import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__      		=					logging.get_logger(__name__)

lowerCAmelCase__      		=					{
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

lowerCAmelCase__      		=					{
    'source_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'
    },
    'target_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'
    },
    'vocab': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'
    },
    'tokenizer_config_file': {
        'Helsinki-NLP/opus-mt-en-de': (
            'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'
        )
    },
}

lowerCAmelCase__      		=					{'Helsinki-NLP/opus-mt-en-de': 512}
lowerCAmelCase__      		=					{}
class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_      					=   VOCAB_FILES_NAMES
 UpperCAmelCase_      					=   PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_      					=   PRETRAINED_INIT_CONFIGURATION
 UpperCAmelCase_      					=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_      					=   ['input_ids', 'attention_mask']
 UpperCAmelCase_      					=   re.compile('>>.+<<'		)  # type: re.Pattern




 def __init__(     self   :    int     ,       lowercase__   :    Dict     ,       lowercase__   :    Dict     ,       lowercase__   :    Dict     ,       lowercase__   :    Optional[int]=None     ,       lowercase__   :    Dict=None     ,       lowercase__   :    Optional[int]=None     ,       lowercase__   :    Optional[int]="<unk>"     ,       lowercase__   :    List[str]="</s>"     ,       lowercase__   :    Optional[int]="<pad>"     ,       lowercase__   :    Optional[Any]=512     ,       lowercase__   :    Optional[Dict[str, Any]] = None     ,       lowercase__   :    Tuple=False     ,       **lowercase__   :    List[Any]     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
            source_lang=lowercase__     ,       target_lang=lowercase__     ,       unk_token=lowercase__     ,       eos_token=lowercase__     ,       pad_token=lowercase__     ,       model_max_length=lowercase__     ,       sp_model_kwargs=self.sp_model_kwargs     ,       target_vocab_file=lowercase__     ,       separate_vocabs=lowercase__     ,       **lowercase__     ,       )
        assert Path(lowercase__).exists(), F"""cannot find spm source {source_spm}"""

        lowerCAmelCase__	      =   separate_vocabs
        lowerCAmelCase__	      =   load_json(lowercase__)
        if self.unk_token not in self.encoder:
               raise KeyError('<unk> token must be in vocab')
        assert self.pad_token in self.encoder

        if separate_vocabs:
               lowerCAmelCase__	      =   load_json(lowercase__)
               lowerCAmelCase__	      =   {v: k for k, v in self.target_encoder.items()}
               lowerCAmelCase__	      =   []
        else:
               lowerCAmelCase__	      =   {v: k for k, v in self.encoder.items()}
               lowerCAmelCase__	      =   [k for k in self.encoder if k.startswith('>>') and k.endswith('<<')]

        lowerCAmelCase__	      =   source_lang
        lowerCAmelCase__	      =   target_lang
        lowerCAmelCase__	      =   [source_spm, target_spm]

        # load SentencePiece model for pre-processing
        lowerCAmelCase__	      =   load_spm(lowercase__     ,       self.sp_model_kwargs)
        lowerCAmelCase__	      =   load_spm(lowercase__     ,       self.sp_model_kwargs)
        lowerCAmelCase__	      =   self.spm_source
        lowerCAmelCase__	      =   self.encoder

        # Multilingual target side: default to using first supported language code.

        self._setup_normalizer()




 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        try:
               from sacremoses import MosesPunctNormalizer

               lowerCAmelCase__	      =   MosesPunctNormalizer(self.source_lang).normalize
        except (ImportError, FileNotFoundError):
               warnings.warn('Recommended: pip install sacremoses.')
               lowerCAmelCase__	      =   lambda lowercase__: x




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    str):







        '''simple docstring'''



        return self.punc_normalizer(lowercase__) if x else ""




 def 						__snake_case		(     self   :    Any     ,       lowercase__   :    Union[str, Any]):







