
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation

__lowerCAmelCase				      =					logging.get_logger(__name__)

__lowerCAmelCase				      =					{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__lowerCAmelCase				      =					{
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
}

__lowerCAmelCase				      =					{
    'gpt2': 1_0_2_4,
    'gpt2-medium': 1_0_2_4,
    'gpt2-large': 1_0_2_4,
    'gpt2-xl': 1_0_2_4,
    'distilgpt2': 1_0_2_4,
}


@lru_cache()
def       a			(   )    ->Dict:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   (
			    list(range(ord('''!'''  )		,	ord('''~'''  ) + 1  )  ) + list(range(ord('''¡'''  )		,	ord('''¬'''  ) + 1  )  ) + list(range(ord('''®'''  )		,	ord('''ÿ'''  ) + 1  )  )
			)
			SCREAMING_SNAKE_CASE    	=   bs[:]
			SCREAMING_SNAKE_CASE    	=   0
			for b in range(2**8  ):
						if b not in bs:
									bs.append(a  )
									cs.append(2**8 + n  )
									n += 1
			SCREAMING_SNAKE_CASE    	=   [chr(a  ) for n in cs]
			return dict(zip(a		,	a  )  )


def       a			(   a  )    ->Tuple:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   set()
			SCREAMING_SNAKE_CASE    	=   word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)  )
						SCREAMING_SNAKE_CASE    	=   char
			return pairs





class 						lowerCamelCase			(					__lowerCamelCase				):
	UpperCamelCase_     :				Optional[Any]								=  VOCAB_FILES_NAMES
	UpperCamelCase_     :				str								=  PRETRAINED_VOCAB_FILES_MAP
	UpperCamelCase_     :				str								=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCamelCase_     :				Dict								=  ['input_ids', 'attention_mask']







	def __init__(    self			:List[str]      ,    lowercase			:List[Any]      ,    lowercase			:Dict      ,    lowercase			:List[Any]="replace"      ,    lowercase			:int="<|endoftext|>"      ,    lowercase			:Optional[Any]="<|endoftext|>"      ,    lowercase			:List[Any]="<|endoftext|>"      ,    lowercase			:Optional[Any]=None      ,    lowercase			:int=False      ,    lowercase			:Any=False      ,    **lowercase			:Union[str, Any]      ,    )		->			str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   AddedToken(lowercase      ,    lstrip=lowercase      ,    rstrip=lowercase ) if isinstance(lowercase      ,    lowercase ) else bos_token
				SCREAMING_SNAKE_CASE    	=   AddedToken(lowercase      ,    lstrip=lowercase      ,    rstrip=lowercase ) if isinstance(lowercase      ,    lowercase ) else eos_token
				SCREAMING_SNAKE_CASE    	=   AddedToken(lowercase      ,    lstrip=lowercase      ,    rstrip=lowercase ) if isinstance(lowercase      ,    lowercase ) else unk_token
				SCREAMING_SNAKE_CASE    	=   AddedToken(lowercase      ,    lstrip=lowercase      ,    rstrip=lowercase ) if isinstance(lowercase      ,    lowercase ) else pad_token
				super().__init__(
				    errors=lowercase      ,    unk_token=lowercase      ,    bos_token=lowercase      ,    eos_token=lowercase      ,    pad_token=lowercase      ,    add_prefix_space=lowercase      ,    add_bos_token=lowercase      ,    **lowercase      ,    )
				SCREAMING_SNAKE_CASE    	=   add_bos_token

