




import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase    :		Any			   =       logging.get_logger(__name__)

lowerCamelCase    :		Dict			   =       "▁"

lowerCamelCase    :		List[Any]			   =       {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowerCamelCase    :		List[str]			   =       {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

lowerCamelCase    :		Tuple			   =       {
    "facebook/m2m100_418M": 1_024,
}

# fmt: off
lowerCamelCase    :		Any			   =       {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						VOCAB_FILES_NAMES
 UpperCamelCase						       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase						       =						PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase						       =						['''input_ids''', '''attention_mask''']

 UpperCamelCase						       =						[]
 UpperCamelCase						       =						[]

 def __init__(    self :  Dict						,							A_ :  Tuple						,							A_ :  Optional[int]						,							A_ :  int=None						,							A_ :  Optional[Any]=None						,							A_ :  int="<s>"						,							A_ :  Dict="</s>"						,							A_ :  List[Any]="</s>"						,							A_ :  int="<pad>"						,							A_ :  str="<unk>"						,							A_ :  int="m2m100"						,							A_ :  Optional[Dict[str, Any]] = None						,							A_ :  Optional[int]=8						,							**A_ :  Optional[Any]						,							)       ->     None:

       """simple docstring"""






       lowerCamelCase_							=   {} if sp_model_kwargs is None else sp_model_kwargs

       lowerCamelCase_							=   language_codes
       lowerCamelCase_							=   FAIRSEQ_LANGUAGE_CODES[language_codes]
       lowerCamelCase_							=   {lang_code: f"""__{lang_code}__""" for lang_code in fairseq_language_code}

       lowerCamelCase_							=   kwargs.get('additional_special_tokens'						,							[]  )
       kwargs["additional_special_tokens"] += [
           self.get_lang_token(A_  )
           for lang_code in fairseq_language_code
           if self.get_lang_token(A_  ) not in kwargs["additional_special_tokens"]
       ]

       super().__init__(
           src_lang=A_						,							tgt_lang=A_						,							bos_token=A_						,							eos_token=A_						,							sep_token=A_						,							unk_token=A_						,							pad_token=A_						,							language_codes=A_						,							sp_model_kwargs=self.sp_model_kwargs						,							num_madeup_words=A_						,							**A_						,							)

       lowerCamelCase_							=   vocab_file
       lowerCamelCase_							=   load_json(A_  )
       lowerCamelCase_							=   {v: k for k, v in self.encoder.items()}
       lowerCamelCase_							=   spm_file
       lowerCamelCase_							=   load_spm(A_						,							self.sp_model_kwargs  )

       lowerCamelCase_							=   len(self.encoder  )

       lowerCamelCase_							=   {
           self.get_lang_token(A_  ): self.encoder_size + i for i, lang_code in enumerate(A_  )
       }
       lowerCamelCase_							=   {lang_code: self.encoder_size + i for i, lang_code in enumerate(A_  )}
       lowerCamelCase_							=   {v: k for k, v in self.lang_token_to_id.items()}

       lowerCamelCase_							=   src_lang if src_lang is not None else 'en'
       lowerCamelCase_							=   tgt_lang
       lowerCamelCase_							=   self.get_lang_id(self._src_lang  )
       self.set_src_lang_special_tokens(self._src_lang  )

       lowerCamelCase_							=   num_madeup_words

 @property
 def     a__			(    self :  Optional[int]  )       ->     int:

       """simple docstring"""






       return len(self.encoder  ) + len(self.lang_token_to_id  )

 @property
 def     a__			(    self :  Any  )       ->     str:

       """simple docstring"""






       return self._src_lang

 @src_lang.setter
 def     a__			(    self :  Optional[Any]						,							A_ :  str  )       ->     None:

       """simple docstring"""






       lowerCamelCase_							=   new_src_lang
       self.set_src_lang_special_tokens(self._src_lang  )

 def     a__			(    self :  List[Any]						,							A_ :  str  )       ->     List[str]:

       """simple docstring"""






       return self.sp_model.encode(A_						,							out_type=A_  )

 def     a__			(    self :  Tuple						,							A_ :  Dict  )       ->     Dict:

       """simple docstring"""






       if token in self.lang_token_to_id:
             return self.lang_token_to_id[token]
       return self.encoder.get(A_						,							self.encoder[self.unk_token]  )

 def     a__			(    self :  int						,							A_ :  int  )       ->     str:

