import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


UpperCAmelCase				 =   logging.get_logger(__name__)

UpperCAmelCase				 =   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase				 =   {
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

UpperCAmelCase				 =   {
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

UpperCAmelCase				 =   {
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    int			      = VOCAB_FILES_NAMES
   UpperCamelCase_					:    Optional[int]			      = PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase_					:    Tuple			      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase_					:    Tuple			      = ['input_ids', 'attention_mask', 'token_type_ids']
   UpperCamelCase_					:    int			      = DebertaTokenizer
   def __init__(     self				,  lowerCAmelCase_=None				,  lowerCAmelCase_=None				,  lowerCAmelCase_=None				,  lowerCAmelCase_="replace"				,  lowerCAmelCase_="[CLS]"				,  lowerCAmelCase_="[SEP]"				,  lowerCAmelCase_="[SEP]"				,  lowerCAmelCase_="[CLS]"				,  lowerCAmelCase_="[UNK]"				,  lowerCAmelCase_="[PAD]"				,  lowerCAmelCase_="[MASK]"				,  lowerCAmelCase_=False				,  **lowerCAmelCase_				,  )						->List[Any]:
        super().__init__(
            _UpperCAmelCase				,  _UpperCAmelCase				,  tokenizer_file=_UpperCAmelCase				,  errors=_UpperCAmelCase				,  bos_token=_UpperCAmelCase				,  eos_token=_UpperCAmelCase				,  unk_token=_UpperCAmelCase				,  sep_token=_UpperCAmelCase				,  cls_token=_UpperCAmelCase				,  pad_token=_UpperCAmelCase				,  mask_token=_UpperCAmelCase				,  add_prefix_space=_UpperCAmelCase				,  **_UpperCAmelCase				,  )
        a	:     Union[str, Any]						      =    kwargs.pop('''add_bos_token'''				,  _UpperCAmelCase     )

        a	:     Optional[Any]						      =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
        if pre_tok_state.get('''add_prefix_space'''				,  _UpperCAmelCase     ) != add_prefix_space:
             a	:     Any						      =    getattr(_UpperCAmelCase				,  pre_tok_state.pop('''type'''     )     )
             a	:     str						      =    add_prefix_space
             a	:     Dict						      =    pre_tok_class(**_UpperCAmelCase     )

        a	:     Any						      =    add_prefix_space
   @property
   def _lowercase(     self     )						->List[str]:
        if self._mask_token is None:
             if self.verbose:
                  logger.error('''Using mask_token, but it is not set yet.'''     )
             return None
        return str(self._mask_token     )
   @mask_token.setter
   def _lowercase(     self				,  lowerCAmelCase_     )						->Optional[int]:
        a	:     Dict						      =    AddedToken(_UpperCAmelCase				,  lstrip=_UpperCAmelCase				,  rstrip=_UpperCAmelCase     ) if isinstance(_UpperCAmelCase				,  _UpperCAmelCase     ) else value
        a	:     Tuple						      =    value
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_ = None     )						->Tuple:
        if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        a	:     Any						      =    [self.cls_token_id]
        a	:     Tuple						      =    [self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_ = None     )						->Optional[int]:
        a	:     List[str]						      =    [self.sep_token_id]
        a	:     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]
   def _lowercase(     self				,  *lowerCAmelCase_				,  **lowerCAmelCase_     )						->Optional[int]:
        a	:     Tuple						      =    kwargs.get('''is_split_into_words'''				,  _UpperCAmelCase     )
        assert self.add_prefix_space or not is_split_into_words, (
            F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*_UpperCAmelCase				,  **_UpperCAmelCase     )
   def _lowercase(     self				,  *lowerCAmelCase_				,  **lowerCAmelCase_     )						->Union[str, Any]:
        a	:     Union[str, Any]						      =    kwargs.get('''is_split_into_words'''				,  _UpperCAmelCase     )

        assert self.add_prefix_space or not is_split_into_words, (
            F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*_UpperCAmelCase				,  **_UpperCAmelCase     )
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_ = None     )						->Optional[Any]:
        a	:     List[str]						      =    self._tokenizer.model.save(_UpperCAmelCase				,  name=_UpperCAmelCase     )
        return tuple(_UpperCAmelCase     )


   def _lowercase(     self				,  lowerCAmelCase_     )						->List[Any]:
        a	:     Optional[int]						      =    []
        for is_user, text in conversation.iter_texts():
             input_ids.extend(self.encode(_UpperCAmelCase				,  add_special_tokens=_UpperCAmelCase     ) + [self.eos_token_id]     )

        if len(_UpperCAmelCase     ) > self.model_max_length:
             a	:     Dict						      =    input_ids[-self.model_max_length :]
        return input_ids






