






"""simple docstring"""

import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


lowercase__          = 'sshleifer/mar_enro_6_3_student'


class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		def    lowerCamelCase       (   self							:	List[str]	):
				super().setUp()

				lowerCAmelCase_   :							Tuple									=			cached_path(
				    "https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"						,			extract_compressed_file=lowerCAmelCase__						,			)
				lowerCAmelCase_   :							Optional[int]									=			f'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''





		@slow
		@require_torch_gpu
		def    lowerCamelCase       (   self							:	List[Any]	):
				MarianMTModel.from_pretrained(lowerCAmelCase__	)





		@slow
		@require_torch_gpu
		def    lowerCamelCase       (   self							:	Dict	):
				lowerCAmelCase_   :							Any									=			{
				    '''$MAX_LEN''': 64,
				    '''$BS''': 64,
				    '''$GAS''': 1,
				    '''$ENRO_DIR''': self.data_dir,
				    '''facebook/mbart-large-cc25''': MARIAN_MODEL,
				    # "val_check_interval=0.25": "val_check_interval=1.0",
				    '''--learning_rate=3e-5''': '''--learning_rate 3e-4''',
				    '''--num_train_epochs 6''': '''--num_train_epochs 1''',
				}

				# Clean up bash script
				lowerCAmelCase_   :							List[Any]									=			(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split("finetune.py"	)[1].strip()
				lowerCAmelCase_   :							Any									=			bash_script.replace("\\\n"						,			""	).strip().replace("\"$@\""						,			""	)
				for k, v in env_vars_to_replace.items():
						lowerCAmelCase_   :							List[Any]									=			bash_script.replace(lowerCAmelCase__						,			str(lowerCAmelCase__	)	)
				lowerCAmelCase_   :							Any									=			self.get_auto_remove_tmp_dir()

				# bash_script = bash_script.replace("--fp16 ", "")
				lowerCAmelCase_   :							Dict									=			f'''
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        '''.split()
				# XXX: args.gpus > 1 : handle multi_gpu in the future

				lowerCAmelCase_   :							Optional[int]									=			['''finetune.py'''] + bash_script.split() + args
				with patch.object(lowerCAmelCase__						,			"argv"						,			lowerCAmelCase__	):
						lowerCAmelCase_   :							int									=			argparse.ArgumentParser()
						lowerCAmelCase_   :							Any									=			pl.Trainer.add_argparse_args(lowerCAmelCase__	)
						lowerCAmelCase_   :							List[str]									=			SummarizationModule.add_model_specific_args(lowerCAmelCase__						,			os.getcwd()	)
						lowerCAmelCase_   :							int									=			parser.parse_args()
						lowerCAmelCase_   :							str									=			main(lowerCAmelCase__	)

				# Check metrics
				lowerCAmelCase_   :							List[str]									=			load_json(model.metrics_save_path	)
				lowerCAmelCase_   :							int									=			metrics['''val'''][0]
				lowerCAmelCase_   :							Dict									=			metrics['''val'''][-1]
				self.assertEqual(len(metrics["val"]	)						,			(args.max_epochs / args.val_check_interval)	)
				assert isinstance(last_step_stats[f'''val_avg_{model.val_metric}''']						,			lowerCAmelCase__	)

				self.assertGreater(last_step_stats["val_avg_gen_time"]						,			0.01	)
				# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
				self.assertLessEqual(last_step_stats["val_avg_gen_time"]						,			1.0	)

				# test learning requirements:

				# 1. BLEU improves over the course of training by more than 2 pts
				self.assertGreater(last_step_stats["val_avg_bleu"] - first_step_stats["val_avg_bleu"]						,			2	)

				# 2. BLEU finishes above 17
				self.assertGreater(last_step_stats["val_avg_bleu"]						,			17	)

				# 3. test BLEU and val BLEU within ~1.1 pt.
				self.assertLess(abs(metrics["val"][-1]["val_avg_bleu"] - metrics["test"][-1]["test_avg_bleu"]	)						,			1.1	)

				# check lightning ckpt can be loaded and has a reasonable statedict
				lowerCAmelCase_   :							Dict									=			os.listdir(lowerCAmelCase__	)
				lowerCAmelCase_   :							Union[str, Any]									=			[x for x in contents if x.endswith(".ckpt"	)][0]
				lowerCAmelCase_   :							int									=			os.path.join(args.output_dir						,			lowerCAmelCase__	)
				lowerCAmelCase_   :							int									=			torch.load(lowerCAmelCase__						,			map_location="cpu"	)
				lowerCAmelCase_   :							Union[str, Any]									=			'''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
				assert expected_key in ckpt["state_dict"]
				assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

