


'''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	:     Tuple	, lowerCamelCase	:     str	, lowerCamelCase	:     Tuple	, lowerCamelCase	:     int				):
   if isinstance(lowerCamelCase	, lowerCamelCase				):
      lowerCAmelCase     =			np.full((len(lowerCamelCase				), sequence_length, 2)	, lowerCamelCase				)
   else:
      lowerCAmelCase     =			np.full((len(lowerCamelCase				), sequence_length)	, lowerCamelCase				)

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

   return out_tensor.tolist()





def 	a_					( lowerCamelCase	:     Optional[int]				):
   lowerCAmelCase     =			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
   lowerCAmelCase     =			unicodedata.category(lowerCamelCase				)
   if cat.startswith('P'				):
      return True
   return False




@dataclass
class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	PreTrainedTokenizerBase
     lowerCamelCase				:	Union[bool, str, PaddingStrategy]		=     True
     lowerCamelCase				:	Optional[int]		=     None
     lowerCamelCase				:	Optional[int]		=     None
     lowerCamelCase				:	int		=     -100
     lowerCamelCase				:	str		=     "pt"





     def 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      Any    )				->  List[Any]:
        import torch

        lowerCAmelCase     =			'label' if 'label' in features[0].keys() else 'labels'
        lowerCAmelCase     =			[feature[label_name] for feature in features] if label_name in features[0].keys() else None
        lowerCAmelCase     =			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

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

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

        return batch







from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel




