


"""simple docstring"""

import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
       import torch

       from transformers import (
           NystromformerForMaskedLM,
           NystromformerForMultipleChoice,
           NystromformerForQuestionAnswering,
           NystromformerForSequenceClassification,
           NystromformerForTokenClassification,
           NystromformerModel,
       )
       from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST





class    lowerCamelCase__						:
      def __init__(		self	:	Union[str, Any]      ,       A_	:	List[str]      ,       A_	:	int=1_3      ,       A_	:	Optional[Any]=7      ,       A_	:	str=True      ,       A_	:	Optional[Any]=True      ,       A_	:	List[Any]=True      ,       A_	:	Dict=True      ,       A_	:	Optional[int]=9_9      ,       A_	:	Optional[int]=3_2      ,       A_	:	Optional[int]=5      ,       A_	:	List[Any]=4      ,       A_	:	List[str]=3_7      ,       A_	:	Optional[int]="gelu"      ,       A_	:	List[str]=0.1      ,       A_	:	Optional[int]=0.1      ,       A_	:	Dict=5_1_2      ,       A_	:	List[str]=1_6      ,       A_	:	Union[str, Any]=2      ,       A_	:	List[str]=0.02      ,       A_	:	str=3      ,       A_	:	Union[str, Any]=4      ,       A_	:	List[str]=None      ,       ):






       '''simple docstring'''


       __lowercase									=	parent
       __lowercase									=	batch_size
       __lowercase									=	seq_length
       __lowercase									=	is_training
       __lowercase									=	use_input_mask
       __lowercase									=	use_token_type_ids
       __lowercase									=	use_labels
       __lowercase									=	vocab_size
       __lowercase									=	hidden_size
       __lowercase									=	num_hidden_layers
       __lowercase									=	num_attention_heads
       __lowercase									=	intermediate_size
       __lowercase									=	hidden_act
       __lowercase									=	hidden_dropout_prob
       __lowercase									=	attention_probs_dropout_prob
       __lowercase									=	max_position_embeddings
       __lowercase									=	type_vocab_size
       __lowercase									=	type_sequence_label_size
       __lowercase									=	initializer_range
       __lowercase									=	num_labels
       __lowercase									=	num_choices
       __lowercase									=	scope
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size							)

       __lowercase									=	None
       if self.use_input_mask:
        __lowercase									=	random_attention_mask([self.batch_size, self.seq_length]							)

       __lowercase									=	None
       if self.use_token_type_ids:
        __lowercase									=	ids_tensor([self.batch_size, self.seq_length]      ,       self.type_vocab_size							)

       __lowercase									=	None
       __lowercase									=	None
       __lowercase									=	None
       if self.use_labels:
        __lowercase									=	ids_tensor([self.batch_size]      ,       self.type_sequence_label_size							)
        __lowercase									=	ids_tensor([self.batch_size, self.seq_length]      ,       self.num_labels							)
        __lowercase									=	ids_tensor([self.batch_size]      ,       self.num_choices							)

       __lowercase									=	self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       return NystromformerConfig(
           vocab_size=self.vocab_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      ,       max_position_embeddings=self.max_position_embeddings      ,       type_vocab_size=self.type_vocab_size      ,       is_decoder=A_      ,       initializer_range=self.initializer_range      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       A_	:	Union[str, Any]      ,       A_	:	str      ,       A_	:	Optional[int]      ,       A_	:	int      ,       A_	:	Tuple      ,       A_	:	str      ,       A_	:	int							):






       '''simple docstring'''


       __lowercase									=	NystromformerModel(config=A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	model(A_      ,       attention_mask=A_      ,       token_type_ids=A_							)
       __lowercase									=	model(A_      ,       token_type_ids=A_							)
       __lowercase									=	model(A_							)
       self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]      ,       A_	:	Optional[Any]      ,       A_	:	Optional[int]      ,       A_	:	Any      ,       A_	:	Tuple      ,       A_	:	Dict      ,       A_	:	Any      ,       A_	:	int							):






       '''simple docstring'''