				# TODO: turn on args.do_predict when PL bug fixed.
				if args.do_predict:
						lowerCAmelCase_   :							Dict									=			{os.path.basename(lowerCAmelCase__	) for p in contents}
						assert "test_generations.txt" in contents
						assert "test_results.txt" in contents
						# assert len(metrics["val"]) ==  desired_n_evals
						assert len(metrics["test"]	) == 1







class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		@timeout_decorator.timeout(6_00	)
		@slow
		@require_torch_gpu
		def    lowerCamelCase       (   self							:	List[Any]	):
				lowerCAmelCase_   :							str									=			f'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
				lowerCAmelCase_   :							Optional[Any]									=			{
				    '''--fp16_opt_level=O1''': '''''',
				    '''$MAX_LEN''': 1_28,
				    '''$BS''': 16,
				    '''$GAS''': 1,
				    '''$ENRO_DIR''': data_dir,
				    '''$m''': '''sshleifer/student_marian_en_ro_6_1''',
				    '''val_check_interval=0.25''': '''val_check_interval=1.0''',
				}

				# Clean up bash script
				lowerCAmelCase_   :							int									=			(
				    (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split("distillation.py"	)[1].strip()
				)
				lowerCAmelCase_   :							Optional[int]									=			bash_script.replace("\\\n"						,			""	).strip().replace("\"$@\""						,			""	)
				lowerCAmelCase_   :							Tuple									=			bash_script.replace("--fp16 "						,			" "	)

				for k, v in env_vars_to_replace.items():
						lowerCAmelCase_   :							Any									=			bash_script.replace(lowerCAmelCase__						,			str(lowerCAmelCase__	)	)
				lowerCAmelCase_   :							Union[str, Any]									=			self.get_auto_remove_tmp_dir()
				lowerCAmelCase_   :							Any									=			bash_script.replace("--fp16"						,			""	)
				lowerCAmelCase_   :							Tuple									=			6
				lowerCAmelCase_   :							str									=			(
				    ['''distillation.py''']
				    + bash_script.split()
				    + [
				        f'''--output_dir={output_dir}''',
				        '''--gpus=1''',
				        '''--learning_rate=1e-3''',
				        f'''--num_train_epochs={epochs}''',
				        '''--warmup_steps=10''',
				        '''--val_check_interval=1.0''',
				        '''--do_predict''',
				    ]
				)
				with patch.object(lowerCAmelCase__						,			"argv"						,			lowerCAmelCase__	):
						lowerCAmelCase_   :							Tuple									=			argparse.ArgumentParser()
						lowerCAmelCase_   :							str									=			pl.Trainer.add_argparse_args(lowerCAmelCase__	)
						lowerCAmelCase_   :							Tuple									=			SummarizationDistiller.add_model_specific_args(lowerCAmelCase__						,			os.getcwd()	)
						lowerCAmelCase_   :							str									=			parser.parse_args()
						# assert args.gpus == gpus THIS BREAKS for multi_gpu

						lowerCAmelCase_   :							Any									=			distill_main(lowerCAmelCase__	)

				# Check metrics
				lowerCAmelCase_   :							List[Any]									=			load_json(model.metrics_save_path	)
				lowerCAmelCase_   :							List[str]									=			metrics['''val'''][0]
				lowerCAmelCase_   :							List[str]									=			metrics['''val'''][-1]
				assert len(metrics["val"]	) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

				assert last_step_stats["val_avg_gen_time"] >= 0.01

				assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
				assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
				assert isinstance(last_step_stats[f'''val_avg_{model.val_metric}''']						,			lowerCAmelCase__	)

				# check lightning ckpt can be loaded and has a reasonable statedict
				lowerCAmelCase_   :							List[str]									=			os.listdir(lowerCAmelCase__	)
				lowerCAmelCase_   :							Any									=			[x for x in contents if x.endswith(".ckpt"	)][0]
				lowerCAmelCase_   :							List[str]									=			os.path.join(args.output_dir						,			lowerCAmelCase__	)
				lowerCAmelCase_   :							Dict									=			torch.load(lowerCAmelCase__						,			map_location="cpu"	)
				lowerCAmelCase_   :							int									=			'''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
				assert expected_key in ckpt["state_dict"]
				assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

