import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification







def      _a					(   lowercase__	:      List[str]			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					str      =		[]
     embed.append(
         (
             f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
             f'''stage{idx}.patch_embed.proj.weight''',
         )			)
     embed.append(
         (
             f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
             f'''stage{idx}.patch_embed.proj.bias''',
         )			)
     embed.append(
         (
             f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
             f'''stage{idx}.patch_embed.norm.weight''',
         )			)
     embed.append(
         (
             f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
             f'''stage{idx}.patch_embed.norm.bias''',
         )			)
     return embed







def      _a					(   lowercase__	:      Union[str, Any]  ,	lowercase__	:      Dict			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					int      =		[]
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
             f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
             f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
             f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
             f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
             f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
             f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
             f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
             f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
         )			)
     attention_weights.append(
         (
             f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
             f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
         )			)
     attention_weights.append(
         (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''')			)
     attention_weights.append(
         (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''')			)
     attention_weights.append(
         (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''')			)
     attention_weights.append(
         (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''')			)
     attention_weights.append(
         (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''')			)
     attention_weights.append(
         (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''')			)
     attention_weights.append(
         (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''')			)
     attention_weights.append(
         (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''')			)
     return attention_weights







def      _a					(   lowercase__	:      Dict			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		[]
     token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token')			)
     return token







def      _a					(   ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		[]
     head.append(('layernorm.weight', 'norm.weight')			)
     head.append(('layernorm.bias', 'norm.bias')			)
     head.append(('classifier.weight', 'head.weight')			)
     head.append(('classifier.bias', 'head.bias')			)
     return head







def      _a					(   lowercase__	:      Optional[Any]  ,	lowercase__	:      Any  ,	lowercase__	:      Optional[int]  ,	lowercase__	:      str			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		'imagenet-1k-id2label.json'
     SCREAMING_SNAKE_CASE__					:					int      =		10_00

     SCREAMING_SNAKE_CASE__					:					Dict      =		'huggingface/label-files'
     SCREAMING_SNAKE_CASE__					:					str      =		num_labels
     SCREAMING_SNAKE_CASE__					:					List[Any]      =		json.load(open(cached_download(hf_hub_url(lowercase__  ,	lowercase__  ,	repo_type='dataset'			)			)  ,	'r'			)			)
     SCREAMING_SNAKE_CASE__					:					Dict      =		{int(lowercase__			): v for k, v in idalabel.items()}

     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		idalabel
     SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		{v: k for k, v in idalabel.items()}

     SCREAMING_SNAKE_CASE__					:					List[str]      =		CvtConfig(num_labels=lowercase__  ,	idalabel=lowercase__  ,	labelaid=lowercase__			)

     # For depth size 13 (13 = 1+2+10)
     if cvt_model.rsplit('/'  ,	1			)[-1][4:6] == "13":
          SCREAMING_SNAKE_CASE__					:					int      =		[1, 2, 10]

     # For depth size 21 (21 = 1+4+16)
     elif cvt_model.rsplit('/'  ,	1			)[-1][4:6] == "21":
          SCREAMING_SNAKE_CASE__					:					Dict      =		[1, 4, 16]

     # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
     else:
          SCREAMING_SNAKE_CASE__					:					Dict      =		[2, 2, 20]
          SCREAMING_SNAKE_CASE__					:					Any      =		[3, 12, 16]
          SCREAMING_SNAKE_CASE__					:					List[str]      =		[1_92, 7_68, 10_24]

     SCREAMING_SNAKE_CASE__					:					List[Any]      =		CvtForImageClassification(lowercase__			)
     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'			)
     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		image_size
     SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.load(lowercase__  ,	map_location=torch.device('cpu'			)			)

     SCREAMING_SNAKE_CASE__					:					Any      =		OrderedDict()
     SCREAMING_SNAKE_CASE__					:					Dict      =		[]

     for idx in range(len(config.depth			)			):
          if config.cls_token[idx]:
               SCREAMING_SNAKE_CASE__					:					Tuple      =		list_of_state_dict + cls_token(lowercase__			)
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		list_of_state_dict + embeddings(lowercase__			)
          for cnt in range(config.depth[idx]			):
               SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		list_of_state_dict + attention(lowercase__  ,	lowercase__			)

     SCREAMING_SNAKE_CASE__					:					Any      =		list_of_state_dict + final()
     for gg in list_of_state_dict:
          print(lowercase__			)
     for i in range(len(lowercase__			)			):
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		original_weights[list_of_state_dict[i][1]]

     model.load_state_dict(lowercase__			)
     model.save_pretrained(lowercase__			)
     image_processor.save_pretrained(lowercase__			)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:       List[str]       						=       argparse.ArgumentParser()
      parser.add_argument(
          "--cvt_model",
          default="cvt-w24",
          type=str,
          help="Name of the cvt model you'd like to convert.",
      )
      parser.add_argument(
          "--image_size",
          default=384,
          type=int,
          help="Input Image Size",
      )
      parser.add_argument(
          "--cvt_file_name",
          default=r"cvtmodels\CvT-w24-384x384-IN-22k.pth",
          type=str,
          help="Input Image Size",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
      )

      SCREAMING_SNAKE_CASE__		:       Dict       						=       parser.parse_args()
      convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





"""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   (     ):
    UpperCAmelCase__	:      List[Any]     							=					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()


UpperCamelCase__          =	load('accuracy')
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					eval_pred
    UpperCAmelCase__	:      Optional[int]     							=					np.argmax(_snake_case    ,axis=1 )
    return metric.compute(predictions=_snake_case    ,references=_snake_case )





class 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_      ):
          super().__init__()
          UpperCAmelCase__	:      List[str]     							=					trainer



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ):
          if control.should_evaluate:
              UpperCAmelCase__	:      int     							=					deepcopy(UpperCamelCase_      )
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,	metric_key_prefix='train'      )
              return control_copy




def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      int     							=					get_args()
    set_seed(args.seed )

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

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

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

    UpperCAmelCase__	:      Optional[Any]     							=					ClassLabel(num_classes=7    ,names=list(set(train_test_validation['train']['complexity'] ) ) )

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

    UpperCAmelCase__	:      List[Any]     							=					train_test_validation.map(
        _snake_case    ,batched=_snake_case    ,remove_columns=train_test_validation['train'].column_names    ,)
    UpperCAmelCase__	:      Tuple     							=					DataCollatorWithPadding(tokenizer=_snake_case )

    UpperCAmelCase__	:      Union[str, 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'    ,)

    UpperCAmelCase__	:      Dict     							=					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()

