

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


SCREAMING_SNAKE_CASE_          =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_          =   {
    '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 					a	(						__A					):
						_lowercase         =      "deberta-v2"


						def __init__(							self    ,       A_=128100    ,       A_=1536    ,       A_=24    ,       A_=24    ,       A_=6144    ,       A_="gelu"    ,       A_=0.1    ,       A_=0.1    ,       A_=512    ,       A_=0    ,       A_=0.02    ,       A_=1e-7    ,       A_=False    ,       A_=-1    ,       A_=0    ,       A_=True    ,       A_=None    ,       A_=0    ,       A_="gelu"    ,       **A_    ,       ):







										'''simple docstring'''


										super().__init__(**A_						)

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

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

										_UpperCAmelCase							:		Dict					=     pos_att_type
										_UpperCAmelCase							:		Dict					=     vocab_size
										_UpperCAmelCase							:		Any					=     layer_norm_eps

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







class 					a	(						__A					):


						@property
						def 							_UpperCAmelCase					(							self						):







										'''simple docstring'''


										if self.task == "multiple-choice":
														_UpperCAmelCase							:		Optional[Any]					=     {0: "batch", 1: "choice", 2: "sequence"}
										else:
														_UpperCAmelCase							:		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						):







										'''simple docstring'''


										return 12





						def 							_UpperCAmelCase					(							self    ,       A_    ,       A_ = -1    ,       A_ = -1    ,       A_ = -1    ,       A_ = False    ,       A_ = None    ,       A_ = 3    ,       A_ = 40    ,       A_ = 40    ,       A_ = None    ,       ):







										'''simple docstring'''


										_UpperCAmelCase							:		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)