


import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)
def        UpperCamelCase_(				)->		Tuple:
			UpperCamelCase__					=       argparse.ArgumentParser()
			parser.add_argument("--model_ckpt"				,     type=_A				,     default="microsoft/unixcoder-base-nine"  )
			parser.add_argument("--num_epochs"				,     type=_A				,     default=5  )
			parser.add_argument("--batch_size"				,     type=_A				,     default=6  )
			parser.add_argument("--gradient_accumulation_steps"				,     type=_A				,     default=1  )
			parser.add_argument("--freeze"				,     type=_A				,     default=_A  )
			parser.add_argument("--learning_rate"				,     type=_A				,     default=5e-4  )
			parser.add_argument("--seed"				,     type=_A				,     default=0  )
			parser.add_argument("--lr_scheduler_type"				,     type=_A				,     default="cosine"  )
			parser.add_argument("--num_warmup_steps"				,     type=_A				,     default=10  )
			parser.add_argument("--weight_decay"				,     type=_A				,     default=0.0_1  )
			parser.add_argument("--output_dir"				,     type=_A				,     default="./results"  )
			return parser.parse_args()


__UpperCamelCase    		=       load('accuracy')
def        UpperCamelCase_(				_A   :int  )->		List[str]:
			UpperCamelCase__,      UpperCamelCase__					=       eval_pred
			UpperCamelCase__					=       np.argmax(_A				,     axis=1  )
			return metric.compute(predictions=_A				,     references=_A  )







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case     ):

						'''simple docstring'''


						super().__init__()
						UpperCamelCase__					=       trainer







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						**snake_case     ):

						'''simple docstring'''


						if control.should_evaluate:
									UpperCamelCase__					=       deepcopy(snake_case     )
									self._trainer.evaluate(eval_dataset=self._trainer.train_dataset      ,						metric_key_prefix="train"     )
									return control_copy



def        UpperCamelCase_(				)->		List[str]:
			UpperCamelCase__					=       get_args()
			set_seed(args.seed  )

			UpperCamelCase__					=       load_dataset("codeparrot/codecomplex"				,     split="train"  )
			UpperCamelCase__					=       dataset.train_test_split(test_size=0.2  )
			UpperCamelCase__					=       train_test["test"].train_test_split(test_size=0.5  )
			UpperCamelCase__					=       DatasetDict(
			    {
			        "train": train_test["train"],
			        "test": test_validation["train"],
			        "valid": test_validation["test"],
			    }  )

			print("Loading tokenizer and model"  )
			UpperCamelCase__					=       AutoTokenizer.from_pretrained(args.model_ckpt  )
			UpperCamelCase__					=       tokenizer.eos_token
			UpperCamelCase__					=       AutoModelForSequenceClassification.from_pretrained(args.model_ckpt				,     num_labels=7  )
			UpperCamelCase__					=       model.config.eos_token_id

			if args.freeze:
						for param in model.roberta.parameters():
									UpperCamelCase__					=       False

			UpperCamelCase__					=       ClassLabel(num_classes=7				,     names=list(set(train_test_validation["train"]["complexity"]  )  )  )

			def tokenize(_A   :str  ):
						UpperCamelCase__					=       tokenizer(example["src"]				,     truncation=_A				,     max_length=10_24  )
						UpperCamelCase__					=       labels.straint(example["complexity"]  )
						return {
						    "input_ids": inputs["input_ids"],
						    "attention_mask": inputs["attention_mask"],
						    "label": label,
						}

			UpperCamelCase__					=       train_test_validation.map(
			    _A				,     batched=_A				,     remove_columns=train_test_validation["train"].column_names				,     )
			UpperCamelCase__					=       DataCollatorWithPadding(tokenizer=_A  )