				with open(lowercase      ,    encoding='''utf-8''' ) as vocab_handle:
							SCREAMING_SNAKE_CASE    	=   json.load(lowercase )
				SCREAMING_SNAKE_CASE    	=   {v: k for k, v in self.encoder.items()}
				SCREAMING_SNAKE_CASE    	=   errors  # how to handle errors in decoding
				SCREAMING_SNAKE_CASE    	=   bytes_to_unicode()
				SCREAMING_SNAKE_CASE    	=   {v: k for k, v in self.byte_encoder.items()}
				with open(lowercase      ,    encoding='''utf-8''' ) as merges_handle:
							SCREAMING_SNAKE_CASE    	=   merges_handle.read().split('''\n''' )[1:-1]
				SCREAMING_SNAKE_CASE    	=   [tuple(merge.split() ) for merge in bpe_merges]
				SCREAMING_SNAKE_CASE    	=   dict(zip(lowercase      ,    range(len(lowercase ) ) ) )
				SCREAMING_SNAKE_CASE    	=   {}
				SCREAMING_SNAKE_CASE    	=   add_prefix_space

				# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
				SCREAMING_SNAKE_CASE    	=   re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )







	@property
	def    snake_case__    (    self			:Optional[int] )		->			List[str]:







				"""simple docstring"""






				return len(self.encoder )







	def    snake_case__    (    self			:Tuple )		->			Any:







				"""simple docstring"""






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







	def    snake_case__    (    self			:List[str]      ,    lowercase			:Optional[Any] )		->			Optional[Any]:







				"""simple docstring"""






				if token in self.cache:
							return self.cache[token]
				SCREAMING_SNAKE_CASE    	=   tuple(lowercase )
				SCREAMING_SNAKE_CASE    	=   get_pairs(lowercase )

				if not pairs:
							return token

				while True:
							SCREAMING_SNAKE_CASE    	=   min(lowercase      ,    key=lambda lowercase : self.bpe_ranks.get(lowercase      ,    float('''inf''' ) ) )
							if bigram not in self.bpe_ranks:
										break
							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   bigram
							SCREAMING_SNAKE_CASE    	=   []
							SCREAMING_SNAKE_CASE    	=   0
							while i < len(lowercase ):
										try:
													SCREAMING_SNAKE_CASE    	=   word.index(lowercase      ,    lowercase )
										except ValueError:
													new_word.extend(word[i:] )
													break
										else:
													new_word.extend(word[i:j] )
													SCREAMING_SNAKE_CASE    	=   j

										if word[i] == first and i < len(lowercase ) - 1 and word[i + 1] == second:
													new_word.append(first + second )
													i += 2
										else:
													new_word.append(word[i] )
													i += 1
							SCREAMING_SNAKE_CASE    	=   tuple(lowercase )
							SCREAMING_SNAKE_CASE    	=   new_word
							if len(lowercase ) == 1:
										break
							else:
										SCREAMING_SNAKE_CASE    	=   get_pairs(lowercase )
				SCREAMING_SNAKE_CASE    	=   ''' '''.join(lowercase )
				SCREAMING_SNAKE_CASE    	=   word
				return word







	def    snake_case__    (    self			:Any      ,    lowercase			:List[str]      ,    lowercase			:str=None )		->			List[str]:







				"""simple docstring"""






				if self.add_bos_token:
							SCREAMING_SNAKE_CASE    	=   [self.bos_token_id]
				else:
							SCREAMING_SNAKE_CASE    	=   []

				SCREAMING_SNAKE_CASE    	=   bos_token_ids + token_ids_a

				if token_ids_a is None:
							return output

				return output + bos_token_ids + token_ids_a







	def    snake_case__    (    self			:Tuple      ,    lowercase			:List[int]      ,    lowercase			:Optional[List[int]] = None      ,    lowercase			:bool = False )		->			List[int]:







				"""simple docstring"""






