



'''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 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Any:
      if isinstance(_lowerCAmelCase     ,   _lowerCAmelCase		):
            snake_case__  :    List[str]    						=      np.full((len(_lowerCAmelCase		), sequence_length, 2)     ,   _lowerCAmelCase		)
      else:
            snake_case__  :    Union[str, Any]    						=      np.full((len(_lowerCAmelCase		), sequence_length)     ,   _lowerCAmelCase		)

      for i, tensor in enumerate(_lowerCAmelCase		):
            if padding_side == "right":
                  if isinstance(_lowerCAmelCase     ,   _lowerCAmelCase		):
                        snake_case__  :    int    						=      tensor[:sequence_length]
                  else:
                        snake_case__  :    Tuple    						=      tensor[:sequence_length]
            else:
                  if isinstance(_lowerCAmelCase     ,   _lowerCAmelCase		):
                        snake_case__  :    Dict    						=      tensor[:sequence_length]
                  else:
                        snake_case__  :    Optional[int]    						=      tensor[:sequence_length]

      return out_tensor.tolist()





def 						__snake_case(				_lowerCAmelCase		)		-> Any:
      snake_case__  :    Tuple    						=      ord(_lowerCAmelCase		)
      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
      snake_case__  :    str    						=      unicodedata.category(_lowerCAmelCase		)
      if cat.startswith("""P"""		):
            return True
      return False







@dataclass
class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







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




    def lowerCamelCase       (    self					:     Dict ,     snake_case_					:     str						):
          import torch

          snake_case__  :    Any    						=      """label""" if """label""" in features[0].keys() else """labels"""
          snake_case__  :    Any    						=      [feature[label_name] for feature in features] if label_name in features[0].keys() else None
          snake_case__  :    Tuple    						=      self.tokenizer.pad(
              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

          snake_case__  :    Optional[int]    						=      torch.tensor(batch["""entity_ids"""]						).shape[1]
          snake_case__  :    Tuple    						=      self.tokenizer.padding_side
          if padding_side == "right":
                snake_case__  :    Union[str, Any]    						=      [
                    list(snake_case_						) + [self.label_pad_token_id] * (sequence_length - len(snake_case_						)) for label in labels
                ]
          else:
                snake_case__  :    Optional[int]    						=      [
                    [self.label_pad_token_id] * (sequence_length - len(snake_case_						)) + list(snake_case_						) for label in labels
                ]

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

          return batch






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase					:       Optional[Any]    					=      {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Tuple    					=      [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		__UpperCamelCase					:       List[Any]    					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

