'''simple docstring'''






import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase						:    int     					=					logging.get_logger(__name__)







@dataclass
class lowercase_					:







   """simple docstring"""

   snake_case_    						=      4_2
   snake_case_    						=      field(default_factory=_UpperCamelCase )
   snake_case_    						=      field(default_factory=_UpperCamelCase )
   def   __UpperCAmelCase      (			self			: Tuple,   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: Tensor,   UpperCamelCase__			: Tensor    )     -> int:
        _A								=     len(list(m.modules()    )    ) == 1 or isinstance(UpperCamelCase__,   nn.Convad    ) or isinstance(UpperCamelCase__,   nn.BatchNormad    )
        if has_not_submodules:
             if not isinstance(UpperCamelCase__,   UpperCamelCase__    ):
                  self.traced.append(UpperCamelCase__    )
   def __call__(			self			: Dict,   UpperCamelCase__			: Tensor    )     -> List[str]:
        for m in self.module.modules():
             self.handles.append(m.register_forward_hook(self._forward_hook    )    )
        self.module(UpperCamelCase__    )
        [x.remove() for x in self.handles]
        return self

   @property
   def   __UpperCAmelCase      (			self			: Dict    )     -> str:
        # check the len of the state_dict keys to see if we have learnable params
        return list(filter(lambda UpperCamelCase__    : len(list(x.state_dict().keys()    )    ) > 0,   self.traced    )    )







@dataclass
class lowercase_					:







   """simple docstring"""

   snake_case_    						=      4_2
   snake_case_    						=      4_2
   snake_case_    						=      0
   snake_case_    						=      field(default_factory=_UpperCamelCase )
   snake_case_    						=      field(default_factory=_UpperCamelCase )
   def __call__(			self			: Optional[int],   UpperCamelCase__			: Tensor    )     -> List[Any]:
        _A								=     Tracker(self.dest    )(UpperCamelCase__    ).parametrized
        _A								=     Tracker(self.src    )(UpperCamelCase__    ).parametrized

        _A								=     list(filter(lambda UpperCamelCase__    : type(UpperCamelCase__    ) not in self.src_skip,   UpperCamelCase__    )    )
        _A								=     list(filter(lambda UpperCamelCase__    : type(UpperCamelCase__    ) not in self.dest_skip,   UpperCamelCase__    )    )

        if len(UpperCamelCase__    ) != len(UpperCamelCase__    ):
             raise Exception(
                 f'Numbers of operations are different. Source module has {len(UpperCamelCase__    )} operations while'
                 f' destination module has {len(UpperCamelCase__    )}.'    )

        for dest_m, src_m in zip(UpperCamelCase__,   UpperCamelCase__    ):
             dest_m.load_state_dict(src_m.state_dict()    )
             if self.verbose == 1:
                  print(f'Transfered from={src_m} to={dest_m}'    )






def    _SCREAMING_SNAKE_CASE		(			__snake_case       : nn.Module      ,		__snake_case       : nn.Module							):
     # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
     _A								=     from_model.state_dict()
     _A								=     our_model.state_dict()
     _A								=     our_model.config
     _A								=     []
     for stage_idx in range(len(config.hidden_sizes							)							):
          for block_id in range(config.depths[stage_idx]							):
               _A								=     F'block{stage_idx + 1}.{block_id}.layer_scale_1'
               _A								=     F'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'

               all_keys.append((from_key, to_key)							)
               _A								=     F'block{stage_idx + 1}.{block_id}.layer_scale_2'
               _A								=     F'van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'

               all_keys.append((from_key, to_key)							)

     for from_key, to_key in all_keys:
          _A								=     from_state_dict.pop(__snake_case							)

     our_model.load_state_dict(__snake_case							)
     return our_model





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : str      ,		__snake_case       : VanConfig      ,		__snake_case       : str      ,		__snake_case       : nn.Module      ,		__snake_case       : Path      ,		__snake_case       : bool = True      ,		):
     print(F'Downloading weights for {name}...'							)
     _A								=     cached_download(__snake_case							)
     print(F'Converting {name}...'							)
     _A								=     torch.load(__snake_case							)['state_dict']
     from_model.load_state_dict(__snake_case							)
     from_model.eval()
     with torch.no_grad():
          _A								=     VanForImageClassification(__snake_case							).eval()
          _A								=     ModuleTransfer(src=__snake_case      ,		dest=__snake_case							)
          _A								=     torch.randn((1, 3, 2_2_4, 2_2_4)							)
          module_transfer(__snake_case							)
          _A								=     copy_parameters(__snake_case      ,		__snake_case							)

     if not torch.allclose(from_model(__snake_case							)      ,		our_model(__snake_case							).logits							):
          raise ValueError('The model logits don\'t match the original one.'							)

