
'''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()
lowerCAmelCase_			:			Union[str, Any]			   =     logging.get_logger(__name__)







@dataclass
class 	__lowerCAmelCase    :
     snake_case   :    nn.Module
     snake_case   :    List[nn.Module]							      =      field(default_factory=__a				)
     snake_case   :    list							      =      field(default_factory=__a				)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  Dict         =    len(list(m.modules()	)	) == 1 or isinstance(lowerCAmelCase__						,	nn.Convad	) or isinstance(lowerCAmelCase__						,	nn.BatchNormad	)
          if has_not_submodules:
               if not isinstance(lowerCAmelCase__						,	lowerCAmelCase__	):
                    self.traced.append(lowerCAmelCase__	)



     def __call__(self						,	lowerCAmelCase__	):
          for m in self.module.modules():
               self.handles.append(m.register_forward_hook(self._forward_hook	)	)
          self.module(lowerCAmelCase__	)
          [x.remove() for x in self.handles]
          return self



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







@dataclass
class 	__lowerCAmelCase    :
     snake_case   :    nn.Module
     snake_case   :    nn.Module
     snake_case   :    int							      =      0
     snake_case   :    List							      =      field(default_factory=__a				)
     snake_case   :    List							      =      field(default_factory=__a				)



     def __call__(self						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  str         =    Tracker(self.dest	)(lowerCAmelCase__	).parametrized
          _UpperCAmelCase					:  List[str]         =    Tracker(self.src	)(lowerCAmelCase__	).parametrized

          _UpperCAmelCase					:  Optional[int]         =    list(filter(lambda lowerCAmelCase__	: type(lowerCAmelCase__	) not in self.src_skip						,	lowerCAmelCase__	)	)
          _UpperCAmelCase					:  Any         =    list(filter(lambda lowerCAmelCase__	: type(lowerCAmelCase__	) not in self.dest_skip						,	lowerCAmelCase__	)	)

          if len(lowerCAmelCase__	) != len(lowerCAmelCase__	):
               raise Exception(
                   F"Numbers of operations are different. Source module has {len(lowerCAmelCase__	)} operations while"
                   F" destination module has {len(lowerCAmelCase__	)}."	)

          for dest_m, src_m in zip(lowerCAmelCase__						,	lowerCAmelCase__	):
               dest_m.load_state_dict(src_m.state_dict()	)
               if self.verbose == 1:
                    print(F"Transfered from={src_m} to={dest_m}"	)







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_       ):
     # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
     _UpperCAmelCase					:  Optional[Any]         =    from_model.state_dict()
     _UpperCAmelCase					:  Union[str, Any]         =    our_model.state_dict()
     _UpperCAmelCase					:  Union[str, Any]         =    our_model.config
     _UpperCAmelCase					:  List[str]         =    []
     for stage_idx in range(len(config.hidden_sizes       )       ):
          for block_id in range(config.depths[stage_idx]       ):
               _UpperCAmelCase					:  Any         =    f"block{stage_idx + 1}.{block_id}.layer_scale_1"
               _UpperCAmelCase					:  Optional[Any]         =    f"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

               all_keys.append((from_key, to_key)       )
               _UpperCAmelCase					:  str         =    f"block{stage_idx + 1}.{block_id}.layer_scale_2"
               _UpperCAmelCase					:  Union[str, Any]         =    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:
          _UpperCAmelCase					:  str         =    from_state_dict.pop(lowerCAmelCase_       )

