


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


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)


SCREAMING_SNAKE_CASE							    =							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE							    =							{
    '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',
    },
}

SCREAMING_SNAKE_CASE							    =							{
    'allenai/led-base-16384': 1_6_3_8_4,
}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      VOCAB_FILES_NAMES
     _lowerCamelCase   =      PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase   =      LEDTokenizer
     _lowerCamelCase   =      ["""input_ids""", """attention_mask"""]


     def __init__(	self  ,       __A=None  ,       __A=None  ,       __A=None  ,       __A="replace"  ,       __A="<s>"  ,       __A="</s>"  ,       __A="</s>"  ,       __A="<s>"  ,       __A="<unk>"  ,       __A="<pad>"  ,       __A="<mask>"  ,       __A=False  ,       __A=True  ,       **__A  ,       ):
        super().__init__(
            lowerCamelCase__  ,       lowerCamelCase__  ,       tokenizer_file=lowerCamelCase__  ,       errors=lowerCamelCase__  ,       bos_token=lowerCamelCase__  ,       eos_token=lowerCamelCase__  ,       sep_token=lowerCamelCase__  ,       cls_token=lowerCamelCase__  ,       unk_token=lowerCamelCase__  ,       pad_token=lowerCamelCase__  ,       mask_token=lowerCamelCase__  ,       add_prefix_space=lowerCamelCase__  ,       trim_offsets=lowerCamelCase__  ,       **lowerCamelCase__  ,       )

        __a					=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
        if pre_tok_state.get("""add_prefix_space"""  ,       lowerCamelCase__    ) != add_prefix_space:
           __a					=  getattr(lowerCamelCase__  ,       pre_tok_state.pop("""type"""    )    )
           __a					=  add_prefix_space
           __a					=  pre_tok_class(**lowerCamelCase__    )

        __a					=  add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        __a					=  """post_processor"""
        __a					=  getattr(self.backend_tokenizer  ,       lowerCamelCase__  ,       lowerCamelCase__    )
        if tokenizer_component_instance:
           __a					=  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:
              __a					=  tuple(state["""sep"""]    )
           if "cls" in state:
              __a					=  tuple(state["""cls"""]    )

           __a					=  False

           if state.get("""add_prefix_space"""  ,       lowerCamelCase__    ) != add_prefix_space:
              __a					=  add_prefix_space
              __a					=  True

           if state.get("""trim_offsets"""  ,       lowerCamelCase__    ) != trim_offsets:
              __a					=  trim_offsets
              __a					=  True

           if changes_to_apply:
              __a					=  getattr(lowerCamelCase__  ,       state.pop("""type"""    )    )
              __a					=  component_class(**lowerCamelCase__    )
              setattr(self.backend_tokenizer  ,       lowerCamelCase__  ,       lowerCamelCase__    )


     @property
     # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
     def 	snake_case_  (	self    ):
        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 	snake_case_  (	self  ,       __A    ):
        __a					=  AddedToken(lowerCamelCase__  ,       lstrip=lowerCamelCase__  ,       rstrip=lowerCamelCase__    ) if isinstance(lowerCamelCase__  ,       lowerCamelCase__    ) else value
        __a					=  value


     def 	snake_case_  (	self  ,       *__A  ,       **__A    ):
        __a					=  kwargs.get("""is_split_into_words"""  ,       lowerCamelCase__    )

        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(*lowerCamelCase__  ,       **lowerCamelCase__    )


     def 	snake_case_  (	self  ,       *__A  ,       **__A    ):
        __a					=  kwargs.get("""is_split_into_words"""  ,       lowerCamelCase__    )

        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(*lowerCamelCase__  ,       **lowerCamelCase__    )


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  self._tokenizer.model.save(lowerCamelCase__  ,       name=lowerCamelCase__    )
        return tuple(lowerCamelCase__    )


     def 	snake_case_  (	self  ,       __A  ,       __A=None    ):
        __a					=  [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 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  [self.sep_token_id]
        __a					=  [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 	snake_case_  (	self  ,       __A  ,       __A = None  ,       __A = PaddingStrategy.DO_NOT_PAD  ,       __A = None  ,       __A = None  ,       ):
        __a					=  super()._pad(
            encoded_inputs=lowerCamelCase__  ,       max_length=lowerCamelCase__  ,       padding_strategy=lowerCamelCase__  ,       pad_to_multiple_of=lowerCamelCase__  ,       return_attention_mask=lowerCamelCase__  ,       )

        # Load from model defaults
        if return_attention_mask is None:
           __a					=  """attention_mask""" in self.model_input_names

