





"""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


_UpperCamelCase					      =   logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							List[str]									=		["""pixel_values"""]




				def __init__(  self	:List[Any]    ,     __lowercase	:bool = True    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:PILImageResampling = PILImageResampling.BILINEAR    ,     __lowercase	:bool = True    ,     __lowercase	:Union[int, float] = 1 / 255    ,     __lowercase	:bool = True    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:bool = True    ,     **__lowercase	:Tuple    ,     ):
											super().__init__(**__lowercase      )
											__lowerCamelCase   :   Optional[Any]       =size if size is not None else {'''shortest_edge''': 224}
											__lowerCamelCase   :   Optional[Any]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											__lowerCamelCase   :   Dict       =crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
											__lowerCamelCase   :   Optional[Any]       =get_size_dict(__lowercase    ,     param_name='''crop_size'''      )

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




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:np.ndarray    ,     __lowercase	:Dict[str, int]    ,     __lowercase	:PILImageResampling = PIL.Image.BILINEAR    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:Optional[int]    ,     ):
											__lowerCamelCase   :   Optional[int]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											if "shortest_edge" not in size:
																		raise ValueError(f'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'      )
											__lowerCamelCase   :   int       =get_resize_output_image_size(__lowercase    ,     size=size['''shortest_edge''']    ,     default_to_square=__lowercase      )
											return resize(__lowercase    ,     size=__lowercase    ,     resample=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:int    ,     __lowercase	:np.ndarray    ,     __lowercase	:Dict[str, int]    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:Any    ,     ):
											__lowerCamelCase   :   List[Any]       =get_size_dict(__lowercase      )
											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(__lowercase    ,     size=(size['''height'''], size['''width'''])    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:np.ndarray    ,     __lowercase	:Union[int, float]    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:Tuple    ,     ):
											return rescale(__lowercase    ,     scale=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:int    ,     __lowercase	:np.ndarray    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None      ):
											return flip_channel_order(__lowercase    ,     data_format=__lowercase      )




				def        __lowercase	(  self	:Dict    ,     __lowercase	:ImageInput    ,     __lowercase	:bool = None    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:PILImageResampling = None    ,     __lowercase	:bool = None    ,     __lowercase	:float = None    ,     __lowercase	:bool = None    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:bool = None    ,     __lowercase	:Optional[Union[str, TensorType]] = None    ,     __lowercase	:ChannelDimension = ChannelDimension.FIRST    ,     **__lowercase	:str    ,     ):
											__lowerCamelCase   :   Union[str, Any]       =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase   :   Optional[int]       =resample if resample is not None else self.resample
											__lowerCamelCase   :   Union[str, Any]       =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase   :   Optional[int]       =rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCamelCase   :   Optional[Any]       =do_center_crop if do_center_crop is not None else self.do_center_crop
											__lowerCamelCase   :   List[str]       =(
											    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
											)

											__lowerCamelCase   :   Optional[int]       =size if size is not None else self.size
											__lowerCamelCase   :   Optional[int]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											__lowerCamelCase   :   List[Any]       =crop_size if crop_size is not None else self.crop_size
											__lowerCamelCase   :   Any       =get_size_dict(__lowercase    ,     param_name='''crop_size'''      )

											__lowerCamelCase   :   Any       =make_list_of_images(__lowercase      )

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

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

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

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

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

											__lowerCamelCase   :   List[str]       =[to_channel_dimension_format(__lowercase    ,     __lowercase      ) for image in images]

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




				def        __lowercase	(  self	:int    ,     __lowercase	:str    ,     __lowercase	:List[Tuple] = None      ):
											__lowerCamelCase   :   str       =outputs.logits

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

																		if is_torch_tensor(__lowercase      ):
																									__lowerCamelCase   :   Tuple       =target_sizes.numpy()

																		__lowerCamelCase   :   List[str]       =[]

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

											return semantic_segmentation











"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_camembert import CamembertTokenizer
else:
							_UpperCamelCase					      =   None


_UpperCamelCase					      =   logging.get_logger(__name__)

_UpperCamelCase					      =   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase					      =   {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase					      =   {
    'camembert-base': 512,
}

_UpperCamelCase					      =   '▁'
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							List[str]									=		VOCAB_FILES_NAMES
				__snake_case :							Optional[int]									=		PRETRAINED_VOCAB_FILES_MAP
				__snake_case :							Dict									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__snake_case :							List[str]									=		["""input_ids""", """attention_mask"""]
				__snake_case :							List[Any]									=		CamembertTokenizer




				def __init__(  self	:List[Any]    ,     __lowercase	:Optional[int]=None    ,     __lowercase	:str=None    ,     __lowercase	:Optional[Any]="<s>"    ,     __lowercase	:List[str]="</s>"    ,     __lowercase	:Tuple="</s>"    ,     __lowercase	:int="<s>"    ,     __lowercase	:Union[str, Any]="<unk>"    ,     __lowercase	:Optional[int]="<pad>"    ,     __lowercase	:Union[str, Any]="<mask>"    ,     __lowercase	:Tuple=["<s>NOTUSED", "</s>NOTUSED"]    ,     **__lowercase	:List[str]    ,     ):
											# Mask token behave like a normal word, i.e. include the space before it
											__lowerCamelCase   :   Optional[int]       =AddedToken(__lowercase    ,     lstrip=__lowercase    ,     rstrip=__lowercase      ) if isinstance(__lowercase    ,     __lowercase      ) else mask_token

											super().__init__(
											    __lowercase    ,     tokenizer_file=__lowercase    ,     bos_token=__lowercase    ,     eos_token=__lowercase    ,     sep_token=__lowercase    ,     cls_token=__lowercase    ,     unk_token=__lowercase    ,     pad_token=__lowercase    ,     mask_token=__lowercase    ,     additional_special_tokens=__lowercase    ,     **__lowercase    ,     )

											__lowerCamelCase   :   Any       =vocab_file
											__lowerCamelCase   :   Union[str, Any]       =False if not self.vocab_file else True




				def        __lowercase	(  self	:List[str]    ,     __lowercase	:List[int]    ,     __lowercase	:Optional[List[int]] = None      ):

											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											__lowerCamelCase   :   Tuple       =[self.cls_token_id]
											__lowerCamelCase   :   str       =[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:List[int]    ,     __lowercase	:Optional[List[int]] = None      ):
											__lowerCamelCase   :   Any       =[self.sep_token_id]
											__lowerCamelCase   :   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        __lowercase	(  self	:List[Any]    ,     __lowercase	:str    ,     __lowercase	:Optional[str] = None      ):
											if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																		    '''tokenizer.'''      )

											if not os.path.isdir(__lowercase      ):
																		logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
																		return
											__lowerCamelCase   :   List[str]       =os.path.join(
											    __lowercase    ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

											if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowercase      ):
																		copyfile(self.vocab_file    ,     __lowercase      )

											return (out_vocab_file,)




