


'''simple docstring'''

import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase							:   str  						=	logging.get_logger(__name__)

lowerCamelCase							:   int  						=	{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Union[str, Any]  					=							"""mvp"""
      lowerCAmelCase__					:     Optional[Any]  					=							["""past_key_values"""]
      lowerCAmelCase__					:     List[str]  					=							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



      def __init__(self    :    Any   ,						UpperCamelCase    :    Optional[int]=50267   ,						UpperCamelCase    :    Tuple=1024   ,						UpperCamelCase    :    int=12   ,						UpperCamelCase    :    Tuple=4096   ,						UpperCamelCase    :    Dict=16   ,						UpperCamelCase    :    int=12   ,						UpperCamelCase    :    Optional[int]=4096   ,						UpperCamelCase    :    Optional[int]=16   ,						UpperCamelCase    :    Tuple=0.0   ,						UpperCamelCase    :    Tuple=0.0   ,						UpperCamelCase    :    List[Any]="gelu"   ,						UpperCamelCase    :    Union[str, Any]=1024   ,						UpperCamelCase    :    Optional[Any]=0.1   ,						UpperCamelCase    :    str=0.0   ,						UpperCamelCase    :    str=0.0   ,						UpperCamelCase    :    Optional[Any]=0.02   ,						UpperCamelCase    :    List[str]=0.0   ,						UpperCamelCase    :    List[str]=False   ,						UpperCamelCase    :    Optional[int]=True   ,						UpperCamelCase    :    Any=1   ,						UpperCamelCase    :    int=0   ,						UpperCamelCase    :    int=2   ,						UpperCamelCase    :    Any=True   ,						UpperCamelCase    :    Optional[Any]=2   ,						UpperCamelCase    :    Optional[Any]=2   ,						UpperCamelCase    :    Tuple=False   ,						UpperCamelCase    :    int=100   ,						UpperCamelCase    :    Optional[Any]=800   ,						**UpperCamelCase    :    str   ,						):

             '''simple docstring'''







             lowercase__      			= vocab_size
             lowercase__      			= max_position_embeddings
             lowercase__      			= d_model
             lowercase__      			= encoder_ffn_dim
             lowercase__      			= encoder_layers
             lowercase__      			= encoder_attention_heads
             lowercase__      			= decoder_ffn_dim
             lowercase__      			= decoder_layers
             lowercase__      			= decoder_attention_heads
             lowercase__      			= dropout
             lowercase__      			= attention_dropout
             lowercase__      			= activation_dropout
             lowercase__      			= activation_function
             lowercase__      			= init_std
             lowercase__      			= encoder_layerdrop
             lowercase__      			= decoder_layerdrop
             lowercase__      			= classifier_dropout
             lowercase__      			= use_cache
             lowercase__      			= encoder_layers
             lowercase__      			= scale_embedding  # scale factor will be sqrt(d_model) if True
             lowercase__      			= use_prompt
             lowercase__      			= prompt_length
             lowercase__      			= prompt_mid_dim

             super().__init__(
                 pad_token_id=UpperCamelCase   ,						bos_token_id=UpperCamelCase   ,						eos_token_id=UpperCamelCase   ,						is_encoder_decoder=UpperCamelCase   ,						decoder_start_token_id=UpperCamelCase   ,						forced_eos_token_id=UpperCamelCase   ,						**UpperCamelCase   ,						)

             if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''   ,						UpperCamelCase ):
                    lowercase__      			= self.bos_token_id
                    warnings.warn(
                        f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                        '''The config can simply be saved and uploaded again to be fixed.''' )









import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
        __a						       =				np.full((len(_UpperCAmelCase				), sequence_length, 2)   , _UpperCAmelCase				)
    else:
        __a						       =				np.full((len(_UpperCAmelCase				), sequence_length)   , _UpperCAmelCase				)

    for i, tensor in enumerate(_UpperCAmelCase				):
        if padding_side == "right":
            if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
                __a						       =				tensor[:sequence_length]
            else:
                __a						       =				tensor[:sequence_length]
        else:
            if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
                __a						       =				tensor[:sequence_length]
            else:
                __a						       =				tensor[:sequence_length]

    return out_tensor.tolist()





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				ord(_UpperCAmelCase				)
    if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
        return True
    __a						       =				unicodedata.category(_UpperCAmelCase				)
    if cat.startswith('''P'''				):
        return True
    return False

@dataclass
class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    PreTrainedTokenizerBase
       UpperCamelCase__  :    Union[bool, str, PaddingStrategy]											=						True
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    int											=						-100
       UpperCamelCase__  :    str											=						"pt"



       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]):


           '''simple docstring'''

           import torch

           __a						       =				'''label''' if '''label''' in features[0].keys() else '''labels'''
           __a						       =				[feature[label_name] for feature in features] if label_name in features[0].keys() else None
           __a						       =				self.tokenizer.pad(
               __SCREAMING_SNAKE_CASE     ,		padding=self.padding     ,		max_length=self.max_length     ,		pad_to_multiple_of=self.pad_to_multiple_of     ,		return_tensors='''pt''' if labels is None else None     ,		)

           if labels is None:
               return batch

           __a						       =				torch.tensor(batch['''entity_ids''']).shape[1]
           __a						       =				self.tokenizer.padding_side
           if padding_side == "right":
               __a						       =				[
                   list(__SCREAMING_SNAKE_CASE) + [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) for label in labels
               ]
           else:
               __a						       =				[
                   [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) + list(__SCREAMING_SNAKE_CASE) for label in labels
               ]

           __a						       =				[feature['''ner_tags'''] for feature in features]
           __a						       =				padding_tensor(__SCREAMING_SNAKE_CASE     ,		-1     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				[feature['''original_entity_spans'''] for feature in features]
           __a						       =				padding_tensor(__SCREAMING_SNAKE_CASE     ,		(-1, -1)     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				{k: torch.tensor(__SCREAMING_SNAKE_CASE     ,		dtype=torch.intaa) for k, v in batch.items()}

           return batch





