




'''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_ 		=      "sshleifer/mar_enro_6_3_student"





class    __lowercase						(  __lowercase ):







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     super().setUp()

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







   @slow
   @require_torch_gpu
   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     MarianMTModel.from_pretrained(__A      )







   @slow
   @require_torch_gpu
   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __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(__A      ,							str(__A      )      )
     __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(__A      ,							'argv'      ,							__A      ):
       __a			=					argparse.ArgumentParser()
       __a			=					pl.Trainer.add_argparse_args(__A      )
       __a			=					SummarizationModule.add_model_specific_args(__A      ,							os.getcwd()      )
       __a			=					parser.parse_args()
       __a			=					main(__A      )

     # 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}"]      ,							__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
     __a			=					os.listdir(__A      )
     __a			=					[x for x in contents if x.endswith('.ckpt'      )][0]
     __a			=					os.path.join(args.output_dir      ,							__A      )
     __a			=					torch.load(__A      ,							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(__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    __lowercase						(  __lowercase ):







   @timeout_decorator.timeout(600      )
   @slow
   @require_torch_gpu
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __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(__A      ,							str(__A      )      )
     __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(__A      ,							'argv'      ,							__A      ):
       __a			=					argparse.ArgumentParser()
       __a			=					pl.Trainer.add_argparse_args(__A      )
       __a			=					SummarizationDistiller.add_model_specific_args(__A      ,							os.getcwd()      )
       __a			=					parser.parse_args()
       # assert args.gpus == gpus THIS BREAKS for multi_gpu

       __a			=					distill_main(__A      )

     # 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"] >= 0.01

     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
     __a			=					os.listdir(__A      )
     __a			=					[x for x in contents if x.endswith('.ckpt'      )][0]
     __a			=					os.path.join(args.output_dir      ,							__A      )
     __a			=					torch.load(__A      ,							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(__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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_ 		=      {
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

UpperCAmelCase_ 		=      {
    "junnyu/roformer_chinese_small": 15_36,
    "junnyu/roformer_chinese_base": 15_36,
    "junnyu/roformer_chinese_char_small": 5_12,
    "junnyu/roformer_chinese_char_base": 5_12,
    "junnyu/roformer_small_discriminator": 1_28,
    "junnyu/roformer_small_generator": 1_28,
}


UpperCAmelCase_ 		=      {
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}





class    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      PRETRAINED_INIT_CONFIGURATION
   _a							=      RoFormerTokenizer







   def __init__(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=True      ,							UpperCamelCase="[UNK]"      ,							UpperCamelCase="[SEP]"      ,							UpperCamelCase="[PAD]"      ,							UpperCamelCase="[CLS]"      ,							UpperCamelCase="[MASK]"      ,							UpperCamelCase=True      ,							UpperCamelCase=None      ,							**UpperCamelCase      ,							)				->    int:
     super().__init__(
         UpperCamelCase      ,							tokenizer_file=UpperCamelCase      ,							do_lower_case=UpperCamelCase      ,							unk_token=UpperCamelCase      ,							sep_token=UpperCamelCase      ,							pad_token=UpperCamelCase      ,							cls_token=UpperCamelCase      ,							mask_token=UpperCamelCase      ,							tokenize_chinese_chars=UpperCamelCase      ,							strip_accents=UpperCamelCase      ,							**UpperCamelCase      ,							)

     __a			=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
     if (
         pre_tok_state.get('lowercase'      ,							UpperCamelCase      ) != do_lower_case
         or pre_tok_state.get('strip_accents'      ,							UpperCamelCase      ) != strip_accents
     ):
       __a			=					getattr(UpperCamelCase      ,							pre_tok_state.pop('type'      )      )
       __a			=					do_lower_case
       __a			=					strip_accents
       __a			=					pre_tok_class(**UpperCamelCase      )

     __a			=					do_lower_case







   def __getstate__(	self      )				->    List[Any]:
     __a			=					self.__dict__.copy()
     __a			=					BertPreTokenizer()
     return state







   def __setstate__(	self      ,							UpperCamelCase      )				->    Optional[int]:
     __a			=					d
     __a			=					self.__dict__['_tokenizer'].get_vocab()
     __a			=					PreTokenizer.custom(JiebaPreTokenizer(UpperCamelCase      )      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    int:
     __a			=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

     return output







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __a			=					[self.sep_token_id]
     __a			=					[self.cls_token_id]
     if token_ids_a is None:
       return len(cls + token_ids_a + sep      ) * [0]
     return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Tuple[str]:
     __a			=					self._tokenizer.model.save(UpperCamelCase      ,							name=UpperCamelCase      )
     return tuple(UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=False      ,							**UpperCamelCase      ,							)				->    List[Any]:
     __a			=					BertPreTokenizer()
     return super().save_pretrained(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )



