
"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


_lowercase							   =				logging.get_logger(__name__)

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

_lowercase							   =				{
    '''vocab_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
        ),
        '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
        ),
    },
}

_lowercase							   =				{
    '''squeezebert/squeezebert-uncased''': 5_12,
    '''squeezebert/squeezebert-mnli''': 5_12,
    '''squeezebert/squeezebert-mnli-headless''': 5_12,
}


_lowercase							   =				{
    '''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    VOCAB_FILES_NAMES
						_lowerCamelCase:    Tuple      =    PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:    str      =    PRETRAINED_INIT_CONFIGURATION
						_lowerCamelCase:    Tuple      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:    List[str]      =    SqueezeBertTokenizer


						def __init__( self     :      Tuple  ,A_     :      List[Any]=None  ,A_     :      int=None  ,A_     :      Optional[Any]=True  ,A_     :      Optional[Any]="[UNK]"  ,A_     :      List[Any]="[SEP]"  ,A_     :      List[str]="[PAD]"  ,A_     :      Dict="[CLS]"  ,A_     :      int="[MASK]"  ,A_     :      str=True  ,A_     :      Any=None  ,**A_     :      int  ,)     ->   str:
							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						   =   json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
							if (
							    normalizer_state.get('lowercase'  ,A_      ) != do_lower_case
							    or normalizer_state.get('strip_accents'  ,A_      ) != strip_accents
							    or normalizer_state.get('handle_chinese_chars'  ,A_      ) != tokenize_chinese_chars
							):
								A						   =   getattr(A_  ,normalizer_state.pop('type'      )      )
								A						   =   do_lower_case
								A						   =   strip_accents
								A						   =   tokenize_chinese_chars
								A						   =   normalizer_class(**A_      )

							A						   =   do_lower_case


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Any  ,A_     :      Dict=None      )     ->   Any:
							A						   =   [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 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      List[int]  ,A_     :      Optional[List[int]] = None      )     ->   List[int]:
							A						   =   [self.sep_token_id]
							A						   =   [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 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple  ,A_     :      str  ,A_     :      Optional[str] = None      )     ->   Tuple[str]:
							A						   =   self._tokenizer.model.save(A_  ,name=A_      )
							return tuple(A_      )

"""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,
    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


_lowercase							   =				logging.get_logger(__name__)







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Dict      =    ['''pixel_values''']


						def __init__( self     :      Optional[Any]  ,A_     :      bool = True  ,A_     :      Optional[Dict[str, int]] = None  ,A_     :      PILImageResampling = PILImageResampling.BILINEAR  ,A_     :      bool = True  ,A_     :      Dict[str, int] = None  ,A_     :      bool = True  ,A_     :      Union[int, float] = 1 / 255  ,A_     :      bool = True  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,**A_     :      Optional[Any]  ,)     ->   None:
							super().__init__(**A_      )
							A						   =   size if size is not None else {'shortest_edge': 256}
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							A						   =   crop_size if crop_size is not None else {'height': 224, 'width': 224}
							A						   =   get_size_dict(A_  ,param_name='crop_size'      )
							A						   =   do_resize
							A						   =   size
							A						   =   resample
							A						   =   do_center_crop
							A						   =   crop_size
							A						   =   do_rescale
							A						   =   rescale_factor
							A						   =   do_normalize
							A						   =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							A						   =   image_std if image_std is not None else IMAGENET_STANDARD_STD


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      np.ndarray  ,A_     :      Dict[str, int]  ,A_     :      PILImageResampling = PILImageResampling.BICUBIC  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      int  ,)     ->   np.ndarray:
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							if "shortest_edge" not in size:
								raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'      )
							A						   =   get_resize_output_image_size(A_  ,size=size['shortest_edge']  ,default_to_square=A_      )
							return resize(A_  ,size=A_  ,resample=A_  ,data_format=A_  ,**A_      )


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      np.ndarray  ,A_     :      float  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      List[str]      )     ->   np.ndarray:
							return rescale(A_  ,scale=A_  ,data_format=A_  ,**A_      )


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      ImageInput  ,A_     :      Optional[bool] = None  ,A_     :      Dict[str, int] = None  ,A_     :      PILImageResampling = None  ,A_     :      bool = None  ,A_     :      Dict[str, int] = None  ,A_     :      Optional[bool] = None  ,A_     :      Optional[float] = None  ,A_     :      Optional[bool] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[str, TensorType]] = None  ,A_     :      Union[str, ChannelDimension] = ChannelDimension.FIRST  ,**A_     :      Tuple  ,)     ->   List[Any]:
							A						   =   do_resize if do_resize is not None else self.do_resize
							A						   =   size if size is not None else self.size
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							A						   =   resample if resample is not None else self.resample
							A						   =   do_center_crop if do_center_crop is not None else self.do_center_crop
							A						   =   crop_size if crop_size is not None else self.crop_size
							A						   =   get_size_dict(A_  ,param_name='crop_size'      )
							A						   =   do_rescale if do_rescale is not None else self.do_rescale
							A						   =   rescale_factor if rescale_factor is not None else self.rescale_factor
							A						   =   do_normalize if do_normalize is not None else self.do_normalize
							A						   =   image_mean if image_mean is not None else self.image_mean
							A						   =   image_std if image_std is not None else self.image_std

							A						   =   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:
								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						   =   [to_numpy_array(A_      ) for image in images]

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

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

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

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

							A						   =   [to_channel_dimension_format(A_  ,A_      ) for image in images]

							A						   =   {'pixel_values': images}
							return BatchFeature(data=A_  ,tensor_type=A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Union[str, Any]  ,A_     :      List[Tuple] = None      )     ->   str:
							A						   =   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						   =   target_sizes.numpy()

								A						   =   []

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

							return semantic_segmentation