        '''simple docstring'''



        return self.current_encoder.get(lowercase__     ,       self.current_encoder[self.unk_token])




 def 						__snake_case		(     self   :    str     ,       lowercase__   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.language_code_re.match(lowercase__)
        lowerCAmelCase__	      =   [match.group(0)] if match else []
        return code, self.language_code_re.sub(''     ,       lowercase__)




 def 						__snake_case		(     self   :    str     ,       lowercase__   :    str):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.remove_language_code(lowercase__)
        lowerCAmelCase__	      =   self.current_spm.encode(lowercase__     ,       out_type=lowercase__)
        return code + pieces




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    int):







        '''simple docstring'''



        return self.decoder.get(lowercase__     ,       self.unk_token)




 def 						__snake_case		(     self   :    Any     ,       lowercase__   :    Tuple     ,       **lowercase__   :    Tuple):







        '''simple docstring'''



        return super().batch_decode(lowercase__     ,       **lowercase__)




 def 						__snake_case		(     self   :    str     ,       lowercase__   :    List[Any]     ,       **lowercase__   :    int):







        '''simple docstring'''



        return super().decode(lowercase__     ,       **lowercase__)




 def 						__snake_case		(     self   :    Dict     ,       lowercase__   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.spm_source if self._decode_use_source_tokenizer else self.spm_target
        lowerCAmelCase__	      =   []
        lowerCAmelCase__	      =   ''
        for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                      out_string += sp_model.decode_pieces(lowercase__) + token + " "
                      lowerCAmelCase__	      =   []
               else:
                      current_sub_tokens.append(lowercase__)
        out_string += sp_model.decode_pieces(lowercase__)
        return out_string.strip()




 def 						__snake_case		(     self   :    Optional[Any]     ,       lowercase__   :    Dict     ,       lowercase__   :    str=None):







        '''simple docstring'''



        if token_ids_a is None:
               return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]




 def 						__snake_case		(     self   :    Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.spm_source
        lowerCAmelCase__	      =   self.encoder




 def 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.spm_target
        if self.separate_vocabs:
               lowerCAmelCase__	      =   self.target_encoder




 @property
 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        return len(self.encoder)




 def 						__snake_case		(     self   :    str     ,       lowercase__   :    str     ,       lowercase__   :    Optional[str] = None):







        '''simple docstring'''



        if not os.path.isdir(lowercase__):
               logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
               return
        lowerCAmelCase__	      =   []

        if self.separate_vocabs:
               lowerCAmelCase__	      =   os.path.join(
                   lowercase__     ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']     ,       )
               lowerCAmelCase__	      =   os.path.join(
                   lowercase__     ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file']     ,       )
               save_json(self.encoder     ,       lowercase__)
               save_json(self.target_encoder     ,       lowercase__)
               saved_files.append(lowercase__)
               saved_files.append(lowercase__)
        else:
               lowerCAmelCase__	      =   os.path.join(
                   lowercase__     ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab'])
               save_json(self.encoder     ,       lowercase__)
               saved_files.append(lowercase__)

        for spm_save_filename, spm_orig_path, spm_model in zip(
            [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']]     ,       self.spm_files     ,       [self.spm_source, self.spm_target]     ,       ):
               lowerCAmelCase__	      =   os.path.join(
                   lowercase__     ,       (filename_prefix + '-' if filename_prefix else '') + spm_save_filename)
               if os.path.abspath(lowercase__) != os.path.abspath(lowercase__) and os.path.isfile(lowercase__):
                      copyfile(lowercase__     ,       lowercase__)
                      saved_files.append(lowercase__)
               elif not os.path.isfile(lowercase__):
                      with open(lowercase__     ,       'wb') as fi:
                             lowerCAmelCase__	      =   spm_model.serialized_model_proto()
                             fi.write(lowercase__)
                      saved_files.append(lowercase__)

        return tuple(lowercase__)




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        return self.get_src_vocab()




 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        return dict(self.encoder     ,       **self.added_tokens_encoder)




 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        return dict(self.target_encoder     ,       **self.added_tokens_decoder)




 def __getstate__(     self   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.__dict__.copy()
        state.update(
            {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']})
        return state




 def __setstate__(     self   :    List[Any]     ,       lowercase__   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__	      =   d