       __lowercase									=	NystromformerForMaskedLM(config=A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	model(A_      ,       attention_mask=A_      ,       token_type_ids=A_      ,       labels=A_							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.vocab_size)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	Any      ,       A_	:	Dict      ,       A_	:	int      ,       A_	:	str      ,       A_	:	int      ,       A_	:	Optional[int]      ,       A_	:	str							):






       '''simple docstring'''


       __lowercase									=	NystromformerForQuestionAnswering(config=A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	model(
           A_      ,       attention_mask=A_      ,       token_type_ids=A_      ,       start_positions=A_      ,       end_positions=A_      ,       )
       self.parent.assertEqual(result.start_logits.shape      ,       (self.batch_size, self.seq_length)							)
       self.parent.assertEqual(result.end_logits.shape      ,       (self.batch_size, self.seq_length)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       A_	:	int      ,       A_	:	int      ,       A_	:	int      ,       A_	:	List[str]      ,       A_	:	Optional[Any]      ,       A_	:	str      ,       A_	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	self.num_labels
       __lowercase									=	NystromformerForSequenceClassification(A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	model(A_      ,       attention_mask=A_      ,       token_type_ids=A_      ,       labels=A_							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Any      ,       A_	:	int      ,       A_	:	Optional[int]      ,       A_	:	Any      ,       A_	:	str      ,       A_	:	List[Any]      ,       A_	:	Optional[int]      ,       A_	:	int							):






       '''simple docstring'''


       __lowercase									=	self.num_labels
       __lowercase									=	NystromformerForTokenClassification(config=A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	model(A_      ,       attention_mask=A_      ,       token_type_ids=A_      ,       labels=A_							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.num_labels)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	Optional[int]      ,       A_	:	Optional[int]      ,       A_	:	Optional[Any]      ,       A_	:	List[str]      ,       A_	:	Union[str, Any]      ,       A_	:	Any      ,       A_	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	self.num_choices
       __lowercase									=	NystromformerForMultipleChoice(config=A_							)
       model.to(A_							)
       model.eval()
       __lowercase									=	input_ids.unsqueeze(1							).expand(-1      ,       self.num_choices      ,       -1							).contiguous()
       __lowercase									=	token_type_ids.unsqueeze(1							).expand(-1      ,       self.num_choices      ,       -1							).contiguous()
       __lowercase									=	input_mask.unsqueeze(1							).expand(-1      ,       self.num_choices      ,       -1							).contiguous()
       __lowercase									=	model(
           A_      ,       attention_mask=A_      ,       token_type_ids=A_      ,       labels=A_      ,       )
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_choices)							)






      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	self.prepare_config_and_inputs()
       (
           (
           __lowercase		
       )							,		(
           __lowercase		
       )							,		(
           __lowercase		
       )							,		(
           __lowercase		
       )							,		(
           __lowercase		
       )							,		(
           __lowercase		
       )							,		(
           __lowercase		
       )							,				
       )									=	config_and_inputs
       __lowercase									=	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
       return config, inputs_dict






@require_torch
class    lowerCamelCase__						(		_a			,   _a			,   unittest.TestCase    ):
      a :     str											=		(
          (
              NystromformerModel,
              NystromformerForMaskedLM,
              NystromformerForMultipleChoice,
              NystromformerForQuestionAnswering,
              NystromformerForSequenceClassification,
              NystromformerForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      a :     str											=		(
          {
              """feature-extraction""": NystromformerModel,
              """fill-mask""": NystromformerForMaskedLM,
              """question-answering""": NystromformerForQuestionAnswering,
              """text-classification""": NystromformerForSequenceClassification,
              """token-classification""": NystromformerForTokenClassification,
              """zero-shot""": NystromformerForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      a :     str											=		False
      a :     List[str]											=		False
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	NystromformerModelTester(self							)
       __lowercase									=	ConfigTester(self      ,       config_class=A_      ,       hidden_size=3_7							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       self.config_tester.run_common_tests()
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
        __lowercase									=	type
        self.model_tester.create_and_check_model(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*A_							)






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowercase									=	NystromformerModel.from_pretrained(A_							)
        self.assertIsNotNone(A_							)






