"""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


_A   					=    'sshleifer/mar_enro_6_3_student'

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Dict:
									super().setUp()

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



					@slow
					@require_torch_gpu
					def 			lowerCAmelCase_					(						self			:				Any		)							->       List[Any]:
									MarianMTModel.from_pretrained(__A		)



					@slow
					@require_torch_gpu
					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				{
									    "$MAX_LEN": 64,
									    "$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
									SCREAMING_SNAKE_CASE__		  =				(self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py"		)[1].strip()
									SCREAMING_SNAKE_CASE__		  =				bash_script.replace("\\\n"   , ""		).strip().replace("\"$@\""   , ""		)
									for k, v in env_vars_to_replace.items():
													SCREAMING_SNAKE_CASE__		  =				bash_script.replace(__A   , str(__A		)		)
									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()

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

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

									# Check metrics
									SCREAMING_SNAKE_CASE__		  =				load_json(model.metrics_save_path		)
									SCREAMING_SNAKE_CASE__		  =				metrics["val"][0]
									SCREAMING_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"]   , 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
									SCREAMING_SNAKE_CASE__		  =				os.listdir(__A		)
									SCREAMING_SNAKE_CASE__		  =				[x for x in contents if x.endswith(".ckpt"		)][0]
									SCREAMING_SNAKE_CASE__		  =				os.path.join(args.output_dir   , __A		)
									SCREAMING_SNAKE_CASE__		  =				torch.load(__A   , map_location="cpu"		)
									SCREAMING_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:
													SCREAMING_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		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					@timeout_decorator.timeout(600		)
					@slow
					@require_torch_gpu
					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				F"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
									SCREAMING_SNAKE_CASE__		  =				{
									    "--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
									SCREAMING_SNAKE_CASE__		  =				(
									    (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py"		)[1].strip()
									)
									SCREAMING_SNAKE_CASE__		  =				bash_script.replace("\\\n"   , ""		).strip().replace("\"$@\""   , ""		)
									SCREAMING_SNAKE_CASE__		  =				bash_script.replace("--fp16 "   , " "		)

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

													SCREAMING_SNAKE_CASE__		  =				distill_main(__A		)

									# Check metrics
									SCREAMING_SNAKE_CASE__		  =				load_json(model.metrics_save_path		)
									SCREAMING_SNAKE_CASE__		  =				metrics["val"][0]
									SCREAMING_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
									SCREAMING_SNAKE_CASE__		  =				os.listdir(__A		)
									SCREAMING_SNAKE_CASE__		  =				[x for x in contents if x.endswith(".ckpt"		)][0]
									SCREAMING_SNAKE_CASE__		  =				os.path.join(args.output_dir   , __A		)
									SCREAMING_SNAKE_CASE__		  =				torch.load(__A   , map_location="cpu"		)
									SCREAMING_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:
													SCREAMING_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





"""simple docstring"""






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_A   					=    ['small', 'medium', 'large']

_A   					=    'lm_head.decoder.weight'
_A   					=    'lm_head.weight'




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				torch.load(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				d.pop(__UpperCAmelCase       )
				os.makedirs(__UpperCAmelCase  ,					exist_ok=__UpperCAmelCase       )
				torch.save(__UpperCAmelCase  ,					os.path.join(__UpperCAmelCase  ,					__UpperCAmelCase       )       )


if __name__ == "__main__":
						_A   					=    argparse.ArgumentParser()
						parser.add_argument('--dialogpt_path', default='.', type=str)
						_A   					=    parser.parse_args()
						for MODEL in DIALOGPT_MODELS:
												_A   					=    os.path.join(args.dialogpt_path, F'{MODEL}_ft.pkl')
												_A   					=    F'./DialoGPT-{MODEL}'
												convert_dialogpt_checkpoint(
												    checkpoint_path,
												    pytorch_dump_folder_path,
												)