        # for backward compatibility
        if not hasattr(self     ,       'sp_model_kwargs'):
               lowerCAmelCase__	      =   {}

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   (load_spm(lowercase__     ,       self.sp_model_kwargs) for f in self.spm_files)
        lowerCAmelCase__	      =   self.spm_source
        self._setup_normalizer()




 def 						__snake_case		(     self   :    Optional[Any]     ,       *lowercase__   :    Dict     ,       **lowercase__   :    Tuple):







        '''simple docstring'''



        return 1




 def 						__snake_case		(     self   :    Tuple     ,       lowercase__   :    List[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   set(self.all_special_ids)  # call it once instead of inside list comp
        all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special
        return [1 if x in all_special_ids else 0 for x in seq]




 def 						__snake_case		(     self   :    Optional[Any]     ,       lowercase__   :    List     ,       lowercase__   :    Optional[List] = None     ,       lowercase__   :    bool = False):







        '''simple docstring'''



        if already_has_special_tokens:
               return self._special_token_mask(lowercase__)
        elif token_ids_a is None:
               return self._special_token_mask(lowercase__) + [1]
        else:
               return self._special_token_mask(token_ids_a + token_ids_a) + [1]






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   sentencepiece.SentencePieceProcessor(**lowerCAmelCase__      )
       spm.Load(lowerCAmelCase__      )
       return spm






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       with open(lowerCAmelCase__      ,    'w'      ) as f:
              json.dump(lowerCAmelCase__      ,    lowerCAmelCase__      ,    indent=2      )






def  __lowerCamelCase						(		lowerCAmelCase__      ):
       with open(lowerCAmelCase__      ,    'r'      ) as f:
              return json.load(lowerCAmelCase__      )

import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__ = False      ,    lowerCAmelCase__ = 1      ,    lowerCAmelCase__ = 1      ,    lowerCAmelCase__ = 1_0_0_0_0      ,    ):
       assert len(timesteps.shape      ) == 1, "Timesteps should be a 1d-array"

       lowerCAmelCase__	      =   embedding_dim // 2
       lowerCAmelCase__	      =   -math.log(lowerCAmelCase__      ) * torch.arange(
           start=0      ,    end=lowerCAmelCase__      ,    dtype=torch.floataa      ,    device=timesteps.device      )
       lowerCAmelCase__	      =   exponent / (half_dim - downscale_freq_shift)

       lowerCAmelCase__	      =   torch.exp(lowerCAmelCase__      )
       lowerCAmelCase__	      =   timesteps[:, None].float() * emb[None, :]

       # scale embeddings
       lowerCAmelCase__	      =   scale * emb

       # concat sine and cosine embeddings
       lowerCAmelCase__	      =   torch.cat([torch.sin(lowerCAmelCase__      ), torch.cos(lowerCAmelCase__      )]      ,    dim=-1      )

       # flip sine and cosine embeddings
       if flip_sin_to_cos:
              lowerCAmelCase__	      =   torch.cat([emb[:, half_dim:], emb[:, :half_dim]]      ,    dim=-1      )

       # zero pad
       if embedding_dim % 2 == 1:
              lowerCAmelCase__	      =   torch.nn.functional.pad(lowerCAmelCase__      ,    (0, 1, 0, 0)      )
       return emb






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__=False      ,    lowerCAmelCase__=0      ):
       lowerCAmelCase__	      =   np.arange(lowerCAmelCase__      ,    dtype=np.floataa      )
       lowerCAmelCase__	      =   np.arange(lowerCAmelCase__      ,    dtype=np.floataa      )
       lowerCAmelCase__	      =   np.meshgrid(lowerCAmelCase__      ,    lowerCAmelCase__      )  # here w goes first
       lowerCAmelCase__	      =   np.stack(lowerCAmelCase__      ,    axis=0      )

       lowerCAmelCase__	      =   grid.reshape([2, 1, grid_size, grid_size]      )
       lowerCAmelCase__	      =   get_ad_sincos_pos_embed_from_grid(lowerCAmelCase__      ,    lowerCAmelCase__      )
       if cls_token and extra_tokens > 0:
              lowerCAmelCase__	      =   np.concatenate([np.zeros([extra_tokens, embed_dim]      ), pos_embed]      ,    axis=0      )
       return pos_embed