@require_torch
class    lowerCamelCase__						(		unittest.TestCase    ):
      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	NystromformerModel.from_pretrained("""uw-madison/nystromformer-512"""							)
       __lowercase									=	torch.tensor([[0, 1, 2, 3, 4, 5]]							)

       with torch.no_grad():
        __lowercase									=	model(A_							)[0]

       __lowercase									=	torch.Size((1, 6, 7_6_8)							)
       self.assertEqual(output.shape      ,       A_							)

       __lowercase									=	torch.tensor(
           [[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]]							)

       self.assertTrue(torch.allclose(output[:, :3, :3]      ,       A_      ,       atol=1e-4							)							)






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	"""the [MASK] of Belgium is Brussels"""

       __lowercase									=	AutoTokenizer.from_pretrained("""uw-madison/nystromformer-512"""							)
       __lowercase									=	NystromformerForMaskedLM.from_pretrained("""uw-madison/nystromformer-512"""							)

       __lowercase									=	tokenizer(A_      ,       return_tensors="""pt"""							)

       with torch.no_grad():
        __lowercase									=	model(encoding.input_ids							).logits

       __lowercase									=	token_logits[:, 2, :].argmax(-1							)[0]

       self.assertEqual(tokenizer.decode(A_							)      ,       """capital"""							)




"""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 lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	argparse.ArgumentParser()
 parser.add_argument("""--model_ckpt"""				,   type=UpperCamelCase__				,   default="""microsoft/unixcoder-base-nine""" )
 parser.add_argument("""--num_epochs"""				,   type=UpperCamelCase__				,   default=5 )
 parser.add_argument("""--batch_size"""				,   type=UpperCamelCase__				,   default=6 )
 parser.add_argument("""--gradient_accumulation_steps"""				,   type=UpperCamelCase__				,   default=1 )
 parser.add_argument("""--freeze"""				,   type=UpperCamelCase__				,   default=UpperCamelCase__ )
 parser.add_argument("""--learning_rate"""				,   type=UpperCamelCase__				,   default=5E-4 )
 parser.add_argument("""--seed"""				,   type=UpperCamelCase__				,   default=0 )
 parser.add_argument("""--lr_scheduler_type"""				,   type=UpperCamelCase__				,   default="""cosine""" )
 parser.add_argument("""--num_warmup_steps"""				,   type=UpperCamelCase__				,   default=10 )
 parser.add_argument("""--weight_decay"""				,   type=UpperCamelCase__				,   default=0.01 )
 parser.add_argument("""--output_dir"""				,   type=UpperCamelCase__				,   default="""./results""" )
 return parser.parse_args()


UpperCAmelCase__  							=load("accuracy")


def lowerCAmelCase_      (				UpperCamelCase__	: List[Any] ):






 """simple docstring"""







 __lowercase							,		__lowercase									=	eval_pred
 __lowercase									=	np.argmax(UpperCamelCase__				,   axis=1 )
 return metric.compute(predictions=UpperCamelCase__				,   references=UpperCamelCase__ )





class    lowerCamelCase__						(		_a    ):
      def __init__(		self	:	Any      ,       A_	:	int							):






       '''simple docstring'''


       super().__init__()
       __lowercase									=	trainer






      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	int      ,       A_	:	str      ,       A_	:	Any      ,       **A_	:	Optional[int]							):






       '''simple docstring'''


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






def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	get_args()
 set_seed(args.seed )

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

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

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

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

 def tokenize(UpperCamelCase__	: Optional[Any] ):
  __lowercase									=	tokenizer(example["""src"""]				,   truncation=UpperCamelCase__				,   max_length=1024 )
  __lowercase									=	labels.straint(example["""complexity"""] )
  return {
      "input_ids": inputs["input_ids"],
      "attention_mask": inputs["attention_mask"],
      "label": label,
  }

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

 __lowercase									=	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=0.01				,   metric_for_best_model="""accuracy"""				,   run_name="""complexity-java"""				,   report_to="""wandb"""				,   )

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

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


if __name__ == "__main__":
       main()