				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowercase      ,    token_ids_a=lowercase      ,    already_has_special_tokens=lowercase )

				if not self.add_bos_token:
							return super().get_special_tokens_mask(
							    token_ids_a=lowercase      ,    token_ids_a=lowercase      ,    already_has_special_tokens=lowercase )

				if token_ids_a is None:
							return [1] + ([0] * len(lowercase ))
				return [1] + ([0] * len(lowercase )) + [1] + ([0] * len(lowercase ))







	def    snake_case__    (    self			:int      ,    lowercase			:Union[str, Any] )		->			str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   []
				for token in re.findall(self.pat      ,    lowercase ):
							SCREAMING_SNAKE_CASE    	=   ''''''.join(
							    self.byte_encoder[b] for b in token.encode('''utf-8''' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
							bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase ).split(''' ''' ) )
				return bpe_tokens







	def    snake_case__    (    self			:str      ,    lowercase			:List[Any] )		->			Any:







				"""simple docstring"""






				return self.encoder.get(lowercase      ,    self.encoder.get(self.unk_token ) )







	def    snake_case__    (    self			:Optional[Any]      ,    lowercase			:Optional[int] )		->			Union[str, Any]:







				"""simple docstring"""






				return self.decoder.get(lowercase )







	def    snake_case__    (    self			:str      ,    lowercase			:Union[str, Any] )		->			Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   ''''''.join(lowercase )
				SCREAMING_SNAKE_CASE    	=   bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8'''      ,    errors=self.errors )
				return text







	def    snake_case__    (    self			:Any      ,    lowercase			:str      ,    lowercase			:Optional[str] = None )		->			Tuple[str]:







				"""simple docstring"""






				if not os.path.isdir(lowercase ):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
							return
				SCREAMING_SNAKE_CASE    	=   os.path.join(
				    lowercase      ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
				SCREAMING_SNAKE_CASE    	=   os.path.join(
				    lowercase      ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )

				with open(lowercase      ,    '''w'''      ,    encoding='''utf-8''' ) as f:
							f.write(json.dumps(self.encoder      ,    indent=2      ,    sort_keys=lowercase      ,    ensure_ascii=lowercase ) + '''\n''' )

				SCREAMING_SNAKE_CASE    	=   0
				with open(lowercase      ,    '''w'''      ,    encoding='''utf-8''' ) as writer:
							writer.write('''#version: 0.2\n''' )
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,    key=lambda lowercase : kv[1] ):
										if index != token_index:
													logger.warning(
													    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
													    ''' Please check that the tokenizer is not corrupted!''' )
													SCREAMING_SNAKE_CASE    	=   token_index
										writer.write(''' '''.join(lowercase ) + '''\n''' )
										index += 1

				return vocab_file, merge_file







	def    snake_case__    (    self			:Dict      ,    lowercase			:Union[str, Any]      ,    lowercase			:List[str]=False      ,    **lowercase			:Optional[int] )		->			str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   kwargs.pop('''add_prefix_space'''      ,    self.add_prefix_space )
				if is_split_into_words or add_prefix_space:
							SCREAMING_SNAKE_CASE    	=   ''' ''' + text
				return (text, kwargs)







	def    snake_case__    (    self			:Optional[int]      ,    lowercase			:"Conversation" )		->			List[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   []
				for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(lowercase      ,    add_special_tokens=lowercase ) + [self.eos_token_id] )
				if len(lowercase ) > self.model_max_length:
							SCREAMING_SNAKE_CASE    	=   input_ids[-self.model_max_length :]
				return input_ids

import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation


def       a			(   a		,	a		,	a = False		,	a = 1		,	a = 1		,	a = 1_0000		,	)    ->Dict:




			'''simple docstring'''


			assert len(timesteps.shape  ) == 1, "Timesteps should be a 1d-array"

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

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

			# scale embeddings
			SCREAMING_SNAKE_CASE    	=   scale * emb

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

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

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


def       a			(   a		,	a		,	a=False		,	a=0  )    ->Union[str, Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   np.arange(a		,	dtype=np.floataa  )
			SCREAMING_SNAKE_CASE    	=   np.arange(a		,	dtype=np.floataa  )
			SCREAMING_SNAKE_CASE    	=   np.meshgrid(a		,	a  )  # here w goes first
			SCREAMING_SNAKE_CASE    	=   np.stack(a		,	axis=0  )

