
"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
 import torch


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)





class 				a__					(						A__       ):
     UpperCAmelCase__         =      ['''pixel_values''']



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







      '''simple docstring'''



      super().__init__(**snake_case__   )
      UpperCamelCase_     :					int   	=size if size is not None else {"shortest_edge": 256}
      UpperCamelCase_     :					Optional[int]   	=get_size_dict(snake_case__      ,  default_to_square=snake_case__   )
      UpperCamelCase_     :					Any   	=crop_size if crop_size is not None else {"height": 224, "width": 224}
      UpperCamelCase_     :					Any   	=get_size_dict(snake_case__      ,  param_name='crop_size'   )
      UpperCamelCase_     :					Tuple   	=do_resize
      UpperCamelCase_     :					int   	=size
      UpperCamelCase_     :					Optional[Any]   	=resample
      UpperCamelCase_     :					Tuple   	=do_center_crop
      UpperCamelCase_     :					int   	=crop_size
      UpperCamelCase_     :					int   	=do_rescale
      UpperCamelCase_     :					Tuple   	=rescale_factor
      UpperCamelCase_     :					Tuple   	=do_normalize
      UpperCamelCase_     :					Union[str, Any]   	=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      UpperCamelCase_     :					Dict   	=image_std if image_std is not None else IMAGENET_STANDARD_STD



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :np.ndarray      ,  _lowerCamelCase :Dict[str, int]      ,  _lowerCamelCase :PILImageResampling = PILImageResampling.BICUBIC      ,  _lowerCamelCase :Optional[Union[str, ChannelDimension]] = None      ,  **_lowerCamelCase :int      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=get_size_dict(snake_case__      ,  default_to_square=snake_case__   )
      if "shortest_edge" not in size:
       raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''   )
      UpperCamelCase_     :					Union[str, Any]   	=get_resize_output_image_size(snake_case__      ,  size=size['shortest_edge']      ,  default_to_square=snake_case__   )
      return resize(snake_case__      ,  size=snake_case__      ,  resample=snake_case__      ,  data_format=snake_case__      ,  **snake_case__   )



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :np.ndarray      ,  _lowerCamelCase :Dict[str, int]      ,  _lowerCamelCase :Optional[Union[str, ChannelDimension]] = None      ,  **_lowerCamelCase :List[Any]      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=get_size_dict(snake_case__   )
      if "height" not in size or "width" not in size:
       raise ValueError(f'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''   )
      return center_crop(snake_case__      ,  size=(size['height'], size['width'])      ,  data_format=snake_case__      ,  **snake_case__   )



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :np.ndarray      ,  _lowerCamelCase :float      ,  _lowerCamelCase :Optional[Union[str, ChannelDimension]] = None      ,  **_lowerCamelCase :Tuple   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :np.ndarray      ,  _lowerCamelCase :Union[float, List[float]]      ,  _lowerCamelCase :Union[float, List[float]]      ,  _lowerCamelCase :Optional[Union[str, ChannelDimension]] = None      ,  **_lowerCamelCase :Optional[Any]      ,  ):







      '''simple docstring'''



      return normalize(snake_case__      ,  mean=snake_case__      ,  std=snake_case__      ,  data_format=snake_case__      ,  **snake_case__   )



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







      '''simple docstring'''



      UpperCamelCase_     :					Union[str, Any]   	=do_resize if do_resize is not None else self.do_resize
      UpperCamelCase_     :					Any   	=size if size is not None else self.size
      UpperCamelCase_     :					Optional[int]   	=get_size_dict(snake_case__      ,  default_to_square=snake_case__   )
      UpperCamelCase_     :					int   	=resample if resample is not None else self.resample
      UpperCamelCase_     :					List[str]   	=do_center_crop if do_center_crop is not None else self.do_center_crop
      UpperCamelCase_     :					str   	=crop_size if crop_size is not None else self.crop_size
      UpperCamelCase_     :					Dict   	=get_size_dict(snake_case__      ,  param_name='crop_size'   )
      UpperCamelCase_     :					Optional[Any]   	=do_rescale if do_rescale is not None else self.do_rescale
      UpperCamelCase_     :					List[Any]   	=rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCamelCase_     :					int   	=do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase_     :					int   	=image_mean if image_mean is not None else self.image_mean
      UpperCamelCase_     :					Tuple   	=image_std if image_std is not None else self.image_std