       """simple docstring"""






       if index in self.id_to_lang_token:
             return self.id_to_lang_token[index]
       return self.decoder.get(A_						,							self.unk_token  )

 def     a__			(    self :  List[str]						,							A_ :  Any  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   []
       lowerCamelCase_							=   ''
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(A_  ) + token
                   lowerCamelCase_							=   []
             else:
                   current_sub_tokens.append(A_  )
       out_string += self.sp_model.decode(A_  )
       return out_string.strip()

 def     a__			(    self :  Union[str, Any]						,							A_ :  List[int]						,							A_ :  Optional[List[int]] = None						,							A_ :  bool = False  )       ->     List[int]:

       """simple docstring"""






       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=A_						,							token_ids_a=A_						,							already_has_special_tokens=A_  )

       lowerCamelCase_							=   [1] * len(self.prefix_tokens  )
       lowerCamelCase_							=   [1] * len(self.suffix_tokens  )
       if token_ids_a is None:
             return prefix_ones + ([0] * len(A_  )) + suffix_ones
       return prefix_ones + ([0] * len(A_  )) + ([0] * len(A_  )) + suffix_ones

 def     a__			(    self :  Tuple						,							A_ :  List[int]						,							A_ :  Optional[List[int]] = None  )       ->     List[int]:

       """simple docstring"""






       if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

 def     a__			(    self :  Union[str, Any]  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   {self.convert_ids_to_tokens(A_  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab

 def __getstate__(    self :  Tuple  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   self.__dict__.copy()
       lowerCamelCase_							=   None
       return state

 def __setstate__(    self :  Any						,							A_ :  Dict  )       ->     None:

       """simple docstring"""






       lowerCamelCase_							=   d

       # for backward compatibility
       if not hasattr(self						,							'sp_model_kwargs'  ):
             lowerCamelCase_							=   {}

       lowerCamelCase_							=   load_spm(self.spm_file						,							self.sp_model_kwargs  )

 def     a__			(    self :  int						,							A_ :  str						,							A_ :  Optional[str] = None  )       ->     Tuple[str]:

       """simple docstring"""






       lowerCamelCase_							=   Path(A_  )
       if not save_dir.is_dir():
             raise OSError(f"""{save_directory} should be a directory"""  )
       lowerCamelCase_							=   save_dir / (
           (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
       )
       lowerCamelCase_							=   save_dir / (
           (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
       )

       save_json(self.encoder						,							A_  )

       if os.path.abspath(self.spm_file  ) != os.path.abspath(A_  ) and os.path.isfile(self.spm_file  ):
             copyfile(self.spm_file						,							A_  )
       elif not os.path.isfile(self.spm_file  ):
             with open(A_						,							'wb'  ) as fi:
                   lowerCamelCase_							=   self.sp_model.serialized_model_proto()
                   fi.write(A_  )

       return (str(A_  ), str(A_  ))

 def     a__			(    self :  Optional[Any]						,							A_ :  List[str]						,							A_ :  str = "en"						,							A_ :  Optional[List[str]] = None						,							A_ :  str = "ro"						,							**A_ :  Any						,							)       ->     BatchEncoding:

       """simple docstring"""






       lowerCamelCase_							=   src_lang
       lowerCamelCase_							=   tgt_lang
       self.set_src_lang_special_tokens(self.src_lang  )
       return super().prepare_seqaseq_batch(A_						,							A_						,							**A_  )

 def     a__			(    self :  int						,							A_ :  Dict						,							A_ :  Optional[str]						,							A_ :  Optional[str]						,							**A_ :  Union[str, Any]  )       ->     str:

       """simple docstring"""






       if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'  )
       lowerCamelCase_							=   src_lang
       lowerCamelCase_							=   self(A_						,							add_special_tokens=A_						,							**A_  )
       lowerCamelCase_							=   self.get_lang_id(A_  )
       lowerCamelCase_							=   tgt_lang_id
       return inputs

 def     a__			(    self :  Dict  )       ->     Any:

       """simple docstring"""






       self.set_src_lang_special_tokens(self.src_lang  )

 def     a__			(    self :  Optional[Any]  )       ->     str:

       """simple docstring"""






       self.set_tgt_lang_special_tokens(self.tgt_lang  )

 def     a__			(    self :  Optional[Any]						,							A_ :  str  )       ->     None:

       """simple docstring"""






       lowerCamelCase_							=   self.get_lang_token(A_  )
       lowerCamelCase_							=   self.lang_token_to_id[lang_token]
       lowerCamelCase_							=   [self.cur_lang_id]
       lowerCamelCase_							=   [self.eos_token_id]

 def     a__			(    self :  Tuple						,							A_ :  str  )       ->     None:

