'''simple docstring'''
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       (      _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Any					):


      '''simple docstring'''


      if isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            UpperCAmelCase_            =  np.full((len(_UpperCamelCase					), sequence_length, 2)							,    _UpperCamelCase					)
      else:
            UpperCAmelCase_            =  np.full((len(_UpperCamelCase					), sequence_length)							,    _UpperCamelCase					)

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

      return out_tensor.tolist()



def  __lowerCamelCase       (      _UpperCamelCase  :  Dict					):


      '''simple docstring'''


      UpperCAmelCase_            =  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
      UpperCAmelCase_            =  unicodedata.category(_UpperCamelCase					)
      if cat.startswith('''P'''					):
            return True
      return False







@dataclass
class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	42
  lowerCAmelCase__										=	True
  lowerCAmelCase__										=	None
  lowerCAmelCase__										=	None
  lowerCAmelCase__										=	-1_00
  lowerCAmelCase__										=	"pt"






  def  lowerCAmelCase__ (      self			:       List[str]  ,							UpperCAmelCase__			:       List[Any]  )				->List[Any]:
        import torch

        UpperCAmelCase_            =  '''label''' if '''label''' in features[0].keys() else '''labels'''
        UpperCAmelCase_            =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
        UpperCAmelCase_            =  self.tokenizer.pad(
            UpperCAmelCase__  ,							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

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

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

        return batch






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase__  :      Optional[Any]        =					{
    "configuration_encodec": [
        "ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EncodecConfig",
    ],
    "feature_extraction_encodec": ["EncodecFeatureExtractor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      List[str]        =					[
          "ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
          "EncodecModel",
          "EncodecPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_encodec import (
          ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EncodecConfig,
      )
      from .feature_extraction_encodec import EncodecFeatureExtractor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_encodec import (
                ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
                EncodecModel,
                EncodecPreTrainedModel,
            )

else:
      import sys

      lowercase__  :      str        =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