     _A								=     name
     print(__snake_case							)

     if push_to_hub:
          our_model.push_to_hub(
              repo_path_or_name=save_directory / checkpoint_name      ,		commit_message='Add model'      ,		use_temp_dir=__snake_case      ,		)

          # we can use the convnext one
          _A								=     AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'							)
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / checkpoint_name      ,		commit_message='Add image processor'      ,		use_temp_dir=__snake_case      ,		)

          print(F'Pushed {checkpoint_name}'							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Path      ,		__snake_case       : str = None      ,		__snake_case       : bool = True							):
     _A								=     'imagenet-1k-id2label.json'
     _A								=     1_0_0_0

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

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

     _A								=     partial(__snake_case      ,		num_labels=__snake_case      ,		idalabel=__snake_case      ,		labelaid=__snake_case							)

     _A								=     {
         'van-tiny': ImageNetPreTrainedConfig(
             hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]      ,		depths=[3, 3, 5, 2]      ,		mlp_ratios=[8, 8, 4, 4]      ,		),
         'van-small': ImageNetPreTrainedConfig(
             hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,		depths=[2, 2, 4, 2]      ,		mlp_ratios=[8, 8, 4, 4]      ,		),
         'van-base': ImageNetPreTrainedConfig(
             hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,		depths=[3, 3, 1_2, 3]      ,		mlp_ratios=[8, 8, 4, 4]      ,		),
         'van-large': ImageNetPreTrainedConfig(
             hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,		depths=[3, 5, 2_7, 3]      ,		mlp_ratios=[8, 8, 4, 4]      ,		),
     }

     _A								=     {
         'van-tiny': van_tiny,
         'van-small': van_small,
         'van-base': van_base,
         'van-large': van_large,
     }

     _A								=     {
         'van-tiny': (
             'https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'
         ),
         'van-small': (
             'https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'
         ),
         'van-base': (
             'https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'
         ),
         'van-large': (
             'https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'
         ),
     }

     if model_name:
          convert_weight_and_push(
              __snake_case      ,		names_to_config[model_name]      ,		checkpoint=names_to_original_checkpoints[model_name]      ,		from_model=names_to_original_models[model_name]()      ,		save_directory=__snake_case      ,		push_to_hub=__snake_case      ,		)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(
                   __snake_case      ,		__snake_case      ,		checkpoint=names_to_original_checkpoints[model_name]      ,		from_model=names_to_original_models[model_name]()      ,		save_directory=__snake_case      ,		push_to_hub=__snake_case      ,		)


if __name__ == "__main__":
   _UpperCAmelCase						:    List[Any]     					=					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--model-name''',
       default=None,
       type=str,
       help=(
           '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
           ''' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''',
       default=None,
       type=Path,
       required=True,
       help='''Path to the output PyTorch model directory.''',
   )
   parser.add_argument(
       '''--van_dir''',
       required=True,
       type=Path,
       help=(
           '''A path to VAN\'s original implementation directory. You can download from here:'''
           ''' https://github.com/Visual-Attention-Network/VAN-Classification'''
       ),
   )
   parser.add_argument(
       '''--push_to_hub''',
       default=True,
       type=bool,
       required=False,
       help='''If True, push model and image processor to the hub.''',
   )

   _UpperCAmelCase						:    str     					=					parser.parse_args()
   _UpperCAmelCase						:    Path     					=					args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   _UpperCAmelCase						:    Optional[Any]     					=					args.van_dir
   # append the path to the parents to maskformer dir
   sys.path.append(str(van_dir.parent))
   from van.models.van import van_base, van_large, van_small, van_tiny

   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




'''simple docstring'''






import argparse
import os
import sys
import urllib.request
import zipfile


_UpperCAmelCase						:    int     					=					['''CoLA''', '''SST''', '''MRPC''', '''QQP''', '''STS''', '''MNLI''', '''SNLI''', '''QNLI''', '''RTE''', '''WNLI''', '''diagnostic''']
_UpperCAmelCase						:    Dict     					=					{
    '''CoLA''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4''',
    '''SST''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8''',
    '''MRPC''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc''',
    '''QQP''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5''',
    '''STS''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5''',
    '''MNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce''',
    '''SNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df''',
    '''QNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601''',
    '''RTE''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb''',
    '''WNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf''',
    '''diagnostic''': '''https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D''',
}

_UpperCAmelCase						:    Any     					=					'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'''
_UpperCAmelCase						:    List[Any]     					=					'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'''





