






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
     lowerCamelCase__       		=  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.""",
     )

     lowerCamelCase__       		=  parser.parse_args()

     lowerCamelCase__       		=  UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

     lowerCamelCase__       		=  CLIPImageProcessor()
     lowerCamelCase__       		=  CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

     lowerCamelCase__       		=  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)










from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {
    """microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}




class       snake_case__ (				lowercase_						,     lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					List[Any]								=			"focalnet"



       def __init__(				self      ,    a__=2_24      ,    a__=4      ,    a__=3      ,    a__=96      ,    a__=False      ,    a__=[1_92, 3_84, 7_68, 7_68]      ,    a__=[2, 2, 6, 2]      ,    a__=[2, 2, 2, 2]      ,    a__=[3, 3, 3, 3]      ,    a__="gelu"      ,    a__=4.0      ,    a__=0.0      ,    a__=0.1      ,    a__=False      ,    a__=1e-4      ,    a__=False      ,    a__=False      ,    a__=False      ,    a__=0.02      ,    a__=1e-5      ,    a__=32      ,    a__=None      ,    a__=None      ,    **a__      ,    )			->   Tuple:






            '''simple docstring'''





            super().__init__(**a__     )

            __snake_case      :Optional[Any] 							=    image_size
            __snake_case      :Dict 							=    patch_size
            __snake_case      :Tuple 							=    num_channels
            __snake_case      :List[str] 							=    embed_dim
            __snake_case      :Any 							=    use_conv_embed
            __snake_case      :List[Any] 							=    hidden_sizes
            __snake_case      :str 							=    depths
            __snake_case      :List[Any] 							=    focal_levels
            __snake_case      :int 							=    focal_windows
            __snake_case      :Any 							=    hidden_act
            __snake_case      :List[str] 							=    mlp_ratio
            __snake_case      :str 							=    hidden_dropout_prob
            __snake_case      :List[str] 							=    drop_path_rate
            __snake_case      :int 							=    use_layerscale
            __snake_case      :int 							=    layerscale_value
            __snake_case      :Tuple 							=    use_post_layernorm
            __snake_case      :Dict 							=    use_post_layernorm_in_modulation
            __snake_case      :Union[str, Any] 							=    normalize_modulator
            __snake_case      :List[str] 							=    initializer_range
            __snake_case      :Optional[Any] 							=    layer_norm_eps
            __snake_case      :Tuple 							=    encoder_stride
            __snake_case      :List[str] 							=    ["""stem"""] + [F'''stage{idx}''' for idx in range(1      ,    len(self.depths     ) + 1     )]
            __snake_case							,			__snake_case      :str 							=    get_aligned_output_features_output_indices(
                out_features=a__      ,    out_indices=a__      ,    stage_names=self.stage_names     )


