

'''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     lowerCAmelCase				(    _a				):


       '''simple docstring'''







       snake_case    							=	42


class     lowerCAmelCase				(    _a   ,				_a				):


       '''simple docstring'''







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







        '''simple docstring'''

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

        if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
         lowerCamelCase      						=    (
             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     )
         lowerCamelCase      						=    '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:
         lowerCamelCase      						=    in_channels

         lowerCamelCase      						=    torch.nn.GroupNorm(num_groups=__snake_case						,	num_channels=__snake_case						,	eps=1e-6						,	affine=__snake_case     )
         if use_linear_projection:
          lowerCamelCase      						=    nn.Linear(__snake_case						,	__snake_case     )
         else:
          lowerCamelCase      						=    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"

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

         lowerCamelCase      						=    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"

         lowerCamelCase      						=    sample_size
         lowerCamelCase      						=    sample_size

         lowerCamelCase      						=    patch_size
         lowerCamelCase      						=    PatchEmbed(
             height=__snake_case						,	width=__snake_case						,	patch_size=__snake_case						,	in_channels=__snake_case						,	embed_dim=__snake_case						,	)

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





       def   lowerCamelCase__						(	self					:  Dict						,	__snake_case					:  torch.Tensor						,	__snake_case					:  Optional[torch.Tensor] = None						,	__snake_case					:  Optional[torch.LongTensor] = None						,	__snake_case					:  Optional[torch.LongTensor] = None						,	__snake_case					:  Dict[str, Any] = None						,	__snake_case					:  Optional[torch.Tensor] = None						,	__snake_case					:  Optional[torch.Tensor] = None						,	__snake_case					:  bool = True						,	)   -> List[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)
         lowerCamelCase      						=    (1 - attention_mask.to(hidden_states.dtype     )) * -10000.0
         lowerCamelCase      						=    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:
         lowerCamelCase      						=    (1 - encoder_attention_mask.to(hidden_states.dtype     )) * -10000.0
         lowerCamelCase      						=    encoder_attention_mask.unsqueeze(1     )

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

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

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

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

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

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







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class     lowerCAmelCase				(    __UpperCamelCase   ,				__UpperCamelCase   ,				__UpperCamelCase				):


       '''simple docstring'''







       @register_to_config
       def __init__(	self					:  Any						,	__snake_case					:  int						,	__snake_case					:  int						,	__snake_case					:  int						,	__snake_case					:  float						,	__snake_case					:  int						,	__snake_case					:  int						,	__snake_case					:  int						,	__snake_case					:  int						,	__snake_case					:  str						,	__snake_case					:  bool = False						,	)   -> int:







        '''simple docstring'''

        super().__init__()

        lowerCamelCase      						=    nn.Linear(__snake_case						,	__snake_case						,	bias=__snake_case     )

        lowerCamelCase      						=    nn.Embedding(__snake_case						,	__snake_case     )
        lowerCamelCase      						=    False

        lowerCamelCase      						=    nn.Dropout(p=__snake_case     )

        lowerCamelCase      						=    TaConfig(
            d_model=__snake_case						,	num_heads=__snake_case						,	d_kv=__snake_case						,	d_ff=__snake_case						,	feed_forward_proj=__snake_case						,	dropout_rate=__snake_case						,	is_decoder=__snake_case						,	is_encoder_decoder=__snake_case						,	)
        lowerCamelCase      						=    nn.ModuleList()
        for lyr_num in range(__snake_case     ):
         lowerCamelCase      						=    TaBlock(__snake_case     )
         self.encoders.append(__snake_case     )

        lowerCamelCase      						=    TaLayerNorm(__snake_case     )
        lowerCamelCase      						=    nn.Dropout(p=__snake_case     )





       def   lowerCamelCase__						(	self					:  str						,	__snake_case					:  List[Any]						,	__snake_case					:  List[Any]     )   -> Dict:







        '''simple docstring'''

        lowerCamelCase      						=    self.input_proj(__snake_case     )

        # terminal relative positional encodings
        lowerCamelCase      						=    encoder_inputs.shape[1]
        lowerCamelCase      						=    torch.arange(__snake_case						,	device=encoder_inputs.device     )

        lowerCamelCase      						=    encoder_inputs_mask.sum(-1     )
        lowerCamelCase      						=    torch.roll(input_positions.unsqueeze(0     )						,	tuple(seq_lens.tolist()     )						,	dims=0     )
        x += self.position_encoding(__snake_case     )

        lowerCamelCase      						=    self.dropout_pre(__snake_case     )

        # inverted the attention mask
        lowerCamelCase      						=    encoder_inputs.size()
        lowerCamelCase      						=    self.get_extended_attention_mask(__snake_case						,	__snake_case     )

        for lyr in self.encoders:
         lowerCamelCase      						=    lyr(__snake_case						,	__snake_case     )[0]
        lowerCamelCase      						=    self.layer_norm(__snake_case     )

        return self.dropout_post(__snake_case     ), encoder_inputs_mask




