

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
			from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


a_      =     logging.get_logger(__name__)

a_      =     {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}






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







			lowerCamelCase__							     ='deberta-v2'
			def __init__(      self				:			List[str]   ,   a				:			int=12_8100   ,   a				:			str=1536   ,   a				:			List[Any]=24   ,   a				:			Union[str, Any]=24   ,   a				:			Union[str, Any]=6144   ,   a				:			Dict="gelu"   ,   a				:			Dict=0.1   ,   a				:			Dict=0.1   ,   a				:			Any=512   ,   a				:			Optional[int]=0   ,   a				:			Any=0.02   ,   a				:			int=1e-7   ,   a				:			Dict=False   ,   a				:			Optional[int]=-1   ,   a				:			int=0   ,   a				:			Union[str, Any]=True   ,   a				:			Dict=None   ,   a				:			List[Any]=0   ,   a				:			Tuple="gelu"   ,   **a				:			str   ,   )				->							Union[str, Any]:

				"""simple docstring"""

				super().__init__(**a						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   int					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Any					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_act
				SCREAMING_SNAKE_CASE :   List[Any]					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Dict					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   int					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							initializer_range
				SCREAMING_SNAKE_CASE :   str					  =							relative_attention
				SCREAMING_SNAKE_CASE :   Dict					  =							max_relative_positions
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pad_token_id
				SCREAMING_SNAKE_CASE :   Tuple					  =							position_biased_input

				# Backwards compatibility
				if type(a						) == str:
					SCREAMING_SNAKE_CASE :   Any					  =							[x.strip() for x in pos_att_type.lower().split("|"						)]

				SCREAMING_SNAKE_CASE :   Dict					  =							pos_att_type
				SCREAMING_SNAKE_CASE :   Dict					  =							vocab_size
				SCREAMING_SNAKE_CASE :   Any					  =							layer_norm_eps

				SCREAMING_SNAKE_CASE :   Any					  =							kwargs.get("pooler_hidden_size"   ,   a						)
				SCREAMING_SNAKE_CASE :   int					  =							pooler_dropout
				SCREAMING_SNAKE_CASE :   List[str]					  =							pooler_hidden_act






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







			@property
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

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

				"""simple docstring"""

				return 12






			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]   ,   a				:			int = -1   ,   a				:			int = -1   ,   a				:			int = -1   ,   a				:			bool = False   ,   a				:			Optional["TensorType"] = None   ,   a				:			int = 3   ,   a				:			int = 40   ,   a				:			int = 40   ,   a				:			"PreTrainedTokenizerBase" = None   ,   )				->							Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							super().generate_dummy_inputs(preprocessor=a   ,   framework=a						)
				if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
					del dummy_inputs["token_type_ids"]
				return dummy_inputs


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)