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  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	torch.FloatTensor







class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      @register_to_config
      def __init__(       self   :	List[str]	,    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	,    ):
          super().__init__()
          __UpperCamelCase											=  use_linear_projection
          __UpperCamelCase											=  num_attention_heads
          __UpperCamelCase											=  attention_head_dim
          __UpperCamelCase											=  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
          __UpperCamelCase											=  (in_channels is not None) and (patch_size is None)
          __UpperCamelCase											=  num_vector_embeds is not None
          __UpperCamelCase											=  in_channels is not None and patch_size is not None

          if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
              __UpperCamelCase											=  (
                  F"The configuration file of this model: {self.__class__} is outdated.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			)
              __UpperCamelCase											=  '''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:
              __UpperCamelCase											=  in_channels

              __UpperCamelCase											=  torch.nn.GroupNorm(num_groups=snake_case	,    num_channels=snake_case	,    eps=1E-6	,    affine=snake_case			)
              if use_linear_projection:
                  __UpperCamelCase											=  nn.Linear(snake_case	,    snake_case			)
              else:
                  __UpperCamelCase											=  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"

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

              __UpperCamelCase											=  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"

              __UpperCamelCase											=  sample_size
              __UpperCamelCase											=  sample_size

              __UpperCamelCase											=  patch_size
              __UpperCamelCase											=  PatchEmbed(
                  height=snake_case	,    width=snake_case	,    patch_size=snake_case	,    in_channels=snake_case	,    embed_dim=snake_case	,    )

          # 3. Define transformers blocks
          __UpperCamelCase											=  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
          __UpperCamelCase											=  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:
                  __UpperCamelCase											=  nn.Linear(snake_case	,    snake_case			)
              else:
                  __UpperCamelCase											=  nn.Convad(snake_case	,    snake_case	,    kernel_size=1	,    stride=1	,    padding=0			)
          elif self.is_input_vectorized:
              __UpperCamelCase											=  nn.LayerNorm(snake_case			)
              __UpperCamelCase											=  nn.Linear(snake_case	,    self.num_vector_embeds - 1			)
          elif self.is_input_patches:
              __UpperCamelCase											=  nn.LayerNorm(snake_case	,    elementwise_affine=snake_case	,    eps=1E-6			)
              __UpperCamelCase											=  nn.Linear(snake_case	,    2 * inner_dim			)
              __UpperCamelCase											=  nn.Linear(snake_case	,    patch_size * patch_size * self.out_channels			)







      def  snake_case		(       self   :	Any	,    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	,    ):
          # 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)
              __UpperCamelCase											=  (1 - attention_mask.to(hidden_states.dtype			)) * -10000.0
              __UpperCamelCase											=  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:
              __UpperCamelCase											=  (1 - encoder_attention_mask.to(hidden_states.dtype			)) * -10000.0
              __UpperCamelCase											=  encoder_attention_mask.unsqueeze(1			)

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

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

          # 2. Blocks
          for block in self.transformer_blocks:
              __UpperCamelCase											=  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:
                  __UpperCamelCase											=  hidden_states.reshape(snake_case	,    snake_case	,    snake_case	,    snake_case			).permute(0	,    3	,    1	,    2			).contiguous()
                  __UpperCamelCase											=  self.proj_out(snake_case			)
              else:
                  __UpperCamelCase											=  self.proj_out(snake_case			)
                  __UpperCamelCase											=  hidden_states.reshape(snake_case	,    snake_case	,    snake_case	,    snake_case			).permute(0	,    3	,    1	,    2			).contiguous()

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

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

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












def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		list:

    """simple docstring"""


    __UpperCamelCase											=  len(lowercase_ )

    # Set the gap value to be decreased by a factor of 1.3
    # after each iteration
    __UpperCamelCase											=  1.3

    # Continue sorting until the gap is 1
    while gap > 1:
        # Decrease the gap value
        __UpperCamelCase											=  int(gap / shrink )

        # Sort the elements using insertion sort
        for i in range(lowercase_   ,       len(lowercase_ ) ):
            __UpperCamelCase											=  collection[i]
            __UpperCamelCase											=  i
            while j >= gap and collection[j - gap] > temp:
                __UpperCamelCase											=  collection[j - gap]
                j -= gap
            __UpperCamelCase											=  temp

    return collection


if __name__ == "__main__":
 import doctest

 doctest.testmod()






