


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 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					42




class 							lowercase__(						snake_case__			,							snake_case__			):






      '''simple docstring'''







      @register_to_config
      def __init__(		self   ,			__SCREAMING_SNAKE_CASE = 16   ,			__SCREAMING_SNAKE_CASE = 88   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = 1   ,			__SCREAMING_SNAKE_CASE = 0.0   ,			__SCREAMING_SNAKE_CASE = 32   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = False   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = "geglu"   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = False   ,			__SCREAMING_SNAKE_CASE = False   ,			__SCREAMING_SNAKE_CASE = False   ,			__SCREAMING_SNAKE_CASE = "layer_norm"   ,			__SCREAMING_SNAKE_CASE = True   ,			)					->						Union[str, Any]:







         """simple docstring"""







         super().__init__()
         UpperCamelCase__ :			Optional[Any] 						=use_linear_projection
         UpperCamelCase__ :			Union[str, Any] 						=num_attention_heads
         UpperCamelCase__ :			Any 						=attention_head_dim
         UpperCamelCase__ :			List[str] 						=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__ :			List[str] 						=(in_channels is not None) and (patch_size is None)
         UpperCamelCase__ :			List[str] 						=num_vector_embeds is not None
         UpperCamelCase__ :			str 						=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__ :			Dict 						=(
                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"   ,			__SCREAMING_SNAKE_CASE   ,			standard_warn=__SCREAMING_SNAKE_CASE)
            UpperCamelCase__ :			List[Any] 						="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__ :			Dict 						=in_channels

            UpperCamelCase__ :			int 						=torch.nn.GroupNorm(num_groups=__SCREAMING_SNAKE_CASE   ,			num_channels=__SCREAMING_SNAKE_CASE   ,			eps=1E-6   ,			affine=__SCREAMING_SNAKE_CASE)
            if use_linear_projection:
               UpperCamelCase__ :			Optional[int] 						=nn.Linear(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)
            else:
               UpperCamelCase__ :			Tuple 						=nn.Convad(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_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__ :			str 						=sample_size
            UpperCamelCase__ :			int 						=sample_size
            UpperCamelCase__ :			str 						=num_vector_embeds
            UpperCamelCase__ :			List[Any] 						=self.height * self.width

            UpperCamelCase__ :			Tuple 						=ImagePositionalEmbeddings(
                num_embed=__SCREAMING_SNAKE_CASE   ,			embed_dim=__SCREAMING_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__ :			Dict 						=sample_size
            UpperCamelCase__ :			int 						=sample_size

            UpperCamelCase__ :			List[str] 						=patch_size
            UpperCamelCase__ :			Union[str, Any] 						=PatchEmbed(
                height=__SCREAMING_SNAKE_CASE   ,			width=__SCREAMING_SNAKE_CASE   ,			patch_size=__SCREAMING_SNAKE_CASE   ,			in_channels=__SCREAMING_SNAKE_CASE   ,			embed_dim=__SCREAMING_SNAKE_CASE   ,			)

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

         # 4. Define output layers
         UpperCamelCase__ :			Optional[Any] 						=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__ :			int 						=nn.Linear(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)
            else:
               UpperCamelCase__ :			Dict 						=nn.Convad(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			kernel_size=1   ,			stride=1   ,			padding=0)
         elif self.is_input_vectorized:
            UpperCamelCase__ :			Tuple 						=nn.LayerNorm(__SCREAMING_SNAKE_CASE)
            UpperCamelCase__ :			Any 						=nn.Linear(__SCREAMING_SNAKE_CASE   ,			self.num_vector_embeds - 1)
         elif self.is_input_patches:
            UpperCamelCase__ :			str 						=nn.LayerNorm(__SCREAMING_SNAKE_CASE   ,			elementwise_affine=__SCREAMING_SNAKE_CASE   ,			eps=1E-6)
            UpperCamelCase__ :			str 						=nn.Linear(__SCREAMING_SNAKE_CASE   ,			2 * inner_dim)
            UpperCamelCase__ :			Union[str, Any] 						=nn.Linear(__SCREAMING_SNAKE_CASE   ,			patch_size * patch_size * self.out_channels)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = True   ,			)					->						int:







