





"""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       (     lowercase_			):
					UpperCamelCase								=							42







class  _UpperCAmelCase       (     lowercase_					,							lowercase_			):


					@register_to_config
					def __init__( self      :Any					,	__UpperCamelCase      :int = 16					,	__UpperCamelCase      :int = 88					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :int = 1					,	__UpperCamelCase      :float = 0.0					,	__UpperCamelCase      :int = 32					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :str = "geglu"					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :str = "layer_norm"					,	__UpperCamelCase      :bool = True					,	):
												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																			    f"The configuration file of this model: {self.__class__} is outdated.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"					,	__UpperCamelCase					,	standard_warn=__UpperCamelCase		)
																			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=__UpperCamelCase					,	num_channels=__UpperCamelCase					,	eps=1e-6					,	affine=__UpperCamelCase		)
																			if use_linear_projection:
																										A		=  nn.Linear(__UpperCamelCase					,	__UpperCamelCase		)
																			else:
																										A		=  nn.Convad(__UpperCamelCase					,	__UpperCamelCase					,	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=__UpperCamelCase					,	embed_dim=__UpperCamelCase					,	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=__UpperCamelCase					,	width=__UpperCamelCase					,	patch_size=__UpperCamelCase					,	in_channels=__UpperCamelCase					,	embed_dim=__UpperCamelCase					,	)

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

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


					def      lowerCamelCase	( self      :Dict					,	__UpperCamelCase      :torch.Tensor					,	__UpperCamelCase      :Optional[torch.Tensor] = None					,	__UpperCamelCase      :Optional[torch.LongTensor] = None					,	__UpperCamelCase      :Optional[torch.LongTensor] = None					,	__UpperCamelCase      :Dict[str, Any] = None					,	__UpperCamelCase      :Optional[torch.Tensor] = None					,	__UpperCamelCase      :Optional[torch.Tensor] = None					,	__UpperCamelCase      :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)
																			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(__UpperCamelCase		)
																			if not self.use_linear_projection:
																										A		=  self.proj_in(__UpperCamelCase		)
																										A		=  hidden_states.shape[1]
																										A		=  hidden_states.permute(0					,	2					,	3					,	1		).reshape(__UpperCamelCase					,	height * width					,	__UpperCamelCase		)
																			else:
																										A		=  hidden_states.shape[1]
																										A		=  hidden_states.permute(0					,	2					,	3					,	1		).reshape(__UpperCamelCase					,	height * width					,	__UpperCamelCase		)
																										A		=  self.proj_in(__UpperCamelCase		)
												elif self.is_input_vectorized:
																			A		=  self.latent_image_embedding(__UpperCamelCase		)
												elif self.is_input_patches:
																			A		=  self.pos_embed(__UpperCamelCase		)

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

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

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

																			# 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"					,	__UpperCamelCase		)
																			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=__UpperCamelCase		)









"""simple docstring"""
import numpy as np
import requests






def  A__    (      ):
							A		=  requests.get(
							    "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
							    "master/Week1/ADRvsRating.csv"	)
							A		=  response.text.splitlines()
							A		=  []
							for item in lines:
														A		=  item.split(","	)
														data.append(UpperCamelCase	)
							data.pop(0	)  # This is for removing the labels from the list
							A		=  np.matrix(UpperCamelCase	)
							return dataset






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							A		=  len_data

							A		=  np.dot(UpperCamelCase ,  data_x.transpose()	)
							prod -= data_y.transpose()
							A		=  np.dot(UpperCamelCase ,  UpperCamelCase	)
							A		=  theta - (alpha / n) * sum_grad
							return theta






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							A		=  np.dot(UpperCamelCase ,  data_x.transpose()	)
							prod -= data_y.transpose()
							A		=  np.sum(np.square(UpperCamelCase	)	)
							A		=  sum_elem / (2 * len_data)
							return error






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  100_000
							A		=  0.0_00_15_50

							A		=  data_x.shape[1]
							A		=  data_x.shape[0] - 1

							A		=  np.zeros((1, no_features)	)

							for i in range(0 ,  UpperCamelCase	):
														A		=  run_steep_gradient_descent(UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	)
														A		=  sum_of_square_error(UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	)
														print(F"At Iteration {i + 1} - Error is {error:.5f}"	)

							return theta






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  sum(abs(y - predicted_y[i]	) for i, y in enumerate(UpperCamelCase	)	)
							return total / len(UpperCamelCase	)






def  A__    (      ):
							A		=  collect_dataset()

							A		=  data.shape[0]
							A		=  np.c_[np.ones(UpperCamelCase	), data[:, :-1]].astype(UpperCamelCase	)
							A		=  data[:, -1].astype(UpperCamelCase	)

							A		=  run_linear_regression(UpperCamelCase ,  UpperCamelCase	)
							A		=  theta.shape[1]
							print("Resultant Feature vector : "	)
							for i in range(0 ,  UpperCamelCase	):
														print(F"{theta[0, i]:.5f}"	)


if __name__ == "__main__":
		main()


