

from collections import OrderedDict
from typing import Mapping

from packaging import version

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


a_      =     logging.get_logger(__name__)

a_      =     {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='beit'
			def __init__(      self				:			str   ,   a				:			List[Any]=8192   ,   a				:			int=768   ,   a				:			Optional[Any]=12   ,   a				:			Union[str, Any]=12   ,   a				:			Any=3072   ,   a				:			Union[str, Any]="gelu"   ,   a				:			str=0.0   ,   a				:			Tuple=0.0   ,   a				:			Optional[Any]=0.02   ,   a				:			Optional[int]=1e-12   ,   a				:			List[str]=224   ,   a				:			Union[str, Any]=16   ,   a				:			Optional[Any]=3   ,   a				:			Union[str, Any]=False   ,   a				:			int=False   ,   a				:			List[Any]=False   ,   a				:			Optional[Any]=False   ,   a				:			List[Any]=0.1   ,   a				:			List[Any]=0.1   ,   a				:			Tuple=True   ,   a				:			Tuple=[3, 5, 7, 11]   ,   a				:			Optional[int]=[1, 2, 3, 6]   ,   a				:			Optional[Any]=True   ,   a				:			Dict=0.4   ,   a				:			Tuple=256   ,   a				:			int=1   ,   a				:			List[str]=False   ,   a				:			Union[str, Any]=255   ,   **a				:			int   ,   )				->							Optional[Any]:

				"""simple docstring"""

				super().__init__(**a						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							vocab_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   List[str]					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   str					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_act
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   Any					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   List[Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   int					  =							layer_norm_eps

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							image_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							patch_size
				SCREAMING_SNAKE_CASE :   List[Any]					  =							num_channels
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							use_mask_token
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							use_absolute_position_embeddings
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							use_relative_position_bias
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							use_shared_relative_position_bias
				SCREAMING_SNAKE_CASE :   str					  =							layer_scale_init_value
				SCREAMING_SNAKE_CASE :   str					  =							drop_path_rate
				SCREAMING_SNAKE_CASE :   List[str]					  =							use_mean_pooling
				# decode head attributes (semantic segmentation)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							out_indices
				SCREAMING_SNAKE_CASE :   Dict					  =							pool_scales
				# auxiliary head attributes (semantic segmentation)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							use_auxiliary_head
				SCREAMING_SNAKE_CASE :   Any					  =							auxiliary_loss_weight
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							auxiliary_channels
				SCREAMING_SNAKE_CASE :   int					  =							auxiliary_num_convs
				SCREAMING_SNAKE_CASE :   List[str]					  =							auxiliary_concat_input
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							semantic_loss_ignore_index






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     =version.parse('1.11' )
			@property
			def 				__UpperCamelCase			(      self				:			Dict						)				->							Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]						)






			@property
			def 				__UpperCamelCase			(      self				:			str						)				->							float:

				"""simple docstring"""

				return 1e-4


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			a_      =     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_      =     parser.parse_args()

			a_      =     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

			a_      =     CLIPImageProcessor()
			a_      =     CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

			a_      =     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)