






"""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  lowercase_     (_UpperCAmelCase  ):
      A__						:		torch.FloatTensor







class  lowercase_     (_UpperCAmelCase,     _UpperCAmelCase  ):






      @register_to_config
      def __init__(     self  ,  a_ = 1_6  ,  a_ = 8_8  ,  a_ = None  ,  a_ = None  ,  a_ = 1  ,  a_ = 0.0  ,  a_ = 3_2  ,  a_ = None  ,  a_ = False  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = "geglu"  ,  a_ = None  ,  a_ = False  ,  a_ = False  ,  a_ = False  ,  a_ = "layer_norm"  ,  a_ = True  ,  )						->Dict:







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              f'''The configuration file of this model: {self.__class__} is outdated.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

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

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






      def       lowerCamelCase__     (     self  ,  a_  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = True  ,  )						->Optional[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(a_						)
          if not self.use_linear_projection:
            _a					      =						self.proj_in(a_						)
            _a					      =						hidden_states.shape[1]
            _a					      =						hidden_states.permute(0  ,  2  ,  3  ,  1						).reshape(a_  ,  height * width  ,  a_						)
          else:
            _a					      =						hidden_states.shape[1]
            _a					      =						hidden_states.permute(0  ,  2  ,  3  ,  1						).reshape(a_  ,  height * width  ,  a_						)
            _a					      =						self.proj_in(a_						)
        elif self.is_input_vectorized:
          _a					      =						self.latent_image_embedding(a_						)
        elif self.is_input_patches:
          _a					      =						self.pos_embed(a_						)

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

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

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

          # 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"  ,  a_						)
          _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=a_						)










"""simple docstring"""




import math






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				list:







  '''simple docstring'''







  if n <= 0:
    raise ValueError("Only positive integers have prime factors"       )
  _a					      =						[]
  while n % 2 == 0:
    pf.append(2       )
    _a					      =						int(n / 2       )
  for i in range(3  ,			int(math.sqrt(UpperCamelCase_       )       ) + 1  ,			2       ):
    while n % i == 0:
      pf.append(UpperCamelCase_       )
      _a					      =						int(n / i       )
  if n > 2:
    pf.append(UpperCamelCase_       )
  return pf






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  if n <= 0:
    raise ValueError("Only positive numbers are accepted"       )
  _a					      =						1
  _a					      =						1
  while n % 2 == 0:
    temp += 1
    _a					      =						int(n / 2       )
  div *= temp
  for i in range(3  ,			int(math.sqrt(UpperCamelCase_       )       ) + 1  ,			2       ):
    _a					      =						1
    while n % i == 0:
      temp += 1
      _a					      =						int(n / i       )
    div *= temp
  if n > 1:
    div *= 2
  return div






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  if n <= 0:
    raise ValueError("Only positive numbers are accepted"       )
  _a					      =						1
  _a					      =						1
  while n % 2 == 0:
    temp += 1
    _a					      =						int(n / 2       )
  if temp > 1:
    s *= (2**temp - 1) / (2 - 1)
  for i in range(3  ,			int(math.sqrt(UpperCamelCase_       )       ) + 1  ,			2       ):
    _a					      =						1
    while n % i == 0:
      temp += 1
      _a					      =						int(n / i       )
    if temp > 1:
      s *= (i**temp - 1) / (i - 1)
  return int(UpperCamelCase_       )






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  _a					      =						n
  for x in set(prime_factors(UpperCamelCase_       )       ):
    s *= (x - 1) / x
  return int(UpperCamelCase_       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


