

from collections import OrderedDict
from typing import Mapping

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


lowercase						:      Dict									= logging.get_logger(__name__)

lowercase						:      Union[str, Any]									= {
    'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
    'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
    'kssteven/ibert-roberta-large-mnli': (
        'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
    ),
}




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'ibert'







	def __init__(      self	:Dict  ,     a	:Union[str, Any]=3_0_5_2_2  ,     a	:Tuple=7_6_8  ,     a	:Optional[int]=1_2  ,     a	:Any=1_2  ,     a	:int=3_0_7_2  ,     a	:Tuple="gelu"  ,     a	:Any=0.1  ,     a	:List[str]=0.1  ,     a	:Any=5_1_2  ,     a	:Any=2  ,     a	:List[str]=0.02  ,     a	:List[str]=1E-1_2  ,     a	:Union[str, Any]=1  ,     a	:Any=0  ,     a	:Any=2  ,     a	:Tuple="absolute"  ,     a	:int=False  ,     a	:Optional[int]="none"  ,     **a	:Any  ,     )	->				Any:
								super().__init__(pad_token_id=a  ,     bos_token_id=a  ,     eos_token_id=a  ,     **a		)

								__UpperCamelCase			:     List[Any]         =      vocab_size
								__UpperCamelCase			:     Any         =      hidden_size
								__UpperCamelCase			:     Tuple         =      num_hidden_layers
								__UpperCamelCase			:     Optional[int]         =      num_attention_heads
								__UpperCamelCase			:     Union[str, Any]         =      hidden_act
								__UpperCamelCase			:     List[str]         =      intermediate_size
								__UpperCamelCase			:     Optional[Any]         =      hidden_dropout_prob
								__UpperCamelCase			:     int         =      attention_probs_dropout_prob
								__UpperCamelCase			:     Dict         =      max_position_embeddings
								__UpperCamelCase			:     Tuple         =      type_vocab_size
								__UpperCamelCase			:     Optional[int]         =      initializer_range
								__UpperCamelCase			:     Any         =      layer_norm_eps
								__UpperCamelCase			:     Union[str, Any]         =      position_embedding_type
								__UpperCamelCase			:     List[Any]         =      quant_mode
								__UpperCamelCase			:     Union[str, Any]         =      force_dequant




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	@property
	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
															__UpperCamelCase			:     str         =      {0: "batch", 1: "choice", 2: "sequence"}
								else:
															__UpperCamelCase			:     int         =      {0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								    ]		)

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase						:      List[str]									= argparse.ArgumentParser()

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		parser.add_argument(
		    '--txt2img_unclip',
		    default='kakaobrain/karlo-v1-alpha',
		    type=str,
		    required=False,
		    help='The pretrained txt2img unclip.',
		)

		lowercase						:      int									= parser.parse_args()

		lowercase						:      str									= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase						:      int									= CLIPImageProcessor()
		lowercase						:      Optional[int]									= CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase						:      int									= UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)