

import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
			import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a__				=		logging.get_logger(__name__)

a__				=		{'''vocab_file''': '''vocab.txt'''}

a__				=		{
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

a__				=		{
    '''openbmb/cpm-ant-10b''': 1024,
}



def    __UpperCAmelCase (	__a      :			List[Any]	)			->     Tuple:

					"""simple docstring"""

					_a	:    Union[str, Any]         =		collections.OrderedDict()
					with open(__a		,'''r'''		,encoding='''utf-8'''	) as reader:
										_a	:    Dict         =		reader.readlines()
					for index, token in enumerate(__a	):
										_a	:    Optional[int]         =		token.rstrip('''\n'''	)
										_a	:    Any         =		index
					return vocab

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							def __init__(			self       ,							_a       ,							_a="<unk>"       ,							_a=2_0_0       )    ->    Optional[Any]:
												_a	:    Union[str, Any]         =		vocab
												_a	:    Tuple         =		unk_token
												_a	:    List[str]         =		max_input_chars_per_word




							def 				__lowercase				(			self       ,							_a       )    ->    List[str]:
												_a	:    Union[str, Any]         =		list(_a       )
												if len(_a       ) > self.max_input_chars_per_word:
																	return [self.unk_token]

												_a	:    List[str]         =		0
												_a	:    Any         =		[]
												while start < len(_a       ):
																	_a	:    Optional[int]         =		len(_a       )
																	_a	:    Tuple         =		None
																	while start < end:
																						_a	:    Dict         =		''''''.join(chars[start:end]       )
																						if substr in self.vocab:
																											_a	:    Any         =		substr
																											break
																						end -= 1
																	if cur_substr is None:
																						sub_tokens.append(self.unk_token       )
																						start += 1
																	else:
																						sub_tokens.append(_a       )
																						_a	:    str         =		end

												return sub_tokens



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[Any]	    =    VOCAB_FILES_NAMES
							UpperCAmelCase__       :						Union[str, Any]	    =    PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase__       :						List[str]	    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase__       :						int	    =    ["input_ids", "attention_mask"]
							UpperCAmelCase__       :						Any	    =    False


							def __init__(			self       ,							_a       ,							_a="<d>"       ,							_a="</d>"       ,							_a="<s>"       ,							_a="</s>"       ,							_a="<pad>"       ,							_a="<unk>"       ,							_a="</n>"       ,							_a="</_>"       ,							_a="left"       ,							**_a       ,							)    ->    Any:
												requires_backends(self       ,							['''jieba''']       )
												super().__init__(
												    bod_token=_a       ,							eod_token=_a       ,							bos_token=_a       ,							eos_token=_a       ,							pad_token=_a       ,							unk_token=_a       ,							line_token=_a       ,							space_token=_a       ,							padding_side=_a       ,							**_a       ,							)
												_a	:    Optional[Any]         =		bod_token
												_a	:    Tuple         =		eod_token
												_a	:    Dict         =		load_vocab(_a       )
												_a	:    List[Any]         =		self.encoder[space_token]
												_a	:    Any         =		self.encoder[line_token]

												del self.encoder[space_token]
												del self.encoder[line_token]

												_a	:    Tuple         =		collections.OrderedDict(sorted(self.encoder.items()       ,							key=lambda _a       : x[1]       )       )
												_a	:    Dict         =		{v: k for k, v in self.encoder.items()}

												_a	:    List[Any]         =		WordpieceTokenizer(vocab=self.encoder       ,							unk_token=self.unk_token       )


							@property
							def 				__lowercase				(			self       )    ->    Dict:
												return self.encoder[self.bod_token]


							@property
							def 				__lowercase				(			self       )    ->    Dict:
												return self.encoder[self.eod_token]


							@property
							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												return self.encoder["\n"]


