





"""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


a						: Tuple     	=			logging.get_logger(__name__)


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					Tuple      					=["""pixel_values"""]






							def __init__(   self      ,					lowerCAmelCase__ = True      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = PILImageResampling.BILINEAR      ,					lowerCAmelCase__ = True      ,					lowerCAmelCase__ = 1 / 255      ,					lowerCAmelCase__ = True      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = True      ,					**lowerCAmelCase__      ,					)	-> None:
								super().__init__(**lowerCAmelCase__      )
								a      :		Union[str, Any]	=				size if size is not None else {"shortest_edge": 224}
								a      :		int	=				get_size_dict(lowerCAmelCase__      ,					default_to_square=lowerCAmelCase__      )
								a      :		int	=				crop_size if crop_size is not None else {"height": 256, "width": 256}
								a      :		str	=				get_size_dict(lowerCAmelCase__      ,					param_name="crop_size"      )

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






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = PIL.Image.BILINEAR      ,					lowerCAmelCase__ = None      ,					**lowerCAmelCase__      ,					)	-> np.ndarray:
								a      :		int	=				get_size_dict(lowerCAmelCase__      ,					default_to_square=lowerCAmelCase__      )
								if "shortest_edge" not in size:
									raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""      )
								a      :		Optional[int]	=				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     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					**lowerCAmelCase__      ,					)	-> np.ndarray:
								a      :		Union[str, Any]	=				get_size_dict(lowerCAmelCase__      )
								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(lowerCAmelCase__      ,					size=(size["height"], size["width"])      ,					data_format=lowerCAmelCase__      ,					**lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					**lowerCAmelCase__      ,					)	-> Optional[Any]:
								return rescale(lowerCAmelCase__      ,					scale=lowerCAmelCase__      ,					data_format=lowerCAmelCase__      ,					**lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> np.ndarray:
								return flip_channel_order(lowerCAmelCase__      ,					data_format=lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = ChannelDimension.FIRST      ,					**lowerCAmelCase__      ,					)	-> PIL.Image.Image:
								a      :		Dict	=				do_resize if do_resize is not None else self.do_resize
								a      :		int	=				resample if resample is not None else self.resample
								a      :		Optional[int]	=				do_rescale if do_rescale is not None else self.do_rescale
								a      :		Any	=				rescale_factor if rescale_factor is not None else self.rescale_factor
								a      :		Optional[int]	=				do_center_crop if do_center_crop is not None else self.do_center_crop
								a      :		List[str]	=				(
								    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
								)

								a      :		str	=				size if size is not None else self.size
								a      :		Optional[int]	=				get_size_dict(lowerCAmelCase__      ,					default_to_square=lowerCAmelCase__      )
								a      :		Tuple	=				crop_size if crop_size is not None else self.crop_size
								a      :		Union[str, Any]	=				get_size_dict(lowerCAmelCase__      ,					param_name="crop_size"      )

								a      :		Tuple	=				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_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.
								a      :		Optional[Any]	=				[to_numpy_array(lowerCAmelCase__      ) for image in images]

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

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

								if do_rescale:
									a      :		Optional[Any]	=				[self.rescale(image=lowerCAmelCase__      ,					scale=lowerCAmelCase__      ) for image in images]

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

								a      :		int	=				[to_channel_dimension_format(lowerCAmelCase__      ,					lowerCAmelCase__      ) for image in images]

								a      :		Optional[int]	=				{"pixel_values": images}
								return BatchFeature(data=lowerCAmelCase__      ,					tensor_type=lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> Tuple:
								a      :		Optional[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      :		Dict	=				target_sizes.numpy()

									a      :		int	=				[]

									for idx in range(len(lowerCAmelCase__      )      ):
										a      :		str	=				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      :		List[str]	=				logits.argmax(dim=1      )
									a      :		str	=				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

								return semantic_segmentation









"""simple docstring"""




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


a						: List[str]     	=			logging.get_logger(__name__)

a						: List[Any]     	=			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a						: str     	=			{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

a						: Tuple     	=			{'''allegro/herbert-base-cased''': 514}
a						: Optional[int]     	=			{}


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					str      					=VOCAB_FILES_NAMES
							lowerCamelCase   :					Dict      					=PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase   :					Dict      					=PRETRAINED_INIT_CONFIGURATION
							lowerCamelCase   :					int      					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase   :					Optional[Any]      					=HerbertTokenizer






							def __init__(   self      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__="<s>"      ,					lowerCAmelCase__="<unk>"      ,					lowerCAmelCase__="<pad>"      ,					lowerCAmelCase__="<mask>"      ,					lowerCAmelCase__="</s>"      ,					**lowerCAmelCase__      ,					)	-> Optional[int]:
								super().__init__(
								    lowerCAmelCase__      ,					lowerCAmelCase__      ,					tokenizer_file=lowerCAmelCase__      ,					cls_token=lowerCAmelCase__      ,					unk_token=lowerCAmelCase__      ,					pad_token=lowerCAmelCase__      ,					mask_token=lowerCAmelCase__      ,					sep_token=lowerCAmelCase__      ,					**lowerCAmelCase__      ,					)






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> List[int]:
								a      :		Optional[Any]	=				[self.cls_token_id]
								a      :		Any	=				[self.sep_token_id]
								if token_ids_a is None:
									return cls + token_ids_a + sep

								return cls + token_ids_a + sep + token_ids_a + sep






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = False      )	-> List[int]:
								if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=lowerCAmelCase__      ,					token_ids_a=lowerCAmelCase__      ,					already_has_special_tokens=lowerCAmelCase__      )

								if token_ids_a is None:
									return [1] + ([0] * len(lowerCAmelCase__      )) + [1]
								return [1] + ([0] * len(lowerCAmelCase__      )) + [1] + ([0] * len(lowerCAmelCase__      )) + [1]






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> List[int]:
								a      :		Dict	=				[self.sep_token_id]
								a      :		Tuple	=				[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      ) * [0] + len(token_ids_a + sep      ) * [1]






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> Tuple[str]:
								a      :		List[str]	=				self._tokenizer.model.save(lowerCAmelCase__      ,					name=lowerCAmelCase__      )
								return tuple(lowerCAmelCase__      )