     our_model.load_state_dict(lowerCAmelCase_       )
     return our_model







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ = True ,       ):
     print(f"Downloading weights for {name}..."       )
     _UpperCAmelCase					:  Tuple         =    cached_download(lowerCAmelCase_       )
     print(f"Converting {name}..."       )
     _UpperCAmelCase					:  List[Any]         =    torch.load(lowerCAmelCase_       )["""state_dict"""]
     from_model.load_state_dict(lowerCAmelCase_       )
     from_model.eval()
     with torch.no_grad():
          _UpperCAmelCase					:  List[str]         =    VanForImageClassification(lowerCAmelCase_       ).eval()
          _UpperCAmelCase					:  int         =    ModuleTransfer(src=lowerCAmelCase_ ,       dest=lowerCAmelCase_       )
          _UpperCAmelCase					:  Union[str, Any]         =    torch.randn((1, 3, 224, 224)       )
          module_transfer(lowerCAmelCase_       )
          _UpperCAmelCase					:  str         =    copy_parameters(lowerCAmelCase_ ,       lowerCAmelCase_       )

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

     _UpperCAmelCase					:  Any         =    name
     print(lowerCAmelCase_       )

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

          # we can use the convnext one
          _UpperCAmelCase					:  Optional[Any]         =    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=lowerCAmelCase_ ,       )

          print(f"Pushed {checkpoint_name}"       )







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_ = None ,       lowerCAmelCase_ = True       ):
     _UpperCAmelCase					:  List[Any]         =    """imagenet-1k-id2label.json"""
     _UpperCAmelCase					:  str         =    1000

     _UpperCAmelCase					:  Optional[int]         =    """huggingface/label-files"""
     _UpperCAmelCase					:  Union[str, Any]         =    num_labels
     _UpperCAmelCase					:  Tuple         =    json.load(open(hf_hub_download(lowerCAmelCase_ ,       lowerCAmelCase_ ,       repo_type="""dataset"""       ) ,       """r"""       )       )
     _UpperCAmelCase					:  str         =    {int(lowerCAmelCase_       ): v for k, v in idalabel.items()}

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

     _UpperCAmelCase					:  List[str]         =    partial(lowerCAmelCase_ ,       num_labels=lowerCAmelCase_ ,       idalabel=lowerCAmelCase_ ,       labelaid=lowerCAmelCase_       )

     _UpperCAmelCase					:  Dict         =    {
         """van-tiny""": ImageNetPreTrainedConfig(
             hidden_sizes=[32, 64, 160, 256] ,       depths=[3, 3, 5, 2] ,       mlp_ratios=[8, 8, 4, 4] ,       ),
         """van-small""": ImageNetPreTrainedConfig(
             hidden_sizes=[64, 128, 320, 512] ,       depths=[2, 2, 4, 2] ,       mlp_ratios=[8, 8, 4, 4] ,       ),
         """van-base""": ImageNetPreTrainedConfig(
             hidden_sizes=[64, 128, 320, 512] ,       depths=[3, 3, 12, 3] ,       mlp_ratios=[8, 8, 4, 4] ,       ),
         """van-large""": ImageNetPreTrainedConfig(
             hidden_sizes=[64, 128, 320, 512] ,       depths=[3, 5, 27, 3] ,       mlp_ratios=[8, 8, 4, 4] ,       ),
     }

     _UpperCAmelCase					:  Tuple         =    {
         """van-tiny""": van_tiny,
         """van-small""": van_small,
         """van-base""": van_base,
         """van-large""": van_large,
     }

     _UpperCAmelCase					:  Tuple         =    {
         """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(
              lowerCAmelCase_ ,       names_to_config[model_name] ,       checkpoint=names_to_original_checkpoints[model_name] ,       from_model=names_to_original_models[model_name]() ,       save_directory=lowerCAmelCase_ ,       push_to_hub=lowerCAmelCase_ ,       )
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(
                   lowerCAmelCase_ ,       lowerCAmelCase_ ,       checkpoint=names_to_original_checkpoints[model_name] ,       from_model=names_to_original_models[model_name]() ,       save_directory=lowerCAmelCase_ ,       push_to_hub=lowerCAmelCase_ ,       )


if __name__ == "__main__":
   lowerCAmelCase_			:			Optional[int]			   =     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.''',
   )

   lowerCAmelCase_			:			str			   =     parser.parse_args()
   lowerCAmelCase_			:			Path			   =     args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   lowerCAmelCase_			:			int			   =     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 concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 	__lowerCAmelCase    (  __a       ,					unittest.TestCase				):
     snake_case   :    Dict							      =      PreTrainedTokenizerFast
     snake_case   :    Optional[Any]							      =      False
     snake_case   :    List[str]							      =      True
     snake_case   :    Any							      =      """tokenizer_file"""