      UpperCamelCase_     :					Optional[int]   	=make_list_of_images(snake_case__   )

      if not valid_images(snake_case__   ):
       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_center_crop and crop_size is None:
       raise ValueError('Crop size must be specified if do_center_crop is True.'   )

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

      if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

      # All transformations expect numpy arrays.
      UpperCamelCase_     :					List[str]   	=[to_numpy_array(snake_case__   ) for image in images]

      if do_resize:
       UpperCamelCase_     :					Any   	=[self.resize(image=snake_case__      ,  size=snake_case__      ,  resample=snake_case__   ) for image in images]

      if do_center_crop:
       UpperCamelCase_     :					Optional[int]   	=[self.center_crop(image=snake_case__      ,  size=snake_case__   ) for image in images]

      if do_rescale:
       UpperCamelCase_     :					Dict   	=[self.rescale(image=snake_case__      ,  scale=snake_case__   ) for image in images]

      if do_normalize:
       UpperCamelCase_     :					Any   	=[self.normalize(image=snake_case__      ,  mean=snake_case__      ,  std=snake_case__   ) for image in images]

      UpperCamelCase_     :					int   	=[to_channel_dimension_format(snake_case__      ,  snake_case__   ) for image in images]

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




     def  lowerCamelCase_						(							self :List[Any]      ,  _lowerCamelCase :List[Any]      ,  _lowerCamelCase :List[Tuple] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=outputs.logits

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

       if is_torch_tensor(snake_case__   ):
        UpperCamelCase_     :					Optional[int]   	=target_sizes.numpy()

       UpperCamelCase_     :					Union[str, Any]   	=[]

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

      return semantic_segmentation








"""simple docstring"""



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

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


_lowerCAmelCase	:	str				=      logging.get_logger(__name__)


_lowerCAmelCase	:	int				=      {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase	:	List[Any]				=      {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

_lowerCAmelCase	:	List[str]				=      {
    """allenai/led-base-16384""": 16_384,
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    =VOCAB_FILES_NAMES
   SCREAMING_SNAKE_CASE_    =PRETRAINED_VOCAB_FILES_MAP
   SCREAMING_SNAKE_CASE_    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   SCREAMING_SNAKE_CASE_    =LEDTokenizer
   SCREAMING_SNAKE_CASE_    =['''input_ids''', '''attention_mask''']
   def __init__(		self      :	Optional[Any]							,			snake_case__      :	str=None							,			snake_case__      :	List[Any]=None							,			snake_case__      :	Dict=None							,			snake_case__      :	List[str]="replace"							,			snake_case__      :	Optional[int]="<s>"							,			snake_case__      :	List[str]="</s>"							,			snake_case__      :	Union[str, Any]="</s>"							,			snake_case__      :	Dict="<s>"							,			snake_case__      :	Tuple="<unk>"							,			snake_case__      :	Any="<pad>"							,			snake_case__      :	Dict="<mask>"							,			snake_case__      :	int=False							,			snake_case__      :	Optional[int]=True							,			**snake_case__      :	List[Any]							,			):


        '''simple docstring'''
        super().__init__(
            snake_case__							,			snake_case__							,			tokenizer_file=snake_case__							,			errors=snake_case__							,			bos_token=snake_case__							,			eos_token=snake_case__							,			sep_token=snake_case__							,			cls_token=snake_case__							,			unk_token=snake_case__							,			pad_token=snake_case__							,			mask_token=snake_case__							,			add_prefix_space=snake_case__							,			trim_offsets=snake_case__							,			**snake_case__							,			)

        UpperCAmelCase__						:    str							=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
        if pre_tok_state.get("add_prefix_space"							,			snake_case__							) != add_prefix_space:
             UpperCAmelCase__						:    Dict							=							getattr(snake_case__							,			pre_tok_state.pop("type"							)							)
             UpperCAmelCase__						:    str							=							add_prefix_space
             UpperCAmelCase__						:    Any							=							pre_tok_class(**snake_case__							)

        UpperCAmelCase__						:    Dict							=							add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        UpperCAmelCase__						:    List[str]							=							"post_processor"
        UpperCAmelCase__						:    List[Any]							=							getattr(self.backend_tokenizer							,			snake_case__							,			snake_case__							)
        if tokenizer_component_instance:
             UpperCAmelCase__						:    int							=							json.loads(tokenizer_component_instance.__getstate__()							)