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : Union[str, Any]							):
     print(F'Downloading and extracting {task}...'							)
     _A								=     F'{task}.zip'
     urllib.request.urlretrieve(TASKaPATH[task]      ,		__snake_case							)
     with zipfile.ZipFile(__snake_case							) as zip_ref:
          zip_ref.extractall(__snake_case							)
     os.remove(__snake_case							)
     print('\tCompleted!'							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : str							):
     print('Processing MRPC...'							)
     _A								=     os.path.join(__snake_case      ,		'MRPC'							)
     if not os.path.isdir(__snake_case							):
          os.mkdir(__snake_case							)
     if path_to_data:
          _A								=     os.path.join(__snake_case      ,		'msr_paraphrase_train.txt'							)
          _A								=     os.path.join(__snake_case      ,		'msr_paraphrase_test.txt'							)
     else:
          print('Local MRPC data not specified, downloading data from %s' % MRPC_TRAIN							)
          _A								=     os.path.join(__snake_case      ,		'msr_paraphrase_train.txt'							)
          _A								=     os.path.join(__snake_case      ,		'msr_paraphrase_test.txt'							)
          urllib.request.urlretrieve(__snake_case      ,		__snake_case							)
          urllib.request.urlretrieve(__snake_case      ,		__snake_case							)
     if not os.path.isfile(__snake_case							):
          raise ValueError(F'Train data not found at {mrpc_train_file}'							)
     if not os.path.isfile(__snake_case							):
          raise ValueError(F'Test data not found at {mrpc_test_file}'							)
     urllib.request.urlretrieve(TASKaPATH['MRPC']      ,		os.path.join(__snake_case      ,		'dev_ids.tsv'							)							)

     _A								=     []
     with open(os.path.join(__snake_case      ,		'dev_ids.tsv'							)      ,		encoding='utf8'							) as ids_fh:
          for row in ids_fh:
               dev_ids.append(row.strip().split('\t'							)							)

     with open(__snake_case      ,		encoding='utf8'							) as data_fh, open(
         os.path.join(__snake_case      ,		'train.tsv'							)      ,		'w'      ,		encoding='utf8'							) as train_fh, open(os.path.join(__snake_case      ,		'dev.tsv'							)      ,		'w'      ,		encoding='utf8'							) as dev_fh:
          _A								=     data_fh.readline()
          train_fh.write(__snake_case							)
          dev_fh.write(__snake_case							)
          for row in data_fh:
               _A		,					_A		,					_A		,					_A		,					_A								=     row.strip().split('\t'							)
               if [ida, ida] in dev_ids:
                    dev_fh.write('%s\t%s\t%s\t%s\t%s\n' % (label, ida, ida, sa, sa)							)
               else:
                    train_fh.write('%s\t%s\t%s\t%s\t%s\n' % (label, ida, ida, sa, sa)							)

     with open(__snake_case      ,		encoding='utf8'							) as data_fh, open(
         os.path.join(__snake_case      ,		'test.tsv'							)      ,		'w'      ,		encoding='utf8'							) as test_fh:
          _A								=     data_fh.readline()
          test_fh.write('index\t#1 ID\t#2 ID\t#1 String\t#2 String\n'							)
          for idx, row in enumerate(__snake_case							):
               _A		,					_A		,					_A		,					_A		,					_A								=     row.strip().split('\t'							)
               test_fh.write('%d\t%s\t%s\t%s\t%s\n' % (idx, ida, ida, sa, sa)							)
     print('\tCompleted!'							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]							):
     print('Downloading and extracting diagnostic...'							)
     if not os.path.isdir(os.path.join(__snake_case      ,		'diagnostic'							)							):
          os.mkdir(os.path.join(__snake_case      ,		'diagnostic'							)							)
     _A								=     os.path.join(__snake_case      ,		'diagnostic'      ,		'diagnostic.tsv'							)
     urllib.request.urlretrieve(TASKaPATH['diagnostic']      ,		__snake_case							)
     print('\tCompleted!'							)
     return





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]							):
     _A								=     task_names.split(','							)
     if "all" in task_names:
          _A								=     TASKS
     else:
          _A								=     []
          for task_name in task_names:
               if task_name not in TASKS:
                    raise ValueError(F'Task {task_name} not found!'							)
               tasks.append(__snake_case							)
     return tasks





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]							):
     _A								=     argparse.ArgumentParser()
     parser.add_argument('--data_dir'      ,		help='directory to save data to'      ,		type=__snake_case      ,		default='glue_data'							)
     parser.add_argument(
         '--tasks'      ,		help='tasks to download data for as a comma separated string'      ,		type=__snake_case      ,		default='all'							)
     parser.add_argument(
         '--path_to_mrpc'      ,		help='path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt'      ,		type=__snake_case      ,		default=''      ,		)
     _A								=     parser.parse_args(__snake_case							)

     if not os.path.isdir(args.data_dir							):
          os.mkdir(args.data_dir							)
     _A								=     get_tasks(args.tasks							)

     for task in tasks:
          if task == "MRPC":
               format_mrpc(args.data_dir      ,		args.path_to_mrpc							)
          elif task == "diagnostic":
               download_diagnostic(args.data_dir							)
          else:
               download_and_extract(__snake_case      ,		args.data_dir							)


if __name__ == "__main__":
   sys.exit(main(sys.argv[1:]))



