






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_   ):
     __lowerCamelCase					:						Any     =    42




class 		lowerCAmelCase_							(     A_							,    A_   ):





     @register_to_config
     def __init__(  self       ,   _UpperCAmelCase = 16       ,   _UpperCAmelCase = 88       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = 1       ,   _UpperCAmelCase = 0.0       ,   _UpperCAmelCase = 32       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = False       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = "geglu"       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = False       ,   _UpperCAmelCase = False       ,   _UpperCAmelCase = False       ,   _UpperCAmelCase = "layer_norm"       ,   _UpperCAmelCase = True       ,   )		->							int:
        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               F'''The configuration file of this model: {self.__class__} is outdated.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"""       ,   _UpperCAmelCase       ,   standard_warn=_UpperCAmelCase     )
           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=_UpperCAmelCase       ,   num_channels=_UpperCAmelCase       ,   eps=1E-6       ,   affine=_UpperCAmelCase     )
           if use_linear_projection:
              lowerCamelCase											=nn.Linear(_UpperCAmelCase       ,   _UpperCAmelCase     )
           else:
              lowerCamelCase											=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"

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

           lowerCamelCase											=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"

           lowerCamelCase											=sample_size
           lowerCamelCase											=sample_size

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

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





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = None       ,   _UpperCAmelCase = True       ,   )		->							Optional[Any]:



        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											=hidden_states.shape
           lowerCamelCase											=hidden_states

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

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

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

           # unpatchify
           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"""       ,   _UpperCAmelCase     )
           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=_UpperCAmelCase     )







from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
_UpperCAmelCase				:	Optional[int]    						=    load_iris()
iris.keys()


print(f"Target names: \n {iris.target_names} ")
print(f"\n Features: \n {iris.feature_names}")

# Train set e Test set
_UpperCAmelCase						,						_UpperCAmelCase						,						_UpperCAmelCase						,						_UpperCAmelCase				:	List[Any]    						=    train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

_UpperCAmelCase				:	Optional[int]    						=    KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
_UpperCAmelCase				:	List[Any]    						=    [[1, 2, 1, 4], [2, 3, 4, 5]]

_UpperCAmelCase				:	Tuple    						=    knn.predict(X_new)

print(
    f"\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"
    f" {iris['target_names'][prediction]}"
)