


'''simple docstring'''
from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


__SCREAMING_SNAKE_CASE					:				str									=		[
    '''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
    ''' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'''
    ''' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.''',
    '''The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'''
    ''' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'''
    ''' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'''
    ''' body.''',
    '''Amnesty International releases its annual report on the death penalty. The report catalogs the use of'''
    ''' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'''
    ''' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'''
    ''' punishment.''',
]

__SCREAMING_SNAKE_CASE					:				Tuple									=		[
    '''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'''
    ''' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'''
    ''' had informed his Lufthansa training school of an episode of severe depression, airline says .''',
    '''Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'''
    ''' Israel and the United States opposed the move, which could open the door to war crimes investigations against'''
    ''' Israelis .''',
    '''Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'''
    ''' death . Organization claims that governments around the world are using the threat of terrorism to advance'''
    ''' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'''
    ''' sentences up by 28% .''',
]







def       a_				(	):
							A_			    =		calculate_rouge(UpperCamelCase_						,  UpperCamelCase_						,  bootstrap_aggregation=UpperCamelCase_						,  rouge_keys=["rouge2", "rougeL"] )
							assert isinstance(UpperCamelCase_						,  UpperCamelCase_ )
							A_			    =		calculate_rouge(UpperCamelCase_						,  UpperCamelCase_						,  bootstrap_aggregation=UpperCamelCase_						,  rouge_keys=["rouge2"] )
							assert (
							    pd.DataFrame(no_aggregation["rouge2"] ).fmeasure.mean()
							    == pd.DataFrame(no_aggregation_just_ra["rouge2"] ).fmeasure.mean()
							)







def       a_				(	):
							A_			    =		"rougeLsum"
							A_			    =		calculate_rouge(UpperCamelCase_						,  UpperCamelCase_						,  newline_sep=UpperCamelCase_						,  rouge_keys=[k] )[k]
							A_			    =		calculate_rouge(UpperCamelCase_						,  UpperCamelCase_						,  newline_sep=UpperCamelCase_						,  rouge_keys=[k] )[k]
							assert score > score_no_sep







def       a_				(	):
							A_			    =		["rouge1", "rouge2", "rougeL"]
							A_			    =		calculate_rouge(UpperCamelCase_						,  UpperCamelCase_						,  newline_sep=UpperCamelCase_						,  rouge_keys=UpperCamelCase_ )
							A_			    =		calculate_rouge(UpperCamelCase_						,  UpperCamelCase_						,  newline_sep=UpperCamelCase_						,  rouge_keys=UpperCamelCase_ )
							assert score_sep == score_no_sep







def       a_				(	):
							A_			    =		[
							    "Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
							    "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
							]
							A_			    =		[
							    "Margot Frank, died in 1945, a month earlier than previously thought.",
							    "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
							    " the final seconds on board Flight 9525.",
							]
							assert calculate_rouge(UpperCamelCase_						,  UpperCamelCase_						,  newline_sep=UpperCamelCase_ ) == calculate_rouge(UpperCamelCase_						,  UpperCamelCase_						,  newline_sep=UpperCamelCase_ )







def       a_				(	):
							A_			    =		[
							    "\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
							]
							A_			    =		[
							    " Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
							]

							A_			    =		calculate_rouge(UpperCamelCase_						,  UpperCamelCase_						,  rouge_keys=["rougeLsum"]						,  newline_sep=UpperCamelCase_ )["rougeLsum"]
							A_			    =		calculate_rouge(UpperCamelCase_						,  UpperCamelCase_						,  rouge_keys=["rougeLsum"] )["rougeLsum"]
							assert new_score > prev_score







def       a_				(	):
							A_			    =		Path("examples/seq2seq/test_data/wmt_en_ro" )
							A_			    =		calculate_rouge_path(data_dir.joinpath("test.source" )						,  data_dir.joinpath("test.target" ) )
							assert isinstance(UpperCamelCase_						,  UpperCamelCase_ )
							A_			    =		calculate_rouge_path(
							    data_dir.joinpath("test.source" )						,  data_dir.joinpath("test.target" )						,  bootstrap_aggregation=UpperCamelCase_ )
							assert isinstance(UpperCamelCase_						,  UpperCamelCase_ )









