




import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__a          =			logging.getLogger(__name__)

@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				42
    lowerCAmelCase        =				42
    lowerCAmelCase        =				42


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				42
    lowerCAmelCase        =				42
    lowerCAmelCase        =				None
    lowerCAmelCase        =				None


class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''train'''
    lowerCAmelCase        =				'''dev'''
    lowerCAmelCase        =				'''test'''


class   __a:






    """simple docstring"""





    @staticmethod
    def 							a__				(				_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		List[InputExample]:
         raise NotImplementedError





    @staticmethod
    def 							a__				(				_SCREAMING_SNAKE_CASE  )			->		List[str]:
         raise NotImplementedError





    @staticmethod
    def 							a__				(				_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE="[CLS]"       ,_SCREAMING_SNAKE_CASE=1       ,_SCREAMING_SNAKE_CASE="[SEP]"       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=0       ,_SCREAMING_SNAKE_CASE=0       ,_SCREAMING_SNAKE_CASE=-100       ,_SCREAMING_SNAKE_CASE=0       ,_SCREAMING_SNAKE_CASE=True       ,)			->		List[InputFeatures]:
         UpperCAmelCase_      :							Union[str, Any]       =      {label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE  )}

         UpperCAmelCase_      :							List[str]       =      []
         for ex_index, example in enumerate(_SCREAMING_SNAKE_CASE  ):
              if ex_index % 10_000 == 0:
                   logger.info('''Writing example %d of %d'''       ,_SCREAMING_SNAKE_CASE       ,len(_SCREAMING_SNAKE_CASE  )  )

              UpperCAmelCase_      :							str       =      []
              UpperCAmelCase_      :							Any       =      []
              for word, label in zip(example.words       ,example.labels  ):
                   UpperCAmelCase_      :							Optional[int]       =      tokenizer.tokenize(_SCREAMING_SNAKE_CASE  )

                   # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                   if len(_SCREAMING_SNAKE_CASE  ) > 0:
                        tokens.extend(_SCREAMING_SNAKE_CASE  )
                        # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                        label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_SCREAMING_SNAKE_CASE  ) - 1)  )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
              UpperCAmelCase_      :							str       =      tokenizer.num_special_tokens_to_add()
              if len(_SCREAMING_SNAKE_CASE  ) > max_seq_length - special_tokens_count:
                   UpperCAmelCase_      :							Union[str, Any]       =      tokens[: (max_seq_length - special_tokens_count)]
                   UpperCAmelCase_      :							Dict       =      label_ids[: (max_seq_length - special_tokens_count)]

              # The convention in BERT is:
              # (a) For sequence pairs:
              #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
              #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
              # (b) For single sequences:
              #  tokens:   [CLS] the dog is hairy . [SEP]
              #  type_ids:   0   0   0   0  0     0   0
              #
              # Where "type_ids" are used to indicate whether this is the first
              # sequence or the second sequence. The embedding vectors for `type=0` and
              # `type=1` were learned during pre-training and are added to the wordpiece
              # embedding vector (and position vector). This is not *strictly* necessary
              # since the [SEP] token unambiguously separates the sequences, but it makes
              # it easier for the model to learn the concept of sequences.
              #
              # For classification tasks, the first vector (corresponding to [CLS]) is
              # used as the "sentence vector". Note that this only makes sense because
              # the entire model is fine-tuned.
              tokens += [sep_token]
              label_ids += [pad_token_label_id]
              if sep_token_extra:
                   # roberta uses an extra separator b/w pairs of sentences
                   tokens += [sep_token]
                   label_ids += [pad_token_label_id]
              UpperCAmelCase_      :							Union[str, Any]       =      [sequence_a_segment_id] * len(_SCREAMING_SNAKE_CASE  )

              if cls_token_at_end:
                   tokens += [cls_token]
                   label_ids += [pad_token_label_id]
                   segment_ids += [cls_token_segment_id]
              else:
                   UpperCAmelCase_      :							Union[str, Any]       =      [cls_token] + tokens
                   UpperCAmelCase_      :							List[Any]       =      [pad_token_label_id] + label_ids
                   UpperCAmelCase_      :							str       =      [cls_token_segment_id] + segment_ids

              UpperCAmelCase_      :							Union[str, Any]       =      tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE  )

              # The mask has 1 for real tokens and 0 for padding tokens. Only real
              # tokens are attended to.
              UpperCAmelCase_      :							str       =      [1 if mask_padding_with_zero else 0] * len(_SCREAMING_SNAKE_CASE  )

