




'''simple docstring'''


import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def  SCREAMING_SNAKE_CASE(					__lowercase						,							__lowercase     )       ->      List[Any]:
     A:						Dict						=						old_name

     if "patch_embed" in old_name:
          A			,  A			,  A:						Optional[Any]						=						old_name.split('''.'''     )

          if layer == "0":
               A:						List[str]						=						old_name.replace('''0'''						,							'''convolution1'''     )
          elif layer == "1":
               A:						Union[str, Any]						=						old_name.replace('''1'''						,							'''batchnorm_before'''     )
          elif layer == "3":
               A:						str						=						old_name.replace('''3'''						,							'''convolution2'''     )
          else:
               A:						Tuple						=						old_name.replace('''4'''						,							'''batchnorm_after'''     )

     if "network" in old_name and re.search(r'''\d\.\d'''						,							UpperCamelCase__     ):
          A:						List[str]						=						r'''\b\d{2}\b'''
          if bool(re.search(UpperCamelCase__						,							UpperCamelCase__     )     ):
               A:						Dict						=						re.search(r'''\d\.\d\d.'''						,							UpperCamelCase__     ).group()
          else:
               A:						str						=						re.search(r'''\d\.\d.'''						,							UpperCamelCase__     ).group()
          if int(match[0]     ) < 6:
               A:						Tuple						=						old_name.replace(UpperCamelCase__						,							''''''     )
               A:						Optional[int]						=						trimmed_name.replace('''network'''						,							match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]     )
               A:						Any						=						'''intermediate_stages.''' + trimmed_name
          else:
               A:						Tuple						=						old_name.replace(UpperCamelCase__						,							''''''     )
               if int(match[2]     ) < num_meta4D_last_stage:
                    A:						str						=						trimmed_name.replace('''network'''						,							'''meta4D_layers.blocks.''' + match[2]     )
               else:
                    A:						List[str]						=						str(int(match[2]     ) - num_meta4D_last_stage     )
                    A:						Optional[Any]						=						trimmed_name.replace('''network'''						,							'''meta3D_layers.blocks.''' + layer_index     )
                    if "norm1" in old_name:
                         A:						Any						=						trimmed_name.replace('''norm1'''						,							'''layernorm1'''     )
                    elif "norm2" in old_name:
                         A:						Dict						=						trimmed_name.replace('''norm2'''						,							'''layernorm2'''     )
                    elif "fc1" in old_name:
                         A:						Any						=						trimmed_name.replace('''fc1'''						,							'''linear_in'''     )
                    elif "fc2" in old_name:
                         A:						int						=						trimmed_name.replace('''fc2'''						,							'''linear_out'''     )

               A:						Tuple						=						'''last_stage.''' + trimmed_name

     elif "network" in old_name and re.search(r'''.\d.'''						,							UpperCamelCase__     ):
          A:						List[Any]						=						old_name.replace('''network'''						,							'''intermediate_stages'''     )

     if "fc" in new_name:
          A:						int						=						new_name.replace('''fc'''						,							'''convolution'''     )
     elif ("norm1" in new_name) and ("layernorm1" not in new_name):
          A:						Tuple						=						new_name.replace('''norm1'''						,							'''batchnorm_before'''     )
     elif ("norm2" in new_name) and ("layernorm2" not in new_name):
          A:						Tuple						=						new_name.replace('''norm2'''						,							'''batchnorm_after'''     )
     if "proj" in new_name:
          A:						Optional[Any]						=						new_name.replace('''proj'''						,							'''projection'''     )
     if "dist_head" in new_name:
          A:						Tuple						=						new_name.replace('''dist_head'''						,							'''distillation_classifier'''     )
     elif "head" in new_name:
          A:						List[str]						=						new_name.replace('''head'''						,							'''classifier'''     )
     elif "patch_embed" in new_name:
          A:						Any						=						'''efficientformer.''' + new_name
     elif new_name == "norm.weight" or new_name == "norm.bias":
          A:						str						=						new_name.replace('''norm'''						,							'''layernorm'''     )
          A:						Any						=						'''efficientformer.''' + new_name
     else:
          A:						Optional[int]						=						'''efficientformer.encoder.''' + new_name

     return new_name
def  SCREAMING_SNAKE_CASE(					__lowercase						,							__lowercase     )       ->      Dict:
     for key in checkpoint.copy().keys():
          A:						str						=						checkpoint.pop(UpperCamelCase__     )
          A:						Union[str, Any]						=						val

     return checkpoint
def  SCREAMING_SNAKE_CASE(					)       ->      Union[str, Any]:
     A:						Any						=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     A:						str						=						Image.open(requests.get(UpperCamelCase__						,							stream=UpperCamelCase__     ).raw     )

     return image
def  SCREAMING_SNAKE_CASE(					__lowercase						,							__lowercase						,							__lowercase						,							__lowercase     )       ->      Optional[int]:
     A:						str						=						torch.load(UpperCamelCase__						,							map_location='''cpu'''     )['''model''']
     A:						Dict						=						EfficientFormerConfig.from_json_file(UpperCamelCase__     )
     A:						str						=						EfficientFormerForImageClassificationWithTeacher(UpperCamelCase__     )
     A:						Any						=						'''_'''.join(checkpoint_path.split('''/'''     )[-1].split('''.'''     )[0].split('''_'''     )[:-1]     )

