


"""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


UpperCAmelCase    :     Dict   						=		'sshleifer/mar_enro_6_3_student'





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												super().setUp()

												__UpperCAmelCase   :						List[str]      							=					cached_path(
												    """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz""" ,       extract_compressed_file=UpperCamelCase ,       )
												__UpperCAmelCase   :						Union[str, Any]      							=					f'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''






					@slow
					@require_torch_gpu
					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												MarianMTModel.from_pretrained(UpperCamelCase  )






					@slow
					@require_torch_gpu
					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					{
												    """$MAX_LEN""": 64,
												    """$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
												__UpperCAmelCase   :						List[Any]      							=					(self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""  )[1].strip()
												__UpperCAmelCase   :						Optional[Any]      							=					bash_script.replace("""\\\n""" ,       """"""  ).strip().replace("""\"$@\"""" ,       """"""  )
												for k, v in env_vars_to_replace.items():
																			__UpperCAmelCase   :						List[str]      							=					bash_script.replace(UpperCamelCase ,       str(UpperCamelCase  )  )
												__UpperCAmelCase   :						List[Any]      							=					self.get_auto_remove_tmp_dir()

												# bash_script = bash_script.replace("--fp16 ", "")
												__UpperCAmelCase   :						str      							=					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

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

												# Check metrics
												__UpperCAmelCase   :						Tuple      							=					load_json(model.metrics_save_path  )
												__UpperCAmelCase   :						str      							=					metrics["""val"""][0]
												__UpperCAmelCase   :						Optional[Any]      							=					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}'''] ,       UpperCamelCase  )

												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
												__UpperCAmelCase   :						List[Any]      							=					os.listdir(UpperCamelCase  )
												__UpperCAmelCase   :						List[Any]      							=					[x for x in contents if x.endswith(""".ckpt"""  )][0]
												__UpperCAmelCase   :						int      							=					os.path.join(args.output_dir ,       UpperCamelCase  )
												__UpperCAmelCase   :						str      							=					torch.load(UpperCamelCase ,       map_location="""cpu"""  )
												__UpperCAmelCase   :						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:
																			__UpperCAmelCase   :						Union[str, Any]      							=					{os.path.basename(UpperCamelCase  ) 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(600  )
					@slow
					@require_torch_gpu
					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					f'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
												__UpperCAmelCase   :						Tuple      							=					{
												    """--fp16_opt_level=O1""": """""",
												    """$MAX_LEN""": 128,
												    """$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
												__UpperCAmelCase   :						Dict      							=					(
												    (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""  )[1].strip()
												)
												__UpperCAmelCase   :						Tuple      							=					bash_script.replace("""\\\n""" ,       """"""  ).strip().replace("""\"$@\"""" ,       """"""  )
												__UpperCAmelCase   :						Any      							=					bash_script.replace("""--fp16 """ ,       """ """  )

												for k, v in env_vars_to_replace.items():
																			__UpperCAmelCase   :						Dict      							=					bash_script.replace(UpperCamelCase ,       str(UpperCamelCase  )  )
												__UpperCAmelCase   :						str      							=					self.get_auto_remove_tmp_dir()
												__UpperCAmelCase   :						int      							=					bash_script.replace("""--fp16""" ,       """"""  )
												__UpperCAmelCase   :						Dict      							=					6
												__UpperCAmelCase   :						Any      							=					(
												    ["""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(UpperCamelCase ,       """argv""" ,       UpperCamelCase  ):
																			__UpperCAmelCase   :						List[str]      							=					argparse.ArgumentParser()
																			__UpperCAmelCase   :						Optional[Any]      							=					pl.Trainer.add_argparse_args(UpperCamelCase  )
																			__UpperCAmelCase   :						int      							=					SummarizationDistiller.add_model_specific_args(UpperCamelCase ,       os.getcwd()  )
																			__UpperCAmelCase   :						List[str]      							=					parser.parse_args()
																			# assert args.gpus == gpus THIS BREAKS for multi_gpu

																			__UpperCAmelCase   :						List[str]      							=					distill_main(UpperCamelCase  )

												# Check metrics
												__UpperCAmelCase   :						Dict      							=					load_json(model.metrics_save_path  )
												__UpperCAmelCase   :						List[str]      							=					metrics["""val"""][0]
												__UpperCAmelCase   :						Union[str, Any]      							=					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}'''] ,       UpperCamelCase  )

												# check lightning ckpt can be loaded and has a reasonable statedict
												__UpperCAmelCase   :						str      							=					os.listdir(UpperCamelCase  )
												__UpperCAmelCase   :						int      							=					[x for x in contents if x.endswith(""".ckpt"""  )][0]
												__UpperCAmelCase   :						Any      							=					os.path.join(args.output_dir ,       UpperCamelCase  )
												__UpperCAmelCase   :						Any      							=					torch.load(UpperCamelCase ,       map_location="""cpu"""  )
												__UpperCAmelCase   :						str      							=					"""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:
																			__UpperCAmelCase   :						Tuple      							=					{os.path.basename(UpperCamelCase  ) 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




