




'''simple docstring'''


import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation
def 		snake_case_    (UpperCamelCase       :  torch.Tensor ,						UpperCamelCase       :  int ,						UpperCamelCase       :  bool = False ,						UpperCamelCase       :  float = 1 ,						UpperCamelCase       :  float = 1 ,						UpperCamelCase       :  int = 1_0000 ,						):





							'''simple docstring'''


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

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

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

							# scale embeddings
							_a	     =				scale * emb

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

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

							# zero pad
							if embedding_dim % 2 == 1:
														_a	     =				torch.nn.functional.pad(UpperCamelCase ,						(0, 1, 0, 0)      )
							return emb
def 		snake_case_    (UpperCamelCase       :  Dict ,						UpperCamelCase       :  Union[str, Any] ,						UpperCamelCase       :  Union[str, Any]=False ,						UpperCamelCase       :  Optional[int]=0      ):





							'''simple docstring'''


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

							_a	     =				grid.reshape([2, 1, grid_size, grid_size]      )
							_a	     =				get_ad_sincos_pos_embed_from_grid(UpperCamelCase ,						UpperCamelCase      )
							if cls_token and extra_tokens > 0:
														_a	     =				np.concatenate([np.zeros([extra_tokens, embed_dim]      ), pos_embed] ,						axis=0      )
							return pos_embed
def 		snake_case_    (UpperCamelCase       :  Tuple ,						UpperCamelCase       :  List[Any]      ):





							'''simple docstring'''


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

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

							_a	     =				np.concatenate([emb_h, emb_w] ,						axis=1      )  # (H*W, D)
							return emb
def 		snake_case_    (UpperCamelCase       :  Optional[Any] ,						UpperCamelCase       :  List[str]      ):





							'''simple docstring'''


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

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

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

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

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

class 			A    (		nn.Module    ):







							def __init__(  self       :  str							,			lowerCAmelCase_       :  Tuple=2_24							,			lowerCAmelCase_       :  List[str]=2_24							,			lowerCAmelCase_       :  int=16							,			lowerCAmelCase_       :  Dict=3							,			lowerCAmelCase_       :  int=7_68							,			lowerCAmelCase_       :  List[Any]=False							,			lowerCAmelCase_       :  Any=True							,			lowerCAmelCase_       :  Union[str, Any]=True							,			)			->							int:


														"""simple docstring"""


														super().__init__()

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

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

														_a	     =				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    __lowerCAmelCase			(  self       :  Any							,			lowerCAmelCase_       :  int					)			->							Optional[int]:


														"""simple docstring"""


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



class 			A    (		nn.Module    ):







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


														"""simple docstring"""


														super().__init__()

														_a	     =				nn.Linear(lowerCAmelCase_							,			lowerCAmelCase_					)

														if cond_proj_dim is not None:
																					_a	     =				nn.Linear(lowerCAmelCase_							,			lowerCAmelCase_							,			bias=lowerCAmelCase_					)
														else:
																					_a	     =				None

														_a	     =				get_activation(lowerCAmelCase_					)

														if out_dim is not None:
																					_a	     =				out_dim
														else:
																					_a	     =				time_embed_dim
														_a	     =				nn.Linear(lowerCAmelCase_							,			lowerCAmelCase_					)

														if post_act_fn is None:
																					_a	     =				None
														else:
																					_a	     =				get_activation(lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  List[str]							,			lowerCAmelCase_       :  Any							,			lowerCAmelCase_       :  Dict=None					)			->							Optional[Any]:


														"""simple docstring"""


														if condition is not None:
																					_a	     =				sample + self.cond_proj(lowerCAmelCase_					)
														_a	     =				self.linear_a(lowerCAmelCase_					)

														if self.act is not None:
																					_a	     =				self.act(lowerCAmelCase_					)

														_a	     =				self.linear_a(lowerCAmelCase_					)

														if self.post_act is not None:
																					_a	     =				self.post_act(lowerCAmelCase_					)
														return sample



class 			A    (		nn.Module    ):







							def __init__(  self       :  Union[str, Any]							,			lowerCAmelCase_       :  int							,			lowerCAmelCase_       :  bool							,			lowerCAmelCase_       :  float					)			->							List[Any]:


														"""simple docstring"""


