"""simple docstring"""







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

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin







@dataclass
class  UpperCamelCase	(    lowercase_						):
   lowercase							=						42







class  UpperCamelCase	(    lowercase_		,   lowercase_						):
   @register_to_config
   def __init__(    self  ,__UpperCamelCase = 32  ,__UpperCamelCase = 64  ,__UpperCamelCase = 20  ,__UpperCamelCase = 768  ,__UpperCamelCase=77  ,__UpperCamelCase=4  ,__UpperCamelCase = 0.0  ,__UpperCamelCase = "silu"  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = "linear"  ,__UpperCamelCase = "prd"  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,)			->   int:


        '''simple docstring'''


        super().__init__()
        lowercase_			:       int									=			num_attention_heads
        lowercase_			:       str									=			attention_head_dim
        lowercase_			:       Tuple									=			num_attention_heads * attention_head_dim
        lowercase_			:       List[Any]									=			additional_embeddings

        lowercase_			:       int									=			time_embed_dim or inner_dim
        lowercase_			:       str									=			embedding_proj_dim or embedding_dim
        lowercase_			:       Optional[Any]									=			clip_embed_dim or embedding_dim

        lowercase_			:       int									=			Timesteps(__UpperCamelCase  ,__UpperCamelCase  ,0    )
        lowercase_			:       Tuple									=			TimestepEmbedding(__UpperCamelCase  ,__UpperCamelCase  ,out_dim=__UpperCamelCase  ,act_fn=__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			nn.Linear(__UpperCamelCase  ,__UpperCamelCase    )

        if embedding_proj_norm_type is None:
             lowercase_			:       Any									=			None
        elif embedding_proj_norm_type == "layer":
             lowercase_			:       Dict									=			nn.LayerNorm(__UpperCamelCase    )
        else:
             raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'''    )

        lowercase_			:       Optional[Any]									=			nn.Linear(__UpperCamelCase  ,__UpperCamelCase    )

        if encoder_hid_proj_type is None:
             lowercase_			:       str									=			None
        elif encoder_hid_proj_type == "linear":
             lowercase_			:       Tuple									=			nn.Linear(__UpperCamelCase  ,__UpperCamelCase    )
        else:
             raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'''    )

        lowercase_			:       Tuple									=			nn.Parameter(torch.zeros(1  ,num_embeddings + additional_embeddings  ,__UpperCamelCase    )    )

        if added_emb_type == "prd":
             lowercase_			:       List[str]									=			nn.Parameter(torch.zeros(1  ,1  ,__UpperCamelCase    )    )
        elif added_emb_type is None:
             lowercase_			:       Dict									=			None
        else:
             raise ValueError(
                 f'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'''    )

        lowercase_			:       Optional[int]									=			nn.ModuleList(
            [
                BasicTransformerBlock(
                    __UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,dropout=__UpperCamelCase  ,activation_fn='gelu'  ,attention_bias=__UpperCamelCase  ,)
                for d in range(__UpperCamelCase    )
            ]    )

        if norm_in_type == "layer":
             lowercase_			:       Dict									=			nn.LayerNorm(__UpperCamelCase    )
        elif norm_in_type is None:
             lowercase_			:       int									=			None
        else:
             raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.'''    )

        lowercase_			:       List[str]									=			nn.LayerNorm(__UpperCamelCase    )

        lowercase_			:       Tuple									=			nn.Linear(__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			torch.full(
            [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]  ,-1_0000.0    )
        causal_attention_mask.triu_(1    )
        lowercase_			:       str									=			causal_attention_mask[None, ...]
        self.register_buffer('causal_attention_mask'  ,__UpperCamelCase  ,persistent=__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			nn.Parameter(torch.zeros(1  ,__UpperCamelCase    )    )
        lowercase_			:       List[str]									=			nn.Parameter(torch.zeros(1  ,__UpperCamelCase    )    )
   @property
   # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
   def     _UpperCAmelCase     (    self    )			->   Dict[str, AttentionProcessor]:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			{}

        def fn_recursive_add_processors(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    ):
             if hasattr(__UpperCamelCase  ,'set_processor'    ):
                  lowercase_			:       Union[str, Any]									=			module.processor

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

             return processors

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

        return processors
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       Any									=			len(self.attn_processors.keys()    )

        if isinstance(__UpperCamelCase  ,__UpperCamelCase    ) and len(__UpperCamelCase    ) != count:
             raise ValueError(
                 f'''A dict of processors was passed, but the number of processors {len(__UpperCamelCase    )} 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(__UpperCamelCase  ,'set_processor'    ):
                  if not isinstance(__UpperCamelCase  ,__UpperCamelCase    ):
                       module.set_processor(__UpperCamelCase    )
                  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}'''  ,__UpperCamelCase  ,__UpperCamelCase    )

        for name, module in self.named_children():
             fn_recursive_attn_processor(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        self.set_attn_processor(AttnProcessor()    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = True  ,)			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			hidden_states.shape[0]

        lowercase_			:       Tuple									=			timestep
        if not torch.is_tensor(__UpperCamelCase    ):
             lowercase_			:       Union[str, Any]									=			torch.tensor([timesteps]  ,dtype=torch.long  ,device=hidden_states.device    )
        elif torch.is_tensor(__UpperCamelCase    ) and len(timesteps.shape    ) == 0:
             lowercase_			:       List[str]									=			timesteps[None].to(hidden_states.device    )

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        lowercase_			:       Tuple									=			timesteps * torch.ones(__UpperCamelCase  ,dtype=timesteps.dtype  ,device=timesteps.device    )

        lowercase_			:       Optional[int]									=			self.time_proj(__UpperCamelCase    )

