import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation


def SCREAMING_SNAKE_CASE (   lowercase_:		torch.Tensor						,  lowercase_:		int						,  lowercase_:		bool = False						,  lowercase_:		float = 1						,  lowercase_:		float = 1						,  lowercase_:		int = 10000						,  ):
       assert len(timesteps.shape ) == 1, "Timesteps should be a 1d-array"

       _UpperCamelCase   :				List[Any]       =     embedding_dim // 2
       _UpperCamelCase   :				str       =     -math.log(lowercase_ ) * torch.arange(
           start=0						,  end=lowercase_						,  dtype=torch.floataa						,  device=timesteps.device )
       _UpperCamelCase   :				Optional[int]       =     exponent / (half_dim - downscale_freq_shift)

       _UpperCamelCase   :				Tuple       =     torch.exp(lowercase_ )
       _UpperCamelCase   :				str       =     timesteps[:, None].float() * emb[None, :]

       # scale embeddings
       _UpperCamelCase   :				List[Any]       =     scale * emb

       # concat sine and cosine embeddings
       _UpperCamelCase   :				Optional[Any]       =     torch.cat([torch.sin(lowercase_ ), torch.cos(lowercase_ )]						,  dim=-1 )

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

       # zero pad
       if embedding_dim % 2 == 1:
              _UpperCamelCase   :				Optional[int]       =     torch.nn.functional.pad(lowercase_						,  (0, 1, 0, 0) )
       return emb


def SCREAMING_SNAKE_CASE (   lowercase_:		Tuple						,  lowercase_:		Tuple						,  lowercase_:		Optional[int]=False						,  lowercase_:		Dict=0 ):
       _UpperCamelCase   :				int       =     np.arange(lowercase_						,  dtype=np.floataa )
       _UpperCamelCase   :				Optional[Any]       =     np.arange(lowercase_						,  dtype=np.floataa )
       _UpperCamelCase   :				List[str]       =     np.meshgrid(lowercase_						,  lowercase_ )  # here w goes first
       _UpperCamelCase   :				Optional[Any]       =     np.stack(lowercase_						,  axis=0 )

       _UpperCamelCase   :				Dict       =     grid.reshape([2, 1, grid_size, grid_size] )
       _UpperCamelCase   :				Union[str, Any]       =     get_ad_sincos_pos_embed_from_grid(lowercase_						,  lowercase_ )
       if cls_token and extra_tokens > 0:
              _UpperCamelCase   :				Tuple       =     np.concatenate([np.zeros([extra_tokens, embed_dim] ), pos_embed]						,  axis=0 )
       return pos_embed


def SCREAMING_SNAKE_CASE (   lowercase_:		int						,  lowercase_:		int ):
       if embed_dim % 2 != 0:
              raise ValueError('embed_dim must be divisible by 2' )

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

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


def SCREAMING_SNAKE_CASE (   lowercase_:		List[str]						,  lowercase_:		Any ):
       if embed_dim % 2 != 0:
              raise ValueError('embed_dim must be divisible by 2' )

       _UpperCamelCase   :				str       =     np.arange(embed_dim // 2						,  dtype=np.floataa )
       omega /= embed_dim / 2.0
       _UpperCamelCase   :				Any       =     1.0 / 10000**omega  # (D/2,)

       _UpperCamelCase   :				Dict       =     pos.reshape(-1 )  # (M,)
       _UpperCamelCase   :				Tuple       =     np.einsum('m,d->md'						,  lowercase_						,  lowercase_ )  # (M, D/2), outer product

       _UpperCamelCase   :				Dict       =     np.sin(lowercase_ )  # (M, D/2)
       _UpperCamelCase   :				Union[str, Any]       =     np.cos(lowercase_ )  # (M, D/2)

       _UpperCamelCase   :				List[str]       =     np.concatenate([emb_sin, emb_cos]						,  axis=1 )  # (M, D)
       return emb




class       __lowerCAmelCase     (				nn.Module  ):



 '''simple docstring'''

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

        _UpperCamelCase   :				Any       =     (height // patch_size) * (width // patch_size)
        _UpperCamelCase   :				Optional[int]       =     flatten
        _UpperCamelCase   :				Dict       =     layer_norm