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







							def    __lowerCAmelCase			(  self       :  int							,			lowerCAmelCase_       :  str					)			->							Dict:


														"""simple docstring"""


														_a	     =				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 			A    (		nn.Module    ):







							def __init__(  self       :  List[str]							,			lowerCAmelCase_       :  int = 2_56							,			lowerCAmelCase_       :  float = 1.0							,			lowerCAmelCase_       :  List[str]=True							,			lowerCAmelCase_       :  Tuple=True							,			lowerCAmelCase_       :  Optional[int]=False					)			->							Optional[int]:


														"""simple docstring"""


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

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

																					_a	     =				self.W







							def    __lowerCAmelCase			(  self       :  Optional[Any]							,			lowerCAmelCase_       :  Optional[int]					)			->							Dict:


														"""simple docstring"""


														if self.log:
																					_a	     =				torch.log(lowerCAmelCase_					)

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

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



class 			A    (		nn.Module    ):







							def __init__(  self       :  Optional[int]							,			lowerCAmelCase_       :  int							,			lowerCAmelCase_       :  int							,			lowerCAmelCase_       :  int							,			lowerCAmelCase_       :  int							,			)			->							Tuple:


														"""simple docstring"""


														super().__init__()

														_a	     =				height
														_a	     =				width
														_a	     =				num_embed
														_a	     =				embed_dim

														_a	     =				nn.Embedding(self.num_embed							,			lowerCAmelCase_					)
														_a	     =				nn.Embedding(self.height							,			lowerCAmelCase_					)
														_a	     =				nn.Embedding(self.width							,			lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  str							,			lowerCAmelCase_       :  Any					)			->							Any:


														"""simple docstring"""


														_a	     =				self.emb(lowerCAmelCase_					)

														_a	     =				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
														_a	     =				height_emb.unsqueeze(2					)

														_a	     =				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
														_a	     =				width_emb.unsqueeze(1					)

														_a	     =				height_emb + width_emb

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

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

														return emb



class 			A    (		nn.Module    ):







							def __init__(  self       :  Any							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  List[Any]							,			lowerCAmelCase_       :  Tuple					)			->							Any:


														"""simple docstring"""


														super().__init__()
														_a	     =				dropout_prob > 0
														_a	     =				nn.Embedding(num_classes + use_cfg_embedding							,			lowerCAmelCase_					)
														_a	     =				num_classes
														_a	     =				dropout_prob







							def    __lowerCAmelCase			(  self       :  Optional[Any]							,			lowerCAmelCase_       :  Tuple							,			lowerCAmelCase_       :  List[str]=None					)			->							Tuple:


														"""simple docstring"""


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







							def    __lowerCAmelCase			(  self       :  int							,			lowerCAmelCase_       :  torch.LongTensor							,			lowerCAmelCase_       :  Dict=None					)			->							List[Any]:


														"""simple docstring"""


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



class 			A    (		nn.Module    ):







							def __init__(  self       :  Dict							,			lowerCAmelCase_       :  int = 10_24							,			lowerCAmelCase_       :  int = 7_68							,			lowerCAmelCase_       :  int = 7_68							,			lowerCAmelCase_       :  int = 10							,			)			->							int:


														"""simple docstring"""


														super().__init__()

														_a	     =				num_image_text_embeds
														_a	     =				nn.Linear(lowerCAmelCase_							,			self.num_image_text_embeds * cross_attention_dim					)
														_a	     =				nn.Linear(lowerCAmelCase_							,			lowerCAmelCase_					)







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


														"""simple docstring"""


														_a	     =				text_embeds.shape[0]

														# image
														_a	     =				self.image_embeds(lowerCAmelCase_					)
														_a	     =				image_text_embeds.reshape(lowerCAmelCase_							,			self.num_image_text_embeds							,			-1					)

														# text
														_a	     =				self.text_proj(lowerCAmelCase_					)

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



class 			A    (		nn.Module    ):







							def __init__(  self       :  Union[str, Any]							,			lowerCAmelCase_       :  int = 7_68							,			lowerCAmelCase_       :  int = 7_68							,			lowerCAmelCase_       :  int = 32							,			)			->							Any:


														"""simple docstring"""


														super().__init__()