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might be fp16, so we need to cast here.
        lowercase_			:       Union[str, Any]									=			timesteps_projected.to(dtype=self.dtype    )
        lowercase_			:       Dict									=			self.time_embedding(__UpperCamelCase    )

        if self.embedding_proj_norm is not None:
             lowercase_			:       Optional[int]									=			self.embedding_proj_norm(__UpperCamelCase    )

        lowercase_			:       Tuple									=			self.embedding_proj(__UpperCamelCase    )
        if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
             lowercase_			:       Dict									=			self.encoder_hidden_states_proj(__UpperCamelCase    )
        elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
             raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'    )

        lowercase_			:       List[str]									=			self.proj_in(__UpperCamelCase    )

        lowercase_			:       Tuple									=			self.positional_embedding.to(hidden_states.dtype    )

        lowercase_			:       int									=			[]
        lowercase_			:       Optional[Any]									=			0

        if encoder_hidden_states is not None:
             additional_embeds.append(__UpperCamelCase    )
             additional_embeddings_len += encoder_hidden_states.shape[1]

        if len(proj_embeddings.shape    ) == 2:
             lowercase_			:       List[str]									=			proj_embeddings[:, None, :]

        if len(hidden_states.shape    ) == 2:
             lowercase_			:       Any									=			hidden_states[:, None, :]

        lowercase_			:       Any									=			additional_embeds + [
            proj_embeddings,
            time_embeddings[:, None, :],
            hidden_states,
        ]

        if self.prd_embedding is not None:
             lowercase_			:       Optional[Any]									=			self.prd_embedding.to(hidden_states.dtype    ).expand(__UpperCamelCase  ,-1  ,-1    )
             additional_embeds.append(__UpperCamelCase    )

        lowercase_			:       Any									=			torch.cat(
            __UpperCamelCase  ,dim=1  ,)

        # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
        lowercase_			:       Tuple									=			additional_embeddings_len + proj_embeddings.shape[1] + 1
        if positional_embeddings.shape[1] < hidden_states.shape[1]:
             lowercase_			:       Optional[Any]									=			F.pad(
                 __UpperCamelCase  ,(
                     0,
                     0,
                     additional_embeddings_len,
                     self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
                 )  ,value=0.0  ,)

        lowercase_			:       str									=			hidden_states + positional_embeddings

        if attention_mask is not None:
             lowercase_			:       str									=			(1 - attention_mask.to(hidden_states.dtype    )) * -1_0000.0
             lowercase_			:       str									=			F.pad(__UpperCamelCase  ,(0, self.additional_embeddings)  ,value=0.0    )
             lowercase_			:       Union[str, Any]									=			(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype    )
             lowercase_			:       Tuple									=			attention_mask.repeat_interleave(self.config.num_attention_heads  ,dim=0    )

        if self.norm_in is not None:
             lowercase_			:       Dict									=			self.norm_in(__UpperCamelCase    )

        for block in self.transformer_blocks:
             lowercase_			:       List[str]									=			block(__UpperCamelCase  ,attention_mask=__UpperCamelCase    )

        lowercase_			:       List[str]									=			self.norm_out(__UpperCamelCase    )

        if self.prd_embedding is not None:
             lowercase_			:       Union[str, Any]									=			hidden_states[:, -1]
        else:
             lowercase_			:       Tuple									=			hidden_states[:, additional_embeddings_len:]

        lowercase_			:       int									=			self.proj_to_clip_embeddings(__UpperCamelCase    )

        if not return_dict:
             return (predicted_image_embedding,)

        return PriorTransformerOutput(predicted_image_embedding=__UpperCamelCase    )







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			(prior_latents * self.clip_std) + self.clip_mean
        return prior_latents


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


__SCREAMING_SNAKE_CASE								=load("accuracy")



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]   ):
     lowercase_							,						lowercase_			:       Any									=			eval_pred
     lowercase_			:       Optional[Any]									=			np.argmax(__SCREAMING_SNAKE_CASE  ,    axis=1   )
     return metric.compute(predictions=__SCREAMING_SNAKE_CASE  ,    references=__SCREAMING_SNAKE_CASE   )







class  UpperCamelCase	(    lowercase_						):
   def __init__(    self  ,__UpperCamelCase    )			->   None:


        '''simple docstring'''


        super().__init__()
        lowercase_			:       str									=			trainer







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,**__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        if control.should_evaluate:
             lowercase_			:       List[str]									=			deepcopy(__UpperCamelCase    )
             self._trainer.evaluate(eval_dataset=self._trainer.train_dataset  ,metric_key_prefix='train'    )
             return control_copy






def     lowercase__( ):
     lowercase_			:       Dict									=			get_args()
     set_seed(args.seed   )

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

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

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

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

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

     lowercase_			:       Optional[int]									=			train_test_validation.map(
         __SCREAMING_SNAKE_CASE  ,    batched=__SCREAMING_SNAKE_CASE  ,    remove_columns=train_test_validation['train'].column_names  ,    )
     lowercase_			:       Any									=			DataCollatorWithPadding(tokenizer=__SCREAMING_SNAKE_CASE   )

     lowercase_			:       int									=			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_			:       Optional[int]									=			Trainer(
         model=__SCREAMING_SNAKE_CASE  ,    args=__SCREAMING_SNAKE_CASE  ,    train_dataset=tokenized_datasets['train']  ,    eval_dataset=tokenized_datasets['valid']  ,    tokenizer=__SCREAMING_SNAKE_CASE  ,    data_collator=__SCREAMING_SNAKE_CASE  ,    compute_metrics=__SCREAMING_SNAKE_CASE  ,    )

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


if __name__ == "__main__":
       main()

