"""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__  ):
				lowerCamelCase__						=       4_2


class 			_lowercase		(   UpperCamelCase__      ,      UpperCamelCase__  ):

				@register_to_config
				def __init__(     self       :    Tuple       ,    _lowercase       :    Optional[Any] = 1_6       ,    _lowercase       :    Tuple = 8_8       ,    _lowercase       :    str = None       ,    _lowercase       :    List[str] = None       ,    _lowercase       :    str = 1       ,    _lowercase       :    Any = 0.0       ,    _lowercase       :    int = 3_2       ,    _lowercase       :    Union[str, Any] = None       ,    _lowercase       :    Optional[int] = False       ,    _lowercase       :    int = None       ,    _lowercase       :    Union[str, Any] = None       ,    _lowercase       :    Optional[Any] = None       ,    _lowercase       :    Union[str, Any] = "geglu"       ,    _lowercase       :    Union[str, Any] = None       ,    _lowercase       :    List[str] = False       ,    _lowercase       :    Any = False       ,    _lowercase       :    Any = False       ,    _lowercase       :    List[str] = "layer_norm"       ,    _lowercase       :    List[Any] = 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"""       ,    snake_case_       ,    standard_warn=snake_case_     )
																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=snake_case_       ,    num_channels=snake_case_       ,    eps=1e-6       ,    affine=snake_case_     )
																if use_linear_projection:
																						UpperCAmelCase__						=		nn.Linear(snake_case_       ,    snake_case_     )
																else:
																						UpperCAmelCase__						=		nn.Convad(snake_case_       ,    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__						=		sample_size
																UpperCAmelCase__						=		sample_size
																UpperCAmelCase__						=		num_vector_embeds
																UpperCAmelCase__						=		self.height * self.width

																UpperCAmelCase__						=		ImagePositionalEmbeddings(
																    num_embed=snake_case_       ,    embed_dim=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__						=		sample_size
																UpperCAmelCase__						=		sample_size

																UpperCAmelCase__						=		patch_size
																UpperCAmelCase__						=		PatchEmbed(
																    height=snake_case_       ,    width=snake_case_       ,    patch_size=snake_case_       ,    in_channels=snake_case_       ,    embed_dim=snake_case_       ,    )

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

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

				def        A (     self       :    List[str]       ,    _lowercase       :    Union[str, Any]       ,    _lowercase       :    Any = None       ,    _lowercase       :    Any = None       ,    _lowercase       :    Dict = None       ,    _lowercase       :    List[Any] = None       ,    _lowercase       :    Optional[int] = None       ,    _lowercase       :    Any = None       ,    _lowercase       :    Union[str, Any] = True       ,    ):


										'''simple docstring'''
										# 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)
																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__						=		hidden_states.shape
																UpperCAmelCase__						=		hidden_states

																UpperCAmelCase__						=		self.norm(snake_case_     )
																if not self.use_linear_projection:
																						UpperCAmelCase__						=		self.proj_in(snake_case_     )
																						UpperCAmelCase__						=		hidden_states.shape[1]
																						UpperCAmelCase__						=		hidden_states.permute(0       ,    2       ,    3       ,    1     ).reshape(snake_case_       ,    height * width       ,    snake_case_     )
																else:
																						UpperCAmelCase__						=		hidden_states.shape[1]
																						UpperCAmelCase__						=		hidden_states.permute(0       ,    2       ,    3       ,    1     ).reshape(snake_case_       ,    height * width       ,    snake_case_     )
																						UpperCAmelCase__						=		self.proj_in(snake_case_     )
										elif self.is_input_vectorized:
																UpperCAmelCase__						=		self.latent_image_embedding(snake_case_     )
										elif self.is_input_patches:
																UpperCAmelCase__						=		self.pos_embed(snake_case_     )

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

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

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

																# unpatchify
																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"""       ,    snake_case_     )
																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=snake_case_     )


"""simple docstring"""







from itertools import combinations_with_replacement


def   a__      (		UpperCAmelCase__       :			int = 1_0_0			)->	int:
						UpperCAmelCase__						=		[*list(range(1		,			2_1			)			), 2_5]
						UpperCAmelCase__						=		[2 * x for x in range(1		,			2_1			)] + [5_0]
						UpperCAmelCase__						=		[3 * x for x in range(1		,			2_1			)]
						UpperCAmelCase__						=		singles + doubles + triples + [0]

						UpperCAmelCase__						=		0
						UpperCAmelCase__						=		42
						UpperCAmelCase__						=		42
						UpperCAmelCase__						=		42
						UpperCAmelCase__						=		42

						for double in doubles:
												for throwa, throwa in combinations_with_replacement(UpperCAmelCase__		,			2			):
																		UpperCAmelCase__						=		double + throwa + throwa
																		if checkout_total < limit:
																								num_checkouts += 1

						return num_checkouts


if __name__ == "__main__":
						print(F"""{solution() = }""")