				# TODO: turn on args.do_predict when PL bug fixed.
				if args.do_predict:
						lowerCAmelCase_   :							Any									=			{os.path.basename(lowerCAmelCase__	) for p in contents}
						assert "test_generations.txt" in contents
						assert "test_results.txt" in contents
						# assert len(metrics["val"]) ==  desired_n_evals
						assert len(metrics["test"]	) == 1




import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


__lowerCAmelCase	: Dict  				=logging.get_logger(__name__)


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		nn.functional.normalize(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		nn.functional.normalize(lowercase__      )
						return torch.mm(lowercase__    ,       normalized_text_embeds.t()      )
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Optional[Any]						=		CLIPConfig

				SCREAMING_SNAKE_CASE__				:			List[str]						=		['''CLIPEncoderLayer''']


				def __init__(       self			:str			,			lowerCAmelCase__			:CLIPConfig	)		->			Tuple:
										super().__init__(lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		CLIPVisionModel(config.vision_config	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		nn.Linear(config.vision_config.hidden_size			,			config.projection_dim			,			bias=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Any					=		nn.Parameter(torch.ones(17			,			config.projection_dim	)			,			requires_grad=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		nn.Parameter(torch.ones(3			,			config.projection_dim	)			,			requires_grad=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Dict					=		nn.Parameter(torch.ones(17	)			,			requires_grad=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     str					=		nn.Parameter(torch.ones(3	)			,			requires_grad=lowerCAmelCase__	)


				@torch.no_grad()
				def 		__magic_name__(       self			:Optional[int]			,			lowerCAmelCase__			:int			,			lowerCAmelCase__			:Optional[int]	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     int					=		self.vision_model(lowerCAmelCase__	)[1]  # pooled_output
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.visual_projection(lowerCAmelCase__	)

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		cosine_distance(lowerCAmelCase__			,			self.special_care_embeds	).cpu().float().numpy()
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		cosine_distance(lowerCAmelCase__			,			self.concept_embeds	).cpu().float().numpy()

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		[]
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		image_embeds.shape[0]
										for i in range(lowerCAmelCase__	):
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		{'''special_scores''': {}, '''special_care''': [], '''concept_scores''': {}, '''bad_concepts''': []}

																# increase this value to create a stronger `nfsw` filter
																# at the cost of increasing the possibility of filtering benign images
																__SCREAMING_SNAKE_CASE						:     List[Any]					=		0.0

																for concept_idx in range(len(special_cos_dist[0]	)	):
																						__SCREAMING_SNAKE_CASE						:     List[str]					=		special_cos_dist[i][concept_idx]
																						__SCREAMING_SNAKE_CASE						:     Any					=		self.special_care_embeds_weights[concept_idx].item()
																						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		round(concept_cos - concept_threshold + adjustment			,			3	)
																						if result_img["special_scores"][concept_idx] > 0:
																												result_img["special_care"].append({concept_idx, result_img['''special_scores'''][concept_idx]}	)
																												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		0.01

																for concept_idx in range(len(cos_dist[0]	)	):
																						__SCREAMING_SNAKE_CASE						:     int					=		cos_dist[i][concept_idx]
																						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.concept_embeds_weights[concept_idx].item()
																						__SCREAMING_SNAKE_CASE						:     Tuple					=		round(concept_cos - concept_threshold + adjustment			,			3	)
																						if result_img["concept_scores"][concept_idx] > 0:
																												result_img["bad_concepts"].append(lowerCAmelCase__	)

																result.append(lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[str]					=		[len(res['''bad_concepts''']	) > 0 for res in result]

										return images, has_nsfw_concepts



				@torch.no_grad()
				def 		__magic_name__(       self			:Optional[int]			,			lowerCAmelCase__			:torch.FloatTensor			,			lowerCAmelCase__			:torch.FloatTensor	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.vision_model(lowerCAmelCase__	)[1]  # pooled_output
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self.visual_projection(lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Dict					=		cosine_distance(lowerCAmelCase__			,			self.special_care_embeds	)
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		cosine_distance(lowerCAmelCase__			,			self.concept_embeds	)

										# increase this value to create a stronger `nsfw` filter
										# at the cost of increasing the possibility of filtering benign images
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		0.0

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		special_cos_dist - self.special_care_embeds_weights + adjustment
										# special_scores = special_scores.round(decimals=3)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		torch.any(special_scores > 0			,			dim=1	)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		special_care * 0.01
										__SCREAMING_SNAKE_CASE						:     int					=		special_adjustment.unsqueeze(1	).expand(-1			,			cos_dist.shape[1]	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		(cos_dist - self.concept_embeds_weights) + special_adjustment
										# concept_scores = concept_scores.round(decimals=3)
										__SCREAMING_SNAKE_CASE						:     Any					=		torch.any(concept_scores > 0			,			dim=1	)

										return images, has_nsfw_concepts
