


from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin







@dataclass
class 				lowerCamelCase__       (	lowerCAmelCase):
     SCREAMING_SNAKE_CASE__				      =      42







class 				lowerCamelCase__       (	lowerCAmelCase       ,							lowerCAmelCase):





     @register_to_config
     def __init__(self		,					UpperCAmelCase = 1_6		,					UpperCAmelCase = 8_8		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = 1		,					UpperCAmelCase = 0.0		,					UpperCAmelCase = 3_2		,					UpperCAmelCase = None		,					UpperCAmelCase = False		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = "geglu"		,					UpperCAmelCase = None		,					UpperCAmelCase = False		,					UpperCAmelCase = False		,					UpperCAmelCase = False		,					UpperCAmelCase = "layer_norm"		,					UpperCAmelCase = True		,					)					->       Any:
      super().__init__()
      _lowercase											=use_linear_projection
      _lowercase											=num_attention_heads
      _lowercase											=attention_head_dim
      _lowercase											=num_attention_heads * attention_head_dim

      # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
      # Define whether input is continuous or discrete depending on configuration
      _lowercase											=(in_channels is not None) and (patch_size is None)
      _lowercase											=num_vector_embeds is not None
      _lowercase											=in_channels is not None and patch_size is not None

      if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
       _lowercase											=(
           f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
           ''' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'''
           ''' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'''
           ''' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'''
           ''' would be very nice if you could open a Pull request for the `transformer/config.json` file'''
       )
       deprecate('''norm_type!=num_embeds_ada_norm'''		,					'''1.0.0'''		,					UpperCAmelCase		,					standard_warn=UpperCAmelCase	)
       _lowercase											='''ada_norm'''

      if self.is_input_continuous and self.is_input_vectorized:
       raise ValueError(
           f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
           ''' sure that either `in_channels` or `num_vector_embeds` is None.'''	)
      elif self.is_input_vectorized and self.is_input_patches:
       raise ValueError(
           f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
           ''' sure that either `num_vector_embeds` or `num_patches` is None.'''	)
      elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
       raise ValueError(
           f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
           f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."	)

      # 2. Define input layers
      if self.is_input_continuous:
       _lowercase											=in_channels

       _lowercase											=torch.nn.GroupNorm(num_groups=UpperCAmelCase		,					num_channels=UpperCAmelCase		,					eps=1e-6		,					affine=UpperCAmelCase	)
       if use_linear_projection:
        _lowercase											=nn.Linear(UpperCAmelCase		,					UpperCAmelCase	)
       else:
        _lowercase											=nn.Convad(UpperCAmelCase		,					UpperCAmelCase		,					kernel_size=1		,					stride=1		,					padding=0	)
      elif self.is_input_vectorized:
       assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
       assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

       _lowercase											=sample_size
       _lowercase											=sample_size
       _lowercase											=num_vector_embeds
       _lowercase											=self.height * self.width

       _lowercase											=ImagePositionalEmbeddings(
           num_embed=UpperCAmelCase		,					embed_dim=UpperCAmelCase		,					height=self.height		,					width=self.width	)
      elif self.is_input_patches:
       assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

       _lowercase											=sample_size
       _lowercase											=sample_size

       _lowercase											=patch_size
       _lowercase											=PatchEmbed(
           height=UpperCAmelCase		,					width=UpperCAmelCase		,					patch_size=UpperCAmelCase		,					in_channels=UpperCAmelCase		,					embed_dim=UpperCAmelCase		,					)

      # 3. Define transformers blocks
      _lowercase											=nn.ModuleList(
          [
              BasicTransformerBlock(
                  UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					dropout=UpperCAmelCase		,					cross_attention_dim=UpperCAmelCase		,					activation_fn=UpperCAmelCase		,					num_embeds_ada_norm=UpperCAmelCase		,					attention_bias=UpperCAmelCase		,					only_cross_attention=UpperCAmelCase		,					upcast_attention=UpperCAmelCase		,					norm_type=UpperCAmelCase		,					norm_elementwise_affine=UpperCAmelCase		,					)
              for d in range(UpperCAmelCase	)
          ]	)

