



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 	lowerCAmelCase_     (  _lowercase				:   Optional[Any]						,   _lowercase				:   Dict						,   _lowercase				:   Dict						,   _lowercase				:   List[str])							->       str:







			"""simple docstring"""
			if isinstance(_UpperCAmelCase						,   _UpperCAmelCase):
						a__   :						Union[str, Any]						       =  np.full((len(_UpperCAmelCase), sequence_length, 2)						,   _UpperCAmelCase)
			else:
						a__   :						Optional[int]						       =  np.full((len(_UpperCAmelCase), sequence_length)						,   _UpperCAmelCase)

			for i, tensor in enumerate(_UpperCAmelCase):
						if padding_side == "right":
									if isinstance(_UpperCAmelCase						,   _UpperCAmelCase):
												a__   :						List[str]						       =  tensor[:sequence_length]
									else:
												a__   :						Optional[int]						       =  tensor[:sequence_length]
						else:
									if isinstance(_UpperCAmelCase						,   _UpperCAmelCase):
												a__   :						List[Any]						       =  tensor[:sequence_length]
									else:
												a__   :						int						       =  tensor[:sequence_length]

			return out_tensor.tolist()

def 	lowerCAmelCase_     (  _lowercase				:   str)							->       Dict:







			"""simple docstring"""
			a__   :						int						       =  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__   :						Optional[int]						       =  unicodedata.category(_UpperCAmelCase)
			if cat.startswith("""P"""):
						return True
			return False





@dataclass
class    snake_case__				(__UpperCAmelCase		):







						"""simple docstring"""




						__lowerCAmelCase     :PreTrainedTokenizerBase
						__lowerCAmelCase     :Union[bool, str, PaddingStrategy]						=     True
						__lowerCAmelCase     :Optional[int]						=     None
						__lowerCAmelCase     :Optional[int]						=     None
						__lowerCAmelCase     :int						=     -100
						__lowerCAmelCase     :str						=     "pt"

						def 							SCREAMING_SNAKE_CASE__(   self					,   __lowercase  )					->      int:
									"""simple docstring"""




									import torch

									a__   :						Optional[Any]						       =  """label""" if """label""" in features[0].keys() else """labels"""
									a__   :						Optional[int]						       =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
									a__   :						Optional[int]						       =  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__   :						List[Any]						       =  torch.tensor(batch["""entity_ids"""]  ).shape[1]
									a__   :						Optional[Any]						       =  self.tokenizer.padding_side
									if padding_side == "right":
												a__   :						Tuple						       =  [
												    list(__SCREAMING_SNAKE_CASE  ) + [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE  )) for label in labels
												]
									else:
												a__   :						int						       =  [
												    [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE  )) + list(__SCREAMING_SNAKE_CASE  ) for label in labels
												]

									a__   :						Optional[int]						       =  [feature["""ner_tags"""] for feature in features]
									a__   :						str						       =  padding_tensor(__SCREAMING_SNAKE_CASE					,   -1					,   __SCREAMING_SNAKE_CASE					,   __SCREAMING_SNAKE_CASE  )
									a__   :						Optional[Any]						       =  [feature["""original_entity_spans"""] for feature in features]
									a__   :						Any						       =  padding_tensor(__SCREAMING_SNAKE_CASE					,   (-1, -1)					,   __SCREAMING_SNAKE_CASE					,   __SCREAMING_SNAKE_CASE  )
									a__   :						Optional[int]						       =  {k: torch.tensor(__SCREAMING_SNAKE_CASE					,   dtype=torch.intaa  ) for k, v in batch.items()}

									return batch









from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     NestedDataStructureLike[PathLike]     ,		__SCREAMING_SNAKE_CASE							:     Optional[NamedSplit] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[Features] = None     ,		__SCREAMING_SNAKE_CASE							:     str = None     ,		__SCREAMING_SNAKE_CASE							:     bool = False     ,		__SCREAMING_SNAKE_CASE							:     bool = False     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,		**__SCREAMING_SNAKE_CASE							:     List[str]     ,		):


           '''simple docstring'''

           super().__init__(
               __SCREAMING_SNAKE_CASE     ,		split=__SCREAMING_SNAKE_CASE     ,		features=__SCREAMING_SNAKE_CASE     ,		cache_dir=__SCREAMING_SNAKE_CASE     ,		keep_in_memory=__SCREAMING_SNAKE_CASE     ,		streaming=__SCREAMING_SNAKE_CASE     ,		num_proc=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE     ,		)
           __a						       =				path_or_paths if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE) else {self.split: path_or_paths}
           __a						       =				Text(
               cache_dir=__SCREAMING_SNAKE_CASE     ,		data_files=__SCREAMING_SNAKE_CASE     ,		features=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE     ,		)





       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           if self.streaming:
               __a						       =				self.builder.as_streaming_dataset(split=self.split)
           # Build regular (map-style) dataset
           else:
               __a						       =				None
               __a						       =				None
               __a						       =				None
               __a						       =				None

               self.builder.download_and_prepare(
                   download_config=__SCREAMING_SNAKE_CASE     ,		download_mode=__SCREAMING_SNAKE_CASE     ,		verification_mode=__SCREAMING_SNAKE_CASE     ,		base_path=__SCREAMING_SNAKE_CASE     ,		num_proc=self.num_proc     ,		)
               __a						       =				self.builder.as_dataset(
                   split=self.split     ,		verification_mode=__SCREAMING_SNAKE_CASE     ,		in_memory=self.keep_in_memory)
           return dataset