       """simple docstring"""






       lowerCamelCase_							=   self.get_lang_token(A_  )
       lowerCamelCase_							=   self.lang_token_to_id[lang_token]
       lowerCamelCase_							=   [self.cur_lang_id]
       lowerCamelCase_							=   [self.eos_token_id]

 def     a__			(    self :  Tuple						,							A_ :  str  )       ->     str:

       """simple docstring"""






       return self.lang_code_to_token[lang]





 def     a__			(    self :  List[str]						,							A_ :  str  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   self.get_lang_token(A_  )
       return self.lang_token_to_id[lang_token]

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	Dict[str, Any]					):


      '''simple docstring'''



      lowerCamelCase_							=   sentencepiece.SentencePieceProcessor(**lowercase					)
      spm.Load(str(lowercase					)					)
      return spm

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str					):


      '''simple docstring'''



      with open(lowercase     ,							'r'					) as f:
            return json.load(lowercase					)






def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Tuple     ,							lowercase     :	str					):


      '''simple docstring'''



      with open(lowercase     ,							'w'					) as f:
            json.dump(lowercase     ,							lowercase     ,							indent=2					)









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    :		Tuple			   =       "sshleifer/mar_enro_6_3_student"







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 def     a__			(    self :  Union[str, Any]  )       ->     str:

       """simple docstring"""






       super().setUp()

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

 @slow
 @require_torch_gpu
 def     a__			(    self :  List[str]  )       ->     int:

       """simple docstring"""






       MarianMTModel.from_pretrained(A_  )





 @slow
 @require_torch_gpu
 def     a__			(    self :  List[str]  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   {
           '$MAX_LEN': 64,
           '$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_							=   (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'  )[1].strip()
       lowerCamelCase_							=   bash_script.replace('\\\n'						,							''  ).strip().replace('"$@"'						,							''  )
       for k, v in env_vars_to_replace.items():
             lowerCamelCase_							=   bash_script.replace(A_						,							str(A_  )  )
       lowerCamelCase_							=   self.get_auto_remove_tmp_dir()

       # bash_script = bash_script.replace("--fp16 ", "")
       lowerCamelCase_							=   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_							=   ['finetune.py'] + bash_script.split() + args
       with patch.object(A_						,							'argv'						,							A_  ):
             lowerCamelCase_							=   argparse.ArgumentParser()
             lowerCamelCase_							=   pl.Trainer.add_argparse_args(A_  )
             lowerCamelCase_							=   SummarizationModule.add_model_specific_args(A_						,							os.getcwd()  )
             lowerCamelCase_							=   parser.parse_args()
             lowerCamelCase_							=   main(A_  )

       # Check metrics
       lowerCamelCase_							=   load_json(model.metrics_save_path  )
       lowerCamelCase_							=   metrics['val'][0]
       lowerCamelCase_							=   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
       lowerCamelCase_							=   os.listdir(A_  )
       lowerCamelCase_							=   [x for x in contents if x.endswith('.ckpt'  )][0]
       lowerCamelCase_							=   os.path.join(args.output_dir						,							A_  )
       lowerCamelCase_							=   torch.load(A_						,							map_location='cpu'  )
       lowerCamelCase_							=   '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_							=   {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   A(						UpperCamelCase       ):



 '''simple docstring'''


 @timeout_decorator.timeout(600  )
 @slow
 @require_torch_gpu
 def     a__			(    self :  List[str]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
       lowerCamelCase_							=   {
           '--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_							=   (
           (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'  )[1].strip()
       )
       lowerCamelCase_							=   bash_script.replace('\\\n'						,							''  ).strip().replace('"$@"'						,							''  )
       lowerCamelCase_							=   bash_script.replace('--fp16 '						,							' '  )

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

             lowerCamelCase_							=   distill_main(A_  )

       # Check metrics
       lowerCamelCase_							=   load_json(model.metrics_save_path  )
       lowerCamelCase_							=   metrics['val'][0]
       lowerCamelCase_							=   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
       lowerCamelCase_							=   os.listdir(A_  )
       lowerCamelCase_							=   [x for x in contents if x.endswith('.ckpt'  )][0]
       lowerCamelCase_							=   os.path.join(args.output_dir						,							A_  )
       lowerCamelCase_							=   torch.load(A_						,							map_location='cpu'  )
       lowerCamelCase_							=   '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_							=   {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



