





'''simple docstring'''

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    (							):
    A__    =      argparse.ArgumentParser()
    parser.add_argument("--model_ckpt"						,	type=_lowerCamelCase						,	default="microsoft/unixcoder-base-nine"	)
    parser.add_argument("--num_epochs"						,	type=_lowerCamelCase						,	default=5	)
    parser.add_argument("--batch_size"						,	type=_lowerCamelCase						,	default=6	)
    parser.add_argument("--gradient_accumulation_steps"						,	type=_lowerCamelCase						,	default=1	)
    parser.add_argument("--freeze"						,	type=_lowerCamelCase						,	default=_lowerCamelCase	)
    parser.add_argument("--learning_rate"						,	type=_lowerCamelCase						,	default=5e-4	)
    parser.add_argument("--seed"						,	type=_lowerCamelCase						,	default=0	)
    parser.add_argument("--lr_scheduler_type"						,	type=_lowerCamelCase						,	default="cosine"	)
    parser.add_argument("--num_warmup_steps"						,	type=_lowerCamelCase						,	default=10	)
    parser.add_argument("--weight_decay"						,	type=_lowerCamelCase						,	default=0.0_1	)
    parser.add_argument("--output_dir"						,	type=_lowerCamelCase						,	default="./results"	)
    return parser.parse_args()


__lowerCAmelCase							:     Optional[Any]		     =load("accuracy")





def 						UpperCamelCase    (							_lowerCamelCase : Union[str, Any]	):
    A__,     A__    =      eval_pred
    A__    =      np.argmax(_lowerCamelCase						,	axis=1	)
    return metric.compute(predictions=_lowerCamelCase						,	references=_lowerCamelCase	)






class        UpperCAmelCase     (	UpperCamelCase__    ):
 def __init__(    self				:Any						,	lowercase_				:str							)->							None:
     super().__init__()
     A__    =      trainer



 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:Tuple						,	lowercase_				:Optional[int]						,	lowercase_				:Dict						,	**lowercase_				:int							)->							Any:
     if control.should_evaluate:
         A__    =      deepcopy(lowercase_							)
         self._trainer.evaluate(eval_dataset=self._trainer.train_dataset						,	metric_key_prefix="train"							)
         return control_copy





def 						UpperCamelCase    (							):
    A__    =      get_args()
    set_seed(args.seed	)

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

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

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

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

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

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

    A__    =      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"						,	)

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

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


if __name__ == "__main__":
  main()










'''simple docstring'''

import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
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,
      TimesformerForVideoClassification,
      TimesformerModel,
  )
  from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from transformers import VideoMAEImageProcessor






class        UpperCAmelCase     :
 def __init__(    self				:Optional[Any]						,	lowercase_				:int						,	lowercase_				:Union[str, Any]=13						,	lowercase_				:Union[str, Any]=10						,	lowercase_				:Any=3						,	lowercase_				:Tuple=2						,	lowercase_				:List[Any]=2						,	lowercase_				:int=True						,	lowercase_				:int=True						,	lowercase_				:List[str]=32						,	lowercase_				:Dict=5						,	lowercase_				:List[Any]=4						,	lowercase_				:List[Any]=37						,	lowercase_				:List[Any]="gelu"						,	lowercase_				:int=0.1						,	lowercase_				:List[Any]=0.1						,	lowercase_				:List[Any]=10						,	lowercase_				:int=0.0_2						,	lowercase_				:Union[str, Any]="divided_space_time"						,	lowercase_				:Tuple=None						,	)->							Tuple:
     A__    =      parent
     A__    =      batch_size
     A__    =      image_size
     A__    =      num_channels
     A__    =      patch_size
     A__    =      num_frames
     A__    =      is_training
     A__    =      use_labels
     A__    =      hidden_size
     A__    =      num_hidden_layers
     A__    =      num_attention_heads
     A__    =      intermediate_size
     A__    =      hidden_act
     A__    =      hidden_dropout_prob
     A__    =      attention_probs_dropout_prob
     A__    =      attention_type
     A__    =      initializer_range
     A__    =      scope
     A__    =      num_labels

     # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
     A__    =      (image_size // patch_size) ** 2
     A__    =      (num_frames) * self.num_patches_per_frame + 1
 def 					UpperCAmelCase_							(    self				:str							)->							str:
     A__    =      floats_tensor(
         [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]							)

     A__    =      None
     if self.use_labels:
         A__    =      ids_tensor([self.batch_size]						,	self.num_labels							)

