




"""simple docstring"""


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = 42


if is_transformers_available() and is_torch_available():
     from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline









"""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  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: Any		,       snake_case__							: List[str]		,       snake_case__							: int   ):
  """simple docstring"""

  if isinstance(snake_case__		,       snake_case__   ):
    _snake_case							:		List[Any]								=  np.full((len(snake_case__   ), sequence_length, 2)		,       snake_case__   )
  else:
    _snake_case							:		Any								=  np.full((len(snake_case__   ), sequence_length)		,       snake_case__   )

  for i, tensor in enumerate(snake_case__   ):
    if padding_side == "right":
      if isinstance(snake_case__		,       snake_case__   ):
        _snake_case							:		Dict								=  tensor[:sequence_length]
      else:
        _snake_case							:		List[Any]								=  tensor[:sequence_length]
    else:
      if isinstance(snake_case__		,       snake_case__   ):
        _snake_case							:		str								=  tensor[:sequence_length]
      else:
        _snake_case							:		Tuple								=  tensor[:sequence_length]

  return out_tensor.tolist()


def  UpperCAmelCase__	(snake_case__							: Optional[int]   ):
  """simple docstring"""

  _snake_case							:		str								=  ord(snake_case__   )
  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
  _snake_case							:		Union[str, Any]								=  unicodedata.category(snake_case__   )
  if cat.startswith("""P"""   ):
    return True
  return False






@dataclass
class  lowercase(					__a			):







 '''simple docstring'''





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


 def 			UpperCamelCase_       (    self:		Any,				a_:		Union[str, Any]		):




   '''simple docstring'''



   import torch

   _snake_case							:		Optional[Any]								=  """label""" if """label""" in features[0].keys() else """labels"""
   _snake_case							:		str								=  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
   _snake_case							:		Any								=  self.tokenizer.pad(
       a_,				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							:		Any								=  self.tokenizer.padding_side
   if padding_side == "right":
     _snake_case							:		Optional[int]								=  [
         list(a_		) + [self.label_pad_token_id] * (sequence_length - len(a_		)) for label in labels
     ]
   else:
     _snake_case							:		Union[str, Any]								=  [
         [self.label_pad_token_id] * (sequence_length - len(a_		)) + list(a_		) for label in labels
     ]

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

   return batch