'''simple docstring'''
from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       torch.FloatTensor






class 					__lowerCAmelCase   (   nn.Module						):

						"""simple docstring"""






						def __init__(  self							:      str ,  lowerCAmelCase							:      Tuple=3 ,  lowerCAmelCase							:      Union[str, Any]=3 ,  lowerCAmelCase							:      str=("DownEncoderBlock2D",) ,  lowerCAmelCase							:      Union[str, Any]=(64,) ,  lowerCAmelCase							:      str=2 ,  lowerCAmelCase							:      List[Any]=32 ,  lowerCAmelCase							:      List[str]="silu" ,  lowerCAmelCase							:      Tuple=True ,  ):
													super().__init__()
													A_			    =		layers_per_block

													A_			    =		torch.nn.Convad(
													    lowerCAmelCase ,  block_out_channels[0] ,  kernel_size=3 ,  stride=1 ,  padding=1 ,  )

													A_			    =		None
													A_			    =		nn.ModuleList([]	)

													# down
													A_			    =		block_out_channels[0]
													for i, down_block_type in enumerate(lowerCAmelCase	):
																				A_			    =		output_channel
																				A_			    =		block_out_channels[i]
																				A_			    =		i == len(lowerCAmelCase	) - 1

																				A_			    =		get_down_block(
																				    lowerCAmelCase ,  num_layers=self.layers_per_block ,  in_channels=lowerCAmelCase ,  out_channels=lowerCAmelCase ,  add_downsample=not is_final_block ,  resnet_eps=1e-6 ,  downsample_padding=0 ,  resnet_act_fn=lowerCAmelCase ,  resnet_groups=lowerCAmelCase ,  attention_head_dim=lowerCAmelCase ,  temb_channels=lowerCAmelCase ,  )
																				self.down_blocks.append(lowerCAmelCase	)

													# mid
													A_			    =		UNetMidBlockaD(
													    in_channels=block_out_channels[-1] ,  resnet_eps=1e-6 ,  resnet_act_fn=lowerCAmelCase ,  output_scale_factor=1 ,  resnet_time_scale_shift="default" ,  attention_head_dim=block_out_channels[-1] ,  resnet_groups=lowerCAmelCase ,  temb_channels=lowerCAmelCase ,  )

													# out
													A_			    =		nn.GroupNorm(num_channels=block_out_channels[-1] ,  num_groups=lowerCAmelCase ,  eps=1e-6	)
													A_			    =		nn.SiLU()

													A_			    =		2 * out_channels if double_z else out_channels
													A_			    =		nn.Convad(block_out_channels[-1] ,  lowerCAmelCase ,  3 ,  padding=1	)

													A_			    =		False






						def 				_UpperCAmelCase       (  self							:      Dict ,  lowerCAmelCase							:      int	):
													A_			    =		x
													A_			    =		self.conv_in(lowerCAmelCase	)

													if self.training and self.gradient_checkpointing:

																				def create_custom_forward(lowerCAmelCase							:      Dict	):
																											def custom_forward(*lowerCAmelCase							:      List[Any]	):
																																		return module(*lowerCAmelCase	)

																											return custom_forward

																				# down
																				if is_torch_version(">=" ,  "1.11.0"	):
																											for down_block in self.down_blocks:
																																		A_			    =		torch.utils.checkpoint.checkpoint(
																																		    create_custom_forward(lowerCAmelCase	) ,  lowerCAmelCase ,  use_reentrant=lowerCAmelCase	)
																											# middle
																											A_			    =		torch.utils.checkpoint.checkpoint(
																											    create_custom_forward(self.mid_block	) ,  lowerCAmelCase ,  use_reentrant=lowerCAmelCase	)
																				else:
																											for down_block in self.down_blocks:
																																		A_			    =		torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCAmelCase	) ,  lowerCAmelCase	)
																											# middle
																											A_			    =		torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block	) ,  lowerCAmelCase	)

													else:
																				# down
																				for down_block in self.down_blocks:
																											A_			    =		down_block(lowerCAmelCase	)

