




'''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     UpperCamelCase					(  __UpperCAmelCase					):
  a    :str      					=				4_2






class     UpperCamelCase					(  __UpperCAmelCase			,		__UpperCAmelCase					):



  @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__()
      _a							:       int       					=			use_linear_projection
      _a							:       Optional[Any]       					=			num_attention_heads
      _a							:       Optional[int]       					=			attention_head_dim
      _a							:       Any       					=			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							:       List[str]       					=			(in_channels is not None) and (patch_size is None)
      _a							:       Optional[int]       					=			num_vector_embeds is not None
      _a							:       Union[str, Any]       					=			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							:       List[Any]       					=			(
              f'''The configuration file of this model: {self.__class__} is outdated.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"""		,    lowercase_		,    standard_warn=lowercase_   )
          _a							:       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:
          _a							:       Any       					=			in_channels

          _a							:       int       					=			torch.nn.GroupNorm(num_groups=lowercase_		,    num_channels=lowercase_		,    eps=1e-6		,    affine=lowercase_   )
          if use_linear_projection:
              _a							:       Optional[int]       					=			nn.Linear(lowercase_		,    lowercase_   )
          else:
              _a							:       Optional[Any]       					=			nn.Convad(lowercase_		,    lowercase_		,    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							:       Tuple       					=			sample_size
          _a							:       List[Any]       					=			sample_size
          _a							:       Tuple       					=			num_vector_embeds
          _a							:       Any       					=			self.height * self.width

          _a							:       Optional[int]       					=			ImagePositionalEmbeddings(
              num_embed=lowercase_		,    embed_dim=lowercase_		,    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							:       Tuple       					=			sample_size
          _a							:       Tuple       					=			sample_size

          _a							:       Optional[Any]       					=			patch_size
          _a							:       str       					=			PatchEmbed(
              height=lowercase_		,    width=lowercase_		,    patch_size=lowercase_		,    in_channels=lowercase_		,    embed_dim=lowercase_		,    )

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

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



  def 						a_       ( 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)
          _a							:       Union[str, Any]       					=			(1 - attention_mask.to(hidden_states.dtype   )) * -1_0000.0
          _a							:       List[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:
          _a							:       Optional[Any]       					=			(1 - encoder_attention_mask.to(hidden_states.dtype   )) * -1_0000.0
          _a							:       str       					=			encoder_attention_mask.unsqueeze(1   )

      # 1. Input
      if self.is_input_continuous:
          _a							:       Any       					=			hidden_states.shape
          _a							:       Optional[int]       					=			hidden_states

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

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

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

          _a							:       int       					=			hidden_states + residual
      elif self.is_input_vectorized:
          _a							:       Dict       					=			self.norm_out(lowercase_   )
          _a							:       List[Any]       					=			self.out(lowercase_   )
          # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
          _a							:       Tuple       					=			logits.permute(0		,    2		,    1   )

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

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








'''simple docstring'''





from math import exp





def UpperCAmelCase						(					UpperCAmelCase_	):
    return 8 * x - 2 * exp(-x	)





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       Optional[int]       					=			lower_bound
    _a							:       str       					=			upper_bound
    for _ in range(0      ,  UpperCAmelCase_	):
        _a					,   _a							:       List[str]       					=			xa, xa - (f(UpperCAmelCase_	) * (xa - xa)) / (f(UpperCAmelCase_	) - f(UpperCAmelCase_	))
    return xa


if __name__ == "__main__":
  print(F"""Example: {secant_method(1, 3, 2)}""")


