





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 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  ["pixel_values"]







						def __init__(	self      :			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 / 2_55	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			Optional[Union[float, List[float]]] = None	,	__lowerCamelCase      :			Optional[Union[float, List[float]]] = None	,	**__lowerCamelCase      :			Any	,	)      ->				None:
									super().__init__(**__lowerCamelCase )
									A							:					Any              =						size if size is not None else {"shortest_edge": 2_56}
									A							:					Optional[Any]              =						get_size_dict(__lowerCamelCase	,	default_to_square=__lowerCamelCase )
									A							:					Tuple              =						crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
									A							:					List[str]              =						get_size_dict(__lowerCamelCase	,	param_name="crop_size" )
									A							:					Optional[int]              =						do_resize
									A							:					Optional[int]              =						size
									A							:					List[Any]              =						resample
									A							:					Optional[int]              =						do_center_crop
									A							:					List[Any]              =						crop_size
									A							:					Dict              =						do_rescale
									A							:					Any              =						rescale_factor
									A							:					Dict              =						do_normalize
									A							:					Any              =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									A							:					Tuple              =						image_std if image_std is not None else IMAGENET_STANDARD_STD







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			np.ndarray	,	__lowerCamelCase      :			Dict[str, int]	,	__lowerCamelCase      :			PILImageResampling = PILImageResampling.BICUBIC	,	__lowerCamelCase      :			Optional[Union[str, ChannelDimension]] = None	,	**__lowerCamelCase      :			Tuple	,	)      ->				np.ndarray:
									A							:					int              =						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()}""" )
									A							:					Union[str, Any]              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			np.ndarray	,	__lowerCamelCase      :			Dict[str, int]	,	__lowerCamelCase      :			Optional[Union[str, ChannelDimension]] = None	,	**__lowerCamelCase      :			Optional[Any]	,	)      ->				np.ndarray:
									A							:					str              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			np.ndarray	,	__lowerCamelCase      :			float	,	__lowerCamelCase      :			Optional[Union[str, ChannelDimension]] = None	,	**__lowerCamelCase      :			List[str] )      ->				np.ndarray:
									return rescale(__lowerCamelCase	,	scale=__lowerCamelCase	,	data_format=__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			np.ndarray	,	__lowerCamelCase      :			Union[float, List[float]]	,	__lowerCamelCase      :			Union[float, List[float]]	,	__lowerCamelCase      :			Optional[Union[str, ChannelDimension]] = None	,	**__lowerCamelCase      :			Union[str, Any]	,	)      ->				np.ndarray:
									return normalize(__lowerCamelCase	,	mean=__lowerCamelCase	,	std=__lowerCamelCase	,	data_format=__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__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      :			List[str]	,	)      ->				str:
									A							:					int              =						do_resize if do_resize is not None else self.do_resize
									A							:					str              =						size if size is not None else self.size
									A							:					List[str]              =						get_size_dict(__lowerCamelCase	,	default_to_square=__lowerCamelCase )
									A							:					Any              =						resample if resample is not None else self.resample
									A							:					int              =						do_center_crop if do_center_crop is not None else self.do_center_crop
									A							:					int              =						crop_size if crop_size is not None else self.crop_size
									A							:					Any              =						get_size_dict(__lowerCamelCase	,	param_name="crop_size" )
									A							:					Dict              =						do_rescale if do_rescale is not None else self.do_rescale
									A							:					Dict              =						rescale_factor if rescale_factor is not None else self.rescale_factor
									A							:					Union[str, Any]              =						do_normalize if do_normalize is not None else self.do_normalize
									A							:					Optional[Any]              =						image_mean if image_mean is not None else self.image_mean
									A							:					Dict              =						image_std if image_std is not None else self.image_std

									A							:					Optional[int]              =						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.
									A							:					Union[str, Any]              =						[to_numpy_array(__lowerCamelCase ) for image in images]

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

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

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

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

									A							:					Optional[Any]              =						[to_channel_dimension_format(__lowerCamelCase	,	__lowerCamelCase ) for image in images]