"""simple docstring"""






import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


UpperCAmelCase    :     str   						=		{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}





class      lowerCamelCase__   (     nn.Module				):







					"""simple docstring"""


					def __init__(       self    : Union[str, Any] ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												super().__init__()
												__UpperCAmelCase   :						Union[str, Any]      							=					torchvision.models.resnetaaa(pretrained=UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					list(model.children()  )[:-2]
												__UpperCAmelCase   :						Tuple      							=					nn.Sequential(*UpperCamelCase  )
												__UpperCAmelCase   :						Tuple      							=					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )






					def lowerCamelCase__							(       self    : List[Any] ,       UpperCamelCase    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					self.pool(self.model(UpperCamelCase  )  )
												__UpperCAmelCase   :						str      							=					torch.flatten(UpperCamelCase ,       start_dim=2  )
												__UpperCAmelCase   :						Union[str, Any]      							=					out.transpose(1 ,       2  ).contiguous()
												return out  # BxNx2048






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __init__(       self    : str ,       UpperCamelCase    : str ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : str ,       UpperCamelCase    : Any ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					[json.loads(UpperCamelCase  ) for l in open(UpperCamelCase  )]
												__UpperCAmelCase   :						Optional[Any]      							=					os.path.dirname(UpperCamelCase  )
												__UpperCAmelCase   :						List[Any]      							=					tokenizer
												__UpperCAmelCase   :						List[str]      							=					labels
												__UpperCAmelCase   :						Optional[int]      							=					len(UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					max_seq_length

												__UpperCAmelCase   :						Tuple      							=					transforms






					def __len__(       self    : Tuple  ):
												'''simple docstring'''






												return len(self.data  )






					def __getitem__(       self    : int ,       UpperCamelCase    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,       add_special_tokens=UpperCamelCase  )  )
												__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase   :						Dict      							=					sentence[0], sentence[1:-1], sentence[-1]
												__UpperCAmelCase   :						Union[str, Any]      							=					sentence[: self.max_seq_length]

												__UpperCAmelCase   :						Tuple      							=					torch.zeros(self.n_classes  )
												__UpperCAmelCase   :						str      							=					1

												__UpperCAmelCase   :						List[str]      							=					Image.open(os.path.join(self.data_dir ,       self.data[index]["""img"""]  )  ).convert("""RGB"""  )
												__UpperCAmelCase   :						Any      							=					self.transforms(UpperCamelCase  )

												return {
												    "image_start_token": start_token,
												    "image_end_token": end_token,
												    "sentence": sentence,
												    "image": image,
												    "label": label,
												}






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					Counter()
												for row in self.data:
																			label_freqs.update(row["""label"""]  )
												return label_freqs






def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]							)		->       Any:

							'''simple docstring'''



							__UpperCAmelCase   :						Union[str, Any]      							=					[len(row["""sentence"""]							) for row in batch]
							__UpperCAmelCase							,__UpperCAmelCase   :						Union[str, Any]      							=					len(_UpperCamelCase							), max(_UpperCamelCase							)

							__UpperCAmelCase   :						int      							=					torch.zeros(_UpperCamelCase       ,			_UpperCamelCase       ,			dtype=torch.long							)
							__UpperCAmelCase   :						Any      							=					torch.zeros(_UpperCamelCase       ,			_UpperCamelCase       ,			dtype=torch.long							)

							for i_batch, (input_row, length) in enumerate(zip(_UpperCamelCase       ,			_UpperCamelCase							)							):
														__UpperCAmelCase   :						List[str]      							=					input_row["""sentence"""]
														__UpperCAmelCase   :						Optional[Any]      							=					1

							__UpperCAmelCase   :						int      							=					torch.stack([row["""image"""] for row in batch]							)
							__UpperCAmelCase   :						Optional[int]      							=					torch.stack([row["""label"""] for row in batch]							)
							__UpperCAmelCase   :						int      							=					torch.stack([row["""image_start_token"""] for row in batch]							)
							__UpperCAmelCase   :						int      							=					torch.stack([row["""image_end_token"""] for row in batch]							)

							return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def      lowerCamelCase       (     )		->       List[str]:

							'''simple docstring'''



							return [
							    "Crime",
							    "Drama",
							    "Thriller",
							    "Action",
							    "Comedy",
							    "Romance",
							    "Documentary",
							    "Short",
							    "Mystery",
							    "History",
							    "Family",
							    "Adventure",
							    "Fantasy",
							    "Sci-Fi",
							    "Western",
							    "Horror",
							    "Sport",
							    "War",
							    "Music",
							    "Musical",
							    "Animation",
							    "Biography",
							    "Film-Noir",
							]
def      lowerCamelCase       (     )		->       Dict:

							'''simple docstring'''



							return transforms.Compose(
							    [
							        transforms.Resize(2_5_6							),
							        transforms.CenterCrop(2_2_4							),
							        transforms.ToTensor(),
							        transforms.Normalize(
							            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]       ,			std=[0.12_221_994, 0.12_145_835, 0.14_380_469]       ,			),
							    ]							)
