code: 
  
  
  
  
  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 			UpperCAmelCase_						(		_a							):
      lowercase__		      =   42
  
  
  
  
  
  
  class 			UpperCAmelCase_						(		_a,     _a							):
  
  
  
  
      @register_to_config
      def __init__(					self		: Optional[int]			,				snake_case_		: int = 16			,				snake_case_		: int = 88			,				snake_case_		: Optional[int] = None			,				snake_case_		: Optional[int] = None			,				snake_case_		: int = 1			,				snake_case_		: float = 0.0			,				snake_case_		: int = 32			,				snake_case_		: Optional[int] = None			,				snake_case_		: bool = False			,				snake_case_		: Optional[int] = None			,				snake_case_		: Optional[int] = None			,				snake_case_		: Optional[int] = None			,				snake_case_		: str = "geglu"			,				snake_case_		: Optional[int] = None			,				snake_case_		: bool = False			,				snake_case_		: bool = False			,				snake_case_		: bool = False			,				snake_case_		: str = "layer_norm"			,				snake_case_		: bool = True			,				)   ->							List[str]:
  
  
  
  
       '''simple docstring'''
  
       super().__init__()
       A__ =       use_linear_projection
       A__ =       num_attention_heads
       A__ =       attention_head_dim
       A__ =       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
       A__ =       (in_channels is not None) and (patch_size is None)
       A__ =       num_vector_embeds is not None
       A__ =       in_channels is not None and patch_size is not None
  
       if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
        A__ =       (
            F"""The configuration file of this model: {self.__class__} is outdated.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"			,				snake_case_			,				standard_warn=snake_case_   )
        A__ =       "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:
        A__ =       in_channels
  
        A__ =       torch.nn.GroupNorm(num_groups=snake_case_			,				num_channels=snake_case_			,				eps=1e-6			,				affine=snake_case_   )
        if use_linear_projection:
         A__ =       nn.Linear(snake_case_			,				snake_case_   )
        else:
         A__ =       nn.Convad(snake_case_			,				snake_case_			,				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"
  
        A__ =       sample_size
        A__ =       sample_size
        A__ =       num_vector_embeds
        A__ =       self.height * self.width
  
        A__ =       ImagePositionalEmbeddings(
            num_embed=snake_case_			,				embed_dim=snake_case_			,				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"
  
        A__ =       sample_size
        A__ =       sample_size
  
        A__ =       patch_size
        A__ =       PatchEmbed(
            height=snake_case_			,				width=snake_case_			,				patch_size=snake_case_			,				in_channels=snake_case_			,				embed_dim=snake_case_			,				)
  
       # 3. Define transformers blocks
       A__ =       nn.ModuleList(
           [
               BasicTransformerBlock(
                   snake_case_			,				snake_case_			,				snake_case_			,				dropout=snake_case_			,				cross_attention_dim=snake_case_			,				activation_fn=snake_case_			,				num_embeds_ada_norm=snake_case_			,				attention_bias=snake_case_			,				only_cross_attention=snake_case_			,				upcast_attention=snake_case_			,				norm_type=snake_case_			,				norm_elementwise_affine=snake_case_			,				)
               for d in range(snake_case_   )
           ]   )
  
       # 4. Define output layers
       A__ =       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:
         A__ =       nn.Linear(snake_case_			,				snake_case_   )
        else:
         A__ =       nn.Convad(snake_case_			,				snake_case_			,				kernel_size=1			,				stride=1			,				padding=0   )
       elif self.is_input_vectorized:
        A__ =       nn.LayerNorm(snake_case_   )
        A__ =       nn.Linear(snake_case_			,				self.num_vector_embeds - 1   )
       elif self.is_input_patches:
        A__ =       nn.LayerNorm(snake_case_			,				elementwise_affine=snake_case_			,				eps=1e-6   )
        A__ =       nn.Linear(snake_case_			,				2 * inner_dim   )
        A__ =       nn.Linear(snake_case_			,				patch_size * patch_size * self.out_channels   )
  