														_a	     =				num_image_text_embeds
														_a	     =				nn.Linear(lowerCAmelCase_							,			self.num_image_text_embeds * cross_attention_dim					)
														_a	     =				nn.LayerNorm(lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  int							,			lowerCAmelCase_       :  torch.FloatTensor					)			->							Optional[Any]:


														"""simple docstring"""


														_a	     =				image_embeds.shape[0]

														# image
														_a	     =				self.image_embeds(lowerCAmelCase_					)
														_a	     =				image_embeds.reshape(lowerCAmelCase_							,			self.num_image_text_embeds							,			-1					)
														_a	     =				self.norm(lowerCAmelCase_					)
														return image_embeds



class 			A    (		nn.Module    ):







							def __init__(  self       :  Optional[Any]							,			lowerCAmelCase_       :  List[Any]							,			lowerCAmelCase_       :  Tuple							,			lowerCAmelCase_       :  int=0.1					)			->							Dict:


														"""simple docstring"""


														super().__init__()

														_a	     =				Timesteps(num_channels=2_56							,			flip_sin_to_cos=lowerCAmelCase_							,			downscale_freq_shift=1					)
														_a	     =				TimestepEmbedding(in_channels=2_56							,			time_embed_dim=lowerCAmelCase_					)
														_a	     =				LabelEmbedding(lowerCAmelCase_							,			lowerCAmelCase_							,			lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  int							,			lowerCAmelCase_       :  Optional[int]							,			lowerCAmelCase_       :  Union[str, Any]							,			lowerCAmelCase_       :  Any=None					)			->							Any:


														"""simple docstring"""


														_a	     =				self.time_proj(lowerCAmelCase_					)
														_a	     =				self.timestep_embedder(timesteps_proj.to(dtype=lowerCAmelCase_					)					)  # (N, D)

														_a	     =				self.class_embedder(lowerCAmelCase_					)  # (N, D)

														_a	     =				timesteps_emb + class_labels  # (N, D)

														return conditioning



class 			A    (		nn.Module    ):







							def __init__(  self       :  List[Any]							,			lowerCAmelCase_       :  int							,			lowerCAmelCase_       :  int							,			lowerCAmelCase_       :  int = 64					)			->							Union[str, Any]:


														"""simple docstring"""


														super().__init__()
														_a	     =				nn.LayerNorm(lowerCAmelCase_					)
														_a	     =				AttentionPooling(lowerCAmelCase_							,			lowerCAmelCase_					)
														_a	     =				nn.Linear(lowerCAmelCase_							,			lowerCAmelCase_					)
														_a	     =				nn.LayerNorm(lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  int							,			lowerCAmelCase_       :  Any					)			->							List[Any]:


														"""simple docstring"""


														_a	     =				self.norma(lowerCAmelCase_					)
														_a	     =				self.pool(lowerCAmelCase_					)
														_a	     =				self.proj(lowerCAmelCase_					)
														_a	     =				self.norma(lowerCAmelCase_					)
														return hidden_states



class 			A    (		nn.Module    ):







							def __init__(  self       :  Tuple							,			lowerCAmelCase_       :  int = 7_68							,			lowerCAmelCase_       :  int = 7_68							,			lowerCAmelCase_       :  int = 15_36					)			->							Union[str, Any]:


														"""simple docstring"""


														super().__init__()
														_a	     =				nn.Linear(lowerCAmelCase_							,			lowerCAmelCase_					)
														_a	     =				nn.LayerNorm(lowerCAmelCase_					)
														_a	     =				nn.Linear(lowerCAmelCase_							,			lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  Optional[Any]							,			lowerCAmelCase_       :  torch.FloatTensor							,			lowerCAmelCase_       :  torch.FloatTensor					)			->							List[str]:


														"""simple docstring"""


														_a	     =				self.text_proj(lowerCAmelCase_					)
														_a	     =				self.text_norm(lowerCAmelCase_					)

														# image
														_a	     =				self.image_proj(lowerCAmelCase_					)

														return time_image_embeds + time_text_embeds



class 			A    (		nn.Module    ):







							def __init__(  self       :  Optional[int]							,			lowerCAmelCase_       :  int = 7_68							,			lowerCAmelCase_       :  int = 15_36					)			->							int:


