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


snake_case__				= logging.getLogger(__name__)



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  'sequence-classification'



     def __init__(					self				,				A_						) ->				str:






           """simple docstring"""
           if type(A_						) == dict:
                 _lowerCamelCase			    =				Namespace(**A_						)
           _lowerCamelCase			    =				glue_output_modes[hparams.task]
           _lowerCamelCase			    =				glue_tasks_num_labels[hparams.task]

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



     def 		UpperCamelCase_						(					self				,				**A_						) ->				Optional[int]:






           """simple docstring"""
           return self.model(**A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

           _lowerCamelCase			    =				self(**A_						)
           _lowerCamelCase			    =				outputs[0]

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



     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.hparams
           _lowerCamelCase			    =				processors[args.task]()
           _lowerCamelCase			    =				processor.get_labels()

           for mode in ["train", "dev"]:
                 _lowerCamelCase			    =				self._feature_file(A_						)
                 if os.path.exists(A_						) and not args.overwrite_cache:
                       logger.info('''Loading features from cached file %s'''				,				A_						)
                 else:
                       logger.info('''Creating features from dataset file at %s'''				,				args.data_dir						)
                       _lowerCamelCase			    =				(
                           processor.get_dev_examples(args.data_dir						)
                           if mode == '''dev'''
                           else processor.get_train_examples(args.data_dir						)
                       )
                       _lowerCamelCase			    =				convert_examples_to_features(
                           A_				,				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'''				,				A_						)
                       torch.save(A_				,				A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_ = False						) ->				DataLoader:






           """simple docstring"""
           _lowerCamelCase			    =				'''dev''' if mode == '''test''' else mode

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

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



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

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

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

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



     def 		UpperCamelCase_						(					self				,				A_						) ->				tuple:






           """simple docstring"""
           _lowerCamelCase			    =				torch.stack([x['''val_loss'''] for x in outputs]						).mean().detach().cpu().item()
           _lowerCamelCase			    =				np.concatenate([x['''pred'''] for x in outputs]				,				axis=0						)

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

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

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

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



     def 		UpperCamelCase_						(					self				,				A_						) ->				dict:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				self._eval_end(A_						)
           _lowerCamelCase			    =				ret['''log''']
           return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



     def 		UpperCamelCase_						(					self				,				A_						) ->				dict:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				self._eval_end(A_						)
           _lowerCamelCase			    =				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_						(					A_				,				A_						) ->				str:






           """simple docstring"""
           BaseTransformer.add_model_specific_args(A_				,				A_						)
           parser.add_argument(
               '''--max_seq_length'''				,				default=1_28				,				type=A_				,				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=A_				,				required=A_				,				help='''The GLUE task to run'''				,				)
           parser.add_argument(
               '''--gpus'''				,				default=0				,				type=A_				,				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       __magic_name__(  )						->    Dict:



      '''simple docstring'''

      _lowerCamelCase			    =				argparse.ArgumentParser()
      add_generic_args(__UpperCAmelCase ,						os.getcwd()		)
      _lowerCamelCase			    =				GLUETransformer.add_model_specific_args(__UpperCAmelCase ,						os.getcwd()		)
      _lowerCamelCase			    =				parser.parse_args()

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

      _lowerCamelCase			    =				GLUETransformer(__UpperCAmelCase		)
      _lowerCamelCase			    =				generic_train(__UpperCAmelCase ,						__UpperCAmelCase		)

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


if __name__ == "__main__":
     main()



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__				= logging.get_logger(__name__)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=False		)						->    List[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				[]
      for i in range(config.num_hidden_layers		):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight')		)
            rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias')		)
            rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight')		)
            rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias')		)
            rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight')		)
            rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias')		)
            rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight')		)
            rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias')		)
            rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight')		)
            rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias')		)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ('''cls_token''', '''vit.embeddings.cls_token'''),
              ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
              ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
              ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
          ]		)

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ('''norm.weight''', '''layernorm.weight'''),
                    ('''norm.bias''', '''layernorm.bias'''),
                ]		)

            # if just the base model, we should remove "vit" from all keys that start with "vit"
            _lowerCamelCase			    =				[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''		) else pair for pair in rename_keys]
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ('''norm.weight''', '''vit.layernorm.weight'''),
                    ('''norm.bias''', '''vit.layernorm.bias'''),
                    ('''head.weight''', '''classifier.weight'''),
                    ('''head.bias''', '''classifier.bias'''),
                ]		)

      return rename_keys
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=False		)						->    str:



      '''simple docstring'''

      for i in range(config.num_hidden_layers		):
            if base_model:
                  _lowerCamelCase			    =				''''''
            else:
                  _lowerCamelCase			    =				'''vit.'''
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            _lowerCamelCase			    =				state_dict.pop(F'blocks.{i}.attn.qkv.weight'		)
            _lowerCamelCase			    =				state_dict.pop(F'blocks.{i}.attn.qkv.bias'		)
            # next, add query, keys and values (in that order) to the state dict
            _lowerCamelCase			    =				in_proj_weight[
                : config.hidden_size, :
            ]
            _lowerCamelCase			    =				in_proj_bias[: config.hidden_size]
            _lowerCamelCase			    =				in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _lowerCamelCase			    =				in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _lowerCamelCase			    =				in_proj_weight[
                -config.hidden_size :, :
            ]
            _lowerCamelCase			    =				in_proj_bias[-config.hidden_size :]
def       __magic_name__(  __UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

      _lowerCamelCase			    =				['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(__UpperCAmelCase ,						__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    List[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				dct.pop(__UpperCAmelCase		)
      _lowerCamelCase			    =				val
def       __magic_name__(  )						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      _lowerCamelCase			    =				Image.open(requests.get(__UpperCAmelCase ,						stream=__UpperCAmelCase		).raw		)
      return im
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=True		)						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				ViTConfig()
      # patch_size
      if model_name[-1] == "8":
            _lowerCamelCase			    =				8
      # set labels if required
      if not base_model:
            _lowerCamelCase			    =				1000
            _lowerCamelCase			    =				'''huggingface/label-files'''
            _lowerCamelCase			    =				'''imagenet-1k-id2label.json'''
            _lowerCamelCase			    =				json.load(open(hf_hub_download(__UpperCAmelCase ,						__UpperCAmelCase ,						repo_type='''dataset'''		) ,						'''r'''		)		)
            _lowerCamelCase			    =				{int(__UpperCAmelCase		): v for k, v in idalabel.items()}
            _lowerCamelCase			    =				idalabel
            _lowerCamelCase			    =				{v: k for k, v in idalabel.items()}
      # size of the architecture
      if model_name in ["dino_vits8", "dino_vits16"]:
            _lowerCamelCase			    =				384
            _lowerCamelCase			    =				1536
            _lowerCamelCase			    =				12
            _lowerCamelCase			    =				6

      # load original model from torch hub
      _lowerCamelCase			    =				torch.hub.load('''facebookresearch/dino:main''' ,						__UpperCAmelCase		)
      original_model.eval()

      # load state_dict of original model, remove and rename some keys
      _lowerCamelCase			    =				original_model.state_dict()
      if base_model:
            remove_classification_head_(__UpperCAmelCase		)
      _lowerCamelCase			    =				create_rename_keys(__UpperCAmelCase ,						base_model=__UpperCAmelCase		)
      for src, dest in rename_keys:
            rename_key(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
      read_in_q_k_v(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)

      # load HuggingFace model
      if base_model:
            _lowerCamelCase			    =				ViTModel(__UpperCAmelCase ,						add_pooling_layer=__UpperCAmelCase		).eval()
      else:
            _lowerCamelCase			    =				ViTForImageClassification(__UpperCAmelCase		).eval()
      model.load_state_dict(__UpperCAmelCase		)

      # Check outputs on an image, prepared by ViTImageProcessor
      _lowerCamelCase			    =				ViTImageProcessor()
      _lowerCamelCase			    =				image_processor(images=prepare_img() ,						return_tensors='''pt'''		)
      _lowerCamelCase			    =				encoding['''pixel_values''']
      _lowerCamelCase			    =				model(__UpperCAmelCase		)

      if base_model:
            _lowerCamelCase			    =				original_model(__UpperCAmelCase		)
            assert torch.allclose(__UpperCAmelCase ,						outputs.last_hidden_state[:, 0, :] ,						atol=1E-1		)
      else:
            _lowerCamelCase			    =				original_model(__UpperCAmelCase		)
            assert logits.shape == outputs.logits.shape
            assert torch.allclose(__UpperCAmelCase ,						outputs.logits ,						atol=1E-3		)

      Path(__UpperCAmelCase		).mkdir(exist_ok=__UpperCAmelCase		)
      print(F'Saving model {model_name} to {pytorch_dump_folder_path}'		)
      model.save_pretrained(__UpperCAmelCase		)
      print(F'Saving image processor to {pytorch_dump_folder_path}'		)
      image_processor.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__				= argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='dino_vitb16',
         type=str,
         help='Name of the model trained with DINO you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )
     parser.add_argument(
         '--base_model',
         action='store_true',
         help='Whether to only convert the base model (no projection head weights).',
     )

     parser.set_defaults(base_model=True)
     snake_case__				= parser.parse_args()
     convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)