'''simple docstring'''






import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


snake_case_     			=						torch.cuda.is_available()
snake_case_     			=						[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
snake_case_     			=						["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
snake_case_     			=						{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}
def 				_lowerCamelCase(	UpperCamelCase__     :			Path     ,							UpperCamelCase__     :			list		)		->	str:
    A							:							List[Any]				=    '''\n'''.join(UpperCamelCase__		)
    Path(UpperCamelCase__		).open('''w'''		).writelines(UpperCamelCase__		)
def 				_lowerCamelCase(	UpperCamelCase__     :			List[Any]		)		->	Dict:
    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(UpperCamelCase__     ,							F'''{split}.source'''		)     ,							UpperCamelCase__		)
        _dump_articles(os.path.join(UpperCamelCase__     ,							F'''{split}.target'''		)     ,							UpperCamelCase__		)
    return tmp_dir





class 	_lowercase    (				a					):


   @classmethod
   def        snake_case					(			cls       ):
       return cls


   @require_torch_multi_gpu
   def        snake_case					(			self       ):
       A							:							Optional[int]				=    {
           '''no_teacher''': True,
           '''freeze_encoder''': True,
           '''gpus''': 2,
           '''overwrite_output_dir''': True,
           '''sortish_sampler''': True,
       }
       self._test_distiller_cli_fork(_UpperCAmelCase     , check_contents=_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=True       ):
       A							:							List[Any]				=    {
           '''label_smoothing''': 0.0,
           '''early_stopping_patience''': -1,
           '''train_batch_size''': 1,
           '''eval_batch_size''': 2,
           '''max_epochs''': 2,
           '''alpha_mlm''': 0.2,
           '''alpha_ce''': 0.8,
           '''do_predict''': True,
           '''model_name_or_path''': '''sshleifer/tinier_bart''',
           '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
           '''val_check_interval''': 0.5,
       }
       default_updates.update(_UpperCAmelCase       )
       A							:							dict				=    CHEAP_ARGS.copy()
       A							:							Union[str, Any]				=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
       A							:							Optional[int]				=    self.get_auto_remove_tmp_dir()
       args_d.update(data_dir=_UpperCAmelCase     , output_dir=_UpperCAmelCase     , **_UpperCAmelCase       )

       def convert(_UpperCAmelCase     , _UpperCAmelCase       ):
           if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
               return ""
           if v is False or v is None:
               return ""
           if v is True:  # or len(str(v))==0:
               return f'''--{k}'''
           return f'''--{k}={v}'''

       A							:							int				=    [x for x in (convert(_UpperCAmelCase     , _UpperCAmelCase       ) for k, v in args_d.items()) if len(_UpperCAmelCase       )]
       A							:							Union[str, Any]				=    [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
       execute_subprocess_async(_UpperCAmelCase     , env=self.get_env()       )

       A							:							Dict				=    os.listdir(_UpperCAmelCase       )
       A							:							Optional[int]				=    {os.path.basename(_UpperCAmelCase       ) for p in contents}
       A							:							Union[str, Any]				=    [p for p in contents if p.endswith('''ckpt'''       )]
       assert len(_UpperCAmelCase       ) > 0

       self.assertIn('''test_generations.txt'''     , _UpperCAmelCase       )
       self.assertIn('''test_results.txt'''     , _UpperCAmelCase       )

       # get the following from the module, (we don't have access to `model` here)
       A							:							Optional[Any]				=    os.path.join(_UpperCAmelCase     , '''metrics.json'''       )
       A							:							List[Any]				=    '''rouge2'''

       A							:							Union[str, Any]				=    load_json(_UpperCAmelCase       )
       # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
       print(_UpperCAmelCase       )
       A							:							Optional[Any]				=    metrics['''val'''][-1]
       self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']     , 0.01       )
       self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']     , _UpperCAmelCase       )
       self.assertEqual(len(metrics['''test''']       )     , 1       )
       A							:							Tuple				=    int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1       )
       self.assertEqual(len(metrics['''val''']       )     , _UpperCAmelCase       )
