

import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation




def        _lowercase  (					a_					:       List[str]       ,a_					:       int       ,a_					:       Dict = False       ,a_					:       Tuple = 1       ,a_					:       List[Any] = 1       ,a_					:       Union[str, Any] = 1_0_0_0_0       ,)					->						List[Any]:




					'''simple docstring'''







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

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

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

					# scale embeddings
					__magic_name__				=     scale * emb

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

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

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




def        _lowercase  (					a_					:       Optional[Any]       ,a_					:       Union[str, Any]       ,a_					:       List[Any]=False       ,a_					:       int=0					)					->						Optional[Any]:




					'''simple docstring'''







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

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




def        _lowercase  (					a_					:       List[Any]       ,a_					:       Tuple					)					->						str:




					'''simple docstring'''







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

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

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




def        _lowercase  (					a_					:       List[Any]       ,a_					:       Optional[Any]					)					->						Tuple:




					'''simple docstring'''







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

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

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

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

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

class 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			Any	,			__UpperCamelCase:			Optional[Any]=2_24	,			__UpperCamelCase:			Optional[int]=2_24	,			__UpperCamelCase:			Optional[int]=16	,			__UpperCamelCase:			Tuple=3	,			__UpperCamelCase:			Dict=7_68	,			__UpperCamelCase:			Any=False	,			__UpperCamelCase:			Dict=True	,			__UpperCamelCase:			Tuple=True	,			):

						'''simple docstring'''





						super().__init__()

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

						__magic_name__				=     nn.Convad(
						    _lowerCamelCase	,			_lowerCamelCase	,			kernel_size=(patch_size, patch_size)	,			stride=_lowerCamelCase	,			bias=_lowerCamelCase   )
						if layer_norm:
											__magic_name__				=     nn.LayerNorm(_lowerCamelCase	,			elementwise_affine=_lowerCamelCase	,			eps=1E-6   )
						else:
											__magic_name__				=     None

						__magic_name__				=     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 						_SCREAMING_SNAKE_CASE				( self:			Dict	,			__UpperCamelCase:			List[Any]   ):

						'''simple docstring'''





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




class 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			List[Any]	,			__UpperCamelCase:			Dict	,			__UpperCamelCase:			int	,			__UpperCamelCase:			Union[str, Any] = "silu"	,			__UpperCamelCase:			Any = None	,			__UpperCamelCase:			List[Any] = None	,			__UpperCamelCase:			List[str]=None	,			):

						'''simple docstring'''





						super().__init__()

						__magic_name__				=     nn.Linear(_lowerCamelCase	,			_lowerCamelCase   )

						if cond_proj_dim is not None:
											__magic_name__				=     nn.Linear(_lowerCamelCase	,			_lowerCamelCase	,			bias=_lowerCamelCase   )
						else:
											__magic_name__				=     None

						__magic_name__				=     get_activation(_lowerCamelCase   )

						if out_dim is not None:
											__magic_name__				=     out_dim
						else:
											__magic_name__				=     time_embed_dim
						__magic_name__				=     nn.Linear(_lowerCamelCase	,			_lowerCamelCase   )

						if post_act_fn is None:
											__magic_name__				=     None
						else:
											__magic_name__				=     get_activation(_lowerCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]	,			__UpperCamelCase:			List[Any]	,			__UpperCamelCase:			Any=None   ):

						'''simple docstring'''





						if condition is not None:
											__magic_name__				=     sample + self.cond_proj(_lowerCamelCase   )
						__magic_name__				=     self.linear_a(_lowerCamelCase   )

						if self.act is not None:
											__magic_name__				=     self.act(_lowerCamelCase   )

						__magic_name__				=     self.linear_a(_lowerCamelCase   )

						if self.post_act is not None:
											__magic_name__				=     self.post_act(_lowerCamelCase   )
						return sample




class 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			Any	,			__UpperCamelCase:			List[Any]	,			__UpperCamelCase:			int	,			__UpperCamelCase:			Dict   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]	,			__UpperCamelCase:			List[str]   ):

						'''simple docstring'''





						__magic_name__				=     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 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			List[str]	,			__UpperCamelCase:			Optional[int] = 2_56	,			__UpperCamelCase:			Optional[Any] = 1.0	,			__UpperCamelCase:			Union[str, Any]=True	,			__UpperCamelCase:			List[Any]=True	,			__UpperCamelCase:			Tuple=False   ):

						'''simple docstring'''





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

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

											__magic_name__				=     self.W







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]	,			__UpperCamelCase:			Tuple   ):

