




'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_ 		=      logging.get_logger(__name__)


if is_vision_available():
       import PIL





class    __lowercase						(  UpperCamelCase__ ):
   _a							=      ["""pixel_values"""]







   def __init__(	self      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = PILImageResampling.BICUBIC      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = True      ,							UpperCamelCase = 1 / 255      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = True      ,							**UpperCamelCase      ,							)				->    None:
     super().__init__(**_a      )
     __a			=					size if size is not None else {"""shortest_edge""": 224}
     __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      ,							default_to_square=_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 OPENAI_CLIP_MEAN
     __a			=					image_std if image_std is not None else OPENAI_CLIP_STD
     __a			=					do_convert_rgb







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = PILImageResampling.BICUBIC      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    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, width). Got {size.keys()}"      )
     return center_crop(_a      ,							size=(size['height'], size['width'])      ,							data_format=_a      ,							**_a      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    Optional[Any]:
     return rescale(_a      ,							scale=_a      ,							data_format=_a      ,							**_a      )







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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = ChannelDimension.FIRST      ,							**UpperCamelCase      ,							)				->    PIL.Image.Image:
     __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      ,							param_name='size'      ,							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'      ,							default_to_square=_a      )
     __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			=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     __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.'      )

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
       __a			=					[convert_to_rgb(_a      ) for image in images]

     # 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      )









'''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_mobilebert import MobileBertTokenizer


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_ 		=      {
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

UpperCAmelCase_ 		=      {"mobilebert-uncased": 5_12}


UpperCAmelCase_ 		=      {}





class    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      PRETRAINED_INIT_CONFIGURATION
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      MobileBertTokenizer







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

     __a			=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
     if (
         normalizer_state.get('lowercase'      ,							UpperCamelCase      ) != do_lower_case
         or normalizer_state.get('strip_accents'      ,							UpperCamelCase      ) != strip_accents
         or normalizer_state.get('handle_chinese_chars'      ,							UpperCamelCase      ) != tokenize_chinese_chars
     ):
       __a			=					getattr(UpperCamelCase      ,							normalizer_state.pop('type'      )      )
       __a			=					do_lower_case
       __a			=					strip_accents
       __a			=					tokenize_chinese_chars
       __a			=					normalizer_class(**UpperCamelCase      )

     __a			=					do_lower_case







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    Tuple:
     __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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = 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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Tuple[str]:
     __a			=					self._tokenizer.model.save(UpperCamelCase      ,							name=UpperCamelCase      )
     return tuple(UpperCamelCase      )



