
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


_snake_case      :							Dict     	=    logging.get_logger(__name__)

_snake_case      :							List[str]     	=    {
    "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'''



						UpperCamelCase      :  Optional[Any]				   =					'''deberta-v2'''





						def __init__(			self							:int		,    lowerCAmelCase__							:Union[str, Any]=128100		,    lowerCAmelCase__							:Optional[Any]=1536		,    lowerCAmelCase__							:Tuple=24		,    lowerCAmelCase__							:List[Any]=24		,    lowerCAmelCase__							:Union[str, Any]=6144		,    lowerCAmelCase__							:List[str]="gelu"		,    lowerCAmelCase__							:Union[str, Any]=0.1		,    lowerCAmelCase__							:Optional[Any]=0.1		,    lowerCAmelCase__							:List[str]=512		,    lowerCAmelCase__							:Optional[int]=0		,    lowerCAmelCase__							:Optional[int]=0.02		,    lowerCAmelCase__							:int=1E-7		,    lowerCAmelCase__							:Optional[int]=False		,    lowerCAmelCase__							:List[Any]=-1		,    lowerCAmelCase__							:str=0		,    lowerCAmelCase__							:str=True		,    lowerCAmelCase__							:Optional[Any]=None		,    lowerCAmelCase__							:List[Any]=0		,    lowerCAmelCase__							:Union[str, Any]="gelu"		,    **lowerCAmelCase__							:str		,    )  ->Optional[Any]:
										super().__init__(**lowerCAmelCase__     )

										lowercase         =      hidden_size
										lowercase         =      num_hidden_layers
										lowercase         =      num_attention_heads
										lowercase         =      intermediate_size
										lowercase         =      hidden_act
										lowercase         =      hidden_dropout_prob
										lowercase         =      attention_probs_dropout_prob
										lowercase         =      max_position_embeddings
										lowercase         =      type_vocab_size
										lowercase         =      initializer_range
										lowercase         =      relative_attention
										lowercase         =      max_relative_positions
										lowercase         =      pad_token_id
										lowercase         =      position_biased_input

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

										lowercase         =      pos_att_type
										lowercase         =      vocab_size
										lowercase         =      layer_norm_eps

										lowercase         =      kwargs.get("pooler_hidden_size"		,    lowerCAmelCase__     )
										lowercase         =      pooler_dropout
										lowercase         =      pooler_hidden_act





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						@property
						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Mapping[str, Mapping[int, str]]:
										if self.task == "multiple-choice":
														lowercase         =      {0: "batch", 1: "choice", 2: "sequence"}
										else:
														lowercase         =      {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        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->int:
										return 12





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:int = -1		,    lowerCAmelCase__							:bool = False		,    lowerCAmelCase__							:Optional["TensorType"] = None		,    lowerCAmelCase__							:int = 3		,    lowerCAmelCase__							:int = 40		,    lowerCAmelCase__							:int = 40		,    lowerCAmelCase__							:"PreTrainedTokenizerBase" = None		,    )  ->Mapping[str, Any]:
										lowercase         =      super().generate_dummy_inputs(preprocessor=lowerCAmelCase__		,    framework=lowerCAmelCase__     )
										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
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_snake_case      :							Tuple     	=    False
_snake_case      :							Optional[int]     	=    True
_snake_case      :							Any     	=    False


if __name__ == "__main__":
						_snake_case      :							Any     	=    argparse.ArgumentParser()

						parser.add_argument(
						    "--repo_path",
						    default=None,
						    type=str,
						    required=True,
						    help="The config json file corresponding to the architecture.",
						)

						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

						_snake_case      :							Tuple     	=    parser.parse_args()

						_snake_case      :							Optional[Any]     	=    {
						    "image_size": "sample_size",
						    "num_res_blocks": "layers_per_block",
						    "block_channels": "block_out_channels",
						    "down_blocks": "down_block_types",
						    "up_blocks": "up_block_types",
						    "downscale_freq_shift": "freq_shift",
						    "resnet_num_groups": "norm_num_groups",
						    "resnet_act_fn": "act_fn",
						    "resnet_eps": "norm_eps",
						    "num_head_channels": "attention_head_dim",
						}

						_snake_case      :							Tuple     	=    {
						    "time_steps": "time_proj",
						    "mid": "mid_block",
						    "downsample_blocks": "down_blocks",
						    "upsample_blocks": "up_blocks",
						}

						_snake_case      :							Any     	=    "" if has_file(args.repo_path, "config.json") else "unet"

						with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
												_snake_case      :							List[Any]     	=    reader.read()
												_snake_case      :							List[Any]     	=    json.loads(text)

						if do_only_config:
												for key in config_parameters_to_change.keys():
																		config.pop(key, None)

						if has_file(args.repo_path, "config.json"):
												_snake_case      :							List[Any]     	=    UNetaDModel(**config)
						else:
												_snake_case      :							int     	=    UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
												_snake_case      :							Optional[Any]     	=    class_name(**config)

						if do_only_config:
												model.save_config(os.path.join(args.repo_path, subfolder))

						_snake_case      :							Any     	=    dict(model.config)

						if do_only_renaming:
												for key, value in config_parameters_to_change.items():
																		if key in config:
																								_snake_case      :							Any     	=    config[key]
																								del config[key]

												_snake_case      :							List[str]     	=    [k.replace("UNetRes", "") for k in config["down_block_types"]]
												_snake_case      :							Any     	=    [k.replace("UNetRes", "") for k in config["up_block_types"]]

						if do_only_weights:
												_snake_case      :							Optional[int]     	=    torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

												_snake_case      :							Any     	=    {}
												for param_key, param_value in state_dict.items():
																		if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
																								continue
																		_snake_case      :							List[Any]     	=    False
																		for key, new_key in key_parameters_to_change.items():
																								if not has_changed and param_key.split(".")[0] == key:
																														_snake_case      :							Dict     	=    param_value
																														_snake_case      :							Any     	=    True
																		if not has_changed:
																								_snake_case      :							int     	=    param_value

												model.load_state_dict(new_state_dict)
												model.save_pretrained(os.path.join(args.repo_path, subfolder))
