





from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder


@dataclass
class 			SCREAMING_SNAKE_CASE   (__a							):
     lowerCAmelCase		      =	42




class 			SCREAMING_SNAKE_CASE   (__a ,		__a							):
     lowerCAmelCase		      =	True


     @register_to_config
     def __init__(						self						,     _UpperCAmelCase = 3						,     _UpperCAmelCase = 3						,     _UpperCAmelCase = ("DownEncoderBlock2D",)						,     _UpperCAmelCase = ("UpDecoderBlock2D",)						,     _UpperCAmelCase = (64,)						,     _UpperCAmelCase = 1						,     _UpperCAmelCase = "silu"						,     _UpperCAmelCase = 4						,     _UpperCAmelCase = 32						,     _UpperCAmelCase = 32						,     _UpperCAmelCase = 0.18215						,     ):

          '''simple docstring'''






          super().__init__()

          # pass init params to Encoder
          __A						:			List[Any]								=	Encoder(
              in_channels=a__						,     out_channels=a__						,     down_block_types=a__						,     block_out_channels=a__						,     layers_per_block=a__						,     act_fn=a__						,     norm_num_groups=a__						,     double_z=a__						,     )

          # pass init params to Decoder
          __A						:			Union[str, Any]								=	Decoder(
              in_channels=a__						,     out_channels=a__						,     up_block_types=a__						,     block_out_channels=a__						,     layers_per_block=a__						,     norm_num_groups=a__						,     act_fn=a__						,     )

          __A						:			Optional[Any]								=	nn.Convad(2 * latent_channels						,     2 * latent_channels						,     1)
          __A						:			List[str]								=	nn.Convad(a__						,     a__						,     1)

          __A						:			List[str]								=	False
          __A						:			Any								=	False

          # only relevant if vae tiling is enabled
          __A						:			Optional[int]								=	self.config.sample_size
          __A						:			Dict								=	(
              self.config.sample_size[0]
              if isinstance(self.config.sample_size						,     (list, tuple))
              else self.config.sample_size
          )
          __A						:			int								=	int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))
          __A						:			List[Any]								=	0.25


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase=False):

          '''simple docstring'''






          if isinstance(a__						,     (Encoder, Decoder)):
               __A						:			Optional[int]								=	value


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase = True):

          '''simple docstring'''






          __A						:			Tuple								=	use_tiling


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          self.enable_tiling(a__)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[str]								=	True


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	False


     @property
     # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	{}

          def fn_recursive_add_processors(_UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):
               if hasattr(a__						,     'set_processor'):
                    __A						:			List[str]								=	module.processor

               for sub_name, child in module.named_children():
                    fn_recursive_add_processors(F'{name}.{sub_name}'						,     a__						,     a__)

               return processors

          for name, module in self.named_children():
               fn_recursive_add_processors(a__						,     a__						,     a__)

          return processors


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			int								=	len(self.attn_processors.keys())

          if isinstance(a__						,     a__) and len(a__) != count:
               raise ValueError(
                   F'A dict of processors was passed, but the number of processors {len(a__)} does not match the'
                   F' number of attention layers: {count}. Please make sure to pass {count} processor classes.')

          def fn_recursive_attn_processor(_UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):
               if hasattr(a__						,     'set_processor'):
                    if not isinstance(a__						,     a__):
                         module.set_processor(a__)
                    else:
                         module.set_processor(processor.pop(F'{name}.processor'))

               for sub_name, child in module.named_children():
                    fn_recursive_attn_processor(F'{name}.{sub_name}'						,     a__						,     a__)

          for name, module in self.named_children():
               fn_recursive_attn_processor(a__						,     a__						,     a__)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          self.set_attn_processor(AttnProcessor())


     @apply_forward_hook
     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase = True):

          '''simple docstring'''






          if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
               return self.tiled_encode(a__						,     return_dict=a__)

          if self.use_slicing and x.shape[0] > 1:
               __A						:			Dict								=	[self.encoder(a__) for x_slice in x.split(1)]
               __A						:			Optional[int]								=	torch.cat(a__)
          else:
               __A						:			Dict								=	self.encoder(a__)

          __A						:			Optional[int]								=	self.quant_conv(a__)
          __A						:			List[str]								=	DiagonalGaussianDistribution(a__)

          if not return_dict:
               return (posterior,)

          return AutoencoderKLOutput(latent_dist=a__)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase = True):

          '''simple docstring'''






          if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
               return self.tiled_decode(a__						,     return_dict=a__)

          __A						:			int								=	self.post_quant_conv(a__)
          __A						:			Optional[Any]								=	self.decoder(a__)

          if not return_dict:
               return (dec,)

          return DecoderOutput(sample=a__)


     @apply_forward_hook
     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase = True):

          '''simple docstring'''






          if self.use_slicing and z.shape[0] > 1:
               __A						:			List[str]								=	[self._decode(a__).sample for z_slice in z.split(1)]
               __A						:			List[Any]								=	torch.cat(a__)
          else:
               __A						:			Optional[Any]								=	self._decode(a__).sample

          if not return_dict:
               return (decoded,)

          return DecoderOutput(sample=a__)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			int								=	min(a.shape[2]						,     b.shape[2]						,     a__)
          for y in range(a__):
               __A						:			str								=	a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
          return b


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Dict								=	min(a.shape[3]						,     b.shape[3]						,     a__)
          for x in range(a__):
               __A						:			Optional[Any]								=	a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
          return b


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase = True):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
          __A						:			Optional[int]								=	int(self.tile_latent_min_size * self.tile_overlap_factor)
          __A						:			Optional[int]								=	self.tile_latent_min_size - blend_extent