			UpperCamelCase__					=       TrainingArguments(
			    output_dir=args.output_dir				,     learning_rate=args.learning_rate				,     lr_scheduler_type=args.lr_scheduler_type				,     evaluation_strategy="epoch"				,     save_strategy="epoch"				,     logging_strategy="epoch"				,     per_device_train_batch_size=args.batch_size				,     per_device_eval_batch_size=args.batch_size				,     num_train_epochs=args.num_epochs				,     gradient_accumulation_steps=args.gradient_accumulation_steps				,     weight_decay=0.0_1				,     metric_for_best_model="accuracy"				,     run_name="complexity-java"				,     report_to="wandb"				,     )

			UpperCamelCase__					=       Trainer(
			    model=_A				,     args=_A				,     train_dataset=tokenized_datasets["train"]				,     eval_dataset=tokenized_datasets["valid"]				,     tokenizer=_A				,     data_collator=_A				,     compute_metrics=_A				,     )

			print("Training..."  )
			trainer.add_callback(CustomCallback(_A  )  )
			trainer.train()


if __name__ == "__main__":
						main()






import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
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 (
						    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
						    VideoMAEForPreTraining,
						    VideoMAEForVideoClassification,
						    VideoMAEModel,
						)
						from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from transformers import VideoMAEImageProcessor







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case=13      ,						snake_case=10      ,						snake_case=3      ,						snake_case=2      ,						snake_case=2      ,						snake_case=2      ,						snake_case=True      ,						snake_case=True      ,						snake_case=32      ,						snake_case=5      ,						snake_case=4      ,						snake_case=37      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=10      ,						snake_case=0.02      ,						snake_case=0.9      ,						snake_case=None      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       parent
						UpperCamelCase__					=       batch_size
						UpperCamelCase__					=       image_size
						UpperCamelCase__					=       num_channels
						UpperCamelCase__					=       patch_size
						UpperCamelCase__					=       tubelet_size
						UpperCamelCase__					=       num_frames
						UpperCamelCase__					=       is_training
						UpperCamelCase__					=       use_labels
						UpperCamelCase__					=       hidden_size
						UpperCamelCase__					=       num_hidden_layers
						UpperCamelCase__					=       num_attention_heads
						UpperCamelCase__					=       intermediate_size
						UpperCamelCase__					=       hidden_act
						UpperCamelCase__					=       hidden_dropout_prob
						UpperCamelCase__					=       attention_probs_dropout_prob
						UpperCamelCase__					=       type_sequence_label_size
						UpperCamelCase__					=       initializer_range
						UpperCamelCase__					=       mask_ratio
						UpperCamelCase__					=       scope

						# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
						UpperCamelCase__					=       (image_size // patch_size) ** 2
						UpperCamelCase__					=       (num_frames // tubelet_size) * self.num_patches_per_frame

						# use this variable to define bool_masked_pos
						UpperCamelCase__					=       int(mask_ratio * self.seq_length     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       floats_tensor(
						    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]     )

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

						UpperCamelCase__					=       self.get_config()

						return config, pixel_values, labels




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return VideoMAEConfig(
						    image_size=self.image_size      ,						patch_size=self.patch_size      ,						num_channels=self.num_channels      ,						num_frames=self.num_frames      ,						tubelet_size=self.tubelet_size      ,						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      ,						is_decoder=snake_case      ,						initializer_range=self.initializer_range      ,						)




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEModel(config=snake_case     )
						model.to(snake_case     )
						model.eval()
						UpperCamelCase__					=       model(snake_case     )
						self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size)     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEForPreTraining(snake_case     )
						model.to(snake_case     )
						model.eval()
						# important: each video needs to have the same number of masked patches
						# hence we define a single mask, which we then repeat for each example in the batch
						UpperCamelCase__					=       torch.ones((self.num_masks,)     )
						UpperCamelCase__					=       torch.cat([mask, torch.zeros(self.seq_length - mask.size(0     )     )]     )
						UpperCamelCase__					=       mask.expand(self.batch_size      ,						-1     ).bool()

