



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


A__	      =       logging.get_logger(__name__)





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				['''pixel_values''']




				def __init__(							self						,				_snake_case = True						,				_snake_case = None						,				_snake_case = PILImageResampling.BILINEAR						,				_snake_case = True						,				_snake_case = None						,				_snake_case = True						,				_snake_case = 1 / 255						,				_snake_case = True						,				_snake_case = None						,				_snake_case = None						,				**_snake_case						,				):



									"""simple docstring"""





									super().__init__(**_snake_case		)
									_lowerCAmelCase						=    size if size is not None else {"""shortest_edge""": 256}
									_lowerCAmelCase						=    get_size_dict(_snake_case						,				default_to_square=_snake_case		)
									_lowerCAmelCase						=    crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
									_lowerCAmelCase						=    get_size_dict(_snake_case						,				param_name="""crop_size"""		)
									_lowerCAmelCase						=    do_resize
									_lowerCAmelCase						=    size
									_lowerCAmelCase						=    resample
									_lowerCAmelCase						=    do_center_crop
									_lowerCAmelCase						=    crop_size
									_lowerCAmelCase						=    do_rescale
									_lowerCAmelCase						=    rescale_factor
									_lowerCAmelCase						=    do_normalize
									_lowerCAmelCase						=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									_lowerCAmelCase						=    image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case = PILImageResampling.BICUBIC						,				_snake_case = None						,				**_snake_case						,				):



									"""simple docstring"""





									_lowerCAmelCase						=    get_size_dict(_snake_case						,				default_to_square=_snake_case		)
									if "shortest_edge" not in size:
														raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'		)
									_lowerCAmelCase						=    get_resize_output_image_size(_snake_case						,				size=size["""shortest_edge"""]						,				default_to_square=_snake_case		)
									return resize(_snake_case						,				size=_snake_case						,				resample=_snake_case						,				data_format=_snake_case						,				**_snake_case		)




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case = None						,				**_snake_case						,				):



									"""simple docstring"""





									_lowerCAmelCase						=    get_size_dict(_snake_case		)
									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(_snake_case						,				size=(size["""height"""], size["""width"""])						,				data_format=_snake_case						,				**_snake_case		)




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case = None						,				**_snake_case		):



									"""simple docstring"""





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




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case = None						,				**_snake_case						,				):



									"""simple docstring"""





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




				def 							snake_case   (							self						,				_snake_case						,				_snake_case = None						,				_snake_case = None						,				_snake_case = None						,				_snake_case = None						,				_snake_case = None						,				_snake_case = None						,				_snake_case = None						,				_snake_case = None						,				_snake_case = None						,				_snake_case = None						,				_snake_case = None						,				_snake_case = ChannelDimension.FIRST						,				**_snake_case						,				):



									"""simple docstring"""





									_lowerCAmelCase						=    do_resize if do_resize is not None else self.do_resize
									_lowerCAmelCase						=    size if size is not None else self.size
									_lowerCAmelCase						=    get_size_dict(_snake_case						,				default_to_square=_snake_case		)
									_lowerCAmelCase						=    resample if resample is not None else self.resample
									_lowerCAmelCase						=    do_center_crop if do_center_crop is not None else self.do_center_crop
									_lowerCAmelCase						=    crop_size if crop_size is not None else self.crop_size
									_lowerCAmelCase						=    get_size_dict(_snake_case						,				param_name="""crop_size"""		)
									_lowerCAmelCase						=    do_rescale if do_rescale is not None else self.do_rescale
									_lowerCAmelCase						=    rescale_factor if rescale_factor is not None else self.rescale_factor
									_lowerCAmelCase						=    do_normalize if do_normalize is not None else self.do_normalize
									_lowerCAmelCase						=    image_mean if image_mean is not None else self.image_mean
									_lowerCAmelCase						=    image_std if image_std is not None else self.image_std