        _UpperCamelCase   :				Tuple       =     nn.Convad(
            lowerCamelCase				,	lowerCamelCase				,	kernel_size=(patch_size, patch_size)				,	stride=lowerCamelCase				,	bias=lowerCamelCase     )
        if layer_norm:
               _UpperCamelCase   :				Optional[Any]       =     nn.LayerNorm(lowerCamelCase				,	elementwise_affine=lowerCamelCase				,	eps=1e-6     )
        else:
               _UpperCamelCase   :				Dict       =     None

        _UpperCamelCase   :				Union[str, Any]       =     get_ad_sincos_pos_embed(lowerCamelCase				,	int(num_patches**0.5     )     )
        self.register_buffer('pos_embed'				,	torch.from_numpy(lowerCamelCase     ).float().unsqueeze(0     )				,	persistent=lowerCamelCase     )

 def     __snake_case    (	self				:						int				,	lowerCamelCase				:						Tuple     )       ->							int:
        _UpperCamelCase   :				Optional[int]       =     self.proj(lowerCamelCase     )
        if self.flatten:
               _UpperCamelCase   :				List[str]       =     latent.flatten(2     ).transpose(1				,	2     )  # BCHW -> BNC
        if self.layer_norm:
               _UpperCamelCase   :				int       =     self.norm(lowerCamelCase     )
        return latent + self.pos_embed







class       __lowerCAmelCase     (				nn.Module  ):



 '''simple docstring'''

 def __init__(	self				:						Optional[int]				,	lowerCamelCase				:						int				,	lowerCamelCase				:						int				,	lowerCamelCase				:						str = "silu"				,	lowerCamelCase				:						int = None				,	lowerCamelCase				:						Optional[str] = None				,	lowerCamelCase				:						Dict=None				,	)       ->							Dict:
        super().__init__()

        _UpperCamelCase   :				List[Any]       =     nn.Linear(lowerCamelCase				,	lowerCamelCase     )

        if cond_proj_dim is not None:
               _UpperCamelCase   :				List[str]       =     nn.Linear(lowerCamelCase				,	lowerCamelCase				,	bias=lowerCamelCase     )
        else:
               _UpperCamelCase   :				Optional[Any]       =     None

        _UpperCamelCase   :				Union[str, Any]       =     get_activation(lowerCamelCase     )

        if out_dim is not None:
               _UpperCamelCase   :				List[str]       =     out_dim
        else:
               _UpperCamelCase   :				Optional[Any]       =     time_embed_dim
        _UpperCamelCase   :				int       =     nn.Linear(lowerCamelCase				,	lowerCamelCase     )

        if post_act_fn is None:
               _UpperCamelCase   :				Dict       =     None
        else:
               _UpperCamelCase   :				str       =     get_activation(lowerCamelCase     )

 def     __snake_case    (	self				:						Union[str, Any]				,	lowerCamelCase				:						Tuple				,	lowerCamelCase				:						Optional[int]=None     )       ->							List[Any]:
        if condition is not None:
               _UpperCamelCase   :				List[Any]       =     sample + self.cond_proj(lowerCamelCase     )
        _UpperCamelCase   :				List[str]       =     self.linear_a(lowerCamelCase     )

        if self.act is not None:
               _UpperCamelCase   :				Optional[int]       =     self.act(lowerCamelCase     )

        _UpperCamelCase   :				Union[str, Any]       =     self.linear_a(lowerCamelCase     )

        if self.post_act is not None:
               _UpperCamelCase   :				Any       =     self.post_act(lowerCamelCase     )
        return sample







class       __lowerCAmelCase     (				nn.Module  ):



 '''simple docstring'''

 def __init__(	self				:						Dict				,	lowerCamelCase				:						int				,	lowerCamelCase				:						bool				,	lowerCamelCase				:						float     )       ->							Any:
        super().__init__()
        _UpperCamelCase   :				Dict       =     num_channels
        _UpperCamelCase   :				Optional[Any]       =     flip_sin_to_cos
        _UpperCamelCase   :				Union[str, Any]       =     downscale_freq_shift

 def     __snake_case    (	self				:						str				,	lowerCamelCase				:						int     )       ->							List[str]:
        _UpperCamelCase   :				List[str]       =     get_timestep_embedding(
            lowerCamelCase				,	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  ):



