





'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


__lowerCAmelCase							:			Optional[Any]				=logging.get_logger(__name__)

__lowerCAmelCase							:			Union[str, Any]				={
    "nghuyong/ernie-1.0-base-zh": "https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json",
    "nghuyong/ernie-2.0-base-en": "https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json",
    "nghuyong/ernie-2.0-large-en": "https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json",
    "nghuyong/ernie-3.0-base-zh": "https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-medium-zh": "https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-mini-zh": "https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-micro-zh": "https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-nano-zh": "https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json",
    "nghuyong/ernie-gram-zh": "https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json",
    "nghuyong/ernie-health-zh": "https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json",
}






class      UpperCAmelCase			( UpperCamelCase__       ):
 __lowercase     	=       """ernie"""
 def __init__(       self   :str		, lowercase_   :Any=3_05_22		, lowercase_   :int=7_68		, lowercase_   :Union[str, Any]=12		, lowercase_   :Optional[int]=12		, lowercase_   :Tuple=30_72		, lowercase_   :int="gelu"		, lowercase_   :Any=0.1		, lowercase_   :Optional[Any]=0.1		, lowercase_   :Optional[int]=5_12		, lowercase_   :Union[str, Any]=2		, lowercase_   :Dict=3		, lowercase_   :List[Any]=False		, lowercase_   :int=0.0_2		, lowercase_   :int=1E-12		, lowercase_   :Optional[int]=0		, lowercase_   :Optional[int]="absolute"		, lowercase_   :str=True		, lowercase_   :List[Any]=None		, **lowercase_   :Tuple		, )->							Union[str, Any]:
     super().__init__(pad_token_id=lowercase_		, **lowercase_							)

     A__       =    vocab_size
     A__       =    hidden_size
     A__       =    num_hidden_layers
     A__       =    num_attention_heads
     A__       =    hidden_act
     A__       =    intermediate_size
     A__       =    hidden_dropout_prob
     A__       =    attention_probs_dropout_prob
     A__       =    max_position_embeddings
     A__       =    type_vocab_size
     A__       =    task_type_vocab_size
     A__       =    use_task_id
     A__       =    initializer_range
     A__       =    layer_norm_eps
     A__       =    position_embedding_type
     A__       =    use_cache
     A__       =    classifier_dropout






class      UpperCAmelCase			( UpperCamelCase__       ):
 @property
 def   UpperCAmelCase_							(       self   :List[str]							)->							Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         A__       =    {0: "batch", 1: "choice", 2: "sequence"}
     else:
         A__       =    {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
             ("token_type_ids", dynamic_axis),
             ("task_type_ids", dynamic_axis),
         ]							)













'''simple docstring'''

import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image





def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Union[str, Any]=2_56 ):
    A__       =    min(img.size )

    if s < target_image_size:
        raise ValueError(F"min dim for image {s} < {target_image_size}" )

    A__       =    target_image_size / s
    A__       =    (round(r * img.size[1] ), round(r * img.size[0] ))
    A__       =    TF.resize(_lowerCamelCase		, _lowerCamelCase		, interpolation=PIL.Image.LANCZOS )
    A__       =    TF.center_crop(_lowerCamelCase		, output_size=2 * [target_image_size] )
    A__       =    torch.unsqueeze(T.ToTensor()(_lowerCamelCase )		, 0 )
    return img





def 		UpperCamelCase       (							_lowerCamelCase      :      int ):
    A__       =    2.0 * x - 1.0
    return x





def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Tuple=True		, _lowerCamelCase      :      int="RGB" ):
    A__       =    x.detach().cpu()
    if process:
        A__       =    post_process_tensor(_lowerCamelCase )
    A__       =    x.numpy()
    if process:
        A__       =    (2_55 * x).astype(np.uinta )
    A__       =    Image.fromarray(_lowerCamelCase )
    if not x.mode == mode:
        A__       =    x.convert(_lowerCamelCase )
    return x





def 		UpperCamelCase       (							_lowerCamelCase      :      Any ):
    A__       =    torch.clamp(_lowerCamelCase		, -1.0		, 1.0 )
    A__       =    (x + 1.0) / 2.0
    A__       =    x.permute(1		, 2		, 0 )
    return x





def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any] ):
    A__       =    post_process_tensor(x.squeeze() )
    return x.permute(2		, 0		, 1 ).unsqueeze(0 )



