





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A							   =							logging.get_logger(__name__)

__A							   =							{
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     "convbert"

 def __init__(self							:  List[str]			,					UpperCAmelCase_							:  Any=30_522			,					UpperCAmelCase_							:  Optional[int]=768			,					UpperCAmelCase_							:  Optional[int]=12			,					UpperCAmelCase_							:  Optional[Any]=12			,					UpperCAmelCase_							:  List[str]=3_072			,					UpperCAmelCase_							:  Dict="gelu"			,					UpperCAmelCase_							:  int=0.1			,					UpperCAmelCase_							:  Any=0.1			,					UpperCAmelCase_							:  List[str]=512			,					UpperCAmelCase_							:  List[str]=2			,					UpperCAmelCase_							:  Tuple=0.02			,					UpperCAmelCase_							:  List[str]=1E-1_2			,					UpperCAmelCase_							:  Optional[int]=1			,					UpperCAmelCase_							:  Union[str, Any]=0			,					UpperCAmelCase_							:  Tuple=2			,					UpperCAmelCase_							:  Optional[int]=768			,					UpperCAmelCase_							:  Dict=2			,					UpperCAmelCase_							:  Optional[int]=9			,					UpperCAmelCase_							:  List[str]=1			,					UpperCAmelCase_							:  Union[str, Any]=None			,					**UpperCAmelCase_							:  Optional[int]			,					)			->List[Any]:



   '''simple docstring'''



   super().__init__(
       pad_token_id=UpperCAmelCase_			,					bos_token_id=UpperCAmelCase_			,					eos_token_id=UpperCAmelCase_			,					**UpperCAmelCase_			,					)

   lowerCamelCase__: Any         =vocab_size
   lowerCamelCase__: Optional[Any]         =hidden_size
   lowerCamelCase__: List[Any]         =num_hidden_layers
   lowerCamelCase__: List[str]         =num_attention_heads
   lowerCamelCase__: Dict         =intermediate_size
   lowerCamelCase__: List[Any]         =hidden_act
   lowerCamelCase__: List[str]         =hidden_dropout_prob
   lowerCamelCase__: List[Any]         =attention_probs_dropout_prob
   lowerCamelCase__: Union[str, Any]         =max_position_embeddings
   lowerCamelCase__: str         =type_vocab_size
   lowerCamelCase__: str         =initializer_range
   lowerCamelCase__: Optional[Any]         =layer_norm_eps
   lowerCamelCase__: Optional[int]         =embedding_size
   lowerCamelCase__: List[str]         =head_ratio
   lowerCamelCase__: Optional[Any]         =conv_kernel_size
   lowerCamelCase__: int         =num_groups
   lowerCamelCase__: List[str]         =classifier_dropout



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any])			->Mapping[str, Mapping[int, str]]:



   '''simple docstring'''



   if self.task == "multiple-choice":
     lowerCamelCase__: Any         ={0: "batch", 1: "choice", 2: "sequence"}
   else:
     lowerCamelCase__: str         ={0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
           ("token_type_ids", dynamic_axis),
       ])








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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):
     __A	:  int							  =   ["""pixel_values"""]





     def __init__(     self				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = PILImageResampling.BICUBIC				,							_UpperCamelCase = True				,							_UpperCamelCase = True				,							_UpperCamelCase = 1 / 255				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        super().__init__(**_UpperCamelCase					)
        _UpperCAmelCase           =   size if size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)
        _UpperCAmelCase           =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							default_to_square=_UpperCamelCase				,							param_name='''crop_size'''					)

        _UpperCAmelCase           =   do_resize
        _UpperCAmelCase           =   do_rescale
        _UpperCAmelCase           =   do_normalize
        _UpperCAmelCase           =   do_center_crop
        _UpperCAmelCase           =   crop_size
        _UpperCAmelCase           =   size
        _UpperCAmelCase           =   resample
        _UpperCAmelCase           =   rescale_factor
        _UpperCAmelCase           =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        _UpperCAmelCase           =   image_std if image_std is not None else IMAGENET_DEFAULT_STD





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = PILImageResampling.BILINEAR				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

        if "shortest_edge" in size:
           _UpperCAmelCase           =   get_resize_output_image_size(_UpperCamelCase				,							size=size['''shortest_edge''']				,							default_to_square=_UpperCamelCase					)
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
           _UpperCAmelCase           =   (size['''height'''], size['''width'''])
        else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''					)
        return resize(_UpperCamelCase				,							size=_UpperCamelCase				,							resample=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)
        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(_UpperCamelCase				,							size=(size['''height'''], size['''width'''])				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase					):
        return rescale(_UpperCamelCase				,							scale=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        return normalize(_UpperCamelCase				,							mean=_UpperCamelCase				,							std=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     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 = ChannelDimension.FIRST				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase           =   do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase           =   do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase           =   do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase           =   crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase				,							param_name='''crop_size'''				,							default_to_square=_UpperCamelCase					)
        _UpperCAmelCase           =   resample if resample is not None else self.resample
        _UpperCAmelCase           =   rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase           =   image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase           =   image_std if image_std is not None else self.image_std

        _UpperCAmelCase           =   size if size is not None else self.size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

        if not is_batched(_UpperCamelCase					):
           _UpperCAmelCase           =   [images]

        if not valid_images(_UpperCamelCase					):
           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.'''					)

        # All transformations expect numpy arrays.
        _UpperCAmelCase           =   [to_numpy_array(_UpperCamelCase					) for image in images]

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

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

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

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

        _UpperCAmelCase           =   [to_channel_dimension_format(_UpperCamelCase				,							_UpperCamelCase					) for image in images]

        _UpperCAmelCase           =   {'''pixel_values''': images}
        return BatchFeature(data=_UpperCamelCase				,							tensor_type=_UpperCamelCase					)