




import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


UpperCamelCase__ :   Union[str, Any]						=							logging.get_logger(__name__)

UpperCamelCase__ :   Optional[int]						=							{'''vocab_file''': '''spiece.model'''}

UpperCamelCase__ :   Any						=							{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

UpperCamelCase__ :   List[str]						=							{
    '''google/bigbird-roberta-base''': 40_96,
    '''google/bigbird-roberta-large''': 40_96,
    '''google/bigbird-base-trivia-itc''': 40_96,
}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       Optional[int]						=     VOCAB_FILES_NAMES
				__a     :       Dict						=     PRETRAINED_VOCAB_FILES_MAP
				__a     :       Dict						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a     :       str						=     ["input_ids", "attention_mask"]
				__a     :       List[int]						=     []






				def __init__(			self      ,snake_case__      ,snake_case__="<unk>"      ,snake_case__="<s>"      ,snake_case__="</s>"      ,snake_case__="<pad>"      ,snake_case__="[SEP]"      ,snake_case__="[MASK]"      ,snake_case__="[CLS]"      ,snake_case__ = None      ,**snake_case__      ,):
							SCREAMING_SNAKE_CASE_   : str					  =      AddedToken(snake_case__      ,lstrip=snake_case__      ,rstrip=snake_case__ ) if isinstance(snake_case__      ,snake_case__ ) else bos_token
							SCREAMING_SNAKE_CASE_   : Any					  =      AddedToken(snake_case__      ,lstrip=snake_case__      ,rstrip=snake_case__ ) if isinstance(snake_case__      ,snake_case__ ) else eos_token
							SCREAMING_SNAKE_CASE_   : Tuple					  =      AddedToken(snake_case__      ,lstrip=snake_case__      ,rstrip=snake_case__ ) if isinstance(snake_case__      ,snake_case__ ) else unk_token
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      AddedToken(snake_case__      ,lstrip=snake_case__      ,rstrip=snake_case__ ) if isinstance(snake_case__      ,snake_case__ ) else pad_token
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      AddedToken(snake_case__      ,lstrip=snake_case__      ,rstrip=snake_case__ ) if isinstance(snake_case__      ,snake_case__ ) else cls_token
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      AddedToken(snake_case__      ,lstrip=snake_case__      ,rstrip=snake_case__ ) if isinstance(snake_case__      ,snake_case__ ) else sep_token

							# Mask token behave like a normal word, i.e. include the space before it
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      AddedToken(snake_case__      ,lstrip=snake_case__      ,rstrip=snake_case__ ) if isinstance(snake_case__      ,snake_case__ ) else mask_token

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=snake_case__      ,eos_token=snake_case__      ,unk_token=snake_case__      ,pad_token=snake_case__      ,sep_token=snake_case__      ,mask_token=snake_case__      ,cls_token=snake_case__      ,sp_model_kwargs=self.sp_model_kwargs      ,**snake_case__      ,)

							SCREAMING_SNAKE_CASE_   : List[str]					  =      vocab_file

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.Load(snake_case__ )






				@property
				def 		snake_case	(			self ):
							return self.sp_model.get_piece_size()






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      {self.convert_ids_to_tokens(snake_case__ ): i for i in range(self.vocab_size )}
							vocab.update(self.added_tokens_encoder )
							return vocab






				def __getstate__(			self ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.__dict__.copy()
							SCREAMING_SNAKE_CASE_   : Tuple					  =      None
							return state






				def __setstate__(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      d

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

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.Load(self.vocab_file )






				def 		snake_case	(			self      ,snake_case__ ):
							return self.sp_model.encode(snake_case__      ,out_type=snake_case__ )






				def 		snake_case	(			self      ,snake_case__ ):
							return self.sp_model.piece_to_id(snake_case__ )






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.sp_model.IdToPiece(snake_case__ )
							return token






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : str					  =      []
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      ''
							SCREAMING_SNAKE_CASE_   : int					  =      False
							for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
													if not prev_is_special:
																out_string += " "
													out_string += self.sp_model.decode(snake_case__ ) + token
													SCREAMING_SNAKE_CASE_   : int					  =      True
													SCREAMING_SNAKE_CASE_   : int					  =      []
										else:
													current_sub_tokens.append(snake_case__ )
													SCREAMING_SNAKE_CASE_   : List[str]					  =      False
							out_string += self.sp_model.decode(snake_case__ )
							return out_string.strip()






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = False      ,snake_case__ = None      ,snake_case__ = True      ,**snake_case__      ,):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      kwargs.pop('use_source_tokenizer'      ,snake_case__ )

							SCREAMING_SNAKE_CASE_   : Dict					  =      self.convert_ids_to_tokens(snake_case__      ,skip_special_tokens=snake_case__ )

							# To avoid mixing byte-level and unicode for byte-level BPT
							# we need to build string separately for added tokens and byte-level tokens
							# cf. https://github.com/huggingface/transformers/issues/1133
							SCREAMING_SNAKE_CASE_   : str					  =      []
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      []
							for token in filtered_tokens:
										if skip_special_tokens and token in self.all_special_ids:
													continue
										if token in self.added_tokens_encoder:
													if current_sub_text:
																sub_texts.append(self.convert_tokens_to_string(snake_case__ ) )
																SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      []
													sub_texts.append(snake_case__ )
										else:
													current_sub_text.append(snake_case__ )
							if current_sub_text:
										sub_texts.append(self.convert_tokens_to_string(snake_case__ ) )