          # Split the image into 512x512 tiles and encode them separately.
          __A						:			List[str]								=	[]
          for i in range(0						,     x.shape[2]						,     a__):
               __A						:			str								=	[]
               for j in range(0						,     x.shape[3]						,     a__):
                    __A						:			int								=	x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                    __A						:			List[str]								=	self.encoder(a__)
                    __A						:			Tuple								=	self.quant_conv(a__)
                    row.append(a__)
               rows.append(a__)
          __A						:			Any								=	[]
          for i, row in enumerate(a__):
               __A						:			List[str]								=	[]
               for j, tile in enumerate(a__):
                    # blend the above tile and the left tile
                    # to the current tile and add the current tile to the result row
                    if i > 0:
                         __A						:			str								=	self.blend_v(rows[i - 1][j]						,     a__						,     a__)
                    if j > 0:
                         __A						:			Optional[Any]								=	self.blend_h(row[j - 1]						,     a__						,     a__)
                    result_row.append(tile[:, :, :row_limit, :row_limit])
               result_rows.append(torch.cat(a__						,     dim=3))

          __A						:			str								=	torch.cat(a__						,     dim=2)
          __A						:			Optional[int]								=	DiagonalGaussianDistribution(a__)

          if not return_dict:
               return (posterior,)

          return AutoencoderKLOutput(latent_dist=a__)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase = True):

          '''simple docstring'''






          __A						:			Optional[int]								=	int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
          __A						:			Dict								=	int(self.tile_sample_min_size * self.tile_overlap_factor)
          __A						:			Optional[Any]								=	self.tile_sample_min_size - blend_extent

          # Split z into overlapping 64x64 tiles and decode them separately.
          # The tiles have an overlap to avoid seams between tiles.
          __A						:			Any								=	[]
          for i in range(0						,     z.shape[2]						,     a__):
               __A						:			int								=	[]
               for j in range(0						,     z.shape[3]						,     a__):
                    __A						:			Tuple								=	z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                    __A						:			List[Any]								=	self.post_quant_conv(a__)
                    __A						:			int								=	self.decoder(a__)
                    row.append(a__)
               rows.append(a__)
          __A						:			List[str]								=	[]
          for i, row in enumerate(a__):
               __A						:			Tuple								=	[]
               for j, tile in enumerate(a__):
                    # blend the above tile and the left tile
                    # to the current tile and add the current tile to the result row
                    if i > 0:
                         __A						:			Union[str, Any]								=	self.blend_v(rows[i - 1][j]						,     a__						,     a__)
                    if j > 0:
                         __A						:			Any								=	self.blend_h(row[j - 1]						,     a__						,     a__)
                    result_row.append(tile[:, :, :row_limit, :row_limit])
               result_rows.append(torch.cat(a__						,     dim=3))

          __A						:			List[Any]								=	torch.cat(a__						,     dim=2)
          if not return_dict:
               return (dec,)

          return DecoderOutput(sample=a__)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase = False						,     _UpperCAmelCase = True						,     _UpperCAmelCase = None						,     ):

          '''simple docstring'''






          __A						:			List[Any]								=	sample
          __A						:			str								=	self.encode(a__).latent_dist
          if sample_posterior:
               __A						:			Union[str, Any]								=	posterior.sample(generator=a__)
          else:
               __A						:			str								=	posterior.mode()
          __A						:			Union[str, Any]								=	self.decode(a__).sample

          if not return_dict:
               return (dec,)

          return DecoderOutput(sample=a__)






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


lowercase__   :				Tuple      =   load('''accuracy''')






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					Any:
     __A			,__A						:			List[Any]								=	eval_pred
     __A						:			Dict								=	np.argmax(__snake_case			,							axis=1      )
     return metric.compute(predictions=__snake_case			,							references=__snake_case      )


class 			SCREAMING_SNAKE_CASE   (a__							):


     def __init__(						self						,     _UpperCAmelCase):

          '''simple docstring'''






          super().__init__()
          __A						:			int								=	trainer


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          if control.should_evaluate:
               __A						:			str								=	deepcopy(_UpperCAmelCase)
               self._trainer.evaluate(eval_dataset=self._trainer.train_dataset						,     metric_key_prefix='train')
               return control_copy






def 	_lowerCAmelCase      (  )					->					str:
     __A						:			List[Any]								=	get_args()
     set_seed(args.seed      )

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

     print('Loading tokenizer and model'      )
     __A						:			Optional[Any]								=	AutoTokenizer.from_pretrained(args.model_ckpt      )
     __A						:			Tuple								=	tokenizer.eos_token
     __A						:			Dict								=	AutoModelForSequenceClassification.from_pretrained(args.model_ckpt			,							num_labels=7      )
     __A						:			Optional[int]								=	model.config.eos_token_id

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

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

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

     __A						:			str								=	train_test_validation.map(
         __snake_case			,							batched=__snake_case			,							remove_columns=train_test_validation['train'].column_names			,							)
     __A						:			str								=	DataCollatorWithPadding(tokenizer=__snake_case      )

     __A						:			str								=	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						:			Tuple								=	Trainer(
         model=__snake_case			,							args=__snake_case			,							train_dataset=tokenized_datasets['train']			,							eval_dataset=tokenized_datasets['valid']			,							tokenizer=__snake_case			,							data_collator=__snake_case			,							compute_metrics=__snake_case			,							)

     print('Training...'      )
     trainer.add_callback(CustomCallback(__snake_case      )      )
     trainer.train()


if __name__ == "__main__":
  main()