         """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__ :			Optional[int] 						=(1 - attention_mask.to(hidden_states.dtype)) * -1_00_00.0
            UpperCamelCase__ :			Union[str, Any] 						=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__ :			int 						=(1 - encoder_attention_mask.to(hidden_states.dtype)) * -1_00_00.0
            UpperCamelCase__ :			str 						=encoder_attention_mask.unsqueeze(1)

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

            UpperCamelCase__ :			Union[str, Any] 						=self.norm(__SCREAMING_SNAKE_CASE)
            if not self.use_linear_projection:
               UpperCamelCase__ :			Optional[int] 						=self.proj_in(__SCREAMING_SNAKE_CASE)
               UpperCamelCase__ :			Optional[Any] 						=hidden_states.shape[1]
               UpperCamelCase__ :			Any 						=hidden_states.permute(0   ,			2   ,			3   ,			1).reshape(__SCREAMING_SNAKE_CASE   ,			height * width   ,			__SCREAMING_SNAKE_CASE)
            else:
               UpperCamelCase__ :			List[Any] 						=hidden_states.shape[1]
               UpperCamelCase__ :			Optional[Any] 						=hidden_states.permute(0   ,			2   ,			3   ,			1).reshape(__SCREAMING_SNAKE_CASE   ,			height * width   ,			__SCREAMING_SNAKE_CASE)
               UpperCamelCase__ :			Tuple 						=self.proj_in(__SCREAMING_SNAKE_CASE)
         elif self.is_input_vectorized:
            UpperCamelCase__ :			Union[str, Any] 						=self.latent_image_embedding(__SCREAMING_SNAKE_CASE)
         elif self.is_input_patches:
            UpperCamelCase__ :			str 						=self.pos_embed(__SCREAMING_SNAKE_CASE)

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

         # 3. Output
         if self.is_input_continuous:
            if not self.use_linear_projection:
               UpperCamelCase__ :			int 						=hidden_states.reshape(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE).permute(0   ,			3   ,			1   ,			2).contiguous()
               UpperCamelCase__ :			Union[str, Any] 						=self.proj_out(__SCREAMING_SNAKE_CASE)
            else:
               UpperCamelCase__ :			Dict 						=self.proj_out(__SCREAMING_SNAKE_CASE)
               UpperCamelCase__ :			Optional[Any] 						=hidden_states.reshape(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE).permute(0   ,			3   ,			1   ,			2).contiguous()

            UpperCamelCase__ :			Optional[Any] 						=hidden_states + residual
         elif self.is_input_vectorized:
            UpperCamelCase__ :			Tuple 						=self.norm_out(__SCREAMING_SNAKE_CASE)
            UpperCamelCase__ :			Optional[int] 						=self.out(__SCREAMING_SNAKE_CASE)
            # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
            UpperCamelCase__ :			str 						=logits.permute(0   ,			2   ,			1)

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

            # unpatchify
            UpperCamelCase__ :			int 						=int(hidden_states.shape[1] ** 0.5)
            UpperCamelCase__ :			str 						=hidden_states.reshape(
                shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels))
            UpperCamelCase__ :			int 						=torch.einsum("nhwpqc->nchpwq"   ,			__SCREAMING_SNAKE_CASE)
            UpperCamelCase__ :			List[Any] 						=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=__SCREAMING_SNAKE_CASE)





def 	_lowerCamelCase  (		A_  :	float							,  A_  :	float							,  A_  :	int      )			->			float:

   '''simple docstring'''






   UpperCamelCase__ :			Union[str, Any] 						=[1 / first_term]
   UpperCamelCase__ :			Tuple 						=1 / first_term
   for _ in range(number_of_terms - 1      ):
      first_term += common_difference
      arithmetic_progression.append(A_      )
   UpperCamelCase__ :			List[str] 						=[1 / step for step in arithmetic_progression]
   return sum(A_      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(sum_of_harmonic_progression(1 / 2, 2, 2))