     A:						List[Any]						=						config.depths[-1] - config.num_metaad_blocks + 1
     A:						str						=						convert_torch_checkpoint(UpperCamelCase__						,							UpperCamelCase__     )

     model.load_state_dict(UpperCamelCase__     )
     model.eval()

     A:						Tuple						=						{
         '''bilinear''': PILImageResampling.BILINEAR,
         '''bicubic''': PILImageResampling.BICUBIC,
         '''nearest''': PILImageResampling.NEAREST,
     }

     # prepare image
     A:						Any						=						prepare_img()
     A:						int						=						2_5_6
     A:						str						=						2_2_4
     A:						Tuple						=						EfficientFormerImageProcessor(
         size={'''shortest_edge''': image_size}						,							crop_size={'''height''': crop_size, '''width''': crop_size}						,							resample=pillow_resamplings['''bicubic''']						,							)
     A:						Any						=						processor(images=UpperCamelCase__						,							return_tensors='''pt'''     ).pixel_values

     # original processing pipeline
     A:						Dict						=						Compose(
         [
             Resize(UpperCamelCase__						,							interpolation=pillow_resamplings['''bicubic''']     ),
             CenterCrop(UpperCamelCase__     ),
             ToTensor(),
             Normalize(UpperCamelCase__						,							UpperCamelCase__     ),
         ]     )
     A:						Dict						=						image_transforms(UpperCamelCase__     ).unsqueeze(0     )

     assert torch.allclose(UpperCamelCase__						,							UpperCamelCase__     )

     A:						Union[str, Any]						=						model(UpperCamelCase__     )
     A:						List[str]						=						outputs.logits

     A:						Any						=						(1, 1_0_0_0)

     if "l1" in model_name:
          A:						Any						=						torch.Tensor(
              [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]     )
          assert torch.allclose(logits[0, :1_0]						,							UpperCamelCase__						,							atol=1E-3     )
          assert logits.shape == expected_shape
     elif "l3" in model_name:
          A:						Union[str, Any]						=						torch.Tensor(
              [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]     )
          assert torch.allclose(logits[0, :1_0]						,							UpperCamelCase__						,							atol=1E-3     )
          assert logits.shape == expected_shape
     elif "l7" in model_name:
          A:						int						=						torch.Tensor(
              [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]     )
          assert logits.shape == expected_shape
     else:
          raise ValueError(
              F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""     )

     # Save Checkpoints
     Path(UpperCamelCase__     ).mkdir(exist_ok=UpperCamelCase__     )
     model.save_pretrained(UpperCamelCase__     )
     print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""     )
     processor.save_pretrained(UpperCamelCase__     )
     print(F"""Processor successfuly saved at {pytorch_dump_path}"""     )

     if push_to_hub:
          print('''Pushing model to the hub...'''     )

          model.push_to_hub(
              repo_id=F"""Bearnardd/{pytorch_dump_path}"""						,							commit_message='''Add model'''						,							use_temp_dir=UpperCamelCase__						,							)
          processor.push_to_hub(
              repo_id=F"""Bearnardd/{pytorch_dump_path}"""						,							commit_message='''Add image processor'''						,							use_temp_dir=UpperCamelCase__						,							)


if __name__ == "__main__":
     UpperCamelCase       			= argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--pytorch_model_path''',
         default=None,
         type=str,
         required=True,
         help='''Path to EfficientFormer pytorch checkpoint.''',
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help='''The json file for EfficientFormer model config.''',
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )

     parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
     parser.add_argument(
         '''--no-push_to_hub''',
         dest='''push_to_hub''',
         action='''store_false''',
         help='''Do not push model and image processor to the hub''',
     )
     parser.set_defaults(push_to_hub=True)

     UpperCamelCase       			= parser.parse_args()
     convert_efficientformer_checkpoint(
         checkpoint_path=args.pytorch_model_path,
         efficientformer_config_file=args.config_file,
         pytorch_dump_path=args.pytorch_dump_path,
         push_to_hub=args.push_to_hub,
     )













import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


a__:      List[str]									=		logging.getLogger(__name__)




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	'''sequence-classification'''



 def __init__(							self,__lowerCamelCase ):
        if type(__lowerCamelCase ) == dict:
               A__											=						Namespace(**__lowerCamelCase )
        A__											=						glue_output_modes[hparams.task]
        A__											=						glue_tasks_num_labels[hparams.task]

        super().__init__(__lowerCamelCase,__lowerCamelCase,self.mode )



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        return self.model(**__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

        if self.config.model_type not in ["distilbert", "bart"]:
               A__											=						batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

        A__											=						self(**__lowerCamelCase )
        A__											=						outputs[0]