      # 4. Define output layers
      _lowercase											=in_channels if out_channels is None else out_channels
      if self.is_input_continuous:
       # TODO: should use out_channels for continuous projections
       if use_linear_projection:
        _lowercase											=nn.Linear(UpperCAmelCase		,					UpperCAmelCase	)
       else:
        _lowercase											=nn.Convad(UpperCAmelCase		,					UpperCAmelCase		,					kernel_size=1		,					stride=1		,					padding=0	)
      elif self.is_input_vectorized:
       _lowercase											=nn.LayerNorm(UpperCAmelCase	)
       _lowercase											=nn.Linear(UpperCAmelCase		,					self.num_vector_embeds - 1	)
      elif self.is_input_patches:
       _lowercase											=nn.LayerNorm(UpperCAmelCase		,					elementwise_affine=UpperCAmelCase		,					eps=1e-6	)
       _lowercase											=nn.Linear(UpperCAmelCase		,					2 * inner_dim	)
       _lowercase											=nn.Linear(UpperCAmelCase		,					patch_size * patch_size * self.out_channels	)





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = True		,					)					->       List[Any]:
      # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
      #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
      #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
      # expects mask of shape:
      #   [batch, key_tokens]
      # adds singleton query_tokens dimension:
      #   [batch,                    1, key_tokens]
      # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
      #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
      #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
      if attention_mask is not None and attention_mask.ndim == 2:
       # assume that mask is expressed as:
       #   (1 = keep,      0 = discard)
       # convert mask into a bias that can be added to attention scores:
       #       (keep = +0,     discard = -10000.0)
       _lowercase											=(1 - attention_mask.to(hidden_states.dtype	)) * -1_0000.0
       _lowercase											=attention_mask.unsqueeze(1	)

      # convert encoder_attention_mask to a bias the same way we do for attention_mask
      if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
       _lowercase											=(1 - encoder_attention_mask.to(hidden_states.dtype	)) * -1_0000.0
       _lowercase											=encoder_attention_mask.unsqueeze(1	)

      # 1. Input
      if self.is_input_continuous:
       _lowercase				,   _lowercase				,   _lowercase				,   _lowercase											=hidden_states.shape
       _lowercase											=hidden_states

       _lowercase											=self.norm(UpperCAmelCase	)
       if not self.use_linear_projection:
        _lowercase											=self.proj_in(UpperCAmelCase	)
        _lowercase											=hidden_states.shape[1]
        _lowercase											=hidden_states.permute(0		,					2		,					3		,					1	).reshape(UpperCAmelCase		,					height * width		,					UpperCAmelCase	)
       else:
        _lowercase											=hidden_states.shape[1]
        _lowercase											=hidden_states.permute(0		,					2		,					3		,					1	).reshape(UpperCAmelCase		,					height * width		,					UpperCAmelCase	)
        _lowercase											=self.proj_in(UpperCAmelCase	)
      elif self.is_input_vectorized:
       _lowercase											=self.latent_image_embedding(UpperCAmelCase	)
      elif self.is_input_patches:
       _lowercase											=self.pos_embed(UpperCAmelCase	)

      # 2. Blocks
      for block in self.transformer_blocks:
       _lowercase											=block(
           UpperCAmelCase		,					attention_mask=UpperCAmelCase		,					encoder_hidden_states=UpperCAmelCase		,					encoder_attention_mask=UpperCAmelCase		,					timestep=UpperCAmelCase		,					cross_attention_kwargs=UpperCAmelCase		,					class_labels=UpperCAmelCase		,					)

