






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


__snake_case			:			List[str]			   =   logging.get_logger(__name__)

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

__snake_case			:			str			   =   {
    """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"""
        ),
    },
}

__snake_case			:			List[str]			   =   {
    """microsoft/deberta-base""": 5_12,
    """microsoft/deberta-large""": 5_12,
    """microsoft/deberta-xlarge""": 5_12,
    """microsoft/deberta-base-mnli""": 5_12,
    """microsoft/deberta-large-mnli""": 5_12,
    """microsoft/deberta-xlarge-mnli""": 5_12,
}

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


class 			__SCREAMING_SNAKE_CASE    (      __lowercase):
						_SCREAMING_SNAKE_CASE		:				Tuple            =    VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE		:				Dict            =    PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE		:				List[str]            =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE		:				str            =    ['''input_ids''', '''attention_mask''', '''token_type_ids''']
						_SCREAMING_SNAKE_CASE		:				Any            =    DebertaTokenizer






						def __init__(		self   ,  _UpperCamelCase=None   ,  _UpperCamelCase=None   ,  _UpperCamelCase=None   ,  _UpperCamelCase="replace"   ,  _UpperCamelCase="[CLS]"   ,  _UpperCamelCase="[SEP]"   ,  _UpperCamelCase="[SEP]"   ,  _UpperCamelCase="[CLS]"   ,  _UpperCamelCase="[UNK]"   ,  _UpperCamelCase="[PAD]"   ,  _UpperCamelCase="[MASK]"   ,  _UpperCamelCase=False   ,  **_UpperCamelCase   ,  ):






												"""simple docstring"""
												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   ,  )
												lowerCAmelCase__			  =  kwargs.pop('add_bos_token'   ,  _UpperCamelCase      )

												lowerCAmelCase__			  =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
												if pre_tok_state.get('add_prefix_space'   ,  _UpperCamelCase      ) != add_prefix_space:
																		lowerCAmelCase__			  =  getattr(_UpperCamelCase   ,  pre_tok_state.pop('type'      )      )
																		lowerCAmelCase__			  =  add_prefix_space
																		lowerCAmelCase__			  =  pre_tok_class(**_UpperCamelCase      )

												lowerCAmelCase__			  =  add_prefix_space






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												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 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  AddedToken(_UpperCamelCase   ,  lstrip=_UpperCamelCase   ,  rstrip=_UpperCamelCase      ) if isinstance(_UpperCamelCase   ,  _UpperCamelCase      ) else value
												lowerCAmelCase__			  =  value






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = None      ):






												"""simple docstring"""
												if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowerCAmelCase__			  =  [self.cls_token_id]
												lowerCAmelCase__			  =  [self.sep_token_id]
												return cls + token_ids_a + sep + token_ids_a + sep






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = None      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  [self.sep_token_id]
												lowerCAmelCase__			  =  [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      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  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 					UpperCamelCase__					(		self   ,  *_UpperCamelCase   ,  **_UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  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 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = None      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self._tokenizer.model.save(_UpperCamelCase   ,  name=_UpperCamelCase      )
												return tuple(_UpperCamelCase      )







						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  []
												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:
																		lowerCAmelCase__			  =  input_ids[-self.model_max_length :]
												return input_ids













import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json







def 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple						)     ->      str:

						"""simple docstring"""



						lowerCAmelCase__			  =  filter(lambda UpperCamelCase_						: p.requires_grad			,				model.parameters()						)
						lowerCAmelCase__			  =  sum([np.prod(p.size()						) for p in model_parameters]						)
						return params


__snake_case			:			Dict			   =   logging.getLogger(__name__)


class 			__SCREAMING_SNAKE_CASE    (      pl.Callback):






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  {F"lr_group_{i}": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
												pl_module.logger.log_metrics(_UpperCamelCase      )






						@rank_zero_only
						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase=True      ):






												"""simple docstring"""
												logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"      )
												lowerCAmelCase__			  =  trainer.callback_metrics
												trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}      )
												# Log results
												lowerCAmelCase__			  =  Path(pl_module.hparams.output_dir      )
												if type_path == "test":
																		lowerCAmelCase__			  =  od / 'test_results.txt'
																		lowerCAmelCase__			  =  od / 'test_generations.txt'
												else:
																		# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
																		# If people want this it will be easy enough to add back.
																		lowerCAmelCase__			  =  od / F"{type_path}_results/{trainer.global_step:05d}.txt"
																		lowerCAmelCase__			  =  od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
																		results_file.parent.mkdir(exist_ok=_UpperCamelCase      )
																		generations_file.parent.mkdir(exist_ok=_UpperCamelCase      )
												with open(_UpperCamelCase   ,  'a+'      ) as writer:
																		for key in sorted(_UpperCamelCase      ):
																								if key in ["log", "progress_bar", "preds"]:
																														continue
																								lowerCAmelCase__			  =  metrics[key]
																								if isinstance(_UpperCamelCase   ,  torch.Tensor      ):
																														lowerCAmelCase__			  =  val.item()
																								lowerCAmelCase__			  =  F"{key}: {val:.6f}\n"
																								writer.write(_UpperCamelCase      )

												if not save_generations:
																		return

												if "preds" in metrics:
																		lowerCAmelCase__			  =  '\n'.join(metrics['preds']      )
																		generations_file.open('w+'      ).write(_UpperCamelCase      )






						@rank_zero_only
						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												try:
																		lowerCAmelCase__			  =  pl_module.model.model.num_parameters()
												except AttributeError:
																		lowerCAmelCase__			  =  pl_module.model.num_parameters()

												lowerCAmelCase__			  =  count_trainable_parameters(_UpperCamelCase      )
												# mp stands for million parameters
												trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}      )






						@rank_zero_only
						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												save_json(pl_module.metrics   ,  pl_module.metrics_save_path      )
												return self._write_logs(_UpperCamelCase   ,  _UpperCamelCase   ,  'test'      )







						@rank_zero_only
						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												save_json(pl_module.metrics   ,  pl_module.metrics_save_path      )
												# Uncommenting this will save val generations
												# return self._write_logs(trainer, pl_module, "valid")







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    List[Any]=1			,				UpperCamelCase_				:    List[str]=False						)     ->      Optional[Any]:

						"""simple docstring"""



						if metric == "rouge2":
												lowerCAmelCase__			  =  '{val_avg_rouge2:.4f}-{step_count}'
						elif metric == "bleu":
												lowerCAmelCase__			  =  '{val_avg_bleu:.4f}-{step_count}'
						elif metric == "loss":
												lowerCAmelCase__			  =  '{val_avg_loss:.4f}-{step_count}'
						else:
												raise NotImplementedError(
												    F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
												    ' this function.'						)

						lowerCAmelCase__			  =  ModelCheckpoint(
						    dirpath=UpperCamelCase_			,				filename=UpperCamelCase_			,				monitor=F"val_{metric}"			,				mode='min' if 'loss' in metric else 'max'			,				save_top_k=UpperCamelCase_			,				)
						return checkpoint_callback







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    int						)     ->      Dict:

						"""simple docstring"""



						return EarlyStopping(
						    monitor=F"val_{metric}"			,				mode='min' if 'loss' in metric else 'max'			,				patience=UpperCamelCase_			,				verbose=UpperCamelCase_			,				)