              # Zero-pad up to the sequence length.
              UpperCAmelCase_      :							Optional[Any]       =      max_seq_length - len(_SCREAMING_SNAKE_CASE  )
              if pad_on_left:
                   UpperCAmelCase_      :							str       =      ([pad_token] * padding_length) + input_ids
                   UpperCAmelCase_      :							Optional[Any]       =      ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                   UpperCAmelCase_      :							int       =      ([pad_token_segment_id] * padding_length) + segment_ids
                   UpperCAmelCase_      :							int       =      ([pad_token_label_id] * padding_length) + label_ids
              else:
                   input_ids += [pad_token] * padding_length
                   input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                   segment_ids += [pad_token_segment_id] * padding_length
                   label_ids += [pad_token_label_id] * padding_length

              assert len(_SCREAMING_SNAKE_CASE  ) == max_seq_length
              assert len(_SCREAMING_SNAKE_CASE  ) == max_seq_length
              assert len(_SCREAMING_SNAKE_CASE  ) == max_seq_length
              assert len(_SCREAMING_SNAKE_CASE  ) == max_seq_length

              if ex_index < 5:
                   logger.info('''*** Example ***'''  )
                   logger.info('''guid: %s'''       ,example.guid  )
                   logger.info('''tokens: %s'''       ,''' '''.join([str(_SCREAMING_SNAKE_CASE  ) for x in tokens]  )  )
                   logger.info('''input_ids: %s'''       ,''' '''.join([str(_SCREAMING_SNAKE_CASE  ) for x in input_ids]  )  )
                   logger.info('''input_mask: %s'''       ,''' '''.join([str(_SCREAMING_SNAKE_CASE  ) for x in input_mask]  )  )
                   logger.info('''segment_ids: %s'''       ,''' '''.join([str(_SCREAMING_SNAKE_CASE  ) for x in segment_ids]  )  )
                   logger.info('''label_ids: %s'''       ,''' '''.join([str(_SCREAMING_SNAKE_CASE  ) for x in label_ids]  )  )

              if "token_type_ids" not in tokenizer.model_input_names:
                   UpperCAmelCase_      :							List[str]       =      None

              features.append(
                  InputFeatures(
                      input_ids=_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE       ,label_ids=_SCREAMING_SNAKE_CASE  )  )
         return features


if is_torch_available():
     import torch
     from torch import nn
     from torch.utils.data import Dataset

     class   __a(					_a       ):






         """simple docstring"""





         lowerCAmelCase        =				42
         lowerCAmelCase        =				nn.CrossEntropyLoss().ignore_index





         def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE = Split.train       ,)			->		Tuple:
              # Load data features from cache or dataset file
              UpperCAmelCase_      :							Dict       =      os.path.join(
                  _SCREAMING_SNAKE_CASE       ,'''cached_{}_{}_{}'''.format(mode.value       ,tokenizer.__class__.__name__       ,str(_SCREAMING_SNAKE_CASE  )  )       ,)

              # Make sure only the first process in distributed training processes the dataset,
              # and the others will use the cache.
              UpperCAmelCase_      :							Optional[Any]       =      cached_features_file + '''.lock'''
              with FileLock(_SCREAMING_SNAKE_CASE  ):
                   if os.path.exists(_SCREAMING_SNAKE_CASE  ) and not overwrite_cache:
                        logger.info(f'''Loading features from cached file {cached_features_file}'''  )
                        UpperCAmelCase_      :							Optional[Any]       =      torch.load(_SCREAMING_SNAKE_CASE  )
                   else:
                        logger.info(f'''Creating features from dataset file at {data_dir}'''  )
                        UpperCAmelCase_      :							str       =      token_classification_task.read_examples_from_file(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
                        # TODO clean up all this to leverage built-in features of tokenizers
                        UpperCAmelCase_      :							Any       =      token_classification_task.convert_examples_to_features(
                            _SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,cls_token_at_end=bool(model_type in ['''xlnet''']  )       ,cls_token=tokenizer.cls_token       ,cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0       ,sep_token=tokenizer.sep_token       ,sep_token_extra=_SCREAMING_SNAKE_CASE       ,pad_on_left=bool(tokenizer.padding_side == '''left'''  )       ,pad_token=tokenizer.pad_token_id       ,pad_token_segment_id=tokenizer.pad_token_type_id       ,pad_token_label_id=self.pad_token_label_id       ,)
                        logger.info(f'''Saving features into cached file {cached_features_file}'''  )
                        torch.save(self.features       ,_SCREAMING_SNAKE_CASE  )





         def __len__(				self  )			->		List[Any]:
              return len(self.features  )





         def __getitem__(				self       ,_SCREAMING_SNAKE_CASE  )			->		InputFeatures:
              return self.features[i]


if is_tf_available():
     import tensorflow as tf

     class   __a:






         """simple docstring"""





         lowerCAmelCase        =				42
         lowerCAmelCase        =				-100





         def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE = Split.train       ,)			->		List[str]:
              UpperCAmelCase_      :							Tuple       =      token_classification_task.read_examples_from_file(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
              # TODO clean up all this to leverage built-in features of tokenizers
              UpperCAmelCase_      :							List[str]       =      token_classification_task.convert_examples_to_features(
                  _SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,cls_token_at_end=bool(model_type in ['''xlnet''']  )       ,cls_token=tokenizer.cls_token       ,cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0       ,sep_token=tokenizer.sep_token       ,sep_token_extra=_SCREAMING_SNAKE_CASE       ,pad_on_left=bool(tokenizer.padding_side == '''left'''  )       ,pad_token=tokenizer.pad_token_id       ,pad_token_segment_id=tokenizer.pad_token_type_id       ,pad_token_label_id=self.pad_token_label_id       ,)

              def gen():
                   for ex in self.features:
                        if ex.token_type_ids is None:
                             yield (
                                 {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                 ex.label_ids,
                             )
                        else:
                             yield (
                                 {
                                     "input_ids": ex.input_ids,
                                     "attention_mask": ex.attention_mask,
                                     "token_type_ids": ex.token_type_ids,
                                 },
                                 ex.label_ids,
                             )

              if "token_type_ids" not in tokenizer.model_input_names:
                   UpperCAmelCase_      :							Any       =      tf.data.Dataset.from_generator(
                       _SCREAMING_SNAKE_CASE       ,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)       ,(
                           {'''input_ids''': tf.TensorShape([None]  ), '''attention_mask''': tf.TensorShape([None]  )},
                           tf.TensorShape([None]  ),
                       )       ,)
              else:
                   UpperCAmelCase_      :							List[Any]       =      tf.data.Dataset.from_generator(
                       _SCREAMING_SNAKE_CASE       ,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)       ,(
                           {
                               '''input_ids''': tf.TensorShape([None]  ),
                               '''attention_mask''': tf.TensorShape([None]  ),
                               '''token_type_ids''': tf.TensorShape([None]  ),
                           },
                           tf.TensorShape([None]  ),
                       )       ,)





         def 							a__				(				self  )			->		Optional[Any]:
              UpperCAmelCase_      :							List[str]       =      self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

              return self.dataset





         def __len__(				self  )			->		List[Any]:
              return len(self.features  )





         def __getitem__(				self       ,_SCREAMING_SNAKE_CASE  )			->		InputFeatures:
              return self.features[i]





import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[Any]       =      SwinConfig()
     UpperCAmelCase_      :							Dict       =      swin_name.split('''_'''				)

     UpperCAmelCase_      :							List[Any]       =      name_split[1]
     UpperCAmelCase_      :							Optional[Any]       =      int(name_split[4]				)
     UpperCAmelCase_      :							Union[str, Any]       =      int(name_split[3][-1]				)

     if model_size == "tiny":
          UpperCAmelCase_      :							Tuple       =      96
          UpperCAmelCase_      :							Optional[int]       =      (2, 2, 6, 2)
          UpperCAmelCase_      :							str       =      (3, 6, 12, 24)
     elif model_size == "small":
          UpperCAmelCase_      :							Dict       =      96
          UpperCAmelCase_      :							str       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							List[str]       =      (3, 6, 12, 24)
     elif model_size == "base":
          UpperCAmelCase_      :							Tuple       =      128
          UpperCAmelCase_      :							Optional[int]       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							Optional[int]       =      (4, 8, 16, 32)
     else:
          UpperCAmelCase_      :							List[str]       =      192
          UpperCAmelCase_      :							str       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							Union[str, Any]       =      (6, 12, 24, 48)

     if "in22k" in swin_name:
          UpperCAmelCase_      :							int       =      21841
     else:
          UpperCAmelCase_      :							Tuple       =      1000
          UpperCAmelCase_      :							Any       =      '''huggingface/label-files'''
          UpperCAmelCase_      :							int       =      '''imagenet-1k-id2label.json'''
          UpperCAmelCase_      :							str       =      json.load(open(hf_hub_download(_lowercase			,						_lowercase			,						repo_type='''dataset'''				)			,						'''r'''				)				)
          UpperCAmelCase_      :							Optional[int]       =      {int(_lowercase				): v for k, v in idalabel.items()}
          UpperCAmelCase_      :							List[Any]       =      idalabel
          UpperCAmelCase_      :							int       =      {v: k for k, v in idalabel.items()}