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if embed_dim % 2 != 0:
              raise ValueError('embed_dim must be divisible by 2'      )

       # use half of dimensions to encode grid_h
       lowerCAmelCase__	      =   get_ad_sincos_pos_embed_from_grid(embed_dim // 2      ,    grid[0]      )  # (H*W, D/2)
       lowerCAmelCase__	      =   get_ad_sincos_pos_embed_from_grid(embed_dim // 2      ,    grid[1]      )  # (H*W, D/2)

       lowerCAmelCase__	      =   np.concatenate([emb_h, emb_w]      ,    axis=1      )  # (H*W, D)
       return emb






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if embed_dim % 2 != 0:
              raise ValueError('embed_dim must be divisible by 2'      )

       lowerCAmelCase__	      =   np.arange(embed_dim // 2      ,    dtype=np.floataa      )
       omega /= embed_dim / 2.0
       lowerCAmelCase__	      =   1.0 / 1_0_0_0_0**omega  # (D/2,)

       lowerCAmelCase__	      =   pos.reshape(-1      )  # (M,)
       lowerCAmelCase__	      =   np.einsum('m,d->md'      ,    lowerCAmelCase__      ,    lowerCAmelCase__      )  # (M, D/2), outer product

       lowerCAmelCase__	      =   np.sin(lowerCAmelCase__      )  # (M, D/2)
       lowerCAmelCase__	      =   np.cos(lowerCAmelCase__      )  # (M, D/2)

       lowerCAmelCase__	      =   np.concatenate([emb_sin, emb_cos]      ,    axis=1      )  # (M, D)
       return emb
class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[int]     ,       lowercase__   :    Any=224     ,       lowercase__   :    Any=224     ,       lowercase__   :    int=16     ,       lowercase__   :    List[Any]=3     ,       lowercase__   :    Dict=768     ,       lowercase__   :    Tuple=False     ,       lowercase__   :    Any=True     ,       lowercase__   :    List[str]=True     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   (height // patch_size) * (width // patch_size)
        lowerCAmelCase__	      =   flatten
        lowerCAmelCase__	      =   layer_norm

        lowerCAmelCase__	      =   nn.Convad(
            lowercase__     ,       lowercase__     ,       kernel_size=(patch_size, patch_size)     ,       stride=lowercase__     ,       bias=lowercase__)
        if layer_norm:
               lowerCAmelCase__	      =   nn.LayerNorm(lowercase__     ,       elementwise_affine=lowercase__     ,       eps=1e-6)
        else:
               lowerCAmelCase__	      =   None

        lowerCAmelCase__	      =   get_ad_sincos_pos_embed(lowercase__     ,       int(num_patches**0.5))
        self.register_buffer('pos_embed'     ,       torch.from_numpy(lowercase__).float().unsqueeze(0)     ,       persistent=lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.proj(lowercase__)
        if self.flatten:
               lowerCAmelCase__	      =   latent.flatten(2).transpose(1     ,       2)  # BCHW -> BNC
        if self.layer_norm:
               lowerCAmelCase__	      =   self.norm(lowercase__)
        return latent + self.pos_embed






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    str     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    str = "silu"     ,       lowercase__   :    int = None     ,       lowercase__   :    Optional[str] = None     ,       lowercase__   :    List[str]=None     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)

        if cond_proj_dim is not None:
               lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__     ,       bias=lowercase__)
        else:
               lowerCAmelCase__	      =   None

        lowerCAmelCase__	      =   get_activation(lowercase__)

        if out_dim is not None:
               lowerCAmelCase__	      =   out_dim
        else:
               lowerCAmelCase__	      =   time_embed_dim
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)

        if post_act_fn is None:
               lowerCAmelCase__	      =   None
        else:
               lowerCAmelCase__	      =   get_activation(lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    Tuple     ,       lowercase__   :    List[Any]=None):







