






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__				(							UpperCAmelCase ):
      lowerCamelCase_							:   torch.FloatTensor




class 	lowerCamelCase__				(							UpperCAmelCase,   UpperCAmelCase ):






      @register_to_config
      def __init__(self					:    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		,		)       ->   Dict:


       """simple docstring"""






       super().__init__()
       lowerCamelCase_							:    str							=     use_linear_projection
       lowerCamelCase_							:    int							=     num_attention_heads
       lowerCamelCase_							:    str							=     attention_head_dim
       lowerCamelCase_							:    str							=     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_							:    Tuple							=     (in_channels is not None) and (patch_size is None)
       lowerCamelCase_							:    List[Any]							=     num_vector_embeds is not None
       lowerCamelCase_							:    List[str]							=     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_							:    List[str]							=     (
            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_							:    str							=     '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_							:    int							=     in_channels

        lowerCamelCase_							:    Union[str, Any]							=     torch.nn.GroupNorm(num_groups=_snake_case		,		num_channels=_snake_case		,		eps=1e-6		,		affine=_snake_case		)
        if use_linear_projection:
         lowerCamelCase_							:    str							=     nn.Linear(_snake_case		,		_snake_case		)
        else:
         lowerCamelCase_							:    Optional[int]							=     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_							:    Dict							=     sample_size
        lowerCamelCase_							:    List[str]							=     sample_size
        lowerCamelCase_							:    Tuple							=     num_vector_embeds
        lowerCamelCase_							:    List[str]							=     self.height * self.width

        lowerCamelCase_							:    Any							=     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_							:    str							=     sample_size
        lowerCamelCase_							:    Optional[int]							=     sample_size

        lowerCamelCase_							:    Tuple							=     patch_size
        lowerCamelCase_							:    List[str]							=     PatchEmbed(
            height=_snake_case		,		width=_snake_case		,		patch_size=_snake_case		,		in_channels=_snake_case		,		embed_dim=_snake_case		,		)

       # 3. Define transformers blocks
       lowerCamelCase_							:    List[Any]							=     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_							:    Union[str, 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:
         lowerCamelCase_							:    Optional[int]							=     nn.Linear(_snake_case		,		_snake_case		)
        else:
         lowerCamelCase_							:    List[str]							=     nn.Convad(_snake_case		,		_snake_case		,		kernel_size=1		,		stride=1		,		padding=0		)
       elif self.is_input_vectorized:
        lowerCamelCase_							:    Dict							=     nn.LayerNorm(_snake_case		)
        lowerCamelCase_							:    Union[str, Any]							=     nn.Linear(_snake_case		,		self.num_vector_embeds - 1		)
       elif self.is_input_patches:
        lowerCamelCase_							:    Any							=     nn.LayerNorm(_snake_case		,		elementwise_affine=_snake_case		,		eps=1e-6		)
        lowerCamelCase_							:    List[Any]							=     nn.Linear(_snake_case		,		2 * inner_dim		)
        lowerCamelCase_							:    Dict							=     nn.Linear(_snake_case		,		patch_size * patch_size * self.out_channels		)






      def      UpperCAmelCase_     (self					:    int		,		_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[str]:


       """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_							:    Dict							=     (1 - attention_mask.to(hidden_states.dtype		)) * -1_0000.0
        lowerCamelCase_							:    Dict							=     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_							:    str							=     (1 - encoder_attention_mask.to(hidden_states.dtype		)) * -1_0000.0
        lowerCamelCase_							:    List[str]							=     encoder_attention_mask.unsqueeze(1		)

       # 1. Input
       if self.is_input_continuous:
        lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_							:    str							=     hidden_states.shape
        lowerCamelCase_							:    Union[str, Any]							=     hidden_states

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

       # 2. Blocks
       for block in self.transformer_blocks:
        lowerCamelCase_							:    Optional[int]							=     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_							:    Optional[Any]							=     hidden_states.reshape(_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		).permute(0		,		3		,		1		,		2		).contiguous()
         lowerCamelCase_							:    List[str]							=     self.proj_out(_snake_case		)
        else:
         lowerCamelCase_							:    Union[str, Any]							=     self.proj_out(_snake_case		)
         lowerCamelCase_							:    str							=     hidden_states.reshape(_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		).permute(0		,		3		,		1		,		2		).contiguous()

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

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

        # unpatchify
        lowerCamelCase_							:    Tuple							=     int(hidden_states.shape[1] ** 0.5		)
        lowerCamelCase_							:    Optional[Any]							=     hidden_states.reshape(
            shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)		)
        lowerCamelCase_							:    int							=     torch.einsum('nhwpqc->nchpwq'		,		_snake_case		)
        lowerCamelCase_							:    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=_snake_case		)








def       _a   (							lowerCamelCase__				)  ->      int:
 return int(lowerCamelCase__				) if x - int(lowerCamelCase__				) >= 0 else int(lowerCamelCase__				) - 1


if __name__ == "__main__":
       import doctest

       doctest.testmod()