							# Mimic the behavior of the Rust tokenizer:
							# No space before [MASK] and [SEP]
							if spaces_between_special_tokens:
										SCREAMING_SNAKE_CASE_   : str					  =      re.sub(R' (\[(MASK|SEP)\])'      ,R'\1'      ,' '.join(snake_case__ ) )
							else:
										SCREAMING_SNAKE_CASE_   : Any					  =      ''.join(snake_case__ )

							SCREAMING_SNAKE_CASE_   : int					  =      (
							    clean_up_tokenization_spaces
							    if clean_up_tokenization_spaces is not None
							    else self.clean_up_tokenization_spaces
							)
							if clean_up_tokenization_spaces:
										SCREAMING_SNAKE_CASE_   : Dict					  =      self.clean_up_tokenization(snake_case__ )
										return clean_text
							else:
										return text






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							if not os.path.isdir(snake_case__ ):
										logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
										return
							SCREAMING_SNAKE_CASE_   : str					  =      os.path.join(
							    snake_case__      ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

							if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ) and os.path.isfile(self.vocab_file ):
										copyfile(self.vocab_file      ,snake_case__ )
							elif not os.path.isfile(self.vocab_file ):
										with open(snake_case__      ,'wb' ) as fi:
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.sp_model.serialized_model_proto()
													fi.write(snake_case__ )

							return (out_vocab_file,)






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [self.cls_token_id]
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      [self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None      ,snake_case__ = False ):
							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=snake_case__      ,token_ids_a=snake_case__      ,already_has_special_tokens=snake_case__ )

							if token_ids_a is None:
										return [1] + ([0] * len(snake_case__ )) + [1]
							return [1] + ([0] * len(snake_case__ )) + [1] + ([0] * len(snake_case__ )) + [1]






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							SCREAMING_SNAKE_CASE_   : str					  =      [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [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]










"""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 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				def        __lowercase	(  self	:List[Any]      ):
											super().setUp()

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




				@slow
				@require_torch_gpu
				def        __lowercase	(  self	:int      ):
											MarianMTModel.from_pretrained(__lowercase      )




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

											# bash_script = bash_script.replace("--fp16 ", "")
											__lowerCamelCase   :   List[Any]       =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

											__lowerCamelCase   :   str       =['''finetune.py'''] + bash_script.split() + args
											with patch.object(__lowercase    ,     '''argv'''    ,     __lowercase      ):
																		__lowerCamelCase   :   str       =argparse.ArgumentParser()
																		__lowerCamelCase   :   Dict       =pl.Trainer.add_argparse_args(__lowercase      )
																		__lowerCamelCase   :   List[Any]       =SummarizationModule.add_model_specific_args(__lowercase    ,     os.getcwd()      )
																		__lowerCamelCase   :   int       =parser.parse_args()
																		__lowerCamelCase   :   Tuple       =main(__lowercase      )

											# Check metrics
											__lowerCamelCase   :   Union[str, Any]       =load_json(model.metrics_save_path      )
											__lowerCamelCase   :   Any       =metrics['''val'''][0]
											__lowerCamelCase   :   int       =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''']    ,     0.01      )
											# 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   :   Any       =os.listdir(__lowercase      )
											__lowerCamelCase   :   Tuple       =[x for x in contents if x.endswith('''.ckpt'''      )][0]
											__lowerCamelCase   :   int       =os.path.join(args.output_dir    ,     __lowercase      )
											__lowerCamelCase   :   List[str]       =torch.load(__lowercase    ,     map_location='''cpu'''      )
											__lowerCamelCase   :   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   :   str       ={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 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				@timeout_decorator.timeout(600      )
				@slow
				@require_torch_gpu
				def        __lowercase	(  self	:str      ):
											__lowerCamelCase   :   int       =f'{self.test_file_dir_str}/test_data/wmt_en_ro'
											__lowerCamelCase   :   List[Any]       ={
											    '''--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   :   Any       =(
											    (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''      )[1].strip()
											)
											__lowerCamelCase   :   Optional[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   :   str       =bash_script.replace(__lowercase    ,     str(__lowercase      )      )
											__lowerCamelCase   :   Any       =self.get_auto_remove_tmp_dir()
											__lowerCamelCase   :   Optional[int]       =bash_script.replace('''--fp16'''    ,     ''''''      )
											__lowerCamelCase   :   Tuple       =6
											__lowerCamelCase   :   str       =(
											    ['''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      ):
																		__lowerCamelCase   :   int       =argparse.ArgumentParser()
																		__lowerCamelCase   :   int       =pl.Trainer.add_argparse_args(__lowercase      )
																		__lowerCamelCase   :   Optional[int]       =SummarizationDistiller.add_model_specific_args(__lowercase    ,     os.getcwd()      )
																		__lowerCamelCase   :   Union[str, Any]       =parser.parse_args()
																		# assert args.gpus == gpus THIS BREAKS for multi_gpu

																		__lowerCamelCase   :   Union[str, Any]       =distill_main(__lowercase      )

											# Check metrics
											__lowerCamelCase   :   Dict       =load_json(model.metrics_save_path      )
											__lowerCamelCase   :   List[str]       =metrics['''val'''][0]
											__lowerCamelCase   :   Tuple       =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}']    ,     __lowercase      )

											# check lightning ckpt can be loaded and has a reasonable statedict
											__lowerCamelCase   :   int       =os.listdir(__lowercase      )
											__lowerCamelCase   :   str       =[x for x in contents if x.endswith('''.ckpt'''      )][0]
											__lowerCamelCase   :   Union[str, Any]       =os.path.join(args.output_dir    ,     __lowercase      )
											__lowerCamelCase   :   Optional[int]       =torch.load(__lowercase    ,     map_location='''cpu'''      )
											__lowerCamelCase   :   int       ='''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(__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