						UpperCamelCase__					=       model(snake_case      ,						snake_case     )
						# model only returns predictions for masked patches
						UpperCamelCase__					=       mask.sum().item()
						UpperCamelCase__					=       3 * self.tubelet_size * self.patch_size**2
						self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, num_masked_patches, decoder_num_labels)     )







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.prepare_config_and_inputs()
						UpperCamelCase__,      UpperCamelCase__,      UpperCamelCase__					=       config_and_inputs
						UpperCamelCase__					=       {"pixel_values": pixel_values}
						return config, inputs_dict







@require_torch
class        lowerCamelCase__	(							UpperCAmelCase					,	UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Optional[Any]    =		(
			    (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
			)
			_UpperCamelCase		:					Union[str, Any]    =		(
			    {'feature-extraction': VideoMAEModel, 'video-classification': VideoMAEForVideoClassification}
			    if is_torch_available()
			    else {}
			)

			_UpperCamelCase		:					Optional[int]    =		False
			_UpperCamelCase		:					Tuple    =		False
			_UpperCamelCase		:					int    =		False
			_UpperCamelCase		:					Any    =		False




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEModelTester(self     )
						UpperCamelCase__					=       ConfigTester(self      ,						config_class=snake_case      ,						has_text_modality=snake_case      ,						hidden_size=37     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case=False     ):

						'''simple docstring'''


						UpperCamelCase__					=       copy.deepcopy(snake_case     )

						if model_class == VideoMAEForPreTraining:
									# important: each video needs to have the same number of masked patches
									# hence we define a single mask, which we then repeat for each example in the batch
									UpperCamelCase__					=       torch.ones((self.model_tester.num_masks,)     )
									UpperCamelCase__					=       torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0     )     )]     )
									UpperCamelCase__					=       mask.expand(self.model_tester.batch_size      ,						-1     ).bool()
									UpperCamelCase__					=       bool_masked_pos.to(snake_case     )

						if return_labels:
									if model_class in [
									    *get_values(snake_case     ),
									]:
												UpperCamelCase__					=       torch.zeros(
												    self.model_tester.batch_size      ,						dtype=torch.long      ,						device=snake_case     )

						return inputs_dict




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						self.config_tester.run_common_tests()




			@unittest.skip(reason="VideoMAE does not use inputs_embeds"     )
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

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




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

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

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




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*snake_case     )




			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCamelCase__					=       VideoMAEModel.from_pretrained(snake_case     )
									self.assertIsNotNone(snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if not self.has_attentions:
									pass

						else:
									UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()
									UpperCamelCase__					=       True

									for model_class in self.all_model_classes:
												UpperCamelCase__					=       self.model_tester.seq_length - self.model_tester.num_masks
												UpperCamelCase__					=       (
												    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
												)

												UpperCamelCase__					=       True
												UpperCamelCase__					=       False
												UpperCamelCase__					=       True
												UpperCamelCase__					=       model_class(snake_case     )
												model.to(snake_case     )
												model.eval()
												with torch.no_grad():
															UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )
												UpperCamelCase__					=       outputs.attentions
												self.assertEqual(len(snake_case     )      ,						self.model_tester.num_hidden_layers     )

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												UpperCamelCase__					=       True
												UpperCamelCase__					=       model_class(snake_case     )
												model.to(snake_case     )
												model.eval()
												with torch.no_grad():
															UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )
												UpperCamelCase__					=       outputs.attentions
												self.assertEqual(len(snake_case     )      ,						self.model_tester.num_hidden_layers     )

												self.assertListEqual(
												    list(attentions[0].shape[-3:]     )      ,						[self.model_tester.num_attention_heads, seq_len, seq_len]      ,						)
												UpperCamelCase__					=       len(snake_case     )

												# Check attention is always last and order is fine
												UpperCamelCase__					=       True
												UpperCamelCase__					=       True
												UpperCamelCase__					=       model_class(snake_case     )
												model.to(snake_case     )
												model.eval()
												with torch.no_grad():
															UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )

												self.assertEqual(out_len + 1      ,						len(snake_case     )     )

