




"""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


a      =							"sshleifer/mar_enro_6_3_student"






class       SCREAMING_SNAKE_CASE__					(					UpperCAmelCase_					):


						def   __lowercase (							self				:			Union[str, Any]					):
										super().setUp()

										lowerCAmelCase  =				cached_path(
										    """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""						,						extract_compressed_file=_lowercase						,						)
										lowerCAmelCase  =				f'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''


						@slow
						@require_torch_gpu
						def   __lowercase (							self				:			int					):
										MarianMTModel.from_pretrained(_lowercase					)




						@slow
						@require_torch_gpu
						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				{
										    '$MAX_LEN': 64,
										    '$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  =				(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split("""finetune.py"""					)[1].strip()
										lowerCAmelCase  =				bash_script.replace("""\\\n"""						,						""""""					).strip().replace("""\"$@\""""						,						""""""					)
										for k, v in env_vars_to_replace.items():
														lowerCAmelCase  =				bash_script.replace(_lowercase						,						str(_lowercase					)					)
										lowerCAmelCase  =				self.get_auto_remove_tmp_dir()

										# bash_script = bash_script.replace("--fp16 ", "")
										lowerCAmelCase  =				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

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

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

										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  =				os.listdir(_lowercase					)
										lowerCAmelCase  =				[x for x in contents if x.endswith(""".ckpt"""					)][0]
										lowerCAmelCase  =				os.path.join(args.output_dir						,						_lowercase					)
										lowerCAmelCase  =				torch.load(_lowercase						,						map_location="""cpu"""					)
										lowerCAmelCase  =				'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  =				{os.path.basename(_lowercase					) 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       SCREAMING_SNAKE_CASE__					(					UpperCAmelCase_					):


						@timeout_decorator.timeout(600					)
						@slow
						@require_torch_gpu
						def   __lowercase (							self				:			Any					):
										lowerCAmelCase  =				f'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
										lowerCAmelCase  =				{
										    '--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
										lowerCAmelCase  =				(
										    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split("""distillation.py"""					)[1].strip()
										)
										lowerCAmelCase  =				bash_script.replace("""\\\n"""						,						""""""					).strip().replace("""\"$@\""""						,						""""""					)
										lowerCAmelCase  =				bash_script.replace("""--fp16 """						,						""" """					)

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

														lowerCAmelCase  =				distill_main(_lowercase					)

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

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

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor






class       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			List[Any]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Optional[int]=13						,						lowerCAmelCase				:			List[str]=30						,						lowerCAmelCase				:			Dict=2						,						lowerCAmelCase				:			Any=3						,						lowerCAmelCase				:			str=True						,						lowerCAmelCase				:			List[Any]=True						,						lowerCAmelCase				:			Dict=32						,						lowerCAmelCase				:			Dict=5						,						lowerCAmelCase				:			str=4						,						lowerCAmelCase				:			List[Any]=37						,						lowerCAmelCase				:			List[Any]="gelu"						,						lowerCAmelCase				:			List[Any]=0.1						,						lowerCAmelCase				:			List[Any]=0.1						,						lowerCAmelCase				:			Optional[int]=10						,						lowerCAmelCase				:			List[str]=0.02						,						lowerCAmelCase				:			List[str]=None						,						):
										lowerCAmelCase  =				parent
										lowerCAmelCase  =				batch_size
										lowerCAmelCase  =				image_size
										lowerCAmelCase  =				patch_size
										lowerCAmelCase  =				num_channels
										lowerCAmelCase  =				is_training
										lowerCAmelCase  =				use_labels
										lowerCAmelCase  =				hidden_size
										lowerCAmelCase  =				num_hidden_layers
										lowerCAmelCase  =				num_attention_heads
										lowerCAmelCase  =				intermediate_size
										lowerCAmelCase  =				hidden_act
										lowerCAmelCase  =				hidden_dropout_prob
										lowerCAmelCase  =				attention_probs_dropout_prob
										lowerCAmelCase  =				type_sequence_label_size
										lowerCAmelCase  =				initializer_range
										lowerCAmelCase  =				scope

										# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										lowerCAmelCase  =				(image_size // patch_size) ** 2
										lowerCAmelCase  =				num_patches + 1