									A							:					int              =						{"pixel_values": images}
									return BatchFeature(data=__lowerCamelCase	,	tensor_type=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			List[Tuple] = None )      ->				Union[str, Any]:
									A							:					Union[str, Any]              =						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 ):
															A							:					List[Any]              =						target_sizes.numpy()

												A							:					Optional[Any]              =						[]

												for idx in range(len(__lowerCamelCase ) ):
															A							:					Optional[int]              =						torch.nn.functional.interpolate(
															    logits[idx].unsqueeze(dim=0 )	,	size=target_sizes[idx]	,	mode="bilinear"	,	align_corners=__lowerCamelCase )
															A							:					Optional[int]              =						resized_logits[0].argmax(dim=0 )
															semantic_segmentation.append(__lowerCamelCase )
									else:
												A							:					Tuple              =						logits.argmax(dim=1 )
												A							:					Union[str, Any]              =						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

									return semantic_segmentation




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""": 16384,
}

class 	lowerCamelCase_    ( _A						):






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







						def __init__(	self      :			int	,	__lowerCamelCase      :			List[str]=None	,	__lowerCamelCase      :			Optional[int]=None	,	__lowerCamelCase      :			Optional[int]=None	,	__lowerCamelCase      :			str="replace"	,	__lowerCamelCase      :			Optional[int]="<s>"	,	__lowerCamelCase      :			str="</s>"	,	__lowerCamelCase      :			Any="</s>"	,	__lowerCamelCase      :			Dict="<s>"	,	__lowerCamelCase      :			Optional[int]="<unk>"	,	__lowerCamelCase      :			Union[str, Any]="<pad>"	,	__lowerCamelCase      :			Dict="<mask>"	,	__lowerCamelCase      :			Union[str, Any]=False	,	__lowerCamelCase      :			str=True	,	**__lowerCamelCase      :			Union[str, Any]	,	)      ->				Optional[int]:
									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							:					Union[str, Any]              =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
									if pre_tok_state.get("add_prefix_space"	,	__lowerCamelCase ) != add_prefix_space:
												A							:					Any              =						getattr(__lowerCamelCase	,	pre_tok_state.pop("type" ) )
												A							:					Any              =						add_prefix_space
												A							:					Tuple              =						pre_tok_class(**__lowerCamelCase )

									A							:					str              =						add_prefix_space

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

												A							:					int              =						False

												if state.get("add_prefix_space"	,	__lowerCamelCase ) != add_prefix_space:
															A							:					List[Any]              =						add_prefix_space
															A							:					Dict              =						True

												if state.get("trim_offsets"	,	__lowerCamelCase ) != trim_offsets:
															A							:					Dict              =						trim_offsets
															A							:					str              =						True

												if changes_to_apply:
															A							:					int              =						getattr(__lowerCamelCase	,	state.pop("type" ) )
															A							:					Dict              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				str:
									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 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Any )      ->				Dict:
									A							:					Tuple              =						AddedToken(__lowerCamelCase	,	lstrip=__lowerCamelCase	,	rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase	,	__lowerCamelCase ) else value
									A							:					Tuple              =						value







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	*__lowerCamelCase      :			Optional[Any]	,	**__lowerCamelCase      :			List[str] )      ->				BatchEncoding:
									A							:					List[str]              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	*__lowerCamelCase      :			str	,	**__lowerCamelCase      :			Optional[Any] )      ->				BatchEncoding:
									A							:					List[str]              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			Optional[str] = None )      ->				Tuple[str]:
									A							:					Optional[Any]              =						self._tokenizer.model.save(__lowerCamelCase	,	name=__lowerCamelCase )
									return tuple(__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			List[Any]=None )      ->				List[str]:
									A							:					Optional[int]              =						[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 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			List[int]	,	__lowerCamelCase      :			Optional[List[int]] = None )      ->				List[int]:
									A							:					str              =						[self.sep_token_id]
									A							:					int              =						[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 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Union[Dict[str, EncodedInput], BatchEncoding]	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			Optional[bool] = None	,	)      ->				dict:
									A							:					Dict              =						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							:					List[Any]              =						"attention_mask" in self.model_input_names

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

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

									return encoded_inputs