     UpperCAmelCase_      :							Tuple       =      img_size
     UpperCAmelCase_      :							List[Any]       =      num_classes
     UpperCAmelCase_      :							str       =      embed_dim
     UpperCAmelCase_      :							Optional[int]       =      depths
     UpperCAmelCase_      :							Dict       =      num_heads
     UpperCAmelCase_      :							Optional[int]       =      window_size

     return config







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if "patch_embed.proj" in name:
          UpperCAmelCase_      :							str       =      name.replace('''patch_embed.proj'''			,						'''embeddings.patch_embeddings.projection'''				)
     if "patch_embed.norm" in name:
          UpperCAmelCase_      :							List[Any]       =      name.replace('''patch_embed.norm'''			,						'''embeddings.norm'''				)
     if "layers" in name:
          UpperCAmelCase_      :							Tuple       =      '''encoder.''' + name
     if "attn.proj" in name:
          UpperCAmelCase_      :							Tuple       =      name.replace('''attn.proj'''			,						'''attention.output.dense'''				)
     if "attn" in name:
          UpperCAmelCase_      :							int       =      name.replace('''attn'''			,						'''attention.self'''				)
     if "norm1" in name:
          UpperCAmelCase_      :							Union[str, Any]       =      name.replace('''norm1'''			,						'''layernorm_before'''				)
     if "norm2" in name:
          UpperCAmelCase_      :							List[Any]       =      name.replace('''norm2'''			,						'''layernorm_after'''				)
     if "mlp.fc1" in name:
          UpperCAmelCase_      :							Tuple       =      name.replace('''mlp.fc1'''			,						'''intermediate.dense'''				)
     if "mlp.fc2" in name:
          UpperCAmelCase_      :							Optional[int]       =      name.replace('''mlp.fc2'''			,						'''output.dense'''				)

     if name == "norm.weight":
          UpperCAmelCase_      :							Optional[Any]       =      '''layernorm.weight'''
     if name == "norm.bias":
          UpperCAmelCase_      :							List[str]       =      '''layernorm.bias'''

     if "head" in name:
          UpperCAmelCase_      :							Union[str, Any]       =      name.replace('''head'''			,						'''classifier'''				)
     else:
          UpperCAmelCase_      :							Union[str, Any]       =      '''swin.''' + name

     return name







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_      :							str       =      orig_state_dict.pop(_lowercase				)

          if "mask" in key:
               continue
          elif "qkv" in key:
               UpperCAmelCase_      :							str       =      key.split('''.'''				)
               UpperCAmelCase_      :							str       =      int(key_split[1]				)
               UpperCAmelCase_      :							Optional[Any]       =      int(key_split[3]				)
               UpperCAmelCase_      :							Optional[int]       =      model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    UpperCAmelCase_      :							Any       =      val[:dim, :]
                    UpperCAmelCase_      :							Optional[int]       =      val[
                        dim : dim * 2, :
                    ]
                    UpperCAmelCase_      :							List[str]       =      val[-dim:, :]
               else:
                    UpperCAmelCase_      :							Any       =      val[
                        :dim
                    ]
                    UpperCAmelCase_      :							Optional[int]       =      val[
                        dim : dim * 2
                    ]
                    UpperCAmelCase_      :							Union[str, Any]       =      val[
                        -dim:
                    ]
          else:
               UpperCAmelCase_      :							Optional[Any]       =      val

     return orig_state_dict







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      timm.create_model(_lowercase			,						pretrained=_lowercase				)
     timm_model.eval()

     UpperCAmelCase_      :							Any       =      get_swin_config(_lowercase				)
     UpperCAmelCase_      :							Union[str, Any]       =      SwinForImageClassification(_lowercase				)
     model.eval()

     UpperCAmelCase_      :							Tuple       =      convert_state_dict(timm_model.state_dict()			,						_lowercase				)
     model.load_state_dict(_lowercase				)

     UpperCAmelCase_      :							List[Any]       =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

     UpperCAmelCase_      :							int       =      AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''			,						'''-'''				)				)				)
     UpperCAmelCase_      :							Optional[Any]       =      Image.open(requests.get(_lowercase			,						stream=_lowercase				).raw				)
     UpperCAmelCase_      :							Union[str, Any]       =      image_processor(images=_lowercase			,						return_tensors='''pt'''				)

     UpperCAmelCase_      :							Optional[Any]       =      timm_model(inputs['''pixel_values''']				)
     UpperCAmelCase_      :							Dict       =      model(**_lowercase				).logits

     assert torch.allclose(_lowercase			,						_lowercase			,						atol=1E-3				)

     print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''				)
     model.save_pretrained(_lowercase				)

     print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
     image_processor.save_pretrained(_lowercase				)


if __name__ == "__main__":
     __a          =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--swin_name',
         default='swin_tiny_patch4_window7_224',
         type=str,
         help='Name of the Swin timm model you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )

     __a          =			parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)