  
  
  
      def 							__magic_name__							(					self		: Dict			,				snake_case_		: torch.Tensor			,				snake_case_		: Optional[torch.Tensor] = None			,				snake_case_		: Optional[torch.LongTensor] = None			,				snake_case_		: Optional[torch.LongTensor] = None			,				snake_case_		: Dict[str, Any] = None			,				snake_case_		: Optional[torch.Tensor] = None			,				snake_case_		: Optional[torch.Tensor] = None			,				snake_case_		: bool = True			,				)   ->							List[Any]:
  
  
  
  
       '''simple docstring'''
  
       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)
        A__ =       (1 - attention_mask.to(hidden_states.dtype   )) * -10_000.0
        A__ =       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:
        A__ =       (1 - encoder_attention_mask.to(hidden_states.dtype   )) * -10_000.0
        A__ =       encoder_attention_mask.unsqueeze(1   )
  
       # 1. Input
       if self.is_input_continuous:
        A__,   A__,   A__,   A__ =       hidden_states.shape
        A__ =       hidden_states
  
        A__ =       self.norm(snake_case_   )
        if not self.use_linear_projection:
         A__ =       self.proj_in(snake_case_   )
         A__ =       hidden_states.shape[1]
         A__ =       hidden_states.permute(0			,				2			,				3			,				1   ).reshape(snake_case_			,				height * width			,				snake_case_   )
        else:
         A__ =       hidden_states.shape[1]
         A__ =       hidden_states.permute(0			,				2			,				3			,				1   ).reshape(snake_case_			,				height * width			,				snake_case_   )
         A__ =       self.proj_in(snake_case_   )
       elif self.is_input_vectorized:
        A__ =       self.latent_image_embedding(snake_case_   )
       elif self.is_input_patches:
        A__ =       self.pos_embed(snake_case_   )
  
       # 2. Blocks
       for block in self.transformer_blocks:
        A__ =       block(
            snake_case_			,				attention_mask=snake_case_			,				encoder_hidden_states=snake_case_			,				encoder_attention_mask=snake_case_			,				timestep=snake_case_			,				cross_attention_kwargs=snake_case_			,				class_labels=snake_case_			,				)
  
       # 3. Output
       if self.is_input_continuous:
        if not self.use_linear_projection:
         A__ =       hidden_states.reshape(snake_case_			,				snake_case_			,				snake_case_			,				snake_case_   ).permute(0			,				3			,				1			,				2   ).contiguous()
         A__ =       self.proj_out(snake_case_   )
        else:
         A__ =       self.proj_out(snake_case_   )
         A__ =       hidden_states.reshape(snake_case_			,				snake_case_			,				snake_case_			,				snake_case_   ).permute(0			,				3			,				1			,				2   ).contiguous()
  
        A__ =       hidden_states + residual
       elif self.is_input_vectorized:
        A__ =       self.norm_out(snake_case_   )
        A__ =       self.out(snake_case_   )
        # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
        A__ =       logits.permute(0			,				2			,				1   )
  
        # log(p(x_0))
        A__ =       F.log_softmax(logits.double()			,				dim=1   ).float()
       elif self.is_input_patches:
        # TODO: cleanup!
        A__ =       self.transformer_blocks[0].norma.emb(
            snake_case_			,				snake_case_			,				hidden_dtype=hidden_states.dtype   )
        A__,   A__ =       self.proj_out_a(F.silu(snake_case_   )   ).chunk(2			,				dim=1   )
        A__ =       self.norm_out(snake_case_   ) * (1 + scale[:, None]) + shift[:, None]
        A__ =       self.proj_out_a(snake_case_   )
  
        # unpatchify
        A__ =       A__ =       int(hidden_states.shape[1] ** 0.5   )
        A__ =       hidden_states.reshape(
            shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)   )
        A__ =       torch.einsum("nhwpqc->nchpwq"			,				snake_case_   )
        A__ =       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=snake_case_   )
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE       = {
      "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
  }
  
  try:
        if not is_vision_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = ["DetaImageProcessor"]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = [
            "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
            "DetaForObjectDetection",
            "DetaModel",
            "DetaPreTrainedModel",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
        try:
              if not is_vision_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .image_processing_deta import DetaImageProcessor
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_deta import (
                  DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DetaForObjectDetection,
                  DetaModel,
                  DetaPreTrainedModel,
              )
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE       = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  