
"""simple docstring"""







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      __a      (       lowerCAmelCase_      ):
       _lowerCamelCase				:				torch.FloatTensor






class      __a      (       lowerCAmelCase_					,						lowerCAmelCase_      ):







       @register_to_config
       def __init__(       self				,lowerCamelCase_ = 16				,lowerCamelCase_ = 88				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = 1				,lowerCamelCase_ = 0.0				,lowerCamelCase_ = 32				,lowerCamelCase_ = None				,lowerCamelCase_ = False				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = "geglu"				,lowerCamelCase_ = None				,lowerCamelCase_ = False				,lowerCamelCase_ = False				,lowerCamelCase_ = False				,lowerCamelCase_ = "layer_norm"				,lowerCamelCase_ = True				,):



          '''simple docstring'''

          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                 f'''The configuration file of this model: {self.__class__} is outdated.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"				,lowerCamelCase_				,standard_warn=lowerCamelCase_		)
             _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=lowerCamelCase_				,num_channels=lowerCamelCase_				,eps=1e-6				,affine=lowerCamelCase_		)
             if use_linear_projection:
                _UpperCAmelCase 						=       nn.Linear(lowerCamelCase_				,lowerCamelCase_		)
             else:
                _UpperCAmelCase 						=       nn.Convad(lowerCamelCase_				,lowerCamelCase_				,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=lowerCamelCase_				,embed_dim=lowerCamelCase_				,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=lowerCamelCase_				,width=lowerCamelCase_				,patch_size=lowerCamelCase_				,in_channels=lowerCamelCase_				,embed_dim=lowerCamelCase_				,)

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

          # 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(lowerCamelCase_				,lowerCamelCase_		)
             else:
                _UpperCAmelCase 						=       nn.Convad(lowerCamelCase_				,lowerCamelCase_				,kernel_size=1				,stride=1				,padding=0		)
          elif self.is_input_vectorized:
             _UpperCAmelCase 						=       nn.LayerNorm(lowerCamelCase_		)
             _UpperCAmelCase 						=       nn.Linear(lowerCamelCase_				,self.num_vector_embeds - 1		)
          elif self.is_input_patches:
             _UpperCAmelCase 						=       nn.LayerNorm(lowerCamelCase_				,elementwise_affine=lowerCamelCase_				,eps=1e-6		)
             _UpperCAmelCase 						=       nn.Linear(lowerCamelCase_				,2 * inner_dim		)
             _UpperCAmelCase 						=       nn.Linear(lowerCamelCase_				,patch_size * patch_size * self.out_channels		)







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = None				,lowerCamelCase_ = True				,):



          '''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)
             _UpperCAmelCase 						=       (1 - attention_mask.to(hidden_states.dtype		)) * -1_0_0_0_0.0
             _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		)) * -1_0_0_0_0.0
             _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(lowerCamelCase_		)
             if not self.use_linear_projection:
                _UpperCAmelCase 						=       self.proj_in(lowerCamelCase_		)
                _UpperCAmelCase 						=       hidden_states.shape[1]
                _UpperCAmelCase 						=       hidden_states.permute(0				,2				,3				,1		).reshape(lowerCamelCase_				,height * width				,lowerCamelCase_		)
             else:
                _UpperCAmelCase 						=       hidden_states.shape[1]
                _UpperCAmelCase 						=       hidden_states.permute(0				,2				,3				,1		).reshape(lowerCamelCase_				,height * width				,lowerCamelCase_		)
                _UpperCAmelCase 						=       self.proj_in(lowerCamelCase_		)
          elif self.is_input_vectorized:
             _UpperCAmelCase 						=       self.latent_image_embedding(lowerCamelCase_		)
          elif self.is_input_patches:
             _UpperCAmelCase 						=       self.pos_embed(lowerCamelCase_		)

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

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

             _UpperCAmelCase 						=       hidden_states + residual
          elif self.is_input_vectorized:
             _UpperCAmelCase 						=       self.norm_out(lowerCamelCase_		)
             _UpperCAmelCase 						=       self.out(lowerCamelCase_		)
             # (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(
                 lowerCamelCase_				,lowerCamelCase_				,hidden_dtype=hidden_states.dtype		)
             _UpperCAmelCase						,				_UpperCAmelCase 						=       self.proj_out_a(F.silu(lowerCamelCase_		)		).chunk(2				,dim=1		)
             _UpperCAmelCase 						=       self.norm_out(lowerCamelCase_		) * (1 + scale[:, None]) + shift[:, None]
             _UpperCAmelCase 						=       self.proj_out_a(lowerCamelCase_		)

             # 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"				,lowerCamelCase_		)
             _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=lowerCamelCase_		)



"""simple docstring"""







def       __UpperCAmelCase		(		a_      :		list[list[int]]					)  ->							bool:
   # must be
   _UpperCAmelCase 						=       [list(a_					) for row in matrix]
   if matrix and isinstance(a_     ,	a_					):
      if isinstance(matrix[0]     ,	a_					):
         _UpperCAmelCase 						=       0
         for row in matrix:
            if prev_len == 0:
               _UpperCAmelCase 						=       len(a_					)
               _UpperCAmelCase 						=       True
            else:
               _UpperCAmelCase 						=       prev_len == len(a_					)
      else:
         _UpperCAmelCase 						=       True
   else:
      _UpperCAmelCase 						=       False

   return result
def       __UpperCAmelCase		(		a_      :		list[list[int]]					)  ->							None:
   if check_matrix(a_					) and len(a_					) > 0:
      _UpperCAmelCase 						=       [list(a_					) for row in a]
      _UpperCAmelCase 						=       len(a_					)
      if isinstance(a[0]     ,	a_					):
         _UpperCAmelCase 						=       len(a[0]					)
      else:
         for dat in a:
            print(a_					)
         return

      # horizotal printing increasing
      for i in range(0     ,	a_					):
         print(a[0][i]					)
      # vertical printing down
      for i in range(1     ,	a_					):
         print(a[i][mat_col - 1]					)
      # horizotal printing decreasing
      if mat_row > 1:
         for i in range(mat_col - 2     ,	-1     ,	-1					):
            print(a[mat_row - 1][i]					)
        # vertical printing up
      for i in range(mat_row - 2     ,	0     ,	-1					):
         print(a[i][0]					)
      _UpperCAmelCase 						=       [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
      if len(a_					) > 0:
         spiral_print_clockwise(a_					)
      else:
         return
   else:
      print("Not a valid matrix"					)
      return
def       __UpperCAmelCase		(		a_      :		list[list]					)  ->							list[int]:
   if matrix:
      return list(matrix.pop(0					)					) + spiral_traversal(list(zip(*a_					)					)[::-1]					)
   else:
      return []


# driver code
if __name__ == "__main__":
  import doctest

  doctest.testmod()

  SCREAMING_SNAKE_CASE_  					= [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
  spiral_print_clockwise(a)

