




"""simple docstring"""


from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
     import PIL

if is_torch_available():
     import torch


A_      =    logging.get_logger(__name__)






class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = ["pixel_values"]


 def __init__(    self:		Dict,				a_:		bool = True,				a_:		Dict[str, int] = None,				a_:		PILImageResampling = PILImageResampling.BILINEAR,				a_:		bool = True,				a_:		Union[int, float] = 1 / 255,				a_:		bool = True,				a_:		Dict[str, int] = None,				a_:		bool = True,				**a_:		Union[str, Any],				):




   '''simple docstring'''



   super().__init__(**a_		)
   _snake_case							:		int								=  size if size is not None else {"""shortest_edge""": 224}
   _snake_case							:		Tuple								=  get_size_dict(a_,				default_to_square=a_		)
   _snake_case							:		Optional[int]								=  crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
   _snake_case							:		List[Any]								=  get_size_dict(a_,				param_name="""crop_size"""		)

   _snake_case							:		Any								=  do_resize
   _snake_case							:		Tuple								=  size
   _snake_case							:		str								=  resample
   _snake_case							:		List[Any]								=  do_rescale
   _snake_case							:		Union[str, Any]								=  rescale_factor
   _snake_case							:		Dict								=  do_center_crop
   _snake_case							:		List[Any]								=  crop_size
   _snake_case							:		int								=  do_flip_channel_order


 def 			UpperCamelCase_       (    self:		int,				a_:		np.ndarray,				a_:		Dict[str, int],				a_:		PILImageResampling = PIL.Image.BILINEAR,				a_:		Optional[Union[str, ChannelDimension]] = None,				**a_:		Optional[int],				):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  get_size_dict(a_,				default_to_square=a_		)
   if "shortest_edge" not in size:
     raise ValueError(f"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"		)
   _snake_case							:		Dict								=  get_resize_output_image_size(a_,				size=size["""shortest_edge"""],				default_to_square=a_		)
   return resize(a_,				size=a_,				resample=a_,				data_format=a_,				**a_		)


 def 			UpperCamelCase_       (    self:		Dict,				a_:		np.ndarray,				a_:		Dict[str, int],				a_:		Optional[Union[str, ChannelDimension]] = None,				**a_:		str,				):




   '''simple docstring'''



   _snake_case							:		Any								=  get_size_dict(a_		)
   if "height" not in size or "width" not in size:
     raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"		)
   return center_crop(a_,				size=(size["""height"""], size["""width"""]),				data_format=a_,				**a_		)


 def 			UpperCamelCase_       (    self:		Optional[int],				a_:		np.ndarray,				a_:		Union[int, float],				a_:		Optional[Union[str, ChannelDimension]] = None,				**a_:		Union[str, Any],				):




   '''simple docstring'''



   return rescale(a_,				scale=a_,				data_format=a_,				**a_		)


 def 			UpperCamelCase_       (    self:		List[str],				a_:		np.ndarray,				a_:		Optional[Union[str, ChannelDimension]] = None		):




   '''simple docstring'''



   return flip_channel_order(a_,				data_format=a_		)


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		ImageInput,				a_:		bool = None,				a_:		Dict[str, int] = None,				a_:		PILImageResampling = None,				a_:		bool = None,				a_:		float = None,				a_:		bool = None,				a_:		Dict[str, int] = None,				a_:		bool = None,				a_:		Optional[Union[str, TensorType]] = None,				a_:		ChannelDimension = ChannelDimension.FIRST,				**a_:		Union[str, Any],				):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  do_resize if do_resize is not None else self.do_resize
   _snake_case							:		Tuple								=  resample if resample is not None else self.resample
   _snake_case							:		Optional[Any]								=  do_rescale if do_rescale is not None else self.do_rescale
   _snake_case							:		Dict								=  rescale_factor if rescale_factor is not None else self.rescale_factor
   _snake_case							:		Union[str, Any]								=  do_center_crop if do_center_crop is not None else self.do_center_crop
   _snake_case							:		List[Any]								=  (
       do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
   )

   _snake_case							:		int								=  size if size is not None else self.size
   _snake_case							:		str								=  get_size_dict(a_,				default_to_square=a_		)
   _snake_case							:		Dict								=  crop_size if crop_size is not None else self.crop_size
   _snake_case							:		str								=  get_size_dict(a_,				param_name="""crop_size"""		)

   _snake_case							:		List[Any]								=  make_list_of_images(a_		)

   if not valid_images(a_		):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray."""		)

   if do_resize and size is None:
     raise ValueError("""Size must be specified if do_resize is True."""		)

   if do_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True."""		)

   if do_center_crop and crop_size is None:
     raise ValueError("""Crop size must be specified if do_center_crop is True."""		)

   # All transformations expect numpy arrays.
   _snake_case							:		Optional[int]								=  [to_numpy_array(a_		) for image in images]

   if do_resize:
     _snake_case							:		int								=  [self.resize(image=a_,				size=a_,				resample=a_		) for image in images]

   if do_center_crop:
     _snake_case							:		Tuple								=  [self.center_crop(image=a_,				size=a_		) for image in images]

   if do_rescale:
     _snake_case							:		Optional[Any]								=  [self.rescale(image=a_,				scale=a_		) for image in images]

   # the pretrained checkpoints assume images are BGR, not RGB
   if do_flip_channel_order:
     _snake_case							:		List[Any]								=  [self.flip_channel_order(image=a_		) for image in images]

   _snake_case							:		Tuple								=  [to_channel_dimension_format(a_,				a_		) for image in images]

   _snake_case							:		List[Any]								=  {"""pixel_values""": images}
   return BatchFeature(data=a_,				tensor_type=a_		)


 def 			UpperCamelCase_       (    self:		str,				a_:		Any,				a_:		List[Tuple] = None		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  outputs.logits

   # Resize logits and compute semantic segmentation maps
   if target_sizes is not None:
     if len(a_		) != len(a_		):
       raise ValueError(
           """Make sure that you pass in as many target sizes as the batch dimension of the logits"""		)

     if is_torch_tensor(a_		):
       _snake_case							:		Tuple								=  target_sizes.numpy()

     _snake_case							:		int								=  []

     for idx in range(len(a_		)		):
       _snake_case							:		List[str]								=  torch.nn.functional.interpolate(
           logits[idx].unsqueeze(dim=0		),				size=target_sizes[idx],				mode="""bilinear""",				align_corners=a_		)
       _snake_case							:		Tuple								=  resized_logits[0].argmax(dim=0		)
       semantic_segmentation.append(a_		)
   else:
     _snake_case							:		Dict								=  logits.argmax(dim=1		)
     _snake_case							:		List[str]								=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

   return semantic_segmentation








"""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