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


def       a			(   a		,	a  )    ->Optional[int]:




			'''simple docstring'''


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

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

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


def       a			(   a		,	a  )    ->List[Any]:




			'''simple docstring'''


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

			SCREAMING_SNAKE_CASE    	=   np.arange(embed_dim // 2		,	dtype=np.floataa  )
			omega /= embed_dim / 2.0
			SCREAMING_SNAKE_CASE    	=   1.0 / 1_0000**omega  # (D/2,)

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

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

			SCREAMING_SNAKE_CASE    	=   np.concatenate([emb_sin, emb_cos]		,	axis=1  )  # (M, D)
			return emb





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:str      ,    lowercase			:int=2_2_4      ,    lowercase			:Dict=2_2_4      ,    lowercase			:List[Any]=1_6      ,    lowercase			:str=3      ,    lowercase			:List[str]=7_6_8      ,    lowercase			:Tuple=False      ,    lowercase			:Optional[int]=True      ,    lowercase			:str=True      ,    )		->			int:







				"""simple docstring"""






				super().__init__()

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

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

				SCREAMING_SNAKE_CASE    	=   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			:Optional[int]      ,    lowercase			:str )		->			Union[str, Any]:







				"""simple docstring"""






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





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:Union[str, Any]      ,    lowercase			:int      ,    lowercase			:int      ,    lowercase			:str = "silu"      ,    lowercase			:int = None      ,    lowercase			:Optional[str] = None      ,    lowercase			:int=None      ,    )		->			Optional[Any]:







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )

				if cond_proj_dim is not None:
							SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase      ,    bias=lowercase )
				else:
							SCREAMING_SNAKE_CASE    	=   None

				SCREAMING_SNAKE_CASE    	=   get_activation(lowercase )

				if out_dim is not None:
							SCREAMING_SNAKE_CASE    	=   out_dim
				else:
							SCREAMING_SNAKE_CASE    	=   time_embed_dim
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )

				if post_act_fn is None:
							SCREAMING_SNAKE_CASE    	=   None
				else:
							SCREAMING_SNAKE_CASE    	=   get_activation(lowercase )







	def    snake_case__    (    self			:Optional[int]      ,    lowercase			:Tuple      ,    lowercase			:Optional[Any]=None )		->			Tuple:







				"""simple docstring"""






				if condition is not None:
							SCREAMING_SNAKE_CASE    	=   sample + self.cond_proj(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.linear_a(lowercase )

				if self.act is not None:
							SCREAMING_SNAKE_CASE    	=   self.act(lowercase )

				SCREAMING_SNAKE_CASE    	=   self.linear_a(lowercase )

				if self.post_act is not None:
							SCREAMING_SNAKE_CASE    	=   self.post_act(lowercase )
				return sample





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:int      ,    lowercase			:int      ,    lowercase			:bool      ,    lowercase			:float )		->			int:







				"""simple docstring"""






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







	def    snake_case__    (    self			:str      ,    lowercase			:str )		->			List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   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 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:List[str]      ,    lowercase			:int = 2_5_6      ,    lowercase			:float = 1.0      ,    lowercase			:int=True      ,    lowercase			:Any=True      ,    lowercase			:List[Any]=False )		->			Optional[int]:







				"""simple docstring"""






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

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

							SCREAMING_SNAKE_CASE    	=   self.W







	def    snake_case__    (    self			:Optional[Any]      ,    lowercase			:Optional[Any] )		->			Optional[Any]:







				"""simple docstring"""






				if self.log:
							SCREAMING_SNAKE_CASE    	=   torch.log(lowercase )

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

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





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:Optional[int]      ,    lowercase			:int      ,    lowercase			:int      ,    lowercase			:int      ,    lowercase			:int      ,    )		->			List[str]:







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE    	=   height
				SCREAMING_SNAKE_CASE    	=   width
				SCREAMING_SNAKE_CASE    	=   num_embed
				SCREAMING_SNAKE_CASE    	=   embed_dim