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Dict         =    False  # because we don't have pretrained_vocab_files_map
          super().setUp()
          _UpperCAmelCase					:  Optional[Any]         =    True

          _UpperCAmelCase					:  int         =    ["""robot-test/dummy-tokenizer-fast""", """robot-test/dummy-tokenizer-wordlevel"""]
          _UpperCAmelCase					:  Union[str, Any]         =    """SaulLu/dummy-tokenizer-bytelevel-bpe"""

          # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
          _UpperCAmelCase					:  List[str]         =    [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

          _UpperCAmelCase					:  Optional[Any]         =    PreTrainedTokenizerFast.from_pretrained(model_paths[0]	)
          tokenizer.save_pretrained(self.tmpdirname	)



     def     snake_case_ (self	):
          # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
          # model
          pass



     def     snake_case_ (self	):
          # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
          # model
          pass



     def     snake_case_ (self	):
          # We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
          # model
          pass



     def     snake_case_ (self	):
          # PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
          pass



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Optional[Any]         =    self.tmpdirname
          # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"	):
                    try:
                         _UpperCAmelCase					:  Optional[int]         =    tempfile.mkdtemp()
                         _UpperCAmelCase					:  Tuple         =    self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__						,	**lowerCAmelCase__	)

                         tokenizer.save_pretrained(self.tmpdirname	)
                         super().test_training_new_tokenizer()
                    finally:
                         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                         # is restored
                         shutil.rmtree(self.tmpdirname	)
                         _UpperCAmelCase					:  Optional[int]         =    tmpdirname_orig



     def     snake_case_ (self	):
          _UpperCAmelCase					:  int         =    self.tmpdirname
          # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"	):
                    try:
                         _UpperCAmelCase					:  Any         =    tempfile.mkdtemp()
                         _UpperCAmelCase					:  Union[str, Any]         =    self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__						,	**lowerCAmelCase__	)

                         tokenizer.save_pretrained(self.tmpdirname	)
                         super().test_training_new_tokenizer_with_special_tokens_change()
                    finally:
                         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                         # is restored
                         shutil.rmtree(self.tmpdirname	)
                         _UpperCAmelCase					:  Dict         =    tmpdirname_orig



     def     snake_case_ (self	):
          _UpperCAmelCase					:  List[Any]         =    self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name	)

          _UpperCAmelCase					:  Dict         =    ("""a""" for _ in range(1_0_0_0	))
          _UpperCAmelCase					:  Optional[int]         =    tokenizer.train_new_from_iterator(text_iterator=lowerCAmelCase__						,	length=1_0_0_0						,	vocab_size=5_0	)

          _UpperCAmelCase					:  int         =    new_tokenizer.encode("""a🤗"""	)
          self.assertEqual(lowerCAmelCase__						,	[6_4, 1_7_2, 2_5_3, 9_7, 2_4_5]	)







@require_tokenizers
class 	__lowerCAmelCase    (  unittest.TestCase				):



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Any         =    AutoTokenizer.from_pretrained("""bert-base-cased"""	)
          _UpperCAmelCase					:  Dict         =    json.loads(tokenizer._tokenizer.to_str()	)
          _UpperCAmelCase					:  List[Any]         =    len(lowerCAmelCase__	)

          with tempfile.TemporaryDirectory() as tmp_dir:
               # Hack to save this in the tokenizer_config.json
               _UpperCAmelCase					:  Union[str, Any]         =    ["""tokenizer.4.0.0.json"""]
               tokenizer.save_pretrained(lowerCAmelCase__	)
               json.dump(lowerCAmelCase__						,	open(os.path.join(lowerCAmelCase__						,	"""tokenizer.4.0.0.json"""	)						,	"""w"""	)	)

               # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
               _UpperCAmelCase					:  Optional[int]         =    AutoTokenizer.from_pretrained(lowerCAmelCase__	)
               self.assertEqual(len(lowerCAmelCase__	)						,	len(lowerCAmelCase__	) + 1	)
               _UpperCAmelCase					:  Tuple         =    json.loads(new_tokenizer._tokenizer.to_str()	)
               self.assertIn("""huggingface"""						,	json_tokenizer["""model"""]["""vocab"""]	)

               # Will need to be adjusted if we reach v42 and this test is still here.
               # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
               shutil.move(os.path.join(lowerCAmelCase__						,	"""tokenizer.4.0.0.json"""	)						,	os.path.join(lowerCAmelCase__						,	"""tokenizer.42.0.0.json"""	)	)
               _UpperCAmelCase					:  Tuple         =    ["""tokenizer.42.0.0.json"""]
               tokenizer.save_pretrained(lowerCAmelCase__	)
               _UpperCAmelCase					:  str         =    AutoTokenizer.from_pretrained(lowerCAmelCase__	)
               self.assertEqual(len(lowerCAmelCase__	)						,	len(lowerCAmelCase__	)	)
               _UpperCAmelCase					:  Union[str, Any]         =    json.loads(new_tokenizer._tokenizer.to_str()	)
               self.assertNotIn("""huggingface"""						,	json_tokenizer["""model"""]["""vocab"""]	)



     def     snake_case_ (self	):
          # This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
          _UpperCAmelCase					:  List[Any]         =    """hf-internal-testing/test-two-tokenizers"""

          # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
          _UpperCAmelCase					:  Dict         =    AutoTokenizer.from_pretrained(lowerCAmelCase__	)
          self.assertEqual(len(lowerCAmelCase__	)						,	2_8_9_9_7	)
          _UpperCAmelCase					:  List[str]         =    json.loads(tokenizer._tokenizer.to_str()	)
          self.assertIn("""huggingface"""						,	json_tokenizer["""model"""]["""vocab"""]	)

          # Testing an older version by monkey-patching the version in the module it's used.
          import transformers as old_transformers

          _UpperCAmelCase					:  Optional[Any]         =    """3.0.0"""
          _UpperCAmelCase					:  str         =    old_transformers.models.auto.AutoTokenizer.from_pretrained(lowerCAmelCase__	)
          self.assertEqual(len(lowerCAmelCase__	)						,	2_8_9_9_6	)
          _UpperCAmelCase					:  Optional[int]         =    json.loads(old_tokenizer._tokenizer.to_str()	)
          self.assertNotIn("""huggingface"""						,	json_tokenizer["""model"""]["""vocab"""]	)







@require_tokenizers
class 	__lowerCAmelCase    (  unittest.TestCase				):



     def     snake_case_ (self	):
          # See https://github.com/huggingface/transformers/pull/12550
          # and https://github.com/huggingface/tokenizers/issues/537
          _UpperCAmelCase					:  int         =    PreTrainedTokenizerFast.from_pretrained("""robot-test/dummy-tokenizer-wordlevel"""	)
          _UpperCAmelCase					:  str         =    """The Matrix is a 1999 science fiction action film."""

          with concurrent.futures.ThreadPoolExecutor() as executor:
               _UpperCAmelCase					:  List[Any]         =    [executor.submit(self.fetch						,	lowerCAmelCase__						,	lowerCAmelCase__	) for i in range(1_0	)]
               _UpperCAmelCase					:  Any         =    [future.result() for future in futures]
               self.assertEqual(lowerCAmelCase__						,	[[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0	)]	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__	):
          return tokenizer.encode(lowerCAmelCase__						,	truncation="""longest_first"""						,	padding="""longest"""	)