																				# middle
																				A_			    =		self.mid_block(lowerCAmelCase	)

													# post-process
													A_			    =		self.conv_norm_out(lowerCAmelCase	)
													A_			    =		self.conv_act(lowerCAmelCase	)
													A_			    =		self.conv_out(lowerCAmelCase	)

													return sample






class 					__lowerCAmelCase   (   nn.Module						):

						"""simple docstring"""






						def __init__(  self							:      Optional[int] ,  lowerCAmelCase							:      Optional[Any]=3 ,  lowerCAmelCase							:      Dict=3 ,  lowerCAmelCase							:      Dict=("UpDecoderBlock2D",) ,  lowerCAmelCase							:      Optional[int]=(64,) ,  lowerCAmelCase							:      str=2 ,  lowerCAmelCase							:      Dict=32 ,  lowerCAmelCase							:      Any="silu" ,  lowerCAmelCase							:      List[Any]="group" ,  ):
													super().__init__()
													A_			    =		layers_per_block

													A_			    =		nn.Convad(
													    lowerCAmelCase ,  block_out_channels[-1] ,  kernel_size=3 ,  stride=1 ,  padding=1 ,  )

													A_			    =		None
													A_			    =		nn.ModuleList([]	)

													A_			    =		in_channels if norm_type == "spatial" else None

													# mid
													A_			    =		UNetMidBlockaD(
													    in_channels=block_out_channels[-1] ,  resnet_eps=1e-6 ,  resnet_act_fn=lowerCAmelCase ,  output_scale_factor=1 ,  resnet_time_scale_shift="default" if norm_type == "group" else norm_type ,  attention_head_dim=block_out_channels[-1] ,  resnet_groups=lowerCAmelCase ,  temb_channels=lowerCAmelCase ,  )

													# up
													A_			    =		list(reversed(lowerCAmelCase	)	)
													A_			    =		reversed_block_out_channels[0]
													for i, up_block_type in enumerate(lowerCAmelCase	):
																				A_			    =		output_channel
																				A_			    =		reversed_block_out_channels[i]

																				A_			    =		i == len(lowerCAmelCase	) - 1

																				A_			    =		get_up_block(
																				    lowerCAmelCase ,  num_layers=self.layers_per_block + 1 ,  in_channels=lowerCAmelCase ,  out_channels=lowerCAmelCase ,  prev_output_channel=lowerCAmelCase ,  add_upsample=not is_final_block ,  resnet_eps=1e-6 ,  resnet_act_fn=lowerCAmelCase ,  resnet_groups=lowerCAmelCase ,  attention_head_dim=lowerCAmelCase ,  temb_channels=lowerCAmelCase ,  resnet_time_scale_shift=lowerCAmelCase ,  )
																				self.up_blocks.append(lowerCAmelCase	)
																				A_			    =		output_channel

													# out
													if norm_type == "spatial":
																				A_			    =		SpatialNorm(block_out_channels[0] ,  lowerCAmelCase	)
													else:
																				A_			    =		nn.GroupNorm(num_channels=block_out_channels[0] ,  num_groups=lowerCAmelCase ,  eps=1e-6	)
													A_			    =		nn.SiLU()
													A_			    =		nn.Convad(block_out_channels[0] ,  lowerCAmelCase ,  3 ,  padding=1	)

													A_			    =		False






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      Union[str, Any]=None	):
													A_			    =		z
													A_			    =		self.conv_in(lowerCAmelCase	)

													A_			    =		next(iter(self.up_blocks.parameters()	)	).dtype
													if self.training and self.gradient_checkpointing:

																				def create_custom_forward(lowerCAmelCase							:      Union[str, Any]	):
																											def custom_forward(*lowerCAmelCase							:      Union[str, Any]	):
																																		return module(*lowerCAmelCase	)

																											return custom_forward

																				if is_torch_version(">=" ,  "1.11.0"	):
																											# middle
																											A_			    =		torch.utils.checkpoint.checkpoint(
																											    create_custom_forward(self.mid_block	) ,  lowerCAmelCase ,  lowerCAmelCase ,  use_reentrant=lowerCAmelCase	)
																											A_			    =		sample.to(lowerCAmelCase	)