 '''simple docstring'''

 def __init__(	self				:						List[str]				,	lowerCamelCase				:						int = 2_5_6				,	lowerCamelCase				:						float = 1.0				,	lowerCamelCase				:						str=True				,	lowerCamelCase				:						Optional[Any]=True				,	lowerCamelCase				:						Optional[int]=False     )       ->							Union[str, Any]:
        super().__init__()
        _UpperCamelCase   :				Optional[int]       =     nn.Parameter(torch.randn(lowerCamelCase     ) * scale				,	requires_grad=lowerCamelCase     )
        _UpperCamelCase   :				Any       =     log
        _UpperCamelCase   :				List[Any]       =     flip_sin_to_cos

        if set_W_to_weight:
               # to delete later
               _UpperCamelCase   :				List[Any]       =     nn.Parameter(torch.randn(lowerCamelCase     ) * scale				,	requires_grad=lowerCamelCase     )

               _UpperCamelCase   :				Optional[int]       =     self.W

 def     __snake_case    (	self				:						Optional[Any]				,	lowerCamelCase				:						Optional[Any]     )       ->							List[str]:
        if self.log:
               _UpperCamelCase   :				Any       =     torch.log(lowerCamelCase     )

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

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







class       __lowerCAmelCase     (				nn.Module  ):



 '''simple docstring'''

 def __init__(	self				:						List[str]				,	lowerCamelCase				:						int				,	lowerCamelCase				:						int				,	lowerCamelCase				:						int				,	lowerCamelCase				:						int				,	)       ->							List[Any]:
        super().__init__()

        _UpperCamelCase   :				str       =     height
        _UpperCamelCase   :				List[Any]       =     width
        _UpperCamelCase   :				Tuple       =     num_embed
        _UpperCamelCase   :				Tuple       =     embed_dim

        _UpperCamelCase   :				Tuple       =     nn.Embedding(self.num_embed				,	lowerCamelCase     )
        _UpperCamelCase   :				Any       =     nn.Embedding(self.height				,	lowerCamelCase     )
        _UpperCamelCase   :				List[Any]       =     nn.Embedding(self.width				,	lowerCamelCase     )

 def     __snake_case    (	self				:						Any				,	lowerCamelCase				:						Optional[Any]     )       ->							int:
        _UpperCamelCase   :				List[Any]       =     self.emb(lowerCamelCase     )

        _UpperCamelCase   :				Tuple       =     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
        _UpperCamelCase   :				str       =     height_emb.unsqueeze(2     )

        _UpperCamelCase   :				int       =     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
        _UpperCamelCase   :				List[str]       =     width_emb.unsqueeze(1     )

        _UpperCamelCase   :				Any       =     height_emb + width_emb

        # 1 x H x W x D -> 1 x L xD
        _UpperCamelCase   :				Union[str, Any]       =     pos_emb.view(1				,	self.height * self.width				,	-1     )

        _UpperCamelCase   :				List[str]       =     emb + pos_emb[:, : emb.shape[1], :]

        return emb







class       __lowerCAmelCase     (				nn.Module  ):



 '''simple docstring'''

 def __init__(	self				:						str				,	lowerCamelCase				:						Union[str, Any]				,	lowerCamelCase				:						str				,	lowerCamelCase				:						Dict     )       ->							List[str]:
        super().__init__()
        _UpperCamelCase   :				str       =     dropout_prob > 0
        _UpperCamelCase   :				Dict       =     nn.Embedding(num_classes + use_cfg_embedding				,	lowerCamelCase     )
        _UpperCamelCase   :				List[Any]       =     num_classes
        _UpperCamelCase   :				Optional[Any]       =     dropout_prob

 def     __snake_case    (	self				:						Optional[int]				,	lowerCamelCase				:						Dict				,	lowerCamelCase				:						str=None     )       ->							Union[str, Any]:
        if force_drop_ids is None:
               _UpperCamelCase   :				str       =     torch.rand(labels.shape[0]				,	device=labels.device     ) < self.dropout_prob
        else:
               _UpperCamelCase   :				Optional[int]       =     torch.tensor(force_drop_ids == 1     )
        _UpperCamelCase   :				List[str]       =     torch.where(lowerCamelCase				,	self.num_classes				,	lowerCamelCase     )
        return labels

