





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__			):
							lowerCamelCase_							=    42




class      UpperCamelCase_				(   UpperCamelCase__ ,     UpperCamelCase__			):


							@register_to_config
							def __init__(							self			:Dict							, __A			:int = 16							, __A			:int = 88							, __A			:Optional[int] = None							, __A			:Optional[int] = None							, __A			:int = 1							, __A			:float = 0.0							, __A			:int = 32							, __A			:Optional[int] = None							, __A			:bool = False							, __A			:Optional[int] = None							, __A			:Optional[int] = None							, __A			:Optional[int] = None							, __A			:str = "geglu"							, __A			:Optional[int] = None							, __A			:bool = False							, __A			:bool = False							, __A			:bool = False							, __A			:str = "layer_norm"							, __A			:bool = True							, )      ->      Any:



									"""simple docstring"""



									super().__init__()
									SCREAMING_SNAKE_CASE__								=					use_linear_projection
									SCREAMING_SNAKE_CASE__								=					num_attention_heads
									SCREAMING_SNAKE_CASE__								=					attention_head_dim
									SCREAMING_SNAKE_CASE__								=					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
									SCREAMING_SNAKE_CASE__								=					(in_channels is not None) and (patch_size is None)
									SCREAMING_SNAKE_CASE__								=					num_vector_embeds is not None
									SCREAMING_SNAKE_CASE__								=					in_channels is not None and patch_size is not None

									if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
											SCREAMING_SNAKE_CASE__								=					(
											    f'''The configuration file of this model: {self.__class__} is outdated.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						)
											SCREAMING_SNAKE_CASE__								=					"""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:
											SCREAMING_SNAKE_CASE__								=					in_channels

											SCREAMING_SNAKE_CASE__								=					torch.nn.GroupNorm(num_groups=__A							, num_channels=__A							, eps=1E-6							, affine=__A						)
											if use_linear_projection:
													SCREAMING_SNAKE_CASE__								=					nn.Linear(__A							, __A						)
											else:
													SCREAMING_SNAKE_CASE__								=					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"

											SCREAMING_SNAKE_CASE__								=					sample_size
											SCREAMING_SNAKE_CASE__								=					sample_size
											SCREAMING_SNAKE_CASE__								=					num_vector_embeds
											SCREAMING_SNAKE_CASE__								=					self.height * self.width

											SCREAMING_SNAKE_CASE__								=					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"

											SCREAMING_SNAKE_CASE__								=					sample_size
											SCREAMING_SNAKE_CASE__								=					sample_size

											SCREAMING_SNAKE_CASE__								=					patch_size
											SCREAMING_SNAKE_CASE__								=					PatchEmbed(
											    height=__A							, width=__A							, patch_size=__A							, in_channels=__A							, embed_dim=__A							, )

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


							def    _snake_case     (							self			:Optional[int]							, __A			:torch.Tensor							, __A			:Optional[torch.Tensor] = None							, __A			:Optional[torch.LongTensor] = None							, __A			:Optional[torch.LongTensor] = None							, __A			:Dict[str, Any] = None							, __A			:Optional[torch.Tensor] = None							, __A			:Optional[torch.Tensor] = None							, __A			:bool = 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)
											SCREAMING_SNAKE_CASE__								=					(1 - attention_mask.to(hidden_states.dtype						)) * -1_0_0_0_0.0
											SCREAMING_SNAKE_CASE__								=					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:
											SCREAMING_SNAKE_CASE__								=					(1 - encoder_attention_mask.to(hidden_states.dtype						)) * -1_0_0_0_0.0
											SCREAMING_SNAKE_CASE__								=					encoder_attention_mask.unsqueeze(1						)

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

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

									# 2. Blocks
									for block in self.transformer_blocks:
											SCREAMING_SNAKE_CASE__								=					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:
													SCREAMING_SNAKE_CASE__								=					hidden_states.reshape(__A							, __A							, __A							, __A						).permute(0							, 3							, 1							, 2						).contiguous()
													SCREAMING_SNAKE_CASE__								=					self.proj_out(__A						)
											else:
													SCREAMING_SNAKE_CASE__								=					self.proj_out(__A						)
													SCREAMING_SNAKE_CASE__								=					hidden_states.reshape(__A							, __A							, __A							, __A						).permute(0							, 3							, 1							, 2						).contiguous()

											SCREAMING_SNAKE_CASE__								=					hidden_states + residual
									elif self.is_input_vectorized:
											SCREAMING_SNAKE_CASE__								=					self.norm_out(__A						)
											SCREAMING_SNAKE_CASE__								=					self.out(__A						)
											# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
											SCREAMING_SNAKE_CASE__								=					logits.permute(0							, 2							, 1						)

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

											# unpatchify
											SCREAMING_SNAKE_CASE__								=					SCREAMING_SNAKE_CASE__								=					int(hidden_states.shape[1] ** 0.5						)
											SCREAMING_SNAKE_CASE__								=					hidden_states.reshape(
											    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)						)
											SCREAMING_SNAKE_CASE__								=					torch.einsum("""nhwpqc->nchpwq"""							, __A						)
											SCREAMING_SNAKE_CASE__								=					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						)






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					SCREAMING_SNAKE_CASE__								=					0
		for letter in data:
				SCREAMING_SNAKE_CASE__								=					(hash_ << 4) + ord(UpperCamelCase__			)
				SCREAMING_SNAKE_CASE__								=					hash_ & 0XF_0_0_0_0_0_0_0
				if x != 0:
						hash_ ^= x >> 24
				hash_ &= ~x
		return hash_


if __name__ == "__main__":
	import doctest

	doctest.testmod()