        '''simple docstring'''



        if condition is not None:
               lowerCAmelCase__	      =   sample + self.cond_proj(lowercase__)
        lowerCAmelCase__	      =   self.linear_a(lowercase__)

        if self.act is not None:
               lowerCAmelCase__	      =   self.act(lowercase__)

        lowerCAmelCase__	      =   self.linear_a(lowercase__)

        if self.post_act is not None:
               lowerCAmelCase__	      =   self.post_act(lowercase__)
        return sample






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[int]     ,       lowercase__   :    int     ,       lowercase__   :    bool     ,       lowercase__   :    float):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   num_channels
        lowerCAmelCase__	      =   flip_sin_to_cos
        lowerCAmelCase__	      =   downscale_freq_shift




 def 						__snake_case		(     self   :    int     ,       lowercase__   :    Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   get_timestep_embedding(
            lowercase__     ,       self.num_channels     ,       flip_sin_to_cos=self.flip_sin_to_cos     ,       downscale_freq_shift=self.downscale_freq_shift     ,       )
        return t_emb






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    List[str]     ,       lowercase__   :    int = 256     ,       lowercase__   :    float = 1.0     ,       lowercase__   :    str=True     ,       lowercase__   :    List[str]=True     ,       lowercase__   :    Any=False):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.Parameter(torch.randn(lowercase__) * scale     ,       requires_grad=lowercase__)
        lowerCAmelCase__	      =   log
        lowerCAmelCase__	      =   flip_sin_to_cos

        if set_W_to_weight:
               # to delete later
               lowerCAmelCase__	      =   nn.Parameter(torch.randn(lowercase__) * scale     ,       requires_grad=lowercase__)

               lowerCAmelCase__	      =   self.W




 def 						__snake_case		(     self   :    Dict     ,       lowercase__   :    List[str]):







        '''simple docstring'''



        if self.log:
               lowerCAmelCase__	      =   torch.log(lowercase__)

        lowerCAmelCase__	      =   x[:, None] * self.weight[None, :] * 2 * np.pi

        if self.flip_sin_to_cos:
               lowerCAmelCase__	      =   torch.cat([torch.cos(lowercase__), torch.sin(lowercase__)]     ,       dim=-1)
        else:
               lowerCAmelCase__	      =   torch.cat([torch.sin(lowercase__), torch.cos(lowercase__)]     ,       dim=-1)
        return out






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    List[str]     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   height
        lowerCAmelCase__	      =   width
        lowerCAmelCase__	      =   num_embed
        lowerCAmelCase__	      =   embed_dim

        lowerCAmelCase__	      =   nn.Embedding(self.num_embed     ,       lowercase__)
        lowerCAmelCase__	      =   nn.Embedding(self.height     ,       lowercase__)
        lowerCAmelCase__	      =   nn.Embedding(self.width     ,       lowercase__)




 def 						__snake_case		(     self   :    Dict     ,       lowercase__   :    Any):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.emb(lowercase__)

        lowerCAmelCase__	      =   self.height_emb(torch.arange(self.height     ,       device=index.device).view(1     ,       self.height))

        # 1 x H x D -> 1 x H x 1 x D
        lowerCAmelCase__	      =   height_emb.unsqueeze(2)

        lowerCAmelCase__	      =   self.width_emb(torch.arange(self.width     ,       device=index.device).view(1     ,       self.width))

        # 1 x W x D -> 1 x 1 x W x D
        lowerCAmelCase__	      =   width_emb.unsqueeze(1)

        lowerCAmelCase__	      =   height_emb + width_emb

        # 1 x H x W x D -> 1 x L xD
        lowerCAmelCase__	      =   pos_emb.view(1     ,       self.height * self.width     ,       -1)

        lowerCAmelCase__	      =   emb + pos_emb[:, : emb.shape[1], :]

        return emb






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Any     ,       lowercase__   :    int     ,       lowercase__   :    Dict     ,       lowercase__   :    Any):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   dropout_prob > 0
        lowerCAmelCase__	      =   nn.Embedding(num_classes + use_cfg_embedding     ,       lowercase__)
        lowerCAmelCase__	      =   num_classes
        lowerCAmelCase__	      =   dropout_prob




 def 						__snake_case		(     self   :    Any     ,       lowercase__   :    List[Any]     ,       lowercase__   :    List[Any]=None):







