
import torch
from torch import nn

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





class   UpperCamelCase_      (      __a		,    __a		):







						'''simple docstring'''



						@register_to_config
						def __init__(			self							:List[Any]		,    *,
						    lowerCAmelCase__							:int = 4		,    lowerCAmelCase__							:int = 768		,    lowerCAmelCase__							:int		,    lowerCAmelCase__							:List[Any]		,    )  ->Union[str, Any]:
										super().__init__()

										lowercase         =      nn.Parameter(torch.zeros(lowerCAmelCase__     )     )

										# parameters for additional clip time embeddings
										lowercase         =      nn.Linear(lowerCAmelCase__		,    lowerCAmelCase__     )
										lowercase         =      nn.Linear(lowerCAmelCase__		,    lowerCAmelCase__     )

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





						def        SCREAMING_SNAKE_CASE(			self							:str		,    *, lowerCAmelCase__							:Tuple		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:int     )  ->Tuple:
										if do_classifier_free_guidance:
														# Add the classifier free guidance embeddings to the image embeddings
														lowercase         =      image_embeddings.shape[0]
														lowercase         =      self.learned_classifier_free_guidance_embeddings.unsqueeze(0     )
														lowercase         =      classifier_free_guidance_embeddings.expand(
														    lowerCAmelCase__		,    -1     )
														lowercase         =      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]

										lowercase         =      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, ...
										lowercase         =      self.embedding_proj(lowerCAmelCase__     )
										lowercase         =      self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__     )
										lowercase         =      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"
										lowercase         =      self.clip_extra_context_tokens_proj(lowerCAmelCase__     )
										lowercase         =      clip_extra_context_tokens.reshape(lowerCAmelCase__		,    -1		,    self.clip_extra_context_tokens     )
										lowercase         =      clip_extra_context_tokens.permute(0		,    2		,    1     )

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

										return text_encoder_hidden_states, additive_clip_time_embeddings


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
