

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


_a:     List[Any]							=      logging.get_logger(__name__)

_a:     Any							=      {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class     __UpperCamelCase     (       lowercase    ):
	SCREAMING_SNAKE_CASE__     					=  '''gpt_neo'''
	SCREAMING_SNAKE_CASE__     					=  ['''past_key_values''']
	SCREAMING_SNAKE_CASE__     					=  {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




	def __init__(		self						:    List[Any]		,						lowerCAmelCase						:    str=50_257		,						lowerCAmelCase						:    Optional[Any]=2_048		,						lowerCAmelCase						:    str=2_048		,						lowerCAmelCase						:    Optional[Any]=24		,						lowerCAmelCase						:    Union[str, Any]=[[["global", "local"], 12]]		,						lowerCAmelCase						:    Tuple=16		,						lowerCAmelCase						:    Optional[Any]=None		,						lowerCAmelCase						:    List[Any]=256		,						lowerCAmelCase						:    Dict="gelu_new"		,						lowerCAmelCase						:    Union[str, Any]=0.0		,						lowerCAmelCase						:    Dict=0.0		,						lowerCAmelCase						:    Dict=0.0		,						lowerCAmelCase						:    Dict=0.1		,						lowerCAmelCase						:    Optional[int]=1e-5		,						lowerCAmelCase						:    List[Any]=0.02		,						lowerCAmelCase						:    Union[str, Any]=True		,						lowerCAmelCase						:    Union[str, Any]=50_256		,						lowerCAmelCase						:    Union[str, Any]=50_256		,						**lowerCAmelCase						:    List[Any]		,						):


		'''simple docstring'''







		UpperCAmelCase_												=      vocab_size
		UpperCAmelCase_												=      max_position_embeddings
		UpperCAmelCase_												=      hidden_size
		UpperCAmelCase_												=      num_layers
		UpperCAmelCase_												=      num_heads
		UpperCAmelCase_												=      intermediate_size
		UpperCAmelCase_												=      window_size
		UpperCAmelCase_												=      activation_function
		UpperCAmelCase_												=      resid_dropout
		UpperCAmelCase_												=      embed_dropout
		UpperCAmelCase_												=      attention_dropout
		UpperCAmelCase_												=      classifier_dropout
		UpperCAmelCase_												=      layer_norm_epsilon
		UpperCAmelCase_												=      initializer_range
		UpperCAmelCase_												=      use_cache

		UpperCAmelCase_												=      bos_token_id
		UpperCAmelCase_												=      eos_token_id

		UpperCAmelCase_												=      attention_types
		UpperCAmelCase_												=      self.expand_attention_types_params(A__				)

		if len(self.attention_layers				) != self.num_layers:
			raise ValueError(
			    "Configuration for convolutional module is incorrect. "
			    "It is required that `len(config.attention_layers)` == `config.num_layers` "
			    F"but is `len(config.attention_layers) = {len(self.attention_layers				)}`, "
			    F"`config.num_layers = {self.num_layers}`. "
			    "`config.attention_layers` is prepared using `config.attention_types`. "
			    "Please verify the value of `config.attention_types` argument."				)

		super().__init__(bos_token_id=A__		,						eos_token_id=A__		,						**A__				)




	@staticmethod
	def    __A       (		lowerCAmelCase						:    Any				):


		'''simple docstring'''







		UpperCAmelCase_												=      []
		for item in attention_types:
			for _ in range(item[1]				):
				attentions.extend(item[0]				)
		return attentions



def    __lowerCAmelCase   (    A							,  A							,  A							,  A     ):
	import torch

	UpperCAmelCase_												=      input.size()
	UpperCAmelCase_												=      len(lowercase_     )
	UpperCAmelCase_												=      shape[dimension]

	UpperCAmelCase_												=      torch.arange(0							,  lowercase_							,  lowercase_     )
	UpperCAmelCase_												=      torch.div(sizedim - size							,  lowercase_							,  rounding_mode="floor"     ) + 1
	UpperCAmelCase_												=      torch.arange(lowercase_     ) + low_indices[:min_length][:, None]

	UpperCAmelCase_												=      [slice(lowercase_     )] * rank
	UpperCAmelCase_												=      indices
	UpperCAmelCase_												=      input[s]

	UpperCAmelCase_												=      list(range(0							,  rank + 1     )     )
	perm.append(perm.pop(dimension + 1     )     )

	return sliced.permute(lowercase_     )



def    __lowerCAmelCase   (    A							,  A     ):
	import torch

	UpperCAmelCase_												=      torch.arange(1							,  lowercase_     )
	UpperCAmelCase_												=      torch.remainder(lowercase_							,  lowercase_     )
	UpperCAmelCase_												=      remainders == 0
	UpperCAmelCase_												=      candidates[divisor_indices]
	UpperCAmelCase_												=      torch.max(lowercase_     )
	return largest_divisor, torch.div(lowercase_							,  lowercase_							,  rounding_mode="floor"     )




class     __UpperCamelCase     (       lowercase    ):




	@property
	def    __A       (		self						:    List[str]				):


		'''simple docstring'''







