


"""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


lowerCAmelCase__      				=   '''sshleifer/mar_enro_6_3_student'''


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





         super().setUp()

         lowerCAmelCase     :     List[str]								=							cached_path(
             "https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"		,     extract_compressed_file=snake_case__		,     )
         lowerCAmelCase     :     Dict								=							f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""

       @slow
       @require_torch_gpu
       def        lowercase__							(						self						):






         """simple docstring"""





         MarianMTModel.from_pretrained(snake_case__						)







       @slow
       @require_torch_gpu
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							{
             "$MAX_LEN": 64,
             "$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
         lowerCAmelCase     :     List[str]								=							(self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py"						)[1].strip()
         lowerCAmelCase     :     Optional[Any]								=							bash_script.replace("\\\n"		,     ""						).strip().replace("\"$@\""		,     ""						)
         for k, v in env_vars_to_replace.items():
           lowerCAmelCase     :     Any								=							bash_script.replace(snake_case__		,     str(snake_case__						)						)
         lowerCAmelCase     :     Optional[Any]								=							self.get_auto_remove_tmp_dir()

         # bash_script = bash_script.replace("--fp16 ", "")
         lowerCAmelCase     :     Optional[Any]								=							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

         lowerCAmelCase     :     Optional[int]								=							["finetune.py"] + bash_script.split() + args
         with patch.object(snake_case__		,     "argv"		,     snake_case__						):
           lowerCAmelCase     :     Union[str, Any]								=							argparse.ArgumentParser()
           lowerCAmelCase     :     List[Any]								=							pl.Trainer.add_argparse_args(snake_case__						)
           lowerCAmelCase     :     Union[str, Any]								=							SummarizationModule.add_model_specific_args(snake_case__		,     os.getcwd()						)
           lowerCAmelCase     :     List[Any]								=							parser.parse_args()
           lowerCAmelCase     :     List[str]								=							main(snake_case__						)

         # Check metrics
         lowerCAmelCase     :     str								=							load_json(model.metrics_save_path						)
         lowerCAmelCase     :     List[str]								=							metrics["val"][0]
         lowerCAmelCase     :     List[str]								=							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}"""]		,     snake_case__						)

         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
         lowerCAmelCase     :     List[str]								=							os.listdir(snake_case__						)
         lowerCAmelCase     :     List[str]								=							[x for x in contents if x.endswith(".ckpt"						)][0]
         lowerCAmelCase     :     int								=							os.path.join(args.output_dir		,     snake_case__						)
         lowerCAmelCase     :     List[str]								=							torch.load(snake_case__		,     map_location="cpu"						)
         lowerCAmelCase     :     List[Any]								=							"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:
           lowerCAmelCase     :     int								=							{os.path.basename(snake_case__						) 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       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       @timeout_decorator.timeout(600						)
       @slow
       @require_torch_gpu
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
         lowerCAmelCase     :     Tuple								=							{
             "--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
         lowerCAmelCase     :     Union[str, Any]								=							(
             (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py"						)[1].strip()
         )
         lowerCAmelCase     :     Any								=							bash_script.replace("\\\n"		,     ""						).strip().replace("\"$@\""		,     ""						)
         lowerCAmelCase     :     Union[str, Any]								=							bash_script.replace("--fp16 "		,     " "						)

         for k, v in env_vars_to_replace.items():
           lowerCAmelCase     :     List[str]								=							bash_script.replace(snake_case__		,     str(snake_case__						)						)
         lowerCAmelCase     :     Union[str, Any]								=							self.get_auto_remove_tmp_dir()
         lowerCAmelCase     :     Union[str, Any]								=							bash_script.replace("--fp16"		,     ""						)
         lowerCAmelCase     :     Dict								=							6
         lowerCAmelCase     :     int								=							(
             ["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(snake_case__		,     "argv"		,     snake_case__						):
           lowerCAmelCase     :     Tuple								=							argparse.ArgumentParser()
           lowerCAmelCase     :     Union[str, Any]								=							pl.Trainer.add_argparse_args(snake_case__						)
           lowerCAmelCase     :     Optional[int]								=							SummarizationDistiller.add_model_specific_args(snake_case__		,     os.getcwd()						)
           lowerCAmelCase     :     Tuple								=							parser.parse_args()
           # assert args.gpus == gpus THIS BREAKS for multi_gpu

           lowerCAmelCase     :     Dict								=							distill_main(snake_case__						)

         # Check metrics
         lowerCAmelCase     :     Any								=							load_json(model.metrics_save_path						)
         lowerCAmelCase     :     int								=							metrics["val"][0]
         lowerCAmelCase     :     Any								=							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}"""]		,     snake_case__						)

         # check lightning ckpt can be loaded and has a reasonable statedict
         lowerCAmelCase     :     Tuple								=							os.listdir(snake_case__						)
         lowerCAmelCase     :     int								=							[x for x in contents if x.endswith(".ckpt"						)][0]
         lowerCAmelCase     :     Any								=							os.path.join(args.output_dir		,     snake_case__						)
         lowerCAmelCase     :     List[str]								=							torch.load(snake_case__		,     map_location="cpu"						)
         lowerCAmelCase     :     List[str]								=							"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:
           lowerCAmelCase     :     int								=							{os.path.basename(snake_case__						) 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 logging

from transformers.configuration_utils import PretrainedConfig


__snake_case						:Any      		=     logging.getLogger(__name__)

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[Any]											=						'''masked_bert'''



       def __init__(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     int=30_522     ,		__SCREAMING_SNAKE_CASE							:     str=768     ,		__SCREAMING_SNAKE_CASE							:     List[str]=12     ,		__SCREAMING_SNAKE_CASE							:     Any=12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=3_072     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="gelu"     ,		__SCREAMING_SNAKE_CASE							:     int=0.1     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.1     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=512     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.02     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1E-12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0     ,		__SCREAMING_SNAKE_CASE							:     List[str]="topK"     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="constant"     ,		__SCREAMING_SNAKE_CASE							:     int=0.0     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]     ,		):


           '''simple docstring'''

           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)

           __a						       =				vocab_size
           __a						       =				hidden_size
           __a						       =				num_hidden_layers
           __a						       =				num_attention_heads
           __a						       =				hidden_act
           __a						       =				intermediate_size
           __a						       =				hidden_dropout_prob
           __a						       =				attention_probs_dropout_prob
           __a						       =				max_position_embeddings
           __a						       =				type_vocab_size
           __a						       =				initializer_range
           __a						       =				layer_norm_eps
           __a						       =				pruning_method
           __a						       =				mask_init
           __a						       =				mask_scale