									_lowerCAmelCase						=    make_list_of_images(_snake_case		)

									if not valid_images(_snake_case		):
														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.
									_lowerCAmelCase						=    [to_numpy_array(_snake_case		) for image in images]

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

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

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

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

									_lowerCAmelCase						=    [to_channel_dimension_format(_snake_case						,				_snake_case		) for image in images]

									_lowerCAmelCase						=    {"""pixel_values""": images}
									return BatchFeature(data=_snake_case						,				tensor_type=_snake_case		)




				def 							snake_case   (							self						,				_snake_case						,				_snake_case = None		):



									"""simple docstring"""





									_lowerCAmelCase						=    outputs.logits

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

														if is_torch_tensor(_snake_case		):
																			_lowerCAmelCase						=    target_sizes.numpy()

														_lowerCAmelCase						=    []

														for idx in range(len(_snake_case		)		):
																			_lowerCAmelCase						=    torch.nn.functional.interpolate(
																			    logits[idx].unsqueeze(dim=0		)						,				size=target_sizes[idx]						,				mode="""bilinear"""						,				align_corners=_snake_case		)
																			_lowerCAmelCase						=    resized_logits[0].argmax(dim=0		)
																			semantic_segmentation.append(_snake_case		)
									else:
														_lowerCAmelCase						=    logits.argmax(dim=1		)
														_lowerCAmelCase						=    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

									return semantic_segmentation






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


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

A_       : Tuple         =			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A_       : List[Any]         =			{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

A_       : Tuple         =			{
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


A_       : Union[str, Any]         =			{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Dict  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Tuple  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   List[str]  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   List[Any]  							=       PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__:   Optional[int]  							=       RoFormerTokenizer






					def __init__( self    ,       A__=None    ,       A__=None    ,       A__=True    ,       A__="[UNK]"    ,       A__="[SEP]"    ,       A__="[PAD]"    ,       A__="[CLS]"    ,       A__="[MASK]"    ,       A__=True    ,       A__=None    ,       **A__    ,       ):
						super().__init__(
						    A__    ,       tokenizer_file=A__    ,       do_lower_case=A__    ,       unk_token=A__    ,       sep_token=A__    ,       pad_token=A__    ,       cls_token=A__    ,       mask_token=A__    ,       tokenize_chinese_chars=A__    ,       strip_accents=A__    ,       **A__    ,       )

						A__							:    Tuple        =		json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
						if (
						    pre_tok_state.get("""lowercase"""    ,       A__			) != do_lower_case
						    or pre_tok_state.get("""strip_accents"""    ,       A__			) != strip_accents
						):
							A__							:    List[Any]        =		getattr(A__    ,       pre_tok_state.pop("""type"""			)			)
							A__							:    Optional[int]        =		do_lower_case
							A__							:    int        =		strip_accents
							A__							:    str        =		pre_tok_class(**A__			)

						A__							:    Any        =		do_lower_case






					def __getstate__( self			):
						A__							:    int        =		self.__dict__.copy()
						A__							:    Union[str, Any]        =		BertPreTokenizer()
						return state






					def __setstate__( self    ,       A__			):
						A__							:    Union[str, Any]        =		d
						A__							:    Union[str, Any]        =		self.__dict__["""_tokenizer"""].get_vocab()
						A__							:    Dict        =		PreTokenizer.custom(JiebaPreTokenizer(A__			)			)






					def   __A			( self    ,       A__    ,       A__=None			):
						A__							:    Optional[Any]        =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Tuple        =		[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    ,       A__    ,       A__ = None			):
						A__							:    Any        =		self._tokenizer.model.save(A__    ,       name=A__			)
						return tuple(A__			)






					def   __A			( self    ,       A__    ,       A__=None    ,       A__=None    ,       A__=False    ,       **A__    ,       ):
						A__							:    str        =		BertPreTokenizer()
						return super().save_pretrained(A__    ,       A__    ,       A__    ,       A__    ,       **A__			)



