
from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase__          =logging.get_logger(__name__)
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				["pixel_values"]
   def __init__(self			: Optional[int] ,    snake_case_			: bool = True ,    snake_case_			: int = 3_2 ,    snake_case_			: List[str]=PILImageResampling.BILINEAR ,    snake_case_			: bool = True ,    **snake_case_			: Optional[Any] ,    ):
    __a			:  List[str]		    =  do_resize
    __a			:  int		    =  do_rescale
    __a			:  List[Any]		    =  size_divisor
    __a			:  List[Any]		    =  resample
    super().__init__(**snake_case_       )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: np.ndarray ,    snake_case_			: int ,    snake_case_			: str ,    snake_case_			: Optional[ChannelDimension] = None ,    **snake_case_			: Any       ):
    __a			:  int		    =  get_image_size(snake_case_       )
    # Rounds the height and width down to the closest multiple of size_divisor
    __a			:  List[str]		    =  height // size_divisor * size_divisor
    __a			:  Optional[Any]		    =  width // size_divisor * size_divisor
    __a			:  Dict		    =  resize(snake_case_ ,    (new_h, new_w) ,    resample=snake_case_ ,    data_format=snake_case_ ,    **snake_case_       )
    return image
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: np.ndarray ,    snake_case_			: float ,    snake_case_			: Optional[ChannelDimension] = None ,    **snake_case_			: int       ):
    return rescale(image=snake_case_ ,    scale=snake_case_ ,    data_format=snake_case_ ,    **snake_case_       )







   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] ,    snake_case_			: Optional[bool] = None ,    snake_case_			: Optional[int] = None ,    snake_case_			: Union[str, Any]=None ,    snake_case_			: Optional[bool] = None ,    snake_case_			: Optional[Union[TensorType, str]] = None ,    snake_case_			: ChannelDimension = ChannelDimension.FIRST ,    **snake_case_			: Optional[int] ,    ):
    __a			:  int		    =  do_resize if do_resize is not None else self.do_resize
    __a			:  Dict		    =  do_rescale if do_rescale is not None else self.do_rescale
    __a			:  List[Any]		    =  size_divisor if size_divisor is not None else self.size_divisor
    __a			:  str		    =  resample if resample is not None else self.resample

    if do_resize and size_divisor is None:
     raise ValueError('''size_divisor is required for resizing'''       )

    __a			:  Optional[Any]		    =  make_list_of_images(snake_case_       )

    if not valid_images(snake_case_       ):
     raise ValueError('''Invalid image(s)'''       )

    # All transformations expect numpy arrays.
    __a			:  Optional[Any]		    =  [to_numpy_array(snake_case_       ) for img in images]

    if do_resize:
     __a			:  Optional[Any]		    =  [self.resize(snake_case_ ,    size_divisor=snake_case_ ,    resample=snake_case_       ) for image in images]

    if do_rescale:
     __a			:  Any		    =  [self.rescale(snake_case_ ,    scale=1 / 2_5_5       ) for image in images]

    __a			:  Optional[Any]		    =  [to_channel_dimension_format(snake_case_ ,    snake_case_       ) for image in images]

    __a			:  int		    =  {'''pixel_values''': images}
    return BatchFeature(data=snake_case_ ,    tensor_type=snake_case_       )


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					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Any  ):
 if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  __a			:  Union[str, Any]		    =  np.full((len(lowerCAmelCase__  ), sequence_length, 2)  ,       lowerCAmelCase__  )
 else:
  __a			:  str		    =  np.full((len(lowerCAmelCase__  ), sequence_length)  ,       lowerCAmelCase__  )

 for i, tensor in enumerate(lowerCAmelCase__  ):
  if padding_side == "right":
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Any		    =  tensor[:sequence_length]
   else:
    __a			:  List[Any]		    =  tensor[:sequence_length]
  else:
   if isinstance(lowerCAmelCase__  ,       lowerCAmelCase__  ):
    __a			:  Dict		    =  tensor[:sequence_length]
   else:
    __a			:  int		    =  tensor[:sequence_length]

 return out_tensor.tolist()




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[Any]  ):
 __a			:  str		    =  ord(lowerCAmelCase__  )
 if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
  return True
 __a			:  List[str]		    =  unicodedata.category(lowerCAmelCase__  )
 if cat.startswith('''P'''  ):
  return True
 return False



@dataclass
class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							PreTrainedTokenizerBase
   _SCREAMING_SNAKE_CASE				:							Union[bool, str, PaddingStrategy]     	=				True
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				None
   _SCREAMING_SNAKE_CASE				:							int     	=				-100
   _SCREAMING_SNAKE_CASE				:							str     	=				"pt"
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Tuple       ):
    import torch

    __a			:  Union[str, Any]		    =  '''label''' if '''label''' in features[0].keys() else '''labels'''
    __a			:  Tuple		    =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __a			:  Union[str, Any]		    =  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

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

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

    return batch