						'''simple docstring'''





						if self.log:
											__magic_name__				=     torch.log(_lowerCamelCase   )

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

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




class 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			Tuple	,			__UpperCamelCase:			List[str]	,			__UpperCamelCase:			Tuple	,			__UpperCamelCase:			int	,			__UpperCamelCase:			Any	,			):

						'''simple docstring'''





						super().__init__()

						__magic_name__				=     height
						__magic_name__				=     width
						__magic_name__				=     num_embed
						__magic_name__				=     embed_dim

						__magic_name__				=     nn.Embedding(self.num_embed	,			_lowerCamelCase   )
						__magic_name__				=     nn.Embedding(self.height	,			_lowerCamelCase   )
						__magic_name__				=     nn.Embedding(self.width	,			_lowerCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]	,			__UpperCamelCase:			Optional[Any]   ):

						'''simple docstring'''





						__magic_name__				=     self.emb(_lowerCamelCase   )

						__magic_name__				=     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
						__magic_name__				=     height_emb.unsqueeze(2   )

						__magic_name__				=     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
						__magic_name__				=     width_emb.unsqueeze(1   )

						__magic_name__				=     height_emb + width_emb

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

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

						return emb




class 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			int	,			__UpperCamelCase:			List[Any]	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						super().__init__()
						__magic_name__				=     dropout_prob > 0
						__magic_name__				=     nn.Embedding(num_classes + use_cfg_embedding	,			_lowerCamelCase   )
						__magic_name__				=     num_classes
						__magic_name__				=     dropout_prob







	def 						_SCREAMING_SNAKE_CASE				( self:			str	,			__UpperCamelCase:			List[str]	,			__UpperCamelCase:			Dict=None   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			Any	,			__UpperCamelCase:			Any	,			__UpperCamelCase:			Optional[int]=None   ):

						'''simple docstring'''





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




class 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			Any	,			__UpperCamelCase:			Optional[Any] = 10_24	,			__UpperCamelCase:			List[str] = 7_68	,			__UpperCamelCase:			Optional[Any] = 7_68	,			__UpperCamelCase:			Union[str, Any] = 10	,			):

						'''simple docstring'''





						super().__init__()

						__magic_name__				=     num_image_text_embeds
						__magic_name__				=     nn.Linear(_lowerCamelCase	,			self.num_image_text_embeds * cross_attention_dim   )
						__magic_name__				=     nn.Linear(_lowerCamelCase	,			_lowerCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]	,			__UpperCamelCase:			Optional[int]	,			__UpperCamelCase:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     text_embeds.shape[0]

						# image
						__magic_name__				=     self.image_embeds(_lowerCamelCase   )
						__magic_name__				=     image_text_embeds.reshape(_lowerCamelCase	,			self.num_image_text_embeds	,			-1   )

						# text
						__magic_name__				=     self.text_proj(_lowerCamelCase   )

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




class 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			int	,			__UpperCamelCase:			Optional[int] = 7_68	,			__UpperCamelCase:			Dict = 7_68	,			__UpperCamelCase:			Optional[int] = 32	,			):

						'''simple docstring'''





						super().__init__()

						__magic_name__				=     num_image_text_embeds
						__magic_name__				=     nn.Linear(_lowerCamelCase	,			self.num_image_text_embeds * cross_attention_dim   )
						__magic_name__				=     nn.LayerNorm(_lowerCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict	,			__UpperCamelCase:			Any   ):

						'''simple docstring'''





						__magic_name__				=     image_embeds.shape[0]

						# image
						__magic_name__				=     self.image_embeds(_lowerCamelCase   )
						__magic_name__				=     image_embeds.reshape(_lowerCamelCase	,			self.num_image_text_embeds	,			-1   )
						__magic_name__				=     self.norm(_lowerCamelCase   )
						return image_embeds




class 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			Optional[int]	,			__UpperCamelCase:			int	,			__UpperCamelCase:			Any	,			__UpperCamelCase:			Any=0.1   ):

						'''simple docstring'''





						super().__init__()

						__magic_name__				=     Timesteps(num_channels=2_56	,			flip_sin_to_cos=_lowerCamelCase	,			downscale_freq_shift=1   )
						__magic_name__				=     TimestepEmbedding(in_channels=2_56	,			time_embed_dim=_lowerCamelCase   )
						__magic_name__				=     LabelEmbedding(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]	,			__UpperCamelCase:			Tuple	,			__UpperCamelCase:			List[str]	,			__UpperCamelCase:			int=None   ):