 def     __snake_case    (	self				:						Union[str, Any]				,	lowerCamelCase				:						torch.LongTensor				,	lowerCamelCase				:						Optional[Any]=None     )       ->							Dict:
        _UpperCamelCase   :				Any       =     self.dropout_prob > 0
        if (self.training and use_dropout) or (force_drop_ids is not None):
               _UpperCamelCase   :				Optional[int]       =     self.token_drop(lowerCamelCase				,	lowerCamelCase     )
        _UpperCamelCase   :				Optional[int]       =     self.embedding_table(lowerCamelCase     )
        return embeddings







class       __lowerCAmelCase     (				nn.Module  ):



 '''simple docstring'''

 def __init__(	self				:						Union[str, Any]				,	lowerCamelCase				:						int = 1_0_2_4				,	lowerCamelCase				:						int = 7_6_8				,	lowerCamelCase				:						int = 7_6_8				,	lowerCamelCase				:						int = 1_0				,	)       ->							int:
        super().__init__()

        _UpperCamelCase   :				Optional[int]       =     num_image_text_embeds
        _UpperCamelCase   :				Dict       =     nn.Linear(lowerCamelCase				,	self.num_image_text_embeds * cross_attention_dim     )
        _UpperCamelCase   :				Dict       =     nn.Linear(lowerCamelCase				,	lowerCamelCase     )

 def     __snake_case    (	self				:						Optional[Any]				,	lowerCamelCase				:						torch.FloatTensor				,	lowerCamelCase				:						torch.FloatTensor     )       ->							Dict:
        _UpperCamelCase   :				List[str]       =     text_embeds.shape[0]

        # image
        _UpperCamelCase   :				Dict       =     self.image_embeds(lowerCamelCase     )
        _UpperCamelCase   :				Optional[Any]       =     image_text_embeds.reshape(lowerCamelCase				,	self.num_image_text_embeds				,	-1     )

        # text
        _UpperCamelCase   :				Optional[int]       =     self.text_proj(lowerCamelCase     )

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







class       __lowerCAmelCase     (				nn.Module  ):



 '''simple docstring'''

 def __init__(	self				:						List[Any]				,	lowerCamelCase				:						int = 7_6_8				,	lowerCamelCase				:						int = 7_6_8				,	lowerCamelCase				:						int = 3_2				,	)       ->							Dict:
        super().__init__()

        _UpperCamelCase   :				Optional[Any]       =     num_image_text_embeds
        _UpperCamelCase   :				Union[str, Any]       =     nn.Linear(lowerCamelCase				,	self.num_image_text_embeds * cross_attention_dim     )
        _UpperCamelCase   :				Optional[Any]       =     nn.LayerNorm(lowerCamelCase     )

 def     __snake_case    (	self				:						Dict				,	lowerCamelCase				:						torch.FloatTensor     )       ->							Union[str, Any]:
        _UpperCamelCase   :				List[Any]       =     image_embeds.shape[0]

        # image
        _UpperCamelCase   :				List[str]       =     self.image_embeds(lowerCamelCase     )
        _UpperCamelCase   :				Any       =     image_embeds.reshape(lowerCamelCase				,	self.num_image_text_embeds				,	-1     )
        _UpperCamelCase   :				int       =     self.norm(lowerCamelCase     )
        return image_embeds







class       __lowerCAmelCase     (				nn.Module  ):



 '''simple docstring'''

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

        _UpperCamelCase   :				Optional[Any]       =     Timesteps(num_channels=2_5_6				,	flip_sin_to_cos=lowerCamelCase				,	downscale_freq_shift=1     )
        _UpperCamelCase   :				Union[str, Any]       =     TimestepEmbedding(in_channels=2_5_6				,	time_embed_dim=lowerCamelCase     )
        _UpperCamelCase   :				List[Any]       =     LabelEmbedding(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase     )

 def     __snake_case    (	self				:						Dict				,	lowerCamelCase				:						Union[str, Any]				,	lowerCamelCase				:						Tuple				,	lowerCamelCase				:						Tuple=None     )       ->							Tuple:
        _UpperCamelCase   :				Optional[int]       =     self.time_proj(lowerCamelCase     )
        _UpperCamelCase   :				Union[str, Any]       =     self.timestep_embedder(timesteps_proj.to(dtype=lowerCamelCase     )     )  # (N, D)