												UpperCamelCase__					=       outputs.attentions

												self.assertEqual(len(snake_case     )      ,						self.model_tester.num_hidden_layers     )
												self.assertListEqual(
												    list(self_attentions[0].shape[-3:]     )      ,						[self.model_tester.num_attention_heads, seq_len, seq_len]      ,						)




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						def check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     ):
									UpperCamelCase__					=       model_class(snake_case     )
									model.to(snake_case     )
									model.eval()

									with torch.no_grad():
												UpperCamelCase__					=       model(**self._prepare_for_class(snake_case      ,						snake_case     )     )

									UpperCamelCase__					=       outputs.hidden_states
									UpperCamelCase__					=       self.model_tester.num_hidden_layers + 1
									self.assertEqual(len(snake_case     )      ,						snake_case     )

									UpperCamelCase__					=       self.model_tester.seq_length - self.model_tester.num_masks
									UpperCamelCase__					=       num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]     )      ,						[seq_length, self.model_tester.hidden_size]      ,						)

						UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCamelCase__					=       True
									check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									UpperCamelCase__					=       True

									check_hidden_states_output(snake_case      ,						snake_case      ,						snake_case     )







			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."     )
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass



def        UpperCamelCase_(				)->		Union[str, Any]:
			UpperCamelCase__					=       hf_hub_download(
			    repo_id="hf-internal-testing/spaghetti-video"				,     filename="eating_spaghetti.npy"				,     repo_type="dataset"  )
			UpperCamelCase__					=       np.load(_A  )
			return list(_A  )







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






			"""simple docstring"""






			@cached_property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return (
						    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]      ,						image_std=[0.5, 0.5, 0.5]     )
						    if is_vision_available()
						    else None
						)




			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics"     ).to(
						    snake_case     )

						UpperCamelCase__					=       self.default_image_processor
						UpperCamelCase__					=       prepare_video()
						UpperCamelCase__					=       image_processor(snake_case      ,						return_tensors="pt"     ).to(snake_case     )

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

						# verify the logits
						UpperCamelCase__					=       torch.Size((1, 400)     )
						self.assertEqual(outputs.logits.shape      ,						snake_case     )

						UpperCamelCase__					=       torch.tensor([0.3669, -0.0688, -0.2421]     ).to(snake_case     )

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







			@slow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"     ).to(snake_case     )

						UpperCamelCase__					=       self.default_image_processor
						UpperCamelCase__					=       prepare_video()
						UpperCamelCase__					=       image_processor(snake_case      ,						return_tensors="pt"     ).to(snake_case     )

						# add boolean mask, indicating which patches to mask
						UpperCamelCase__					=       hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos"      ,						filename="bool_masked_pos.pt"     )
						UpperCamelCase__					=       torch.load(snake_case     )

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

						# verify the logits
						UpperCamelCase__					=       torch.Size([1, 1408, 1536]     )
						UpperCamelCase__					=       torch.tensor(
						    [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]      ,						device=snake_case     )
						self.assertEqual(outputs.logits.shape      ,						snake_case     )
						self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]      ,						snake_case      ,						atol=1E-4     )     )

						# verify the loss (`config.norm_pix_loss` = `True`)
						UpperCamelCase__					=       torch.tensor([0.5142]      ,						device=snake_case     )
						self.assertTrue(torch.allclose(outputs.loss      ,						snake_case      ,						atol=1E-4     )     )

						# verify the loss (`config.norm_pix_loss` = `False`)
						UpperCamelCase__					=       VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"      ,						norm_pix_loss=snake_case     ).to(
						    snake_case     )

						with torch.no_grad():
									UpperCamelCase__					=       model(**snake_case     )

						UpperCamelCase__					=       torch.tensor(torch.tensor([0.6469]     )      ,						device=snake_case     )
						self.assertTrue(torch.allclose(outputs.loss      ,						snake_case      ,						atol=1E-4     )     )


