



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

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


a__   :					Optional[int] 						=      logging.getLogger(__name__)



@dataclass(frozen=lowerCamelCase						)
class    UpperCAmelCase__:

   '''simple docstring'''




   A     :	str
   A     :	str
   A     :	Optional[str]												=     None
   A     :	Optional[str]												=     None
   A     :	Optional[str]												=     None



@dataclass(frozen=lowerCamelCase						)
class    UpperCAmelCase__:

   '''simple docstring'''




   A     :	List[int]
   A     :	Optional[List[int]]												=     None
   A     :	Optional[List[int]]												=     None
   A     :	Optional[Union[int, float]]												=     None
   A     :	Optional[int]												=     None


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



       class    UpperCAmelCase__(				lowerCamelCase						):

          '''simple docstring'''




          A     :	List[InputFeatures]


          def __init__(    self	:      Optional[Any]      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      PreTrainedTokenizer      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      Optional[int] = None      ,					lowerCAmelCase	:      Tuple=False      ,					lowerCAmelCase	:      bool = False      ,					)						->					str:




                """simple docstring"""

                lowercase__  =	hans_processors[task]()

                lowercase__  =	os.path.join(
                    lowerCAmelCase      ,					'cached_{}_{}_{}_{}'.format(
                        'dev' if evaluate else 'train'      ,					tokenizer.__class__.__name__      ,					str(lowerCAmelCase)      ,					lowerCAmelCase      ,					)      ,					)
                lowercase__  =	processor.get_labels()
                if tokenizer.__class__ in (
                    RobertaTokenizer,
                    RobertaTokenizerFast,
                    XLMRobertaTokenizer,
                    BartTokenizer,
                    BartTokenizerFast,
                ):
                      # HACK(label indices are swapped in RoBERTa pretrained model)
                      lowercase__,						lowercase__  =	label_list[2], label_list[1]
                lowercase__  =	label_list

                # Make sure only the first process in distributed training processes the dataset,
                # and the others will use the cache.
                lowercase__  =	cached_features_file + '.lock'
                with FileLock(lowerCAmelCase):
                      if os.path.exists(lowerCAmelCase) and not overwrite_cache:
                            logger.info(f'''Loading features from cached file {cached_features_file}''')
                            lowercase__  =	torch.load(lowerCAmelCase)
                      else:
                            logger.info(f'''Creating features from dataset file at {data_dir}''')

                            lowercase__  =	(
                                processor.get_dev_examples(lowerCAmelCase) if evaluate else processor.get_train_examples(lowerCAmelCase)
                            )

                            logger.info('Training examples: %s'      ,					len(lowerCAmelCase))
                            lowercase__  =	hans_convert_examples_to_features(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)
                            logger.info('Saving features into cached file %s'      ,					lowerCAmelCase)
                            torch.save(self.features      ,					lowerCAmelCase)


          def __len__(    self	:      Tuple)						->					Dict:




                """simple docstring"""

                return len(self.features)


          def __getitem__(    self	:      Dict      ,					lowerCAmelCase	:      Optional[int])						->					InputFeatures:




                """simple docstring"""

                return self.features[i]



          def       UpperCAmelCase		(    self	:      List[Any])						->					Tuple:




                """simple docstring"""

                return self.label_list


if is_tf_available():
       import tensorflow as tf



       class    UpperCAmelCase__:

          '''simple docstring'''




          A     :	List[InputFeatures]


          def __init__(    self	:      Dict      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      PreTrainedTokenizer      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      Optional[int] = 1_28      ,					lowerCAmelCase	:      Union[str, Any]=False      ,					lowerCAmelCase	:      bool = False      ,					)						->					Union[str, Any]:




                """simple docstring"""

                lowercase__  =	hans_processors[task]()
                lowercase__  =	processor.get_labels()
                if tokenizer.__class__ in (
                    RobertaTokenizer,
                    RobertaTokenizerFast,
                    XLMRobertaTokenizer,
                    BartTokenizer,
                    BartTokenizerFast,
                ):
                      # HACK(label indices are swapped in RoBERTa pretrained model)
                      lowercase__,						lowercase__  =	label_list[2], label_list[1]
                lowercase__  =	label_list

                lowercase__  =	processor.get_dev_examples(lowerCAmelCase) if evaluate else processor.get_train_examples(lowerCAmelCase)
                lowercase__  =	hans_convert_examples_to_features(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)

                def gen():
                      for ex_index, ex in tqdm.tqdm(enumerate(self.features)      ,					desc='convert examples to features'):
                            if ex_index % 1_00_00 == 0:
                                  logger.info('Writing example %d of %d' % (ex_index, len(lowerCAmelCase)))