				SCREAMING_SNAKE_CASE    	=   nn.Embedding(self.num_embed      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.Embedding(self.height      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.Embedding(self.width      ,    lowercase )







	def    snake_case__    (    self			:int      ,    lowercase			:Tuple )		->			str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.emb(lowercase )

				SCREAMING_SNAKE_CASE    	=   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
				SCREAMING_SNAKE_CASE    	=   height_emb.unsqueeze(2 )

				SCREAMING_SNAKE_CASE    	=   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
				SCREAMING_SNAKE_CASE    	=   width_emb.unsqueeze(1 )

				SCREAMING_SNAKE_CASE    	=   height_emb + width_emb

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

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

				return emb





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:Optional[Any]      ,    lowercase			:Tuple      ,    lowercase			:int      ,    lowercase			:Union[str, Any] )		->			Union[str, Any]:







				"""simple docstring"""






				super().__init__()
				SCREAMING_SNAKE_CASE    	=   dropout_prob > 0
				SCREAMING_SNAKE_CASE    	=   nn.Embedding(num_classes + use_cfg_embedding      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   num_classes
				SCREAMING_SNAKE_CASE    	=   dropout_prob







	def    snake_case__    (    self			:int      ,    lowercase			:Tuple      ,    lowercase			:List[str]=None )		->			Dict:







				"""simple docstring"""






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







	def    snake_case__    (    self			:List[Any]      ,    lowercase			:torch.LongTensor      ,    lowercase			:Tuple=None )		->			Dict:







				"""simple docstring"""






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





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:Any      ,    lowercase			:int = 1_0_2_4      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 1_0      ,    )		->			Optional[int]:







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE    	=   num_image_text_embeds
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    self.num_image_text_embeds * cross_attention_dim )
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )







	def    snake_case__    (    self			:Tuple      ,    lowercase			:torch.FloatTensor      ,    lowercase			:torch.FloatTensor )		->			Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   text_embeds.shape[0]

				# image
				SCREAMING_SNAKE_CASE    	=   self.image_embeds(lowercase )
				SCREAMING_SNAKE_CASE    	=   image_text_embeds.reshape(lowercase      ,    self.num_image_text_embeds      ,    -1 )

				# text
				SCREAMING_SNAKE_CASE    	=   self.text_proj(lowercase )

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





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:Optional[int]      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 3_2      ,    )		->			Dict:







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE    	=   num_image_text_embeds
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    self.num_image_text_embeds * cross_attention_dim )
				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )







	def    snake_case__    (    self			:List[str]      ,    lowercase			:torch.FloatTensor )		->			Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   image_embeds.shape[0]

				# image
				SCREAMING_SNAKE_CASE    	=   self.image_embeds(lowercase )
				SCREAMING_SNAKE_CASE    	=   image_embeds.reshape(lowercase      ,    self.num_image_text_embeds      ,    -1 )
				SCREAMING_SNAKE_CASE    	=   self.norm(lowercase )
				return image_embeds





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:List[str]      ,    lowercase			:int      ,    lowercase			:Union[str, Any]      ,    lowercase			:Optional[Any]=0.1 )		->			int:







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE    	=   Timesteps(num_channels=2_5_6      ,    flip_sin_to_cos=lowercase      ,    downscale_freq_shift=1 )
				SCREAMING_SNAKE_CASE    	=   TimestepEmbedding(in_channels=2_5_6      ,    time_embed_dim=lowercase )
				SCREAMING_SNAKE_CASE    	=   LabelEmbedding(lowercase      ,    lowercase      ,    lowercase )







	def    snake_case__    (    self			:int      ,    lowercase			:Optional[int]      ,    lowercase			:int      ,    lowercase			:Optional[int]=None )		->			List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.time_proj(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.timestep_embedder(timesteps_proj.to(dtype=lowercase ) )  # (N, D)

