
'''simple docstring'''
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


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    '''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 	snake_case__       (      __a					):







		"""simple docstring"""

		lowerCamelCase     							=						'deberta-v2'



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






				"""simple docstring"""







				super().__init__(**_lowerCamelCase      )

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

				# Backwards compatibility
				if type(_lowerCamelCase      ) == str:
						snake_case  :       List[Any]     			=						[x.strip() for x in pos_att_type.lower().split('''|'''      )]

				snake_case  :       Any     			=						pos_att_type
				snake_case  :       str     			=						vocab_size
				snake_case  :       Any     			=						layer_norm_eps

				snake_case  :       Optional[int]     			=						kwargs.get('''pooler_hidden_size'''  ,	_lowerCamelCase      )
				snake_case  :       Any     			=						pooler_dropout
				snake_case  :       int     			=						pooler_hidden_act







class 	snake_case__       (      __a					):







		"""simple docstring"""

		@property
		def       lowerCAmelCase				(      self				:			int      )						->			List[Any]:






				"""simple docstring"""







				if self.task == "multiple-choice":
						snake_case  :       str     			=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
						snake_case  :       str     			=						{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       lowerCAmelCase				(      self				:			Optional[Any]      )						->			List[str]:






				"""simple docstring"""







				return 12



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			List[str] = -1  ,	UpperCamelCase__				:			Dict = -1  ,	UpperCamelCase__				:			List[Any] = -1  ,	UpperCamelCase__				:			Dict = False  ,	UpperCamelCase__				:			List[Any] = None  ,	UpperCamelCase__				:			List[str] = 3  ,	UpperCamelCase__				:			str = 40  ,	UpperCamelCase__				:			Optional[Any] = 40  ,	UpperCamelCase__				:			str = None  ,	)						->			str:






				"""simple docstring"""







				snake_case  :       Any     			=						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



'''simple docstring'''

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							Union[str, Any]=False)			->					Optional[int]:
   lowerCAmelCase_			=      OmegaConf.load(__snake_case)
   if display:
      print(yaml.dump(OmegaConf.to_container(__snake_case)))
   return config



def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int]=None	,   __snake_case  :							str=None)			->					str:
   if conf_path is None:
      lowerCAmelCase_			=      '''./model_checkpoints/vqgan_only.yaml'''
   lowerCAmelCase_			=      load_config(__snake_case	,   display=__snake_case)
   lowerCAmelCase_			=      VQModel(**config.model.params)
   if ckpt_path is None:
      lowerCAmelCase_			=      '''./model_checkpoints/vqgan_only.pt'''
   lowerCAmelCase_			=      torch.load(__snake_case	,   map_location=__snake_case)
   if ".ckpt" in ckpt_path:
      lowerCAmelCase_			=      sd['''state_dict''']
   model.load_state_dict(__snake_case	,   strict=__snake_case)
   model.to(__snake_case)
   del sd
   return model



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							str)			->					Optional[int]:
   lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      model.encode(__snake_case)
   print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''')
   lowerCAmelCase_			=      model.decode(__snake_case)
   return xrec



def snake_case_      (					__snake_case  :							int	,   __snake_case  :							List[Any]=False)			->					Union[str, Any]:
   lowerCAmelCase_	,lowerCAmelCase_			=      string.rsplit('''.'''	,   1)
   if reload:
      lowerCAmelCase_			=      importlib.import_module(__snake_case)
      importlib.reload(__snake_case)
   return getattr(importlib.import_module(__snake_case	,   package=__snake_case)	,   cls)



def snake_case_      (					__snake_case  :							Tuple)			->					Union[str, Any]:
   if "target" not in config:
      raise KeyError('''Expected key `target` to instantiate.''')
   return get_obj_from_str(config['''target'''])(**config.get('''params'''	,   {}))



def snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Any	,   __snake_case  :							Optional[int]=True	,   __snake_case  :							Any=True)			->					List[str]:
   lowerCAmelCase_			=      instantiate_from_config(__snake_case)
   if sd is not None:
      model.load_state_dict(__snake_case)
   if gpu:
      model.cuda()
   if eval_mode:
      model.eval()
   return {"model": model}






def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int]	,   __snake_case  :							Dict	,   __snake_case  :							str)			->					Any:
   # load the specified checkpoint
   if ckpt:
      lowerCAmelCase_			=      torch.load(__snake_case	,   map_location='''cpu''')
      lowerCAmelCase_			=      pl_sd['''global_step''']
      print(F'''loaded model from global step {global_step}.''')
   else:
      lowerCAmelCase_			=      {'''state_dict''': None}
      lowerCAmelCase_			=      None
   lowerCAmelCase_			=      load_model_from_config(config.model	,   pl_sd['''state_dict''']	,   gpu=__snake_case	,   eval_mode=__snake_case)['''model''']
   return model, global_step