        A__											=						self.trainer.lr_schedulers[0]['''scheduler''']
        A__											=						{'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
        return {"loss": loss, "log": tensorboard_logs}



 def 				UpperCamelCase			(							self ):
        A__											=						self.hparams
        A__											=						processors[args.task]()
        A__											=						processor.get_labels()

        for mode in ["train", "dev"]:
               A__											=						self._feature_file(__lowerCamelCase )
               if os.path.exists(__lowerCamelCase ) and not args.overwrite_cache:
                      logger.info('''Loading features from cached file %s''',__lowerCamelCase )
               else:
                      logger.info('''Creating features from dataset file at %s''',args.data_dir )
                      A__											=						(
                          processor.get_dev_examples(args.data_dir )
                          if mode == '''dev'''
                          else processor.get_train_examples(args.data_dir )
                      )
                      A__											=						convert_examples_to_features(
                          __lowerCamelCase,self.tokenizer,max_length=args.max_seq_length,label_list=self.labels,output_mode=args.glue_output_mode,)
                      logger.info('''Saving features into cached file %s''',__lowerCamelCase )
                      torch.save(__lowerCamelCase,__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = False ):
        A__											=						'''dev''' if mode == '''test''' else mode

        A__											=						self._feature_file(__lowerCamelCase )
        logger.info('''Loading features from cached file %s''',__lowerCamelCase )
        A__											=						torch.load(__lowerCamelCase )
        A__											=						torch.tensor([f.input_ids for f in features],dtype=torch.long )
        A__											=						torch.tensor([f.attention_mask for f in features],dtype=torch.long )
        A__											=						torch.tensor([f.token_type_ids for f in features],dtype=torch.long )
        if self.hparams.glue_output_mode == "classification":
               A__											=						torch.tensor([f.label for f in features],dtype=torch.long )
        elif self.hparams.glue_output_mode == "regression":
               A__											=						torch.tensor([f.label for f in features],dtype=torch.float )

        return DataLoader(
            TensorDataset(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ),batch_size=__lowerCamelCase,shuffle=__lowerCamelCase,)



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

        if self.config.model_type not in ["distilbert", "bart"]:
               A__											=						batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

        A__											=						self(**__lowerCamelCase )
        A__					,	A__											=						outputs[:2]
        A__											=						logits.detach().cpu().numpy()
        A__											=						inputs['''labels'''].detach().cpu().numpy()

        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
        A__											=						np.concatenate([x['''pred'''] for x in outputs],axis=0 )

        if self.hparams.glue_output_mode == "classification":
               A__											=						np.argmax(__lowerCamelCase,axis=1 )
        elif self.hparams.glue_output_mode == "regression":
               A__											=						np.squeeze(__lowerCamelCase )

        A__											=						np.concatenate([x['''target'''] for x in outputs],axis=0 )
        A__											=						[[] for _ in range(out_label_ids.shape[0] )]
        A__											=						[[] for _ in range(out_label_ids.shape[0] )]

        A__											=						{**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task,__lowerCamelCase,__lowerCamelCase )}

        A__											=						dict(results.items() )
        A__											=						results
        return ret, preds_list, out_label_list



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__					,	A__					,	A__											=						self._eval_end(__lowerCamelCase )
        A__											=						ret['''log''']
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__					,	A__					,	A__											=						self._eval_end(__lowerCamelCase )
        A__											=						ret['''log''']
        # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
        return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




 @staticmethod
 def 				UpperCamelCase			(							__lowerCamelCase,__lowerCamelCase ):
        BaseTransformer.add_model_specific_args(__lowerCamelCase,__lowerCamelCase )
        parser.add_argument(
            '''--max_seq_length''',default=128,type=__lowerCamelCase,help=(
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            ),)

        parser.add_argument(
            '''--task''',default='''''',type=__lowerCamelCase,required=__lowerCamelCase,help='''The GLUE task to run''',)
        parser.add_argument(
            '''--gpus''',default=0,type=__lowerCamelCase,help='''The number of GPUs allocated for this, it is by default 0 meaning none''',)

        parser.add_argument(
            '''--overwrite_cache''',action='''store_true''',help='''Overwrite the cached training and evaluation sets''' )

        return parser


def 	UpperCamelCase__(       )->Any:
       A__											=						argparse.ArgumentParser()
       add_generic_args(UpperCamelCase__ ,     os.getcwd()  )
       A__											=						GLUETransformer.add_model_specific_args(UpperCamelCase__ ,     os.getcwd()  )
       A__											=						parser.parse_args()

       # If output_dir not provided, a folder will be generated in pwd
       if args.output_dir is None:
              A__											=						os.path.join(
                  '''./results''' ,     f"{args.task}_{time.strftime('%Y%m%d_%H%M%S'  )}" ,     )
              os.makedirs(args.output_dir  )

       A__											=						GLUETransformer(UpperCamelCase__  )
       A__											=						generic_train(UpperCamelCase__ ,     UpperCamelCase__  )

       # Optionally, predict on dev set and write to output_dir
       if args.do_predict:
              A__											=						sorted(glob.glob(os.path.join(args.output_dir ,     '''checkpoint-epoch=*.ckpt'''  ) ,     recursive=UpperCamelCase__  )  )
              A__											=						model.load_from_checkpoint(checkpoints[-1]  )
              return trainer.test(UpperCamelCase__  )


if __name__ == "__main__":
       main()