        _UpperCamelCase   :				List[Any]       =     self.class_embedder(lowerCamelCase     )  # (N, D)

        _UpperCamelCase   :				Any       =     timesteps_emb + class_labels  # (N, D)

        return conditioning







class       __lowerCAmelCase     (				nn.Module  ):



 '''simple docstring'''

 def __init__(	self				:						Tuple				,	lowerCamelCase				:						int				,	lowerCamelCase				:						int				,	lowerCamelCase				:						int = 6_4     )       ->							Optional[Any]:
        super().__init__()
        _UpperCamelCase   :				Optional[int]       =     nn.LayerNorm(lowerCamelCase     )
        _UpperCamelCase   :				Tuple       =     AttentionPooling(lowerCamelCase				,	lowerCamelCase     )
        _UpperCamelCase   :				List[Any]       =     nn.Linear(lowerCamelCase				,	lowerCamelCase     )
        _UpperCamelCase   :				List[str]       =     nn.LayerNorm(lowerCamelCase     )

 def     __snake_case    (	self				:						Any				,	lowerCamelCase				:						Union[str, Any]     )       ->							Optional[int]:
        _UpperCamelCase   :				Optional[Any]       =     self.norma(lowerCamelCase     )
        _UpperCamelCase   :				List[str]       =     self.pool(lowerCamelCase     )
        _UpperCamelCase   :				Union[str, Any]       =     self.proj(lowerCamelCase     )
        _UpperCamelCase   :				int       =     self.norma(lowerCamelCase     )
        return hidden_states







class       __lowerCAmelCase     (				nn.Module  ):



 '''simple docstring'''

 def __init__(	self				:						Dict				,	lowerCamelCase				:						int = 7_6_8				,	lowerCamelCase				:						int = 7_6_8				,	lowerCamelCase				:						int = 1_5_3_6     )       ->							Tuple:
        super().__init__()
        _UpperCamelCase   :				str       =     nn.Linear(lowerCamelCase				,	lowerCamelCase     )
        _UpperCamelCase   :				int       =     nn.LayerNorm(lowerCamelCase     )
        _UpperCamelCase   :				List[Any]       =     nn.Linear(lowerCamelCase				,	lowerCamelCase     )

 def     __snake_case    (	self				:						List[Any]				,	lowerCamelCase				:						torch.FloatTensor				,	lowerCamelCase				:						torch.FloatTensor     )       ->							List[Any]:
        # text
        _UpperCamelCase   :				Dict       =     self.text_proj(lowerCamelCase     )
        _UpperCamelCase   :				int       =     self.text_norm(lowerCamelCase     )

        # image
        _UpperCamelCase   :				Tuple       =     self.image_proj(lowerCamelCase     )

        return time_image_embeds + time_text_embeds







class       __lowerCAmelCase     (				nn.Module  ):



 '''simple docstring'''

 def __init__(	self				:						Any				,	lowerCamelCase				:						int = 7_6_8				,	lowerCamelCase				:						int = 1_5_3_6     )       ->							List[Any]:
        super().__init__()
        _UpperCamelCase   :				Optional[Any]       =     nn.Linear(lowerCamelCase				,	lowerCamelCase     )
        _UpperCamelCase   :				List[Any]       =     nn.LayerNorm(lowerCamelCase     )

 def     __snake_case    (	self				:						Tuple				,	lowerCamelCase				:						torch.FloatTensor     )       ->							Any:
        # image
        _UpperCamelCase   :				List[str]       =     self.image_proj(lowerCamelCase     )
        _UpperCamelCase   :				Union[str, Any]       =     self.image_norm(lowerCamelCase     )
        return time_image_embeds







class       __lowerCAmelCase     (				nn.Module  ):



