


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


snake_case							:	Dict					      =				'''sshleifer/mar_enro_6_3_student'''







class 	snake_case_       (snake_case_				):
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   List[str]:
												super().setUp()

												a__					=					cached_path(
												    'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'      ,extract_compressed_file=__snake_case      ,)
												a__					=					F'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'
					@slow
					@require_torch_gpu
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Optional[Any]:
												MarianMTModel.from_pretrained(__snake_case				)



					@slow
					@require_torch_gpu
					def 			lowerCamelCase__(				self       :Any				) ->   Tuple:
												a__					=					{
												    '$MAX_LEN': 64,
												    '$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
												a__					=					(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'				)[1].strip()
												a__					=					bash_script.replace('\\\n'      ,''				).strip().replace('\"$@\"'      ,''				)
												for k, v in env_vars_to_replace.items():
																			a__					=					bash_script.replace(__snake_case      ,str(__snake_case				)				)
												a__					=					self.get_auto_remove_tmp_dir()

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

												a__					=					['finetune.py'] + bash_script.split() + args
												with patch.object(__snake_case      ,'argv'      ,__snake_case				):
																			a__					=					argparse.ArgumentParser()
																			a__					=					pl.Trainer.add_argparse_args(__snake_case				)
																			a__					=					SummarizationModule.add_model_specific_args(__snake_case      ,os.getcwd()				)
																			a__					=					parser.parse_args()
																			a__					=					main(__snake_case				)

												# Check metrics
												a__					=					load_json(model.metrics_save_path				)
												a__					=					metrics['val'][0]
												a__					=					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}']      ,__snake_case				)

												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
												a__					=					os.listdir(__snake_case				)
												a__					=					[x for x in contents if x.endswith('.ckpt'				)][0]
												a__					=					os.path.join(args.output_dir      ,__snake_case				)
												a__					=					torch.load(__snake_case      ,map_location='cpu'				)
												a__					=					'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:
																			a__					=					{os.path.basename(__snake_case				) 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 	snake_case_       (snake_case_				):
					@timeout_decorator.timeout(6_00				)
					@slow
					@require_torch_gpu
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   List[str]:
												a__					=					F'{self.test_file_dir_str}/test_data/wmt_en_ro'
												a__					=					{
												    '--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
												a__					=					(
												    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'				)[1].strip()
												)
												a__					=					bash_script.replace('\\\n'      ,''				).strip().replace('\"$@\"'      ,''				)
												a__					=					bash_script.replace('--fp16 '      ,' '				)

												for k, v in env_vars_to_replace.items():
																			a__					=					bash_script.replace(__snake_case      ,str(__snake_case				)				)
												a__					=					self.get_auto_remove_tmp_dir()
												a__					=					bash_script.replace('--fp16'      ,''				)
												a__					=					6
												a__					=					(
												    ['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(__snake_case      ,'argv'      ,__snake_case				):
																			a__					=					argparse.ArgumentParser()
																			a__					=					pl.Trainer.add_argparse_args(__snake_case				)
																			a__					=					SummarizationDistiller.add_model_specific_args(__snake_case      ,os.getcwd()				)
																			a__					=					parser.parse_args()
																			# assert args.gpus == gpus THIS BREAKS for multi_gpu

																			a__					=					distill_main(__snake_case				)

												# Check metrics
												a__					=					load_json(model.metrics_save_path				)
												a__					=					metrics['val'][0]
												a__					=					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}']      ,__snake_case				)

												# check lightning ckpt can be loaded and has a reasonable statedict
												a__					=					os.listdir(__snake_case				)
												a__					=					[x for x in contents if x.endswith('.ckpt'				)][0]
												a__					=					os.path.join(args.output_dir      ,__snake_case				)
												a__					=					torch.load(__snake_case      ,map_location='cpu'				)
												a__					=					'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:
																			a__					=					{os.path.basename(__snake_case				) 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








from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case							:	Optional[int]					      =				logging.get_logger(__name__)

snake_case							:	Union[str, Any]					      =				{
    '''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-pre''': '''https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-vcr''': '''https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-pre''': '''https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-nlvr2''': '''https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-pre''': '''https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'''
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Dict   				=      '''visual_bert'''
					def __init__(				self       :Optional[int]      ,__snake_case       :Any=3_05_22      ,__snake_case       :str=7_68      ,__snake_case       :Any=5_12      ,__snake_case       :Any=12      ,__snake_case       :int=12      ,__snake_case       :str=30_72      ,__snake_case       :int="gelu"      ,__snake_case       :Optional[int]=0.1      ,__snake_case       :str=0.1      ,__snake_case       :Union[str, Any]=5_12      ,__snake_case       :Tuple=2      ,__snake_case       :Union[str, Any]=0.02      ,__snake_case       :Optional[Any]=1E-12      ,__snake_case       :Optional[Any]=False      ,__snake_case       :int=True      ,__snake_case       :Any=1      ,__snake_case       :Optional[int]=0      ,__snake_case       :Tuple=2      ,**__snake_case       :Any      ,) ->   Union[str, Any]:
												super().__init__(pad_token_id=__snake_case      ,bos_token_id=__snake_case      ,eos_token_id=__snake_case      ,**__snake_case				)

												a__					=					vocab_size
												a__					=					max_position_embeddings
												a__					=					hidden_size
												a__					=					visual_embedding_dim
												a__					=					num_hidden_layers
												a__					=					num_attention_heads
												a__					=					intermediate_size
												a__					=					hidden_act
												a__					=					hidden_dropout_prob
												a__					=					attention_probs_dropout_prob
												a__					=					initializer_range
												a__					=					type_vocab_size
												a__					=					layer_norm_eps
												a__					=					bypass_transformer
												a__					=					special_visual_initialize




