

"""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 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> Tuple:
      if isinstance(lowerCAmelCase			,	lowerCAmelCase    ):
            UpperCAmelCase__			:							Optional[int]							=    np.full((len(lowerCAmelCase    ), sequence_length, 2)			,	lowerCAmelCase    )
      else:
            UpperCAmelCase__			:							Dict							=    np.full((len(lowerCAmelCase    ), sequence_length)			,	lowerCAmelCase    )

      for i, tensor in enumerate(lowerCAmelCase    ):
            if padding_side == "right":
                  if isinstance(lowerCAmelCase			,	lowerCAmelCase    ):
                        UpperCAmelCase__			:							Any							=    tensor[:sequence_length]
                  else:
                        UpperCAmelCase__			:							Tuple							=    tensor[:sequence_length]
            else:
                  if isinstance(lowerCAmelCase			,	lowerCAmelCase    ):
                        UpperCAmelCase__			:							Union[str, Any]							=    tensor[:sequence_length]
                  else:
                        UpperCAmelCase__			:							Optional[int]							=    tensor[:sequence_length]

      return out_tensor.tolist()




def 					a__						(						lowerCAmelCase    )	-> Dict:
      UpperCAmelCase__			:							Optional[int]							=    ord(lowerCAmelCase    )
      if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
            return True
      UpperCAmelCase__			:							str							=    unicodedata.category(lowerCAmelCase    )
      if cat.startswith("""P"""    ):
            return True
      return False





@dataclass
class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  42
  SCREAMING_SNAKE_CASE					      =  True
  SCREAMING_SNAKE_CASE					      =  None
  SCREAMING_SNAKE_CASE					      =  None
  SCREAMING_SNAKE_CASE					      =  -1_0_0
  SCREAMING_SNAKE_CASE					      =  "pt"



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        import torch

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

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

        return batch




"""simple docstring"""
# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)