							@property
							def 				__lowercase				(			self       )    ->    int:
												return len(self.encoder       )


							def 				__lowercase				(			self       )    ->    Tuple:
												return dict(self.encoder       ,							**self.added_tokens_encoder       )


							def 				__lowercase				(			self       ,							_a       )    ->    List[str]:
												_a	:    str         =		[]
												for x in jieba.cut(_a       ,							cut_all=_a       ):
																	output_tokens.extend(self.wordpiece_tokenizer.tokenize(_a       )       )
												return output_tokens


							def 				__lowercase				(			self       ,							_a       ,							**_a       )    ->    str:
												_a	:    List[str]         =		[i for i in token_ids if i >= 0]
												_a	:    Tuple         =		[
												    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
												]
												return super()._decode(_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       )    ->    List[str]:
												return token in self.encoder


							def 				__lowercase				(			self       ,							_a       )    ->    str:
												return "".join(_a       )


							def 				__lowercase				(			self       ,							_a       )    ->    Optional[int]:
												return self.encoder.get(_a       ,							self.encoder.get(self.unk_token       )       )


							def 				__lowercase				(			self       ,							_a       )    ->    str:
												return self.decoder.get(_a       ,							self.unk_token       )


							def 				__lowercase				(			self       ,							_a       ,							_a = None       )    ->    Tuple[str]:
												if os.path.isdir(_a       ):
																	_a	:    List[str]         =		os.path.join(
																	    _a       ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
												else:
																	_a	:    int         =		(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
												_a	:    Optional[Any]         =		0
												if " " in self.encoder:
																	_a	:    str         =		self.encoder[''' ''']
																	del self.encoder[" "]
												if "\n" in self.encoder:
																	_a	:    int         =		self.encoder['''\n''']
																	del self.encoder["\n"]
												_a	:    List[Any]         =		collections.OrderedDict(sorted(self.encoder.items()       ,							key=lambda _a       : x[1]       )       )
												with open(_a       ,							'''w'''       ,							encoding='''utf-8'''       ) as writer:
																	for token, token_index in self.encoder.items():
																						if index != token_index:
																											logger.warning(
																											    F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
																											    ''' Please check that the vocabulary is not corrupted!'''       )
																											_a	:    str         =		token_index
																						writer.write(token + '''\n'''       )
																						index += 1
												return (vocab_file,)


							def 				__lowercase				(			self       ,							_a       ,							_a = None       )    ->    List[int]:
												if token_ids_a is None:
																	return [self.bos_token_id] + token_ids_a
												return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a




							def 				__lowercase				(			self       ,							_a       ,							_a = None       ,							_a = False       )    ->    List[int]:

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_a       ,							token_ids_a=_a       ,							already_has_special_tokens=_a       )

												if token_ids_a is not None:
																	return [1] + ([0] * len(_a       )) + [1] + ([0] * len(_a       ))
												return [1] + ([0] * len(_a       ))






import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
			import torch

if is_vision_available():
			import PIL


a__				=		logging.get_logger(__name__)



def    __UpperCAmelCase (	__a      :			np.ndarray		,__a      :			Union[int, Iterable[int]]		,__a      :			bool		,__a      :			int	)			->     Tuple[int, int]:

					"""simple docstring"""

					def constraint_to_multiple_of(__a      :			List[str]		,__a      :			Dict		,__a      :			Any=0		,__a      :			int=None	):
										_a	:    Dict         =		round(val / multiple	) * multiple

										if max_val is not None and x > max_val:
															_a	:    Any         =		math.floor(val / multiple	) * multiple

										if x < min_val:
															_a	:    Dict         =		math.ceil(val / multiple	) * multiple

										return x

					_a	:    Union[str, Any]         =		(output_size, output_size) if isinstance(__a		,__a	) else output_size

					_a      ,					_a	:    List[Any]         =		get_image_size(__a	)
					_a      ,					_a	:    Any         =		output_size

					# determine new height and width
					_a	:    Union[str, Any]         =		output_height / input_height
					_a	:    Tuple         =		output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width	) < abs(1 - scale_height	):
															# fit width
															_a	:    Optional[Any]         =		scale_width
										else:
															# fit height
															_a	:    Tuple         =		scale_height