        '''simple docstring'''



        if force_drop_ids is None:
               lowerCAmelCase__	      =   torch.rand(labels.shape[0]     ,       device=labels.device) < self.dropout_prob
        else:
               lowerCAmelCase__	      =   torch.tensor(force_drop_ids == 1)
        lowerCAmelCase__	      =   torch.where(lowercase__     ,       self.num_classes     ,       lowercase__)
        return labels




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.LongTensor     ,       lowercase__   :    str=None):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.dropout_prob > 0
        if (self.training and use_dropout) or (force_drop_ids is not None):
               lowerCAmelCase__	      =   self.token_drop(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   self.embedding_table(lowercase__)
        return embeddings






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[Any]     ,       lowercase__   :    int = 1_024     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 10     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   num_image_text_embeds
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       self.num_image_text_embeds * cross_attention_dim)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   text_embeds.shape[0]

        # image
        lowerCAmelCase__	      =   self.image_embeds(lowercase__)
        lowerCAmelCase__	      =   image_text_embeds.reshape(lowercase__     ,       self.num_image_text_embeds     ,       -1)

        # text
        lowerCAmelCase__	      =   self.text_proj(lowercase__)

        return torch.cat([image_text_embeds, text_embeds]     ,       dim=1)






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    str     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 32     ,       ):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   num_image_text_embeds
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       self.num_image_text_embeds * cross_attention_dim)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   image_embeds.shape[0]

        # image
        lowerCAmelCase__	      =   self.image_embeds(lowercase__)
        lowerCAmelCase__	      =   image_embeds.reshape(lowercase__     ,       self.num_image_text_embeds     ,       -1)
        lowerCAmelCase__	      =   self.norm(lowercase__)
        return image_embeds






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    str     ,       lowercase__   :    Optional[int]     ,       lowercase__   :    Dict     ,       lowercase__   :    Tuple=0.1):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__	      =   Timesteps(num_channels=256     ,       flip_sin_to_cos=lowercase__     ,       downscale_freq_shift=1)
        lowerCAmelCase__	      =   TimestepEmbedding(in_channels=256     ,       time_embed_dim=lowercase__)
        lowerCAmelCase__	      =   LabelEmbedding(lowercase__     ,       lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    List[str]     ,       lowercase__   :    Dict     ,       lowercase__   :    Dict=None):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.time_proj(lowercase__)
        lowerCAmelCase__	      =   self.timestep_embedder(timesteps_proj.to(dtype=lowercase__))  # (N, D)

        lowerCAmelCase__	      =   self.class_embedder(lowercase__)  # (N, D)

        lowerCAmelCase__	      =   timesteps_emb + class_labels  # (N, D)

        return conditioning






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[Any]     ,       lowercase__   :    int     ,       lowercase__   :    int     ,       lowercase__   :    int = 64):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)
        lowerCAmelCase__	      =   AttentionPooling(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)




 def 						__snake_case		(     self   :    Optional[int]     ,       lowercase__   :    Any):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.norma(lowercase__)
        lowerCAmelCase__	      =   self.pool(lowercase__)
        lowerCAmelCase__	      =   self.proj(lowercase__)
        lowerCAmelCase__	      =   self.norma(lowercase__)
        return hidden_states






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    List[str]     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 1_536):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.text_proj(lowercase__)
        lowerCAmelCase__	      =   self.text_norm(lowercase__)

        # image
        lowerCAmelCase__	      =   self.image_proj(lowercase__)

        return time_image_embeds + time_text_embeds






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    List[Any]     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 1_536):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_proj(lowercase__)
        lowerCAmelCase__	      =   self.image_norm(lowercase__)
        return time_image_embeds






