


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


_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   logging.getLogger(__name__)

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'sequence-classification'
    def __init__(	self      , __snake_case   ):
         if type(UpperCAmelCase_   ) == dict:
              snake_case					=    Namespace(**UpperCAmelCase_   )
         snake_case					=    glue_output_modes[hparams.task]
         snake_case					=    glue_tasks_num_labels[hparams.task]

         super().__init__(UpperCAmelCase_      , UpperCAmelCase_      , self.mode   )
    def 		a_      (	self      , **__snake_case   ):
         return self.model(**UpperCAmelCase_   )
    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

         snake_case					=    self(**UpperCAmelCase_   )
         snake_case					=    outputs[0]

         snake_case					=    self.trainer.lr_schedulers[0]["scheduler"]
         snake_case					=    {"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss, "log": tensorboard_logs}
    def 		a_      (	self   ):
         snake_case					=    self.hparams
         snake_case					=    processors[args.task]()
         snake_case					=    processor.get_labels()

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

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

         return DataLoader(
             TensorDataset(UpperCAmelCase_      , UpperCAmelCase_      , UpperCAmelCase_      , UpperCAmelCase_   )      , batch_size=UpperCAmelCase_      , shuffle=UpperCAmelCase_      , )
    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

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

         snake_case					=    self(**UpperCAmelCase_   )
         snake_case					=    outputs[:2]
         snake_case					=    logits.detach().cpu().numpy()
         snake_case					=    inputs["labels"].detach().cpu().numpy()

         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    torch.stack([x['''val_loss'''] for x in outputs]   ).mean().detach().cpu().item()
         snake_case					=    np.concatenate([x['''pred'''] for x in outputs]      , axis=0   )

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

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

         snake_case					=    {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task      , UpperCAmelCase_      , UpperCAmelCase_   )}

         snake_case					=    dict(results.items()   )
         snake_case					=    results
         return ret, preds_list, out_label_list
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    self._eval_end(UpperCAmelCase_   )
         snake_case					=    ret["log"]
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    self._eval_end(UpperCAmelCase_   )
         snake_case					=    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 		a_      (	__snake_case      , __snake_case   ):
         BaseTransformer.add_model_specific_args(UpperCAmelCase_      , UpperCAmelCase_   )
         parser.add_argument(
             '''--max_seq_length'''      , default=1_2_8      , type=UpperCAmelCase_      , 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=UpperCAmelCase_      , required=UpperCAmelCase_      , help='''The GLUE task to run'''      , )
         parser.add_argument(
             '''--gpus'''      , default=0      , type=UpperCAmelCase_      , 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__						():



     """simple docstring"""






     snake_case					=    argparse.ArgumentParser()
     add_generic_args(_UpperCAmelCase							,os.getcwd()    )
     snake_case					=    GLUETransformer.add_model_specific_args(_UpperCAmelCase							,os.getcwd()    )
     snake_case					=    parser.parse_args()

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

     snake_case					=    GLUETransformer(_UpperCAmelCase    )
     snake_case					=    generic_train(_UpperCAmelCase							,_UpperCAmelCase    )

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


if __name__ == "__main__":
 main()








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       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     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       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')    )
     return token
def       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    []
     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       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    '''imagenet-1k-id2label.json'''
     snake_case					=    10_00

     snake_case					=    '''huggingface/label-files'''
     snake_case					=    num_labels
     snake_case					=    json.load(open(cached_download(hf_hub_url(UpperCamelCase_							,UpperCamelCase_							,repo_type='''dataset'''    )    )							,'''r'''    )    )
     snake_case					=    {int(UpperCamelCase_    ): v for k, v in idalabel.items()}

     snake_case					=    idalabel
     snake_case					=    {v: k for k, v in idalabel.items()}

     snake_case					=    snake_case					=    CvtConfig(num_labels=UpperCamelCase_							,idalabel=UpperCamelCase_							,labelaid=UpperCamelCase_    )

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

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

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

     snake_case					=    CvtForImageClassification(UpperCamelCase_    )
     snake_case					=    AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''    )
     snake_case					=    image_size
     snake_case					=    torch.load(UpperCamelCase_							,map_location=torch.device('''cpu'''    )    )

     snake_case					=    OrderedDict()
     snake_case					=    []

     for idx in range(len(config.depth    )    ):
          if config.cls_token[idx]:
               snake_case					=    list_of_state_dict + cls_token(UpperCamelCase_    )
          snake_case					=    list_of_state_dict + embeddings(UpperCamelCase_    )
          for cnt in range(config.depth[idx]    ):
               snake_case					=    list_of_state_dict + attention(UpperCamelCase_							,UpperCamelCase_    )

     snake_case					=    list_of_state_dict + final()
     for gg in list_of_state_dict:
          print(UpperCamelCase_    )
     for i in range(len(UpperCamelCase_    )    ):
          snake_case					=    original_weights[list_of_state_dict[i][1]]

     model.load_state_dict(UpperCamelCase_    )
     model.save_pretrained(UpperCamelCase_    )
     image_processor.save_pretrained(UpperCamelCase_    )


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

if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			Optional[int]      				=   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=3_84,
     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      :			Optional[int]      				=   parser.parse_args()
 convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