     A__    =      self.get_config()

     return config, pixel_values, labels
 def 					UpperCAmelCase_							(    self				:int							)->							Any:
     A__    =      TimesformerConfig(
         image_size=self.image_size						,	patch_size=self.patch_size						,	num_channels=self.num_channels						,	num_frames=self.num_frames						,	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						,	attention_type=self.attention_type						,	)
     A__    =      self.num_labels
     return config
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:List[str]						,	lowercase_				:List[Any]						,	lowercase_				:Tuple							)->							Optional[int]:
     A__    =      TimesformerModel(config=lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)							)
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:Tuple						,	lowercase_				:Tuple						,	lowercase_				:Dict							)->							Tuple:
     A__    =      TimesformerForVideoClassification(lowercase_							)
     model.to(lowercase_							)
     model.eval()

     A__    =      model(lowercase_							)

     # verify the logits shape
     A__    =      torch.Size((self.batch_size, self.num_labels)							)
     self.parent.assertEqual(result.logits.shape						,	lowercase_							)



 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							str:
     A__    =      self.prepare_config_and_inputs()
     A__,     A__,     A__    =      config_and_inputs
     A__    =      {"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class        UpperCAmelCase     (	UpperCamelCase__      ,	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
 __lowercase       		=    (
     {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
     if is_torch_available()
     else {}
 )

 __lowercase       		=    False
 __lowercase       		=    False
 __lowercase       		=    False
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Optional[int]:
     A__    =      TimesformerModelTester(self							)
     A__    =      ConfigTester(
         self						,	config_class=lowercase_						,	has_text_modality=lowercase_						,	hidden_size=37							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:int						,	lowercase_				:Dict						,	lowercase_				:int=False							)->							str:
     A__    =      copy.deepcopy(lowercase_							)

     if return_labels:
         if model_class in get_values(lowercase_							):
             A__    =      torch.zeros(
                 self.model_tester.batch_size						,	dtype=torch.long						,	device=lowercase_							)

     return inputs_dict
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							List[Any]:
     self.config_tester.run_common_tests()
 @unittest.skip(reason="TimeSformer does not use inputs_embeds"							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     pass
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[Any]:
     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         A__    =      model_class(lowercase_							)
         self.assertIsInstance(model.get_input_embeddings()						,	(nn.Module)							)
         A__    =      model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(lowercase_						,	nn.Linear							)							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Dict:
     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

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

         A__    =      ["pixel_values"]
         self.assertListEqual(arg_names[:1]						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_video_classification(*lowercase_							)
 @slow
 def 					UpperCAmelCase_							(    self				:Any							)->							List[Any]:
     for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         A__    =      TimesformerModel.from_pretrained(lowercase_							)
         self.assertIsNotNone(lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     if not self.has_attentions:
         pass

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

         for model_class in self.all_model_classes:
             A__    =      self.model_tester.seq_length
             A__    =      self.model_tester.num_frames

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

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

             # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
             self.assertListEqual(
                 list(attentions[0].shape[-3:]							)						,	[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]						,	)
             A__    =      len(lowercase_							)

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

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

             A__    =      outputs.attentions

             self.assertEqual(len(lowercase_							)						,	self.model_tester.num_hidden_layers							)

             # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]							)						,	[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]						,	)



 def 					UpperCAmelCase_							(    self				:List[Any]							)->							List[str]:
     def check_hidden_states_output(lowercase_				:Dict						,	lowercase_				:int						,	lowercase_				:List[Any]							):
         A__    =      model_class(lowercase_							)
         model.to(lowercase_							)
         model.eval()

         with torch.no_grad():
             A__    =      model(**self._prepare_for_class(lowercase_						,	lowercase_							)							)

         A__    =      outputs.hidden_states
         A__    =      self.model_tester.num_hidden_layers + 1
         self.assertEqual(len(lowercase_							)						,	lowercase_							)

         A__    =      self.model_tester.seq_length

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

     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         A__    =      True
         check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_							)

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

         check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_							)





def 						UpperCamelCase    (							):
    A__    =      hf_hub_download(
        repo_id="hf-internal-testing/spaghetti-video"						,	filename="eating_spaghetti.npy"						,	repo_type="dataset"	)
    A__    =      np.load(_lowerCamelCase	)
    return list(_lowerCamelCase	)






@require_torch
@require_vision
class        UpperCAmelCase     (	unittest.TestCase    ):
 @cached_property
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							int:
     # logits were tested with a different mean and std, so we use the same here
     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 					UpperCAmelCase_							(    self				:int							)->							Any:
     A__    =      TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400"							).to(
         lowercase_							)

     A__    =      self.default_image_processor
     A__    =      prepare_video()
     A__    =      image_processor(video[:8]						,	return_tensors="pt"							).to(lowercase_							)

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

     # verify the logits
     A__    =      torch.Size((1, 4_00)							)
     self.assertEqual(outputs.logits.shape						,	lowercase_							)

     A__    =      torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]							).to(lowercase_							)

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