 '''simple docstring'''

 def __init__(	self				:						List[Any]				,	lowerCamelCase				:						int = 7_6_8				,	lowerCamelCase				:						int = 1_5_3_6     )       ->							Dict:
        super().__init__()
        _UpperCamelCase   :				List[Any]       =     nn.Linear(lowerCamelCase				,	lowerCamelCase     )
        _UpperCamelCase   :				Dict       =     nn.LayerNorm(lowerCamelCase     )
        _UpperCamelCase   :				Optional[Any]       =     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				:						Optional[int]				,	lowerCamelCase				:						torch.FloatTensor				,	lowerCamelCase				:						torch.FloatTensor     )       ->							Optional[int]:
        # image
        _UpperCamelCase   :				Optional[int]       =     self.image_proj(lowerCamelCase     )
        _UpperCamelCase   :				int       =     self.image_norm(lowerCamelCase     )
        _UpperCamelCase   :				Union[str, Any]       =     self.input_hint_block(lowerCamelCase     )
        return time_image_embeds, hint







class       __lowerCAmelCase     (				nn.Module  ):



 '''simple docstring'''

 def __init__(	self				:						Tuple				,	lowerCamelCase				:						int				,	lowerCamelCase				:						Optional[Any]				,	lowerCamelCase				:						Tuple=None     )       ->							str:
        super().__init__()
        _UpperCamelCase   :				Optional[int]       =     dtype
        _UpperCamelCase   :				Tuple       =     nn.Parameter(torch.randn(1				,	lowerCamelCase     ) / embed_dim**0.5     )
        _UpperCamelCase   :				Dict       =     nn.Linear(lowerCamelCase				,	lowerCamelCase				,	dtype=self.dtype     )
        _UpperCamelCase   :				Any       =     nn.Linear(lowerCamelCase				,	lowerCamelCase				,	dtype=self.dtype     )
        _UpperCamelCase   :				Any       =     nn.Linear(lowerCamelCase				,	lowerCamelCase				,	dtype=self.dtype     )
        _UpperCamelCase   :				Optional[int]       =     num_heads
        _UpperCamelCase   :				Tuple       =     embed_dim // self.num_heads

 def     __snake_case    (	self				:						int				,	lowerCamelCase				:						str     )       ->							Union[str, Any]:
        _UpperCamelCase  ,					_UpperCamelCase  ,					_UpperCamelCase   :				Dict       =     x.size()

        def shape(lowerCamelCase				:						Union[str, Any]     ):
               # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
               _UpperCamelCase   :				Optional[Any]       =     x.view(lowerCamelCase				,	-1				,	self.num_heads				,	self.dim_per_head     )
               # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
               _UpperCamelCase   :				int       =     x.transpose(1				,	2     )
               # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
               _UpperCamelCase   :				Optional[Any]       =     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)
               _UpperCamelCase   :				str       =     x.transpose(1				,	2     )
               return x

        _UpperCamelCase   :				List[str]       =     x.mean(dim=1				,	keepdim=lowerCamelCase     ) + self.positional_embedding.to(x.dtype     )
        _UpperCamelCase   :				Optional[Any]       =     torch.cat([class_token, x]				,	dim=1     )  # (bs, length+1, width)

        # (bs*n_heads, class_token_length, dim_per_head)
        _UpperCamelCase   :				Union[str, Any]       =     shape(self.q_proj(lowerCamelCase     )     )
        # (bs*n_heads, length+class_token_length, dim_per_head)
        _UpperCamelCase   :				Optional[Any]       =     shape(self.k_proj(lowerCamelCase     )     )
        _UpperCamelCase   :				int       =     shape(self.v_proj(lowerCamelCase     )     )

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

        # (bs*n_heads, dim_per_head, class_token_length)
        _UpperCamelCase   :				Any       =     torch.einsum('bts,bcs->bct'				,	lowerCamelCase				,	lowerCamelCase     )

        # (bs, length+1, width)
        _UpperCamelCase   :				Union[str, Any]       =     a.reshape(lowerCamelCase				,	-1				,	1     ).transpose(1				,	2     )

        return a[:, 0, :]  # cls_token




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

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
 if is_torch_available():
  import torch
 if is_tf_available():
  import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


UpperCAmelCase							= logging.get_logger(__name__)