                            yield (
                                {
                                    "example_id": 0,
                                    "input_ids": ex.input_ids,
                                    "attention_mask": ex.attention_mask,
                                    "token_type_ids": ex.token_type_ids,
                                },
                                ex.label,
                            )

                lowercase__  =	tf.data.Dataset.from_generator(
                    lowerCAmelCase      ,					(
                        {
                            'example_id': tf.intaa,
                            'input_ids': tf.intaa,
                            'attention_mask': tf.intaa,
                            'token_type_ids': tf.intaa,
                        },
                        tf.intaa,
                    )      ,					(
                        {
                            'example_id': tf.TensorShape([]),
                            'input_ids': tf.TensorShape([None, None]),
                            'attention_mask': tf.TensorShape([None, None]),
                            'token_type_ids': tf.TensorShape([None, None]),
                        },
                        tf.TensorShape([]),
                    )      ,					)


          def       UpperCAmelCase		(    self	:      Optional[Any])						->					List[str]:




                """simple docstring"""

                return self.dataset


          def __len__(    self	:      List[str])						->					Optional[Any]:




                """simple docstring"""

                return len(self.features)


          def __getitem__(    self	:      Any      ,					lowerCAmelCase	:      Optional[Any])						->					InputFeatures:




                """simple docstring"""

                return self.features[i]



          def       UpperCAmelCase		(    self	:      Dict)						->					List[Any]:




                """simple docstring"""

                return self.label_list



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      Union[str, Any])						->					int:




         """simple docstring"""

         return self._create_examples(self._read_tsv(os.path.join(lowerCAmelCase      ,					'heuristics_train_set.txt'))      ,					'train')


   def       UpperCAmelCase		(    self	:      List[Any]      ,					lowerCAmelCase	:      Optional[int])						->					Tuple:




         """simple docstring"""

         return self._create_examples(self._read_tsv(os.path.join(lowerCAmelCase      ,					'heuristics_evaluation_set.txt'))      ,					'dev')


   def       UpperCAmelCase		(    self	:      List[str])						->					int:




         """simple docstring"""

         return ["contradiction", "entailment", "neutral"]



   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Optional[int])						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	[]
         for i, line in enumerate(lowerCAmelCase):
               if i == 0:
                     continue
               lowercase__  =	'%s-%s' % (set_type, line[0])
               lowercase__  =	line[5]
               lowercase__  =	line[6]
               lowercase__  =	line[7][2:] if line[7].startswith('ex') else line[7]
               lowercase__  =	line[0]
               examples.append(InputExample(guid=lowerCAmelCase      ,					text_a=lowerCAmelCase      ,					text_b=lowerCAmelCase      ,					label=lowerCAmelCase      ,					pairID=lowerCAmelCase))
         return examples

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__    ,	):
      lowercase__  =	{label: i for i, label in enumerate(A__						)}

      lowercase__  =	[]
      for ex_index, example in tqdm.tqdm(enumerate(A__						)    ,	desc='convert examples to features'						):
            if ex_index % 10_000 == 0:
                  logger.info('Writing example %d' % (ex_index)						)

            lowercase__  =	tokenizer(
                example.text_a    ,	example.text_b    ,	add_special_tokens=A__    ,	max_length=A__    ,	padding='max_length'    ,	truncation=A__    ,	return_overflowing_tokens=A__    ,	)

            lowercase__  =	label_map[example.label] if example.label in label_map else 0

            lowercase__  =	int(example.pairID						)

            features.append(InputFeatures(**A__    ,	label=A__    ,	pairID=A__						)						)

      for i, example in enumerate(examples[:5]						):
            logger.info('*** Example ***'						)
            logger.info(F'''guid: {example}'''						)
            logger.info(F'''features: {features[i]}'''						)

      return features


a__   :					List[str] 						=      {
    "hans": 3,
}

a__   :					Any 						=      {
    "hans": HansProcessor,
}





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	SwinConfig(image_size=192						)

      if "base" in model_name:
            lowercase__  =	6
            lowercase__  =	128
            lowercase__  =	(2, 2, 18, 2)
            lowercase__  =	(4, 8, 16, 32)
      elif "large" in model_name:
            lowercase__  =	12
            lowercase__  =	192
            lowercase__  =	(2, 2, 18, 2)
            lowercase__  =	(6, 12, 24, 48)
      else:
            raise ValueError('Model not supported, only supports base and large variants'						)