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

           if needs_to_be_padded:
              __a					=  len(lowerCamelCase__    ) - 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`
                 __a					=  (
                     encoded_inputs["""global_attention_mask"""] + [-1] * difference
                 )
              elif self.padding_side == "left":
                 __a					=  [-1] * difference + encoded_inputs[
                     """global_attention_mask"""
                 ]
              else:
                 raise ValueError("""Invalid padding strategy:""" + str(self.padding_side    )    )

        return encoded_inputs









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


__A								=       logging.get_logger(__name__)







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			['''pixel_values''']


			def __init__( self     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = PILImageResampling.BILINEAR     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = 1 / 255     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> None:



							'''simple docstring'''
							super().__init__(**lowerCamelCase__				)
							__lowerCamelCase									=       size if size is not None else {'shortest_edge': 256}
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							__lowerCamelCase									=       crop_size if crop_size is not None else {'height': 224, 'width': 224}
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		param_name='crop_size'				)
							__lowerCamelCase									=       do_resize
							__lowerCamelCase									=       size
							__lowerCamelCase									=       resample
							__lowerCamelCase									=       do_center_crop
							__lowerCamelCase									=       crop_size
							__lowerCamelCase									=       do_rescale
							__lowerCamelCase									=       rescale_factor
							__lowerCamelCase									=       do_normalize
							__lowerCamelCase									=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							__lowerCamelCase									=       image_std if image_std is not None else IMAGENET_STANDARD_STD


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = PILImageResampling.BICUBIC     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							if "shortest_edge" not in size:
											raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
							__lowerCamelCase									=       get_resize_output_image_size(lowerCamelCase__     ,		size=size['shortest_edge']     ,		default_to_square=lowerCamelCase__				)
							return resize(lowerCamelCase__     ,		size=lowerCamelCase__     ,		resample=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__				)
							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(lowerCamelCase__     ,		size=(size['height'], size['width'])     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__				)       -> np.ndarray:



							'''simple docstring'''
							return rescale(lowerCamelCase__     ,		scale=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							return normalize(lowerCamelCase__     ,		mean=lowerCamelCase__     ,		std=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = ChannelDimension.FIRST     ,		**lowerCamelCase__     ,		)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       do_resize if do_resize is not None else self.do_resize
							__lowerCamelCase									=       size if size is not None else self.size
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							__lowerCamelCase									=       resample if resample is not None else self.resample
							__lowerCamelCase									=       do_center_crop if do_center_crop is not None else self.do_center_crop
							__lowerCamelCase									=       crop_size if crop_size is not None else self.crop_size
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		param_name='crop_size'				)
							__lowerCamelCase									=       do_rescale if do_rescale is not None else self.do_rescale
							__lowerCamelCase									=       rescale_factor if rescale_factor is not None else self.rescale_factor
							__lowerCamelCase									=       do_normalize if do_normalize is not None else self.do_normalize
							__lowerCamelCase									=       image_mean if image_mean is not None else self.image_mean
							__lowerCamelCase									=       image_std if image_std is not None else self.image_std

							__lowerCamelCase									=       make_list_of_images(lowerCamelCase__				)

							if not valid_images(lowerCamelCase__				):
											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.
							__lowerCamelCase									=       [to_numpy_array(lowerCamelCase__				) for image in images]

							if do_resize:
											__lowerCamelCase									=       [self.resize(image=lowerCamelCase__     ,		size=lowerCamelCase__     ,		resample=lowerCamelCase__				) for image in images]

							if do_center_crop:
											__lowerCamelCase									=       [self.center_crop(image=lowerCamelCase__     ,		size=lowerCamelCase__				) for image in images]

							if do_rescale:
											__lowerCamelCase									=       [self.rescale(image=lowerCamelCase__     ,		scale=lowerCamelCase__				) for image in images]

							if do_normalize:
											__lowerCamelCase									=       [self.normalize(image=lowerCamelCase__     ,		mean=lowerCamelCase__     ,		std=lowerCamelCase__				) for image in images]

							__lowerCamelCase									=       [to_channel_dimension_format(lowerCamelCase__     ,		lowerCamelCase__				) for image in images]

							__lowerCamelCase									=       {'pixel_values': images}
							return BatchFeature(data=lowerCamelCase__     ,		tensor_type=lowerCamelCase__				)






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       outputs.logits

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

											if is_torch_tensor(lowerCamelCase__				):
															__lowerCamelCase									=       target_sizes.numpy()

											__lowerCamelCase									=       []

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

							return semantic_segmentation