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

UpperCAmelCase							= {
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
}

UpperCAmelCase							= {
    '''Salesforce/codegen-350M-mono''': 20_48,
}


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


def SCREAMING_SNAKE_CASE (   lowercase_:		int ):
       _UpperCamelCase   :				List[str]       =     set()
       _UpperCamelCase   :				Optional[int]       =     word[0]
       for char in word[1:]:
              pairs.add((prev_char, char) )
              _UpperCamelCase   :				Optional[Any]       =     char
       return pairs





class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Union[str, Any] 			=       VOCAB_FILES_NAMES
 _lowerCamelCase  :		Optional[int] 			=       PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase  :		List[str] 			=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase  :		Optional[int] 			=       ['input_ids', 'attention_mask']

 def __init__(	self				:						Union[str, Any]				,	lowerCamelCase				:						Tuple				,	lowerCamelCase				:						str				,	lowerCamelCase				:						Optional[Any]="replace"				,	lowerCamelCase				:						int="<|endoftext|>"				,	lowerCamelCase				:						Dict="<|endoftext|>"				,	lowerCamelCase				:						Dict="<|endoftext|>"				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						List[Any]=False				,	lowerCamelCase				:						Optional[int]=False				,	**lowerCamelCase				:						Optional[Any]				,	)       ->							int:
        _UpperCamelCase   :				int       =     AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase     ) if isinstance(lowerCamelCase				,	lowerCamelCase     ) else bos_token
        _UpperCamelCase   :				Any       =     AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase     ) if isinstance(lowerCamelCase				,	lowerCamelCase     ) else eos_token
        _UpperCamelCase   :				Dict       =     AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase     ) if isinstance(lowerCamelCase				,	lowerCamelCase     ) else unk_token
        _UpperCamelCase   :				Dict       =     AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase     ) if isinstance(lowerCamelCase				,	lowerCamelCase     ) else pad_token
        super().__init__(
            errors=lowerCamelCase				,	unk_token=lowerCamelCase				,	bos_token=lowerCamelCase				,	eos_token=lowerCamelCase				,	pad_token=lowerCamelCase				,	add_prefix_space=lowerCamelCase				,	add_bos_token=lowerCamelCase				,	**lowerCamelCase				,	)
        _UpperCamelCase   :				List[str]       =     add_bos_token

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

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        _UpperCamelCase   :				Union[str, Any]       =     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[Any]     )       ->							Union[str, Any]:
        return len(self.encoder     )

 def     __snake_case    (	self				:						Union[str, Any]     )       ->							Optional[Any]:
        return dict(self.encoder				,	**self.added_tokens_encoder     )

 def     __snake_case    (	self				:						int				,	lowerCamelCase				:						Dict     )       ->							Union[str, Any]:
        if token in self.cache:
               return self.cache[token]
        _UpperCamelCase   :				Dict       =     tuple(lowerCamelCase     )
        _UpperCamelCase   :				Union[str, Any]       =     get_pairs(lowerCamelCase     )

        if not pairs:
               return token

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

                      if word[i] == first and i < len(lowerCamelCase     ) - 1 and word[i + 1] == second:
                             new_word.append(first + second     )
                             i += 2
                      else:
                             new_word.append(word[i]     )
                             i += 1
               _UpperCamelCase   :				List[str]       =     tuple(lowerCamelCase     )
               _UpperCamelCase   :				Tuple       =     new_word
               if len(lowerCamelCase     ) == 1:
                      break
               else:
                      _UpperCamelCase   :				int       =     get_pairs(lowerCamelCase     )
        _UpperCamelCase   :				List[Any]       =     ' '.join(lowerCamelCase     )
        _UpperCamelCase   :				List[str]       =     word
        return word

 def     __snake_case    (	self				:						Optional[Any]				,	lowerCamelCase				:						str				,	lowerCamelCase				:						Any=None     )       ->							Any:
        if self.add_bos_token:
               _UpperCamelCase   :				List[str]       =     [self.bos_token_id]
        else:
               _UpperCamelCase   :				List[str]       =     []