						'''simple docstring'''





						__magic_name__				=     self.time_proj(_lowerCamelCase   )
						__magic_name__				=     self.timestep_embedder(timesteps_proj.to(dtype=_lowerCamelCase   )   )  # (N, D)

						__magic_name__				=     self.class_embedder(_lowerCamelCase   )  # (N, D)

						__magic_name__				=     timesteps_emb + class_labels  # (N, D)

						return conditioning




class 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			Any	,			__UpperCamelCase:			Dict	,			__UpperCamelCase:			Union[str, Any] = 64   ):

						'''simple docstring'''





						super().__init__()
						__magic_name__				=     nn.LayerNorm(_lowerCamelCase   )
						__magic_name__				=     AttentionPooling(_lowerCamelCase	,			_lowerCamelCase   )
						__magic_name__				=     nn.Linear(_lowerCamelCase	,			_lowerCamelCase   )
						__magic_name__				=     nn.LayerNorm(_lowerCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict	,			__UpperCamelCase:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     self.norma(_lowerCamelCase   )
						__magic_name__				=     self.pool(_lowerCamelCase   )
						__magic_name__				=     self.proj(_lowerCamelCase   )
						__magic_name__				=     self.norma(_lowerCamelCase   )
						return hidden_states




class 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			str	,			__UpperCamelCase:			Tuple = 7_68	,			__UpperCamelCase:			Optional[int] = 7_68	,			__UpperCamelCase:			Dict = 15_36   ):

						'''simple docstring'''





						super().__init__()
						__magic_name__				=     nn.Linear(_lowerCamelCase	,			_lowerCamelCase   )
						__magic_name__				=     nn.LayerNorm(_lowerCamelCase   )
						__magic_name__				=     nn.Linear(_lowerCamelCase	,			_lowerCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Any	,			__UpperCamelCase:			Tuple	,			__UpperCamelCase:			Union[str, Any]   ):

						'''simple docstring'''





						__magic_name__				=     self.text_proj(_lowerCamelCase   )
						__magic_name__				=     self.text_norm(_lowerCamelCase   )

						# image
						__magic_name__				=     self.image_proj(_lowerCamelCase   )

						return time_image_embeds + time_text_embeds




class 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			Dict	,			__UpperCamelCase:			str = 7_68	,			__UpperCamelCase:			List[Any] = 15_36   ):

						'''simple docstring'''





						super().__init__()
						__magic_name__				=     nn.Linear(_lowerCamelCase	,			_lowerCamelCase   )
						__magic_name__				=     nn.LayerNorm(_lowerCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict	,			__UpperCamelCase:			Union[str, Any]   ):

						'''simple docstring'''





						__magic_name__				=     self.image_proj(_lowerCamelCase   )
						__magic_name__				=     self.image_norm(_lowerCamelCase   )
						return time_image_embeds




class 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			List[Any]	,			__UpperCamelCase:			Dict = 7_68	,			__UpperCamelCase:			List[Any] = 15_36   ):

						'''simple docstring'''





						super().__init__()
						__magic_name__				=     nn.Linear(_lowerCamelCase	,			_lowerCamelCase   )
						__magic_name__				=     nn.LayerNorm(_lowerCamelCase   )
						__magic_name__				=     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 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			Dict	,			__UpperCamelCase:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     self.image_proj(_lowerCamelCase   )
						__magic_name__				=     self.image_norm(_lowerCamelCase   )
						__magic_name__				=     self.input_hint_block(_lowerCamelCase   )
						return time_image_embeds, hint




class 					__UpperCamelCase					(       nn.Module      ):







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			List[str]	,			__UpperCamelCase:			Union[str, Any]	,			__UpperCamelCase:			Any=None   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     x.size()

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

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

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

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

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

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

						return a[:, 0, :]  # cls_token






import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


a__				:      Any    		=     logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


a__				:      Optional[int]    		=     namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = BertTokenizer.from_pretrained('''bert-base-uncased''' ,						do_lower_case=a__   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = BertAbs.from_pretrained('''remi/bertabs-finetuned-extractive-abstractive-summarization'''   )
    model.to(args.device   )
    model.eval()