		UpperCAmelCase_												=      OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}				)
		if self.use_past:
			self.fill_with_past_key_values_(A__		,						direction="inputs"				)
			UpperCAmelCase_												=      {0: """batch""", 1: """past_sequence + sequence"""}
		else:
			UpperCAmelCase_												=      {0: """batch""", 1: """sequence"""}

		return common_inputs




	@property
	def    __A       (		self						:    Optional[int]				):


		'''simple docstring'''







		return self._config.num_heads




	def    __A       (		self						:    Any		,						lowerCAmelCase						:    Optional[int]		,						lowerCAmelCase						:    List[str] = -1		,						lowerCAmelCase						:    Optional[Any] = -1		,						lowerCAmelCase						:    Tuple = False		,						lowerCAmelCase						:    Union[str, Any] = None		,						):


		'''simple docstring'''







		UpperCAmelCase_												=      super(A__		,						self				).generate_dummy_inputs(
		    A__		,						batch_size=A__		,						seq_length=A__		,						is_pair=A__		,						framework=A__				)

		# We need to order the input in the way they appears in the forward()
		UpperCAmelCase_												=      OrderedDict({"input_ids": common_inputs["input_ids"]}				)

		# Need to add the past_keys
		if self.use_past:
			if not is_torch_available():
				raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
			else:
				import torch

				UpperCAmelCase_												=      common_inputs["""input_ids"""].shape
				# Not using the same length for past_key_values
				UpperCAmelCase_												=      seqlen + 2
				UpperCAmelCase_												=      (
				    batch,
				    self.num_attention_heads,
				    past_key_values_length,
				    self._config.hidden_size // self.num_attention_heads,
				)
				UpperCAmelCase_												=      [
				    (torch.zeros(A__				), torch.zeros(A__				)) for _ in range(self.num_layers				)
				]

		UpperCAmelCase_												=      common_inputs["""attention_mask"""]
		if self.use_past:
			UpperCAmelCase_												=      ordered_inputs["""attention_mask"""].dtype
			UpperCAmelCase_												=      torch.cat(
			    [ordered_inputs["attention_mask"], torch.ones(A__		,						A__		,						dtype=A__				)]		,						dim=1				)

		return ordered_inputs




	@property
	def    __A       (		self						:    Any				):


		'''simple docstring'''







		return 13


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   ()	-> Union[str, Any]:
	A__				:       int	  =						argparse.ArgumentParser()
	parser.add_argument("""--model_ckpt"""       , type=lowercase_       , default="""microsoft/unixcoder-base-nine"""   )
	parser.add_argument("""--num_epochs"""       , type=lowercase_       , default=5   )
	parser.add_argument("""--batch_size"""       , type=lowercase_       , default=6   )
	parser.add_argument("""--gradient_accumulation_steps"""       , type=lowercase_       , default=1   )
	parser.add_argument("""--freeze"""       , type=lowercase_       , default=lowercase_   )
	parser.add_argument("""--learning_rate"""       , type=lowercase_       , default=5E-4   )
	parser.add_argument("""--seed"""       , type=lowercase_       , default=0   )
	parser.add_argument("""--lr_scheduler_type"""       , type=lowercase_       , default="""cosine"""   )
	parser.add_argument("""--num_warmup_steps"""       , type=lowercase_       , default=10   )
	parser.add_argument("""--weight_decay"""       , type=lowercase_       , default=0.01   )
	parser.add_argument("""--output_dir"""       , type=lowercase_       , default="""./results"""   )
	return parser.parse_args()


A_			:					Any    			=     load('accuracy')

def 		UpperCamelCase   (lowercase_:					Dict   )	-> Any:
	A__  ,  A__				:       int	  =						eval_pred
	A__				:       List[str]	  =						np.argmax(lowercase_       , axis=1   )
	return metric.compute(predictions=lowercase_       , references=lowercase_   )



class        _a		(__magic_name__							):


					'''simple docstring'''






					def __init__(					self       ,			A__     ):
						super().__init__()
						A__				:       Any	  =						trainer






					def     __A     (					self       ,			A__       ,			A__       ,			A__       ,			**A__     ):
						if control.should_evaluate:
							A__				:       Dict	  =						deepcopy(A__     )
							self._trainer.evaluate(eval_dataset=self._trainer.train_dataset       ,			metric_key_prefix="""train"""     )
							return control_copy



def 		UpperCamelCase   ()	-> Optional[int]:
	A__				:       Optional[Any]	  =						get_args()
	set_seed(args.seed   )

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

	print("""Loading tokenizer and model"""   )
	A__				:       Dict	  =						AutoTokenizer.from_pretrained(args.model_ckpt   )
	A__				:       List[str]	  =						tokenizer.eos_token
	A__				:       List[Any]	  =						AutoModelForSequenceClassification.from_pretrained(args.model_ckpt       , num_labels=7   )
	A__				:       Dict	  =						model.config.eos_token_id

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

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

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

	A__				:       int	  =						train_test_validation.map(
	    lowercase_       , batched=lowercase_       , remove_columns=train_test_validation["""train"""].column_names       , )
	A__				:       List[str]	  =						DataCollatorWithPadding(tokenizer=lowercase_   )

	A__				:       Any	  =						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"""       , )

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

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


if __name__ == "__main__":
	main()



