

'''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


UpperCamelCase_    						=       logging.get_logger(__name__)


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

UpperCamelCase_    						=       {
    """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""",
    },
}

UpperCamelCase_    						=       {
    """allenai/led-base-16384""": 16384,
}





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    VOCAB_FILES_NAMES
				lowerCamelCase_		       =    PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_		       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_		       =    LEDTokenizer
				lowerCamelCase_		       =    ['input_ids', 'attention_mask']







				def __init__(       self     :		Optional[int]		,					UpperCAmelCase__     :		Tuple=None		,					UpperCAmelCase__     :		Optional[Any]=None		,					UpperCAmelCase__     :		List[str]=None		,					UpperCAmelCase__     :		Dict="replace"		,					UpperCAmelCase__     :		Any="<s>"		,					UpperCAmelCase__     :		Any="</s>"		,					UpperCAmelCase__     :		Union[str, Any]="</s>"		,					UpperCAmelCase__     :		Dict="<s>"		,					UpperCAmelCase__     :		List[str]="<unk>"		,					UpperCAmelCase__     :		List[Any]="<pad>"		,					UpperCAmelCase__     :		Tuple="<mask>"		,					UpperCAmelCase__     :		int=False		,					UpperCAmelCase__     :		int=True		,					**UpperCAmelCase__     :		Optional[Any]		,					):
										'''simple docstring'''
										super().__init__(
										    UpperCAmelCase__		,					UpperCAmelCase__		,					tokenizer_file=UpperCAmelCase__		,					errors=UpperCAmelCase__		,					bos_token=UpperCAmelCase__		,					eos_token=UpperCAmelCase__		,					sep_token=UpperCAmelCase__		,					cls_token=UpperCAmelCase__		,					unk_token=UpperCAmelCase__		,					pad_token=UpperCAmelCase__		,					mask_token=UpperCAmelCase__		,					add_prefix_space=UpperCAmelCase__		,					trim_offsets=UpperCAmelCase__		,					**UpperCAmelCase__		,					)

										lowercase :							Optional[int]    		=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
										if pre_tok_state.get('''add_prefix_space'''		,					UpperCAmelCase__   ) != add_prefix_space:
																lowercase :							Any    		=getattr(UpperCAmelCase__		,					pre_tok_state.pop('''type'''   )   )
																lowercase :							Optional[Any]    		=add_prefix_space
																lowercase :							Union[str, Any]    		=pre_tok_class(**UpperCAmelCase__   )

										lowercase :							str    		=add_prefix_space

										# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
										lowercase :							Tuple    		='''post_processor'''
										lowercase :							Union[str, Any]    		=getattr(self.backend_tokenizer		,					UpperCAmelCase__		,					UpperCAmelCase__   )
										if tokenizer_component_instance:
																lowercase :							List[str]    		=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:
																						lowercase :							int    		=tuple(state['''sep''']   )
																if "cls" in state:
																						lowercase :							Tuple    		=tuple(state['''cls''']   )

																lowercase :							Union[str, Any]    		=False

																if state.get('''add_prefix_space'''		,					UpperCAmelCase__   ) != add_prefix_space:
																						lowercase :							List[Any]    		=add_prefix_space
																						lowercase :							List[str]    		=True

																if state.get('''trim_offsets'''		,					UpperCAmelCase__   ) != trim_offsets:
																						lowercase :							Tuple    		=trim_offsets
																						lowercase :							Optional[int]    		=True

																if changes_to_apply:
																						lowercase :							Any    		=getattr(UpperCAmelCase__		,					state.pop('''type'''   )   )
																						lowercase :							str    		=component_class(**UpperCAmelCase__   )
																						setattr(self.backend_tokenizer		,					UpperCAmelCase__		,					UpperCAmelCase__   )







				@property
				# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''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  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=AddedToken(UpperCAmelCase__		,					lstrip=UpperCAmelCase__		,					rstrip=UpperCAmelCase__   ) if isinstance(UpperCAmelCase__		,					UpperCAmelCase__   ) else value
										lowercase :							Tuple    		=value







				def  lowerCamelCase_	(       self     :		int		,					*UpperCAmelCase__     :		List[str]		,					**UpperCAmelCase__     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=kwargs.get('''is_split_into_words'''		,					UpperCAmelCase__   )

										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(*UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Optional[int]		,					*UpperCAmelCase__     :		Any		,					**UpperCAmelCase__     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Tuple    		=kwargs.get('''is_split_into_words'''		,					UpperCAmelCase__   )