        _UpperCamelCase   :				Union[str, Any]       =     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				:						Union[str, Any]				,	lowerCamelCase				:						Union[str, Any]     )       ->							str:
        _UpperCamelCase   :				Tuple       =     []
        for token in re.findall(self.pat				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     ''.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(lowerCamelCase     ).split(' '     )     )
        return bpe_tokens

 def     __snake_case    (	self				:						int				,	lowerCamelCase				:						Any     )       ->							List[str]:
        return self.encoder.get(lowerCamelCase				,	self.encoder.get(self.unk_token     )     )

 def     __snake_case    (	self				:						Optional[int]				,	lowerCamelCase				:						List[Any]     )       ->							List[str]:
        return self.decoder.get(lowerCamelCase     )

 def     __snake_case    (	self				:						Optional[int]				,	lowerCamelCase				:						Optional[Any]     )       ->							List[Any]:
        _UpperCamelCase   :				Union[str, Any]       =     ''.join(lowerCamelCase     )
        _UpperCamelCase   :				Tuple       =     bytearray([self.byte_decoder[c] for c in text]     ).decode('utf-8'				,	errors=self.errors     )
        return text

 def     __snake_case    (	self				:						Any				,	lowerCamelCase				:						str				,	lowerCamelCase				:						Optional[str] = None     )       ->							Tuple[str]:
        if not os.path.isdir(lowerCamelCase     ):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
               return
        _UpperCamelCase   :				Any       =     os.path.join(
            lowerCamelCase				,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
        _UpperCamelCase   :				Optional[int]       =     os.path.join(
            lowerCamelCase				,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']     )

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

        _UpperCamelCase   :				Any       =     0
        with open(lowerCamelCase				,	'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 lowerCamelCase     : 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!'     )
                             _UpperCamelCase   :				Dict       =     token_index
                      writer.write(' '.join(lowerCamelCase     ) + '\n'     )
                      index += 1

        return vocab_file, merge_file

 def     __snake_case    (	self				:						Any				,	lowerCamelCase				:						Optional[int]				,	lowerCamelCase				:						int=False				,	**lowerCamelCase				:						Any     )       ->							Tuple:
        _UpperCamelCase   :				Optional[int]       =     kwargs.pop('add_prefix_space'				,	self.add_prefix_space     )
        if is_split_into_words or add_prefix_space:
               _UpperCamelCase   :				Union[str, Any]       =     ' ' + text
        return (text, kwargs)

 def     __snake_case    (	self				:						str				,	lowerCamelCase				:						Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]				,	lowerCamelCase				:						bool = False				,	lowerCamelCase				:						bool = None				,	lowerCamelCase				:						Optional[List[str]] = None				,	**lowerCamelCase				:						Union[str, Any]				,	)       ->							str:
        _UpperCamelCase   :				List[str]       =     super()._decode(
            token_ids=lowerCamelCase				,	skip_special_tokens=lowerCamelCase				,	clean_up_tokenization_spaces=lowerCamelCase				,	**lowerCamelCase				,	)

        if truncate_before_pattern is not None and len(lowerCamelCase     ) > 0:
               _UpperCamelCase   :				Union[str, Any]       =     self.truncate(lowerCamelCase				,	lowerCamelCase     )

        return decoded_text

 def     __snake_case    (	self				:						Any				,	lowerCamelCase				:						Dict				,	lowerCamelCase				:						Optional[int]     )       ->							Any:
        def find_re(lowerCamelCase				:						int				,	lowerCamelCase				:						int				,	lowerCamelCase				:						List[Any]     ):
               _UpperCamelCase   :				Dict       =     pattern.search(lowerCamelCase				,	lowerCamelCase     )
               return m.start() if m else -1

        _UpperCamelCase   :				int       =     [re.compile(lowerCamelCase				,	re.MULTILINE     ) for pattern in truncate_before_pattern]

        _UpperCamelCase   :				Union[str, Any]       =     list(re.finditer('^print'				,	lowerCamelCase				,	re.MULTILINE     )     )

        if len(lowerCamelCase     ) > 1:
               _UpperCamelCase   :				List[Any]       =     completion[: prints[1].start()]

        _UpperCamelCase   :				Dict       =     list(re.finditer('^def'				,	lowerCamelCase				,	re.MULTILINE     )     )

        if len(lowerCamelCase     ) > 1:
               _UpperCamelCase   :				Tuple       =     completion[: defs[1].start()]

        _UpperCamelCase   :				Optional[Any]       =     0

        _UpperCamelCase   :				int       =     [
            pos for pos in [find_re(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase     ) for terminal in terminals] if pos != -1
        ]

        if len(lowerCamelCase     ) > 0:
               return completion[: min(lowerCamelCase     )]
        else:
               return completion