														"""simple docstring"""


														super().__init__()
														_a	     =				nn.Linear(lowerCAmelCase_							,			lowerCAmelCase_					)
														_a	     =				nn.LayerNorm(lowerCAmelCase_					)







							def    __lowerCAmelCase			(  self       :  Any							,			lowerCAmelCase_       :  torch.FloatTensor					)			->							Any:


														"""simple docstring"""


														_a	     =				self.image_proj(lowerCAmelCase_					)
														_a	     =				self.image_norm(lowerCAmelCase_					)
														return time_image_embeds



class 			A    (		nn.Module    ):







							def __init__(  self       :  List[str]							,			lowerCAmelCase_       :  int = 7_68							,			lowerCAmelCase_       :  int = 15_36					)			->							Dict:


														"""simple docstring"""


														super().__init__()
														_a	     =				nn.Linear(lowerCAmelCase_							,			lowerCAmelCase_					)
														_a	     =				nn.LayerNorm(lowerCAmelCase_					)
														_a	     =				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							,			2_56							,			3							,			padding=1							,			stride=2					)							,			nn.SiLU()							,			nn.Convad(2_56							,			4							,			3							,			padding=1					)							,			)







							def    __lowerCAmelCase			(  self       :  Tuple							,			lowerCAmelCase_       :  torch.FloatTensor							,			lowerCAmelCase_       :  torch.FloatTensor					)			->							str:


														"""simple docstring"""


														_a	     =				self.image_proj(lowerCAmelCase_					)
														_a	     =				self.image_norm(lowerCAmelCase_					)
														_a	     =				self.input_hint_block(lowerCAmelCase_					)
														return time_image_embeds, hint



class 			A    (		nn.Module    ):







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


														"""simple docstring"""


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







							def    __lowerCAmelCase			(  self       :  Optional[Any]							,			lowerCAmelCase_       :  Optional[int]					)			->							List[str]:


														"""simple docstring"""


														_a     ,							_a     ,							_a	     =				x.size()

														def shape(lowerCAmelCase_       :  Tuple					):
																					# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
																					_a	     =				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)
																					_a	     =				x.transpose(1							,			2					)
																					# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
																					_a	     =				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)
																					_a	     =				x.transpose(1							,			2					)
																					return x

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

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

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

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

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

														return a[:, 0, :]  # cls_token







'''simple docstring'''


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_reformer import ReformerTokenizer
else:
				_snake_case      :      int   							=		None


_snake_case      :      Union[str, Any]   							=		logging.get_logger(__name__)


_snake_case      :      Union[str, Any]   							=		'▁'

_snake_case      :      Any   							=		{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case      :      List[str]   							=		{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

_snake_case      :      Union[str, Any]   							=		{
    'google/reformer-crime-and-punishment': 524288,
}

class 			A    (		_a    ):
							lowercase_							=				VOCAB_FILES_NAMES
							lowercase_							=				PRETRAINED_VOCAB_FILES_MAP
							lowercase_							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_							=				['input_ids', 'attention_mask']
							lowercase_							=				ReformerTokenizer







							def __init__(  self       :  Any							,			lowerCAmelCase_       :  int=None							,			lowerCAmelCase_       :  Optional[Any]=None							,			lowerCAmelCase_       :  List[str]="</s>"							,			lowerCAmelCase_       :  Any="<unk>"							,			lowerCAmelCase_       :  List[str]=[]							,			**lowerCAmelCase_       :  List[Any]							,			)			->							Dict:


														"""simple docstring"""


														super().__init__(
														    lowerCAmelCase_							,			tokenizer_file=lowerCAmelCase_							,			eos_token=lowerCAmelCase_							,			unk_token=lowerCAmelCase_							,			additional_special_tokens=lowerCAmelCase_							,			**lowerCAmelCase_							,			)

														_a	     =				vocab_file
														_a	     =				False if not self.vocab_file else True







							def    __lowerCAmelCase			(  self       :  List[Any]							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Optional[str] = None					)			->							Tuple[str]:


														"""simple docstring"""


														if not self.can_save_slow_tokenizer:
																					raise ValueError(
																					    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																					    '''tokenizer.'''					)

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

														if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase_					):
																					copyfile(self.vocab_file							,			lowerCAmelCase_					)

														return (out_vocab_file,)