      lowercase__  =	window_size
      lowercase__  =	embed_dim
      lowercase__  =	depths
      lowercase__  =	num_heads

      return config

def 	_lowerCAmelCase (    A__						):
      if "encoder.mask_token" in name:
            lowercase__  =	name.replace('encoder.mask_token'    ,	'embeddings.mask_token'						)
      if "encoder.patch_embed.proj" in name:
            lowercase__  =	name.replace('encoder.patch_embed.proj'    ,	'embeddings.patch_embeddings.projection'						)
      if "encoder.patch_embed.norm" in name:
            lowercase__  =	name.replace('encoder.patch_embed.norm'    ,	'embeddings.norm'						)
      if "attn.proj" in name:
            lowercase__  =	name.replace('attn.proj'    ,	'attention.output.dense'						)
      if "attn" in name:
            lowercase__  =	name.replace('attn'    ,	'attention.self'						)
      if "norm1" in name:
            lowercase__  =	name.replace('norm1'    ,	'layernorm_before'						)
      if "norm2" in name:
            lowercase__  =	name.replace('norm2'    ,	'layernorm_after'						)
      if "mlp.fc1" in name:
            lowercase__  =	name.replace('mlp.fc1'    ,	'intermediate.dense'						)
      if "mlp.fc2" in name:
            lowercase__  =	name.replace('mlp.fc2'    ,	'output.dense'						)

      if name == "encoder.norm.weight":
            lowercase__  =	'layernorm.weight'
      if name == "encoder.norm.bias":
            lowercase__  =	'layernorm.bias'

      if "decoder" in name:
            pass
      else:
            lowercase__  =	'swin.' + name

      return name

def 	_lowerCAmelCase (    A__    ,	A__						):
      for key in orig_state_dict.copy().keys():
            lowercase__  =	orig_state_dict.pop(A__						)

            if "attn_mask" in key:
                  pass
            elif "qkv" in key:
                  lowercase__  =	key.split('.'						)
                  lowercase__  =	int(key_split[2]						)
                  lowercase__  =	int(key_split[4]						)
                  lowercase__  =	model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        lowercase__  =	val[:dim, :]
                        lowercase__  =	val[
                            dim : dim * 2, :
                        ]
                        lowercase__  =	val[-dim:, :]
                  else:
                        lowercase__  =	val[
                            :dim
                        ]
                        lowercase__  =	val[
                            dim : dim * 2
                        ]
                        lowercase__  =	val[
                            -dim:
                        ]
            else:
                  lowercase__  =	val

      return orig_state_dict

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__						):
      lowercase__  =	torch.load(A__    ,	map_location='cpu'						)['model']

      lowercase__  =	get_swin_config(A__						)
      lowercase__  =	SwinForMaskedImageModeling(A__						)
      model.eval()

      lowercase__  =	convert_state_dict(A__    ,	A__						)
      model.load_state_dict(A__						)

      lowercase__  =	'http://images.cocodataset.org/val2017/000000039769.jpg'

      lowercase__  =	ViTImageProcessor(size={'height': 192, 'width': 192}						)
      lowercase__  =	Image.open(requests.get(A__    ,	stream=A__						).raw						)
      lowercase__  =	image_processor(images=A__    ,	return_tensors='pt'						)

      with torch.no_grad():
            lowercase__  =	model(**A__						).logits

      print(outputs.keys()						)
      print('Looks ok!'						)

      if pytorch_dump_folder_path is not None:
            print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''						)
            model.save_pretrained(A__						)

            print(F'''Saving image processor to {pytorch_dump_folder_path}'''						)
            image_processor.save_pretrained(A__						)

      if push_to_hub:
            print(F'''Pushing model and image processor for {model_name} to hub'''						)
            model.push_to_hub(F'''microsoft/{model_name}'''						)
            image_processor.push_to_hub(F'''microsoft/{model_name}'''						)


if __name__ == "__main__":
       a__   :					Optional[Any] 						=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="swin-base-simmim-window6-192",
           type=str,
           choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
           help="Name of the Swin SimMIM model you'd like to convert.",
       )
       parser.add_argument(
           "--checkpoint_path",
           default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
           type=str,
           help="Path to the original PyTorch checkpoint (.pth file).",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )
       parser.add_argument(
           "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
       )

       a__   :					Optional[int] 						=      parser.parse_args()
       convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