				SCREAMING_SNAKE_CASE    	=   self.class_embedder(lowercase )  # (N, D)

				SCREAMING_SNAKE_CASE    	=   timesteps_emb + class_labels  # (N, D)

				return conditioning





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:str      ,    lowercase			:int      ,    lowercase			:int      ,    lowercase			:int = 6_4 )		->			str:







				"""simple docstring"""






				super().__init__()
				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )
				SCREAMING_SNAKE_CASE    	=   AttentionPooling(lowercase      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )







	def    snake_case__    (    self			:int      ,    lowercase			:Optional[int] )		->			Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.norma(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.pool(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.proj(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.norma(lowercase )
				return hidden_states





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:Tuple      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 1_5_3_6 )		->			Union[str, Any]:







				"""simple docstring"""






				super().__init__()
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )







	def    snake_case__    (    self			:str      ,    lowercase			:torch.FloatTensor      ,    lowercase			:torch.FloatTensor )		->			List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.text_proj(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.text_norm(lowercase )

				# image
				SCREAMING_SNAKE_CASE    	=   self.image_proj(lowercase )

				return time_image_embeds + time_text_embeds





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:int      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 1_5_3_6 )		->			List[str]:







				"""simple docstring"""






				super().__init__()
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )







	def    snake_case__    (    self			:Any      ,    lowercase			:torch.FloatTensor )		->			Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.image_proj(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.image_norm(lowercase )
				return time_image_embeds





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:List[str]      ,    lowercase			:int = 7_6_8      ,    lowercase			:int = 1_5_3_6 )		->			str:







				"""simple docstring"""






				super().__init__()
				SCREAMING_SNAKE_CASE    	=   nn.Linear(lowercase      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.LayerNorm(lowercase )
				SCREAMING_SNAKE_CASE    	=   nn.Sequential(
				    nn.Convad(3      ,    1_6      ,    3      ,    padding=1 )      ,    nn.SiLU()      ,    nn.Convad(1_6      ,    1_6      ,    3      ,    padding=1 )      ,    nn.SiLU()      ,    nn.Convad(1_6      ,    3_2      ,    3      ,    padding=1      ,    stride=2 )      ,    nn.SiLU()      ,    nn.Convad(3_2      ,    3_2      ,    3      ,    padding=1 )      ,    nn.SiLU()      ,    nn.Convad(3_2      ,    9_6      ,    3      ,    padding=1      ,    stride=2 )      ,    nn.SiLU()      ,    nn.Convad(9_6      ,    9_6      ,    3      ,    padding=1 )      ,    nn.SiLU()      ,    nn.Convad(9_6      ,    2_5_6      ,    3      ,    padding=1      ,    stride=2 )      ,    nn.SiLU()      ,    nn.Convad(2_5_6      ,    4      ,    3      ,    padding=1 )      ,    )







	def    snake_case__    (    self			:Any      ,    lowercase			:torch.FloatTensor      ,    lowercase			:torch.FloatTensor )		->			Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.image_proj(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.image_norm(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.input_hint_block(lowercase )
				return time_image_embeds, hint





class 						lowerCamelCase			(					nn.Module				):







	def __init__(    self			:List[Any]      ,    lowercase			:Any      ,    lowercase			:Union[str, Any]      ,    lowercase			:int=None )		->			Optional[Any]:







				"""simple docstring"""






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







	def    snake_case__    (    self			:int      ,    lowercase			:Optional[int] )		->			List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   x.size()

				def shape(lowercase			:List[Any] ):
							# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
							SCREAMING_SNAKE_CASE    	=   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)
							SCREAMING_SNAKE_CASE    	=   x.transpose(1      ,    2 )
							# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
							SCREAMING_SNAKE_CASE    	=   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)
							SCREAMING_SNAKE_CASE    	=   x.transpose(1      ,    2 )
							return x

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

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

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

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

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

				return a[:, 0, :]  # cls_token