


from collections import OrderedDict
from typing import Mapping

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


_A		:				List[Any]									=       logging.get_logger(__name__)

_A		:				str									=       {
    '''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''',
    '''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''',
    '''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''',
    '''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''',
    '''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''',
    '''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''',
    '''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''',
}



class        a					(			_a   ):
					UpperCAmelCase__ :  Optional[Any]						      =	"xmod"





					def __init__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str=30522    ,	SCREAMING_SNAKE_CASE_					:				str=768    ,	SCREAMING_SNAKE_CASE_					:				int=12    ,	SCREAMING_SNAKE_CASE_					:				int=12    ,	SCREAMING_SNAKE_CASE_					:				int=3072    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]="gelu"    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=0.1    ,	SCREAMING_SNAKE_CASE_					:				str=0.1    ,	SCREAMING_SNAKE_CASE_					:				Dict=512    ,	SCREAMING_SNAKE_CASE_					:				Dict=2    ,	SCREAMING_SNAKE_CASE_					:				List[str]=0.02    ,	SCREAMING_SNAKE_CASE_					:				Tuple=1E-1_2    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=1    ,	SCREAMING_SNAKE_CASE_					:				Any=0    ,	SCREAMING_SNAKE_CASE_					:				List[str]=2    ,	SCREAMING_SNAKE_CASE_					:				str="absolute"    ,	SCREAMING_SNAKE_CASE_					:				Tuple=True    ,	SCREAMING_SNAKE_CASE_					:				List[str]=None    ,	SCREAMING_SNAKE_CASE_					:				Tuple=False    ,	SCREAMING_SNAKE_CASE_					:				int=2    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=False    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=True    ,	SCREAMING_SNAKE_CASE_					:				Any=("en_XX",)    ,	SCREAMING_SNAKE_CASE_					:				Any=None    ,	**SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	):
								super().__init__(pad_token_id=_A    ,	bos_token_id=_A    ,	eos_token_id=_A    ,	**_A							)

								__lowerCamelCase:				Any     =	vocab_size
								__lowerCamelCase:				Tuple     =	hidden_size
								__lowerCamelCase:				Dict     =	num_hidden_layers
								__lowerCamelCase:				Dict     =	num_attention_heads
								__lowerCamelCase:				Optional[Any]     =	hidden_act
								__lowerCamelCase:				Optional[Any]     =	intermediate_size
								__lowerCamelCase:				Any     =	hidden_dropout_prob
								__lowerCamelCase:				Optional[Any]     =	attention_probs_dropout_prob
								__lowerCamelCase:				List[str]     =	max_position_embeddings
								__lowerCamelCase:				Tuple     =	type_vocab_size
								__lowerCamelCase:				List[Any]     =	initializer_range
								__lowerCamelCase:				int     =	layer_norm_eps
								__lowerCamelCase:				Dict     =	position_embedding_type
								__lowerCamelCase:				str     =	use_cache
								__lowerCamelCase:				Optional[int]     =	classifier_dropout
								__lowerCamelCase:				Optional[Any]     =	pre_norm
								__lowerCamelCase:				Dict     =	adapter_reduction_factor
								__lowerCamelCase:				Optional[int]     =	adapter_layer_norm
								__lowerCamelCase:				str     =	adapter_reuse_layer_norm
								__lowerCamelCase:				Any     =	ln_before_adapter
								__lowerCamelCase:				Optional[int]     =	list(_A							)
								__lowerCamelCase:				Dict     =	default_language



class        a					(			_a   ):





					@property
					def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
								if self.task == "multiple-choice":
											__lowerCamelCase:				int     =	{0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
											__lowerCamelCase:				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__":
   _A		:				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.''',
   )

   _A		:				Optional[Any]									=       parser.parse_args()

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

   _A		:				Optional[Any]									=       CLIPImageProcessor()
   _A		:				List[Any]									=       CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

   _A		:				str									=       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)