    SCREAMING_SNAKE_CASE				:				int				      = {
        '''BOS''': tokenizer.vocab['''[unused0]'''],
        '''EOS''': tokenizer.vocab['''[unused1]'''],
        '''PAD''': tokenizer.vocab['''[PAD]'''],
    }

    if args.compute_rouge:
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = []
        SCREAMING_SNAKE_CASE				:				Dict				      = []

        import nltk
        import rouge

        nltk.download('''punkt'''   )
        SCREAMING_SNAKE_CASE				:				Tuple				      = rouge.Rouge(
            metrics=['''rouge-n''', '''rouge-l'''] ,						max_n=2 ,						limit_length=a__ ,						length_limit=args.beam_size ,						length_limit_type='''words''' ,						apply_avg=a__ ,						apply_best=a__ ,						alpha=0.5 ,						weight_factor=1.2 ,						stemming=a__ ,						)

    # these (unused) arguments are defined to keep the compatibility
    # with the legacy code and will be deleted in a next iteration.
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = ''''''
    SCREAMING_SNAKE_CASE				:				Tuple				      = ''''''

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = build_data_iterator(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				str				      = build_predictor(a__ ,						a__ ,						a__ ,						a__   )

    logger.info('''***** Running evaluation *****'''   )
    logger.info('''  Number examples = %d''' ,						len(data_iterator.dataset   )   )
    logger.info('''  Batch size = %d''' ,						args.batch_size   )
    logger.info(''''''   )
    logger.info('''***** Beam Search parameters *****'''   )
    logger.info('''  Beam size = %d''' ,						args.beam_size   )
    logger.info('''  Minimum length = %d''' ,						args.min_length   )
    logger.info('''  Maximum length = %d''' ,						args.max_length   )
    logger.info('''  Alpha (length penalty) = %.2f''' ,						args.alpha   )
    logger.info('''  Trigrams %s be blocked''' ,						('''will''' if args.block_trigram else '''will NOT''')   )

    for batch in tqdm(a__   ):
        SCREAMING_SNAKE_CASE				:				Tuple				      = predictor.translate_batch(a__   )
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = predictor.from_batch(a__   )
        SCREAMING_SNAKE_CASE				:				List[str]				      = [format_summary(a__   ) for t in translations]
        save_summaries(a__ ,						args.summaries_output_dir ,						batch.document_names   )

        if args.compute_rouge:
            reference_summaries += batch.tgt_str
            generated_summaries += summaries

    if args.compute_rouge:
        SCREAMING_SNAKE_CASE				:				List[Any]				      = rouge_evaluator.get_scores(a__ ,						a__   )
        SCREAMING_SNAKE_CASE				:				Dict				      = format_rouge_scores(a__   )
        save_rouge_scores(a__   )
        print(a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    for summary, document_name in zip(a__ ,						a__   ):
        # Prepare the summary file's name
        if "." in document_name:
            SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''.'''.join(document_name.split('''.'''   )[:-1]   )
            SCREAMING_SNAKE_CASE				:				int				      = document_name.split('''.'''   )[-1]
            SCREAMING_SNAKE_CASE				:				Tuple				      = bare_document_name + '''_summary.''' + extension
        else:
            SCREAMING_SNAKE_CASE				:				Optional[int]				      = document_name + '''_summary'''

        SCREAMING_SNAKE_CASE				:				List[Any]				      = os.path.join(a__ ,						a__   )
        with open(a__ ,						'''w'''   ) as output:
            output.write(a__   )







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				Any				      = translation
    SCREAMING_SNAKE_CASE				:				int				      = (
        raw_summary.replace('''[unused0]''' ,						''''''   )
        .replace('''[unused3]''' ,						''''''   )
        .replace('''[PAD]''' ,						''''''   )
        .replace('''[unused1]''' ,						''''''   )
        .replace(r''' +''' ,						''' '''   )
        .replace(''' [unused2] ''' ,						'''. '''   )
        .replace('''[unused2]''' ,						''''''   )
        .strip()
    )

    return summary







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
        scores['''rouge-1''']['''f'''] ,						scores['''rouge-1''']['''p'''] ,						scores['''rouge-1''']['''r'''] ,						scores['''rouge-2''']['''f'''] ,						scores['''rouge-2''']['''p'''] ,						scores['''rouge-2''']['''r'''] ,						scores['''rouge-l''']['''f'''] ,						scores['''rouge-l''']['''p'''] ,						scores['''rouge-l''']['''r'''] ,						)







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    with open('''rouge_scores.txt''' ,						'''w'''   ) as output:
        output.write(a__   )







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = load_and_cache_examples(a__ ,						a__   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = SequentialSampler(a__   )

    def collate_fn(a__   ):
        return collate(a__ ,						a__ ,						block_size=512 ,						device=args.device   )

