							=			[x for x in contents if x.endswith('.ckpt'		)][0]
								A       							=			os.path.join(args.output_dir      ,      lowercase__		)
								A       							=			torch.load(lowercase__      ,      map_location='cpu'		)
								A       							=			"""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:
										A       							=			{os.path.basename(lowercase__		) 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__    ( snake_case_      ):







						@timeout_decorator.timeout(600		)
						@slow
						@require_torch_gpu
						def __a    (				self :     Optional[int]		):
								A       							=			f'{self.test_file_dir_str}/test_data/wmt_en_ro'
								A       							=			{
								    """--fp16_opt_level=O1""": """""",
								    """$MAX_LEN""": 128,
								    """$BS""": 16,
								    """$GAS""": 1,
								    """$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
								A       							=			(
								    (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split('distillation.py'		)[1].strip()
								)
								A       							=			bash_script.replace('\\\n'      ,      ''		).strip().replace('\"$@\"'      ,      ''		)
								A       							=			bash_script.replace('--fp16 '      ,      ' '		)

								for k, v in env_vars_to_replace.items():
										A       							=			bash_script.replace(lowercase__      ,      str(lowercase__		)		)
								A       							=			self.get_auto_remove_tmp_dir()
								A       							=			bash_script.replace('--fp16'      ,      ''		)
								A       							=			6
								A       							=			(
								    ["""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(lowercase__      ,      'argv'      ,      lowercase__		):
										A       							=			argparse.ArgumentParser()
										A       							=			pl.Trainer.add_argparse_args(lowercase__		)
										A       							=			SummarizationDistiller.add_model_specific_args(lowercase__      ,      os.getcwd()		)
										A       							=			parser.parse_args()
										# assert args.gpus == gpus THIS BREAKS for multi_gpu

										A       							=			distill_main(lowercase__		)

								# Check metrics
								A       							=			load_json(model.metrics_save_path		)
								A       							=			metrics["""val"""][0]
								A       							=			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"] >= 0.0_1

								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}']      ,      lowercase__		)

								# check lightning ckpt can be loaded and has a reasonable statedict
								A       							=			os.listdir(lowercase__		)
								A       							=			[x for x in contents if x.endswith('.ckpt'		)][0]
								A       							=			os.path.join(args.output_dir      ,      lowercase__		)
								A       							=			torch.load(lowercase__      ,      map_location='cpu'		)
								A       							=			"""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:
										A       							=			{os.path.basename(lowercase__		) 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











'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase_      :  Dict				     =  {
    'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  int				     =  [
     'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GraphormerForGraphClassification',
     'GraphormerModel',
     'GraphormerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_      :  Tuple				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