																											# up
																											for up_block in self.up_blocks:
																																		A_			    =		torch.utils.checkpoint.checkpoint(
																																		    create_custom_forward(lowerCAmelCase	) ,  lowerCAmelCase ,  lowerCAmelCase ,  use_reentrant=lowerCAmelCase	)
																				else:
																											# middle
																											A_			    =		torch.utils.checkpoint.checkpoint(
																											    create_custom_forward(self.mid_block	) ,  lowerCAmelCase ,  lowerCAmelCase	)
																											A_			    =		sample.to(lowerCAmelCase	)

																											# up
																											for up_block in self.up_blocks:
																																		A_			    =		torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCAmelCase	) ,  lowerCAmelCase ,  lowerCAmelCase	)
													else:
																				# middle
																				A_			    =		self.mid_block(lowerCAmelCase ,  lowerCAmelCase	)
																				A_			    =		sample.to(lowerCAmelCase	)

																				# up
																				for up_block in self.up_blocks:
																											A_			    =		up_block(lowerCAmelCase ,  lowerCAmelCase	)

        # post-process
													if latent_embeds is None:
																				A_			    =		self.conv_norm_out(lowerCAmelCase	)
													else:
																				A_			    =		self.conv_norm_out(lowerCAmelCase ,  lowerCAmelCase	)
													A_			    =		self.conv_act(lowerCAmelCase	)
													A_			    =		self.conv_out(lowerCAmelCase	)

													return sample






class 					__lowerCAmelCase   (   nn.Module						):

						"""simple docstring"""






						def __init__(  self							:      Dict ,  lowerCAmelCase							:      Dict ,  lowerCAmelCase							:      Union[str, Any] ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      List[str]=None ,  lowerCAmelCase							:      str="random" ,  lowerCAmelCase							:      Union[str, Any]=False ,  lowerCAmelCase							:      Optional[Any]=True	):
													super().__init__()
													A_			    =		n_e
													A_			    =		vq_embed_dim
													A_			    =		beta
													A_			    =		legacy

													A_			    =		nn.Embedding(self.n_e ,  self.vq_embed_dim	)
													self.embedding.weight.data.uniform_(-1.0 / self.n_e ,  1.0 / self.n_e	)

													A_			    =		remap
													if self.remap is not None:
																				self.register_buffer("used" ,  torch.tensor(np.load(self.remap	)	)	)
																				A_			    =		self.used.shape[0]
																				A_			    =		unknown_index  # "random" or "extra" or integer
																				if self.unknown_index == "extra":
																											A_			    =		self.re_embed
																											A_			    =		self.re_embed + 1
																				print(
																				    F"Remapping {self.n_e} indices to {self.re_embed} indices. "
																				    F"Using {self.unknown_index} for unknown indices."	)
													else:
																				A_			    =		n_e

													A_			    =		sane_index_shape






						def 				_UpperCAmelCase       (  self							:      int ,  lowerCAmelCase							:      Union[str, Any]	):
													A_			    =		inds.shape
													assert len(lowerCAmelCase	) > 1
													A_			    =		inds.reshape(ishape[0] ,  -1	)
													A_			    =		self.used.to(lowerCAmelCase	)
													A_			    =		(inds[:, :, None] == used[None, None, ...]).long()
													A_			    =		match.argmax(-1	)
													A_			    =		match.sum(2	) < 1
													if self.unknown_index == "random":
																				A_			    =		torch.randint(0 ,  self.re_embed ,  size=new[unknown].shape	).to(device=new.device	)
													else:
																				A_			    =		self.unknown_index
													return new.reshape(lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      Tuple	):
													A_			    =		inds.shape
													assert len(lowerCAmelCase	) > 1
													A_			    =		inds.reshape(ishape[0] ,  -1	)
													A_			    =		self.used.to(lowerCAmelCase	)
													if self.re_embed > self.used.shape[0]:  # extra token
																				A_			    =		0  # simply set to zero
													A_			    =		torch.gather(used[None, :][inds.shape[0] * [0], :] ,  1 ,  lowerCAmelCase	)
													return back.reshape(lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      int ,  lowerCAmelCase							:      List[Any]	):
													# reshape z -> (batch, height, width, channel) and flatten
													A_			    =		z.permute(0 ,  2 ,  3 ,  1	).contiguous()
													A_			    =		z.view(-1 ,  self.vq_embed_dim	)

													# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
													A_			    =		torch.argmin(torch.cdist(lowerCAmelCase ,  self.embedding.weight	) ,  dim=1	)

													A_			    =		self.embedding(lowerCAmelCase	).view(z.shape	)
													A_			    =		None
													A_			    =		None

													# compute loss for embedding
													if not self.legacy:
																				A_			    =		self.beta * torch.mean((z_q.detach() - z) ** 2	) + torch.mean((z_q - z.detach()) ** 2	)
													else:
																				A_			    =		torch.mean((z_q.detach() - z) ** 2	) + self.beta * torch.mean((z_q - z.detach()) ** 2	)

													# preserve gradients
													A_			    =		z + (z_q - z).detach()

													# reshape back to match original input shape
													A_			    =		z_q.permute(0 ,  3 ,  1 ,  2	).contiguous()

													if self.remap is not None:
																				A_			    =		min_encoding_indices.reshape(z.shape[0] ,  -1	)  # add batch axis
																				A_			    =		self.remap_to_used(lowerCAmelCase	)
																				A_			    =		min_encoding_indices.reshape(-1 ,  1	)  # flatten

													if self.sane_index_shape:
																				A_			    =		min_encoding_indices.reshape(z_q.shape[0] ,  z_q.shape[2] ,  z_q.shape[3]	)

													return z_q, loss, (perplexity, min_encodings, min_encoding_indices)






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int	):
													# shape specifying (batch, height, width, channel)
													if self.remap is not None:
																				A_			    =		indices.reshape(shape[0] ,  -1	)  # add batch axis
																				A_			    =		self.unmap_to_all(lowerCAmelCase	)
																				A_			    =		indices.reshape(-1	)  # flatten again

													# get quantized latent vectors
													A_			    =		self.embedding(lowerCAmelCase	)

													if shape is not None:
																				A_			    =		z_q.view(lowerCAmelCase	)
																				# reshape back to match original input shape
																				A_			    =		z_q.permute(0 ,  3 ,  1 ,  2	).contiguous()

													return z_q






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						def __init__(  self							:      Dict ,  lowerCAmelCase							:      Union[str, Any] ,  lowerCAmelCase							:      Union[str, Any]=False	):
													A_			    =		parameters
													A_    ,					A_			    =		torch.chunk(lowerCAmelCase ,  2 ,  dim=1	)
													A_			    =		torch.clamp(self.logvar ,  -3_0.0 ,  2_0.0	)
													A_			    =		deterministic
													A_			    =		torch.exp(0.5 * self.logvar	)
													A_			    =		torch.exp(self.logvar	)
													if self.deterministic:
																				A_			    =		A_			    =		torch.zeros_like(
																				    self.mean ,  device=self.parameters.device ,  dtype=self.parameters.dtype	)






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      Optional[torch.Generator] = None	):
													# make sure sample is on the same device as the parameters and has same dtype
													A_			    =		randn_tensor(
													    self.mean.shape ,  generator=lowerCAmelCase ,  device=self.parameters.device ,  dtype=self.parameters.dtype	)
													A_			    =		self.mean + self.std * sample
													return x






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      str=None	):
													if self.deterministic:
																				return torch.Tensor([0.0]	)
													else:
																				if other is None:
																											return 0.5 * torch.sum(torch.pow(self.mean ,  2	) + self.var - 1.0 - self.logvar ,  dim=[1, 2, 3]	)
																				else:
																											return 0.5 * torch.sum(
																											    torch.pow(self.mean - other.mean ,  2	) / other.var
																											    + self.var / other.var
																											    - 1.0
																											    - self.logvar
																											    + other.logvar ,  dim=[1, 2, 3] ,  )






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      Optional[int]=[1, 2, 3]	):
													if self.deterministic:
																				return torch.Tensor([0.0]	)
													A_			    =		np.log(2.0 * np.pi	)
													return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean ,  2	) / self.var ,  dim=lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													return self.mean