    SCREAMING_SNAKE_CASE				:				Dict				      = DataLoader(
        a__ ,						sampler=a__ ,						batch_size=args.batch_size ,						collate_fn=a__ ,						)

    return iterator







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = CNNDMDataset(args.documents_dir   )
    return dataset







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = [x for x in data if not len(x[1]   ) == 0]  # remove empty_files
    SCREAMING_SNAKE_CASE				:				int				      = [name for name, _, _ in data]
    SCREAMING_SNAKE_CASE				:				List[str]				      = [''' '''.join(a__   ) for _, _, summary_list in data]

    SCREAMING_SNAKE_CASE				:				Dict				      = [encode_for_summarization(a__ ,						a__ ,						a__   ) for _, story, summary in data]
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.tensor(
        [truncate_or_pad(a__ ,						a__ ,						tokenizer.pad_token_id   ) for story, _ in encoded_text]   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = compute_token_type_ids(a__ ,						tokenizer.cls_token_id   )
    SCREAMING_SNAKE_CASE				:				int				      = build_mask(a__ ,						tokenizer.pad_token_id   )

    SCREAMING_SNAKE_CASE				:				Optional[int]				      = Batch(
        document_names=a__ ,						batch_size=len(a__   ) ,						src=encoded_stories.to(a__   ) ,						segs=encoder_token_type_ids.to(a__   ) ,						mask_src=encoder_mask.to(a__   ) ,						tgt_str=a__ ,						)

    return batch







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = summary_tokens.to('''cpu'''   ).numpy()
    SCREAMING_SNAKE_CASE				:				str				      = tokenizer.decode(a__   )
    SCREAMING_SNAKE_CASE				:				int				      = summary.split('''.'''   )
    SCREAMING_SNAKE_CASE				:				Dict				      = [s + '''.''' for s in sentences]
    return sentences







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				str				      = argparse.ArgumentParser()
    parser.add_argument(
        '''--documents_dir''' ,						default=a__ ,						type=a__ ,						required=a__ ,						help='''The folder where the documents to summarize are located.''' ,						)
    parser.add_argument(
        '''--summaries_output_dir''' ,						default=a__ ,						type=a__ ,						required=a__ ,						help='''The folder in wich the summaries should be written. Defaults to the folder where the documents are''' ,						)
    parser.add_argument(
        '''--compute_rouge''' ,						default=a__ ,						type=a__ ,						required=a__ ,						help='''Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.''' ,						)
    # EVALUATION options
    parser.add_argument(
        '''--no_cuda''' ,						default=a__ ,						type=a__ ,						help='''Whether to force the execution on CPU.''' ,						)
    parser.add_argument(
        '''--batch_size''' ,						default=4 ,						type=a__ ,						help='''Batch size per GPU/CPU for training.''' ,						)
    # BEAM SEARCH arguments
    parser.add_argument(
        '''--min_length''' ,						default=50 ,						type=a__ ,						help='''Minimum number of tokens for the summaries.''' ,						)
    parser.add_argument(
        '''--max_length''' ,						default=200 ,						type=a__ ,						help='''Maixmum number of tokens for the summaries.''' ,						)
    parser.add_argument(
        '''--beam_size''' ,						default=5 ,						type=a__ ,						help='''The number of beams to start with for each example.''' ,						)
    parser.add_argument(
        '''--alpha''' ,						default=0.95 ,						type=a__ ,						help='''The value of alpha for the length penalty in the beam search.''' ,						)
    parser.add_argument(
        '''--block_trigram''' ,						default=a__ ,						type=a__ ,						help='''Whether to block the existence of repeating trigrams in the text generated by beam search.''' ,						)
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = parser.parse_args()

    # Select device (distibuted not available)
    SCREAMING_SNAKE_CASE				:				List[Any]				      = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''   )

    # Check the existence of directories
    if not args.summaries_output_dir:
        SCREAMING_SNAKE_CASE				:				Tuple				      = args.documents_dir

    if not documents_dir_is_valid(args.documents_dir   ):
        raise FileNotFoundError(
            '''We could not find the directory you specified for the documents to summarize, or it was empty. Please'''
            ''' specify a valid path.'''   )
    os.makedirs(args.summaries_output_dir ,						exist_ok=a__   )

    evaluate(a__   )







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    if not os.path.exists(a__   ):
        return False

    SCREAMING_SNAKE_CASE				:				List[str]				      = os.listdir(a__   )
    if len(a__   ) == 0:
        return False

    return True


if __name__ == "__main__":
      main()