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                  UpperCAmelCase__						:    Optional[Any]							=							tuple(state["sep"]							)
             if "cls" in state:
                  UpperCAmelCase__						:    Any							=							tuple(state["cls"]							)

             UpperCAmelCase__						:    Any							=							False

             if state.get("add_prefix_space"							,			snake_case__							) != add_prefix_space:
                  UpperCAmelCase__						:    Union[str, Any]							=							add_prefix_space
                  UpperCAmelCase__						:    List[Any]							=							True

             if state.get("trim_offsets"							,			snake_case__							) != trim_offsets:
                  UpperCAmelCase__						:    Optional[int]							=							trim_offsets
                  UpperCAmelCase__						:    List[Any]							=							True

             if changes_to_apply:
                  UpperCAmelCase__						:    List[str]							=							getattr(snake_case__							,			state.pop("type"							)							)
                  UpperCAmelCase__						:    Optional[int]							=							component_class(**snake_case__							)
                  setattr(self.backend_tokenizer							,			snake_case__							,			snake_case__							)
   @property
   # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        if self._mask_token is None:
             if self.verbose:
                  logger.error("Using mask_token, but it is not set yet."							)
             return None
        return str(self._mask_token							)
   @mask_token.setter
   def       __a							(		self      :	Any							,			snake_case__      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__							) if isinstance(snake_case__							,			snake_case__							) else value
        UpperCAmelCase__						:    Dict							=							value
   def       __a							(		self      :	str							,			*snake_case__      :	Any							,			**snake_case__      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							kwargs.get("is_split_into_words"							,			snake_case__							)

        if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                 "to use it with pretokenized inputs."							)

        return super()._batch_encode_plus(*snake_case__							,			**snake_case__							)
   def       __a							(		self      :	List[str]							,			*snake_case__      :	Union[str, Any]							,			**snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							kwargs.get("is_split_into_words"							,			snake_case__							)

        if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                 "to use it with pretokenized inputs."							)

        return super()._encode_plus(*snake_case__							,			**snake_case__							)
   def       __a							(		self      :	Tuple							,			snake_case__      :	str							,			snake_case__      :	Optional[str] = None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							self._tokenizer.model.save(snake_case__							,			name=snake_case__							)
        return tuple(snake_case__							)
   def       __a							(		self      :	str							,			snake_case__      :	List[Any]							,			snake_case__      :	str=None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
             return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
   def       __a							(		self      :	str							,			snake_case__      :	List[int]							,			snake_case__      :	Optional[List[int]] = None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							[self.sep_token_id]
        UpperCAmelCase__						:    Optional[Any]							=							[self.cls_token_id]

        if token_ids_a is None:
             return len(cls + token_ids_a + sep							) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]




   def       __a							(		self      :	Any							,			snake_case__      :	Union[Dict[str, EncodedInput], BatchEncoding]							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	Optional[bool] = None							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							super()._pad(
            encoded_inputs=snake_case__							,			max_length=snake_case__							,			padding_strategy=snake_case__							,			pad_to_multiple_of=snake_case__							,			return_attention_mask=snake_case__							,			)

        # Load from model defaults
        if return_attention_mask is None:
             UpperCAmelCase__						:    Optional[int]							=							"attention_mask" in self.model_input_names

        if return_attention_mask and "global_attention_mask" in encoded_inputs:
             UpperCAmelCase__						:    List[Any]							=							encoded_inputs[self.model_input_names[0]]
             # `global_attention_mask` need to have the same length as other (sequential) inputs.
             UpperCAmelCase__						:    Any							=							len(encoded_inputs["global_attention_mask"]							) != len(snake_case__							)

             if needs_to_be_padded:
                  UpperCAmelCase__						:    List[str]							=							len(snake_case__							) - len(encoded_inputs["global_attention_mask"]							)

                  if self.padding_side == "right":
                       # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                       UpperCAmelCase__						:    Dict							=							(
                           encoded_inputs["global_attention_mask"] + [-1] * difference
                       )
                  elif self.padding_side == "left":
                       UpperCAmelCase__						:    Dict							=							[-1] * difference + encoded_inputs[
                           "global_attention_mask"
                       ]
                  else:
                       raise ValueError("Invalid padding strategy:" + str(self.padding_side							)							)

        return encoded_inputs