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Optional[int]     ,       lowercase__   :    int = 768     ,       lowercase__   :    int = 1_536):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   nn.LayerNorm(lowercase__)
        lowerCAmelCase__	      =   nn.Sequential(
            nn.Convad(3     ,       16     ,       3     ,       padding=1)     ,       nn.SiLU()     ,       nn.Convad(16     ,       16     ,       3     ,       padding=1)     ,       nn.SiLU()     ,       nn.Convad(16     ,       32     ,       3     ,       padding=1     ,       stride=2)     ,       nn.SiLU()     ,       nn.Convad(32     ,       32     ,       3     ,       padding=1)     ,       nn.SiLU()     ,       nn.Convad(32     ,       96     ,       3     ,       padding=1     ,       stride=2)     ,       nn.SiLU()     ,       nn.Convad(96     ,       96     ,       3     ,       padding=1)     ,       nn.SiLU()     ,       nn.Convad(96     ,       256     ,       3     ,       padding=1     ,       stride=2)     ,       nn.SiLU()     ,       nn.Convad(256     ,       4     ,       3     ,       padding=1)     ,       )




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    torch.FloatTensor     ,       lowercase__   :    torch.FloatTensor):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.image_proj(lowercase__)
        lowerCAmelCase__	      =   self.image_norm(lowercase__)
        lowerCAmelCase__	      =   self.input_hint_block(lowercase__)
        return time_image_embeds, hint






class 							a_					(		nn.Module		):






 '''simple docstring'''






 def __init__(     self   :    Union[str, Any]     ,       lowercase__   :    Optional[Any]     ,       lowercase__   :    Optional[int]     ,       lowercase__   :    Optional[int]=None):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__	      =   dtype
        lowerCAmelCase__	      =   nn.Parameter(torch.randn(1     ,       lowercase__) / embed_dim**0.5)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__     ,       dtype=self.dtype)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__     ,       dtype=self.dtype)
        lowerCAmelCase__	      =   nn.Linear(lowercase__     ,       lowercase__     ,       dtype=self.dtype)
        lowerCAmelCase__	      =   num_heads
        lowerCAmelCase__	      =   embed_dim // self.num_heads




 def 						__snake_case		(     self   :    List[Any]     ,       lowercase__   :    List[Any]):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__	      =   x.size()

        def shape(lowercase__   :    Dict):
               # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
               lowerCAmelCase__	      =   x.view(lowercase__     ,       -1     ,       self.num_heads     ,       self.dim_per_head)
               # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
               lowerCAmelCase__	      =   x.transpose(1     ,       2)
               # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
               lowerCAmelCase__	      =   x.reshape(bs * self.num_heads     ,       -1     ,       self.dim_per_head)
               # (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
               lowerCAmelCase__	      =   x.transpose(1     ,       2)
               return x

        lowerCAmelCase__	      =   x.mean(dim=1     ,       keepdim=lowercase__) + self.positional_embedding.to(x.dtype)
        lowerCAmelCase__	      =   torch.cat([class_token, x]     ,       dim=1)  # (bs, length+1, width)

        # (bs*n_heads, class_token_length, dim_per_head)
        lowerCAmelCase__	      =   shape(self.q_proj(lowercase__))
        # (bs*n_heads, length+class_token_length, dim_per_head)
        lowerCAmelCase__	      =   shape(self.k_proj(lowercase__))
        lowerCAmelCase__	      =   shape(self.v_proj(lowercase__))

        # (bs*n_heads, class_token_length, length+class_token_length):
        lowerCAmelCase__	      =   1 / math.sqrt(math.sqrt(self.dim_per_head))
        lowerCAmelCase__	      =   torch.einsum('bct,bcs->bts'     ,       q * scale     ,       k * scale)  # More stable with f16 than dividing afterwards
        lowerCAmelCase__	      =   torch.softmax(weight.float()     ,       dim=-1).type(weight.dtype)

        # (bs*n_heads, dim_per_head, class_token_length)
        lowerCAmelCase__	      =   torch.einsum('bts,bcs->bct'     ,       lowercase__     ,       lowercase__)

        # (bs, length+1, width)
        lowerCAmelCase__	      =   a.reshape(lowercase__     ,       -1     ,       1).transpose(1     ,       2)

        return a[:, 0, :]  # cls_token