					_a	:    Optional[Any]         =		constraint_to_multiple_of(scale_height * input_height		,multiple=__a	)
					_a	:    int         =		constraint_to_multiple_of(scale_width * input_width		,multiple=__a	)

					return (new_height, new_width)







class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    ["pixel_values"]


							def __init__(			self       ,							_a = True       ,							_a = None       ,							_a = PILImageResampling.BILINEAR       ,							_a = False       ,							_a = 1       ,							_a = True       ,							_a = 1 / 2_5_5       ,							_a = True       ,							_a = None       ,							_a = None       ,							**_a       ,							)    ->    None:
												super().__init__(**_a       )
												_a	:    Optional[int]         =		size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
												_a	:    Optional[Any]         =		get_size_dict(_a       )
												_a	:    Any         =		do_resize
												_a	:    Dict         =		size
												_a	:    str         =		keep_aspect_ratio
												_a	:    Any         =		ensure_multiple_of
												_a	:    Optional[Any]         =		resample
												_a	:    List[Any]         =		do_rescale
												_a	:    int         =		rescale_factor
												_a	:    Any         =		do_normalize
												_a	:    Union[str, Any]         =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												_a	:    List[str]         =		image_std if image_std is not None else IMAGENET_STANDARD_STD


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = False       ,							_a = 1       ,							_a = PILImageResampling.BICUBIC       ,							_a = None       ,							**_a       ,							)    ->    np.ndarray:
												_a	:    str         =		get_size_dict(_a       )
												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()}"""       )
												_a	:    Optional[Any]         =		get_resize_output_image_size(
												    _a       ,							output_size=(size['''height'''], size['''width'''])       ,							keep_aspect_ratio=_a       ,							multiple=_a       ,							)
												return resize(_a       ,							size=_a       ,							resample=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = None       ,							**_a       ,							)    ->    int:
												return rescale(_a       ,							scale=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a = None       ,							**_a       ,							)    ->    np.ndarray:
												return normalize(_a       ,							mean=_a       ,							std=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = ChannelDimension.FIRST       ,							**_a       ,							)    ->    PIL.Image.Image:
												_a	:    Optional[int]         =		do_resize if do_resize is not None else self.do_resize
												_a	:    Union[str, Any]         =		size if size is not None else self.size
												_a	:    str         =		get_size_dict(_a       )
												_a	:    List[Any]         =		keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												_a	:    Optional[Any]         =		ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												_a	:    str         =		resample if resample is not None else self.resample
												_a	:    str         =		do_rescale if do_rescale is not None else self.do_rescale
												_a	:    Union[str, Any]         =		rescale_factor if rescale_factor is not None else self.rescale_factor
												_a	:    List[Any]         =		do_normalize if do_normalize is not None else self.do_normalize
												_a	:    str         =		image_mean if image_mean is not None else self.image_mean
												_a	:    Tuple         =		image_std if image_std is not None else self.image_std

												_a	:    Dict         =		make_list_of_images(_a       )

												if not valid_images(_a       ):
																	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 or resample is None:
																	raise ValueError('''Size and resample 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_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	:    Dict         =		[to_numpy_array(_a       ) for image in images]

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

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

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

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

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




							def 				__lowercase				(			self       ,							_a       ,							_a = None       )    ->    Any:
												_a	:    Optional[Any]         =		outputs.logits

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

																	if is_torch_tensor(_a       ):
																						_a	:    List[Any]         =		target_sizes.numpy()

																	_a	:    str         =		[]

																	for idx in range(len(_a       )       ):
																						_a	:    str         =		torch.nn.functional.interpolate(
																						    logits[idx].unsqueeze(dim=0       )       ,							size=target_sizes[idx]       ,							mode='''bilinear'''       ,							align_corners=_a       )
																						_a	:    Union[str, Any]         =		resized_logits[0].argmax(dim=0       )
																						semantic_segmentation.append(_a       )
												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



