
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				:Union[str, Any]									=							[
    '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				:List[str]									=							[
    '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 		UpperCAmelCase     (      )						->      Optional[int]:







				"""simple docstring"""



				__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 		UpperCAmelCase     (      )						->      Optional[Any]:







				"""simple docstring"""



				__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 		UpperCAmelCase     (      )						->      Optional[Any]:







				"""simple docstring"""



				__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 		UpperCAmelCase     (      )						->      Optional[Any]:







				"""simple docstring"""



				__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 		UpperCAmelCase     (      )						->      Tuple:







				"""simple docstring"""



				__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 		UpperCAmelCase     (      )						->      str:







				"""simple docstring"""



				__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		)





import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json


def A    (							__UpperCamelCase       )    ->   Optional[int]:
			A__	  =     filter(lambda __UpperCamelCase       : p.requires_grad			,						model.parameters()       )
			A__	  =     sum([np.prod(p.size()       ) for p in model_parameters]       )
			return params


SCREAMING_SNAKE_CASE__   			=   logging.getLogger(__name__)


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Dict:
			if metric == "rouge2":
						A__	  =     '{val_avg_rouge2:.4f}-{step_count}'
			elif metric == "bleu":
						A__	  =     '{val_avg_bleu:.4f}-{step_count}'
			elif metric == "em":
						A__	  =     '{val_avg_em:.4f}-{step_count}'
			elif metric == "loss":
						A__	  =     '{val_avg_loss:.4f}-{step_count}'
			else:
						raise NotImplementedError(
						    f'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
						    ' function.'       )

			A__	  =     ModelCheckpoint(
			    dirpath=__UpperCamelCase			,						filename=__UpperCamelCase			,						monitor=f'''val_{metric}'''			,						mode='max'			,						save_top_k=1			,						every_n_epochs=1			,						)

			return checkpoint_callback


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Any:
			return EarlyStopping(
			    monitor=f'''val_{metric}'''			,						mode='min' if 'loss' in metric else 'max'			,						patience=__UpperCamelCase			,						verbose=__UpperCamelCase			,						)






class   __lowerCAmelCase  (		pl.Callback   ):
					"""simple docstring"""






					def      _a				(  self       :							Dict					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							str       ):






								"""simple docstring"""


								A__	  =     {F'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups       )}
								pl_module.logger.log_metrics(_snake_case       )



					@rank_zero_only
					def      _a				(  self       :							Union[str, Any]					,       _snake_case       :							pl.Trainer					,       _snake_case       :							pl.LightningModule					,       _snake_case       :							str					,       _snake_case       :							Optional[Any]=True       ):






								"""simple docstring"""


								logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''       )
								A__	  =     trainer.callback_metrics
								trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}       )
								# Log results
								A__	  =     Path(pl_module.hparams.output_dir       )
								if type_path == "test":
											A__	  =     od / 'test_results.txt'
											A__	  =     od / 'test_generations.txt'
								else:
											# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
											# If people want this it will be easy enough to add back.
											A__	  =     od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
											A__	  =     od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
											results_file.parent.mkdir(exist_ok=_snake_case       )
											generations_file.parent.mkdir(exist_ok=_snake_case       )
								with open(_snake_case					,       'a+'       ) as writer:
											for key in sorted(_snake_case       ):
														if key in ["log", "progress_bar", "preds"]:
																	continue
														A__	  =     metrics[key]
														if isinstance(_snake_case					,       torch.Tensor       ):
																	A__	  =     val.item()
														A__	  =     F'''{key}: {val:.6f}\n'''
														writer.write(_snake_case       )

								if not save_generations:
											return

								if "preds" in metrics:
											A__	  =     '\n'.join(metrics['preds']       )
											generations_file.open('w+'       ).write(_snake_case       )



					@rank_zero_only
					def      _a				(  self       :							Dict					,       _snake_case       :							List[str]					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


								try:
											A__	  =     pl_module.model.model.num_parameters()
								except AttributeError:
											A__	  =     pl_module.model.num_parameters()

								A__	  =     count_trainable_parameters(_snake_case       )
								# mp stands for million parameters
								trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}       )



					@rank_zero_only
					def      _a				(  self       :							int					,       _snake_case       :							pl.Trainer					,       _snake_case       :							pl.LightningModule       ):






								"""simple docstring"""


								save_json(pl_module.metrics					,       pl_module.metrics_save_path       )
								return self._write_logs(_snake_case					,       _snake_case					,       'test'       )



					@rank_zero_only
					def      _a				(  self       :							Optional[Any]					,       _snake_case       :							pl.Trainer					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


								save_json(pl_module.metrics					,       pl_module.metrics_save_path       )
								# Uncommenting this will save val generations
								# return self._write_logs(trainer, pl_module, "valid")




