

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				(					_UpperCAmelCase			):
	_lowercase  :   Dict       				=  """deberta-v2"""







	def __init__(   self: Optional[int]  , __UpperCamelCase: Union[str, Any]=12_81_00  , __UpperCamelCase: List[str]=15_36  , __UpperCamelCase: Optional[Any]=24  , __UpperCamelCase: str=24  , __UpperCamelCase: Optional[Any]=61_44  , __UpperCamelCase: Any="gelu"  , __UpperCamelCase: Any=0.1  , __UpperCamelCase: List[str]=0.1  , __UpperCamelCase: Union[str, Any]=5_12  , __UpperCamelCase: int=0  , __UpperCamelCase: List[str]=0.02  , __UpperCamelCase: str=1E-7  , __UpperCamelCase: str=False  , __UpperCamelCase: Tuple=-1  , __UpperCamelCase: Dict=0  , __UpperCamelCase: Union[str, Any]=True  , __UpperCamelCase: Dict=None  , __UpperCamelCase: Optional[int]=0  , __UpperCamelCase: List[str]="gelu"  , **__UpperCamelCase: str  , ):

						'''simple docstring'''





						super().__init__(**lowercase__      )

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

						# Backwards compatibility
						if type(lowercase__      ) == str:
											__magic_name__							=    [x.strip() for x in pos_att_type.lower().split('|'      )]

						__magic_name__							=    pos_att_type
						__magic_name__							=    vocab_size
						__magic_name__							=    layer_norm_eps

						__magic_name__							=    kwargs.get('pooler_hidden_size'  , lowercase__      )
						__magic_name__							=    pooler_dropout
						__magic_name__							=    pooler_hidden_act




class 				__UpperCamelCase				(					_UpperCAmelCase			):







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





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

						'''simple docstring'''





						return 12







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , __UpperCamelCase: Dict  , __UpperCamelCase: List[Any] = -1  , __UpperCamelCase: int = -1  , __UpperCamelCase: Union[str, Any] = -1  , __UpperCamelCase: Any = False  , __UpperCamelCase: Any = None  , __UpperCamelCase: Tuple = 3  , __UpperCamelCase: List[Any] = 40  , __UpperCamelCase: int = 40  , __UpperCamelCase: str = None  , ):

						'''simple docstring'''





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







import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			):







	@register_to_config
	def __init__(   self: Optional[Any]  , *,
	    __UpperCamelCase: int = 4  , __UpperCamelCase: int = 7_68  , __UpperCamelCase: int  , __UpperCamelCase: Any  , ):

						'''simple docstring'''





						super().__init__()

						__magic_name__							=    nn.Parameter(torch.zeros(__UpperCamelCase      )      )

						# parameters for additional clip time embeddings
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )

						# parameters for encoder hidden states
						__magic_name__							=    clip_extra_context_tokens
						__magic_name__							=    nn.Linear(
						    __UpperCamelCase  , self.clip_extra_context_tokens * cross_attention_dim      )
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    nn.LayerNorm(__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , *, __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Any      ):

						'''simple docstring'''





						if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__magic_name__							=    image_embeddings.shape[0]
											__magic_name__							=    self.learned_classifier_free_guidance_embeddings.unsqueeze(0      )
											__magic_name__							=    classifier_free_guidance_embeddings.expand(
											    __UpperCamelCase  , -1      )
											__magic_name__							=    torch.cat([classifier_free_guidance_embeddings, image_embeddings]  , dim=0      )

						# The image embeddings batch size and the text embeddings batch size are equal
						assert image_embeddings.shape[0] == prompt_embeds.shape[0]

						__magic_name__							=    prompt_embeds.shape[0]

						# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
						# adding CLIP embeddings to the existing timestep embedding, ...
						__magic_name__							=    self.embedding_proj(__UpperCamelCase      )
						__magic_name__							=    self.clip_image_embeddings_project_to_time_embeddings(__UpperCamelCase      )
						__magic_name__							=    time_projected_image_embeddings + time_projected_prompt_embeds

						# ... and by projecting CLIP embeddings into four
						# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
						__magic_name__							=    self.clip_extra_context_tokens_proj(__UpperCamelCase      )
						__magic_name__							=    clip_extra_context_tokens.reshape(__UpperCamelCase  , -1  , self.clip_extra_context_tokens      )
						__magic_name__							=    clip_extra_context_tokens.permute(0  , 2  , 1      )

						__magic_name__							=    self.encoder_hidden_states_proj(__UpperCamelCase      )
						__magic_name__							=    self.text_encoder_hidden_states_norm(__UpperCamelCase      )
						__magic_name__							=    torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]  , dim=1      )

						return text_encoder_hidden_states, additive_clip_time_embeddings