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

										lowerCAmelCase  =				None
										if self.use_labels:
														lowerCAmelCase  =				ids_tensor([self.batch_size]						,						self.type_sequence_label_size					)

										lowerCAmelCase  =				self.get_config()

										return config, pixel_values, labels


						def   __lowercase (							self				:			Tuple					):
										return ViTMSNConfig(
										    image_size=self.image_size						,						patch_size=self.patch_size						,						num_channels=self.num_channels						,						hidden_size=self.hidden_size						,						num_hidden_layers=self.num_hidden_layers						,						num_attention_heads=self.num_attention_heads						,						intermediate_size=self.intermediate_size						,						hidden_act=self.hidden_act						,						hidden_dropout_prob=self.hidden_dropout_prob						,						attention_probs_dropout_prob=self.attention_probs_dropout_prob						,						initializer_range=self.initializer_range						,						)


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Tuple					):
										lowerCAmelCase  =				ViTMSNModel(config=lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.eval()
										lowerCAmelCase  =				model(lowerCAmelCase					)
										self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size)					)


						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				self.type_sequence_label_size
										lowerCAmelCase  =				ViTMSNForImageClassification(lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.eval()
										lowerCAmelCase  =				model(lowerCAmelCase						,						labels=lowerCAmelCase					)
										print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""					)
										print("""Labels: {labels}"""					)
										self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size)					)

										# test greyscale images
										lowerCAmelCase  =				1
										lowerCAmelCase  =				ViTMSNForImageClassification(lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.eval()

										lowerCAmelCase  =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
										lowerCAmelCase  =				model(lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size)					)




						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase  =				self.prepare_config_and_inputs()
										lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				config_and_inputs
										lowerCAmelCase  =				{"""pixel_values""": pixel_values}
										return config, inputs_dict






@require_torch
class       SCREAMING_SNAKE_CASE__					(					_a						,				_a						,				unittest.TestCase					):
						_a    						=						(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
						_a    						=						(
						    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_a    						=						False
						_a    						=						False
						_a    						=						False
						_a    						=						False


						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase  =				ViTMSNModelTester(self					)
										lowerCAmelCase  =				ConfigTester(self						,						config_class=lowerCAmelCase						,						has_text_modality=lowerCAmelCase						,						hidden_size=37					)


						def   __lowercase (							self				:			Dict					):
										self.config_tester.run_common_tests()


						@unittest.skip(reason="""ViTMSN does not use inputs_embeds"""					)
						def   __lowercase (							self				:			Optional[Any]					):
										pass


						def   __lowercase (							self				:			int					):
										lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowerCAmelCase  =				model_class(lowerCAmelCase					)
														self.assertIsInstance(model.get_input_embeddings()						,						(nn.Module)					)
														lowerCAmelCase  =				model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(lowerCAmelCase						,						nn.Linear					)					)


						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowerCAmelCase  =				model_class(lowerCAmelCase					)
														lowerCAmelCase  =				inspect.signature(model.forward					)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														lowerCAmelCase  =				[*signature.parameters.keys()]

														lowerCAmelCase  =				["""pixel_values"""]
														self.assertListEqual(arg_names[:1]						,						lowerCAmelCase					)


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase					)


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase					)




						@slow
						def   __lowercase (							self				:			Optional[Any]					):
										for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase  =				ViTMSNModel.from_pretrained(lowerCAmelCase					)
														self.assertIsNotNone(lowerCAmelCase					)





def 							lowercase					()     ->  Dict:


				'''simple docstring'''






				lowerCAmelCase  =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
				return image






@require_torch
@require_vision
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						@cached_property
						def   __lowercase (							self				:			Dict					):
										return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""					) if is_vision_available() else None




						@slow
						def   __lowercase (							self				:			Tuple					):
										torch.manual_seed(2					)
										lowerCAmelCase  =				ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""					).to(lowerCAmelCase					)

										lowerCAmelCase  =				self.default_image_processor
										lowerCAmelCase  =				prepare_img()
										lowerCAmelCase  =				image_processor(images=lowerCAmelCase						,						return_tensors="""pt"""					).to(lowerCAmelCase					)

										# forward pass
										with torch.no_grad():
														lowerCAmelCase  =				model(**lowerCAmelCase					)

										# verify the logits
										lowerCAmelCase  =				torch.Size((1, 1000)					)
										self.assertEqual(outputs.logits.shape						,						lowerCAmelCase					)

										lowerCAmelCase  =				torch.tensor([-0.0803, -0.4454, -0.2375]					).to(lowerCAmelCase					)

										self.assertTrue(torch.allclose(outputs.logits[0, :3]						,						lowerCAmelCase						,						atol=1e-4					)					)