										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(*UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		str		,					UpperCAmelCase__     :		Optional[str] = None   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self._tokenizer.model.save(UpperCAmelCase__		,					name=UpperCAmelCase__   )
										return tuple(UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		int		,					UpperCAmelCase__     :		List[Any]		,					UpperCAmelCase__     :		Tuple=None   ):
										'''simple docstring'''
										lowercase :							str    		=[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  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		List[int]		,					UpperCAmelCase__     :		Optional[List[int]] = None   ):
										'''simple docstring'''
										lowercase :							List[str]    		=[self.sep_token_id]
										lowercase :							List[str]    		=[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  lowerCamelCase_	(       self     :		Optional[Any]		,					UpperCAmelCase__     :		Union[Dict[str, EncodedInput], BatchEncoding]		,					UpperCAmelCase__     :		Optional[int] = None		,					UpperCAmelCase__     :		PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,					UpperCAmelCase__     :		Optional[int] = None		,					UpperCAmelCase__     :		Optional[bool] = None		,					):
										'''simple docstring'''
										lowercase :							Optional[int]    		=super()._pad(
										    encoded_inputs=UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding_strategy=UpperCAmelCase__		,					pad_to_multiple_of=UpperCAmelCase__		,					return_attention_mask=UpperCAmelCase__		,					)

										# Load from model defaults
										if return_attention_mask is None:
																lowercase :							int    		='''attention_mask''' in self.model_input_names

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

																if needs_to_be_padded:
																						lowercase :							List[Any]    		=len(UpperCAmelCase__   ) - 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`
																												lowercase :							Dict    		=(
																												    encoded_inputs['''global_attention_mask'''] + [-1] * difference
																												)
																						elif self.padding_side == "left":
																												lowercase :							Union[str, Any]    		=[-1] * difference + encoded_inputs[
																												    '''global_attention_mask'''
																												]
																						else:
																												raise ValueError('''Invalid padding strategy:''' + str(self.padding_side   )   )

										return encoded_inputs











'''simple docstring'''


from typing import Dict, List, Optional, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_snake_case : Dict  				=						logging.get_logger(__name__)

class      A       (						_a       ):
							lowercase_							     =							['pixel_values']







							def __init__(    self			:    List[Any]				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Dict[str, int]] = None				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BICUBIC				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Union[int, float] = 1 / 2_55				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				None:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_   )
														_a      	=							size if size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a      	=							get_size_dict(lowerCAmelCase_   )
														_a      	=							crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_				,     param_name='''crop_size'''   )

														_a      	=							do_resize
														_a      	=							do_rescale
														_a      	=							do_normalize
														_a      	=							do_center_crop
														_a      	=							crop_size
														_a      	=							size
														_a      	=							resample
														_a      	=							rescale_factor
														_a      	=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
														_a      	=							image_std if image_std is not None else IMAGENET_DEFAULT_STD







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BILINEAR				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_   )

														if "shortest_edge" in size:
																					_a      	=							get_resize_output_image_size(lowerCAmelCase_				,     size=size['''shortest_edge''']				,     default_to_square=lowerCAmelCase_   )
																					# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
														elif "height" in size and "width" in size:
																					_a      	=							(size['''height'''], size['''width'''])
														else:
																					raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'   )
														return resize(lowerCAmelCase_				,     size=lowerCAmelCase_				,     resample=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    Dict				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							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, width). Got {size.keys()}'   )
														return center_crop(lowerCAmelCase_				,     size=(size['''height'''], size['''width'''])				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    List[Any]   )     ->				np.ndarray:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    List[Any]				,     )     ->				np.ndarray:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    ImageInput				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    PILImageResampling = None				,     lowerCAmelCase_			:    bool = None				,     lowerCAmelCase_			:    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_			:    List[str]				,     )     ->				BatchFeature:


														"""simple docstring"""


														_a      	=							do_resize if do_resize is not None else self.do_resize
														_a      	=							do_rescale if do_rescale is not None else self.do_rescale
														_a      	=							do_normalize if do_normalize is not None else self.do_normalize
														_a      	=							do_center_crop if do_center_crop is not None else self.do_center_crop
														_a      	=							crop_size if crop_size is not None else self.crop_size
														_a      	=							get_size_dict(lowerCAmelCase_				,     param_name='''crop_size'''				,     default_to_square=lowerCAmelCase_   )
														_a      	=							resample if resample is not None else self.resample
														_a      	=							rescale_factor if rescale_factor is not None else self.rescale_factor
														_a      	=							image_mean if image_mean is not None else self.image_mean
														_a      	=							image_std if image_std is not None else self.image_std

														_a      	=							size if size is not None else self.size
														_a      	=							get_size_dict(lowerCAmelCase_   )

														if not is_batched(lowerCAmelCase_   ):
																					_a      	=							[images]

														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.'''   )

														# All transformations expect numpy arrays.
														_a      	=							[to_numpy_array(lowerCAmelCase_   ) for image in images]

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

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

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

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

														_a      	=							[to_channel_dimension_format(lowerCAmelCase_				,     lowerCAmelCase_   ) for image in images]

														_a      	=							{'''pixel_values''': images}
														return BatchFeature(data=lowerCAmelCase_				,     tensor_type=lowerCAmelCase_   )