      # 3. Output
      if self.is_input_continuous:
       if not self.use_linear_projection:
        _lowercase											=hidden_states.reshape(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase	).permute(0		,					3		,					1		,					2	).contiguous()
        _lowercase											=self.proj_out(UpperCAmelCase	)
       else:
        _lowercase											=self.proj_out(UpperCAmelCase	)
        _lowercase											=hidden_states.reshape(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase	).permute(0		,					3		,					1		,					2	).contiguous()

       _lowercase											=hidden_states + residual
      elif self.is_input_vectorized:
       _lowercase											=self.norm_out(UpperCAmelCase	)
       _lowercase											=self.out(UpperCAmelCase	)
       # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
       _lowercase											=logits.permute(0		,					2		,					1	)

       # log(p(x_0))
       _lowercase											=F.log_softmax(logits.double()		,					dim=1	).float()
      elif self.is_input_patches:
       # TODO: cleanup!
       _lowercase											=self.transformer_blocks[0].norma.emb(
           UpperCAmelCase		,					UpperCAmelCase		,					hidden_dtype=hidden_states.dtype	)
       _lowercase				,   _lowercase											=self.proj_out_a(F.silu(UpperCAmelCase	)	).chunk(2		,					dim=1	)
       _lowercase											=self.norm_out(UpperCAmelCase	) * (1 + scale[:, None]) + shift[:, None]
       _lowercase											=self.proj_out_a(UpperCAmelCase	)

       # unpatchify
       _lowercase											=_lowercase											=int(hidden_states.shape[1] ** 0.5	)
       _lowercase											=hidden_states.reshape(
           shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)	)
       _lowercase											=torch.einsum('''nhwpqc->nchpwq'''		,					UpperCAmelCase	)
       _lowercase											=hidden_states.reshape(
           shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)	)

      if not return_dict:
       return (output,)

      return TransformeraDModelOutput(sample=UpperCAmelCase	)





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


UpperCAmelCase__            =							logging.get_logger(__name__)

UpperCAmelCase__            =							{
    '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/config.json''',
    '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/config.json''',
    '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json''',
    '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json''',
    '''microsoft/deberta-xlarge-mnli''': '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json''',
}







class 				lowerCamelCase__       (	lowerCAmelCase):
     SCREAMING_SNAKE_CASE__				      =      '''deberta'''





     def __init__(self		,					UpperCAmelCase=5_0_2_6_5		,					UpperCAmelCase=7_6_8		,					UpperCAmelCase=1_2		,					UpperCAmelCase=1_2		,					UpperCAmelCase=3_0_7_2		,					UpperCAmelCase="gelu"		,					UpperCAmelCase=0.1		,					UpperCAmelCase=0.1		,					UpperCAmelCase=5_1_2		,					UpperCAmelCase=0		,					UpperCAmelCase=0.02		,					UpperCAmelCase=1e-7		,					UpperCAmelCase=False		,					UpperCAmelCase=-1		,					UpperCAmelCase=0		,					UpperCAmelCase=True		,					UpperCAmelCase=None		,					UpperCAmelCase=0		,					UpperCAmelCase="gelu"		,					**UpperCAmelCase		,					)					->       Dict:
      super().__init__(**UpperCAmelCase	)

      _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(UpperCAmelCase	) == 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'''		,					UpperCAmelCase	)
      _lowercase											=pooler_dropout
      _lowercase											=pooler_hidden_act







class 				lowerCamelCase__       (	lowerCAmelCase):





     @property
     def 					__A (self	)					->       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 					__A (self	)					->       int:
      return 1_2





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase = -1		,					UpperCAmelCase = -1		,					UpperCAmelCase = -1		,					UpperCAmelCase = False		,					UpperCAmelCase = None		,					UpperCAmelCase = 3		,					UpperCAmelCase = 4_0		,					UpperCAmelCase = 4_0		,					UpperCAmelCase = None		,					)					->       Mapping[str, Any]:
      _lowercase											=super().generate_dummy_inputs(preprocessor=UpperCAmelCase		,					framework=UpperCAmelCase	)
      if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
       del dummy_inputs["token_type_ids"]
      return dummy_inputs

