





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_mbart import MBartTokenizer
else:
       a__:      Optional[Any]									=		None


a__:      Optional[int]									=		logging.get_logger(__name__)


a__:      Dict									=		{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

a__:      Dict									=		{
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

a__:      Optional[int]									=		{
    'facebook/mbart-large-en-ro': 1_024,
    'facebook/mbart-large-cc25': 1_024,
}

# fmt: off
a__:      Dict									=		['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE							       =	['''input_ids''', '''attention_mask''']
 __SCREAMING_SNAKE_CASE							       =	MBartTokenizer

 __SCREAMING_SNAKE_CASE							       =	[]
 __SCREAMING_SNAKE_CASE							       =	[]



 def __init__(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase="<s>",__lowerCamelCase="</s>",__lowerCamelCase="</s>",__lowerCamelCase="<s>",__lowerCamelCase="<unk>",__lowerCamelCase="<pad>",__lowerCamelCase="<mask>",__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,**__lowerCamelCase,):
        # Mask token behave like a normal word, i.e. include the space before it
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else mask_token

        super().__init__(
            vocab_file=__lowerCamelCase,tokenizer_file=__lowerCamelCase,bos_token=__lowerCamelCase,eos_token=__lowerCamelCase,sep_token=__lowerCamelCase,cls_token=__lowerCamelCase,unk_token=__lowerCamelCase,pad_token=__lowerCamelCase,mask_token=__lowerCamelCase,src_lang=__lowerCamelCase,tgt_lang=__lowerCamelCase,additional_special_tokens=__lowerCamelCase,**__lowerCamelCase,)

        A__											=						vocab_file
        A__											=						False if not self.vocab_file else True

        A__											=						FAIRSEQ_LANGUAGE_CODES.copy()

        if additional_special_tokens is not None:
               # Only add those special tokens if they are not already there.
               _additional_special_tokens.extend(
                   [t for t in additional_special_tokens if t not in _additional_special_tokens] )

        self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
        A__											=						{
            lang_code: self.convert_tokens_to_ids(__lowerCamelCase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        A__											=						src_lang if src_lang is not None else '''en_XX'''
        A__											=						self.convert_tokens_to_ids(self._src_lang )
        A__											=						tgt_lang
        self.set_src_lang_special_tokens(self._src_lang )



 @property
 def 				UpperCamelCase			(							self ):
        return self._src_lang



 @src_lang.setter
 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						new_src_lang
        self.set_src_lang_special_tokens(self._src_lang )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        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 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        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 + sep + token_ids_a + sep ) * [0]



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ):
        if src_lang is None or tgt_lang is None:
               raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
        A__											=						src_lang
        A__											=						self(__lowerCamelCase,add_special_tokens=__lowerCamelCase,return_tensors=__lowerCamelCase,**__lowerCamelCase )
        A__											=						self.convert_tokens_to_ids(__lowerCamelCase )
        A__											=						tgt_lang_id
        return inputs



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = "en_XX",__lowerCamelCase = None,__lowerCamelCase = "ro_RO",**__lowerCamelCase,):
        A__											=						src_lang
        A__											=						tgt_lang
        return super().prepare_seqaseq_batch(__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        return self.set_src_lang_special_tokens(self.src_lang )



 def 				UpperCamelCase			(							self ):
        return self.set_tgt_lang_special_tokens(self.tgt_lang )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						self.convert_tokens_to_ids(__lowerCamelCase )
        A__											=						[]
        A__											=						[self.eos_token_id, self.cur_lang_code]

        A__											=						self.convert_ids_to_tokens(self.prefix_tokens )
        A__											=						self.convert_ids_to_tokens(self.suffix_tokens )

        A__											=						processors.TemplateProcessing(
            single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str,pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,self.prefix_tokens + self.suffix_tokens ) ),)



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						self.convert_tokens_to_ids(__lowerCamelCase )
        A__											=						[]
        A__											=						[self.eos_token_id, self.cur_lang_code]

        A__											=						self.convert_ids_to_tokens(self.prefix_tokens )
        A__											=						self.convert_ids_to_tokens(self.suffix_tokens )

        A__											=						processors.TemplateProcessing(
            single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str,pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,self.prefix_tokens + self.suffix_tokens ) ),)




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        if not self.can_save_slow_tokenizer:
               raise ValueError(
                   '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                   '''tokenizer.''' )

        if not os.path.isdir(__lowerCamelCase ):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory." )
               return
        A__											=						os.path.join(
            __lowerCamelCase,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

        if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCamelCase ):
               copyfile(self.vocab_file,__lowerCamelCase )

        return (out_vocab_file,)












import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

a__:      List[Any]									=		logging.getLogger()


def 	UpperCamelCase__(       )->Union[str, Any]:
       A__											=						argparse.ArgumentParser()
       parser.add_argument('''-f'''  )
       A__											=						parser.parse_args()
       return args.f




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self ):
        A__											=						logging.StreamHandler(sys.stdout )
        logger.addHandler(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						get_gpu_count()

        if n_gpu > 1:
               pass
               # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
               # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
               # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
               # cmd = [sys.executable] + distributed_args + args
               # execute_subprocess_async(cmd, env=self.get_env())
               # XXX: test the results - need to save them first into .json file
        else:
               args.insert(0,'''run_glue_deebert.py''' )
               with patch.object(__lowerCamelCase,'''argv''',__lowerCamelCase ):
                      A__											=						run_glue_deebert.main()
                      for value in result.values():
                             self.assertGreaterEqual(__lowerCamelCase,0.666 )




 @slow
 @require_torch_non_multi_gpu
 def 				UpperCamelCase			(							self ):
        A__											=						'''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
        self.run_and_check(__lowerCamelCase )

        A__											=						'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
        self.run_and_check(__lowerCamelCase )

        A__											=						'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
        self.run_and_check(__lowerCamelCase )





