






"""simple docstring"""





import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
      from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
      from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
      from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


A__					: Dict=	DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

A__					: List[str]=	"""main"""
# Default branch name
A__					: List[str]=	"""f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"""
# One particular commit (not the top of `main`)
A__					: Dict=	"""aaaaaaa"""
# This commit does not exist, so we should 404.

A__					: Optional[Any]=	"""d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"""
# Sha-1 of config.json on the top of `main`, for checking purposes
A__					: Union[str, Any]=	"""4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"""







@contextlib.contextmanager
def   lowerCAmelCase_( )					->	str:
    """simple docstring"""

    print('Welcome!'					)
    yield
    print('Bye!'					)







@contextlib.contextmanager
def   lowerCAmelCase_( )					->	List[str]:
    """simple docstring"""

    print('Bonjour!'					)
    yield
    print('Au revoir!'					)
class    __lowerCamelCase    (  unittest.TestCase  ):

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       assert transformers.__spec__ is not None
       assert importlib.util.find_spec('transformers'      ) is not None







class    __lowerCamelCase    (  unittest.TestCase  ):

   @unittest.mock.patch('sys.stdout' ,				new_callable=io.StringIO      )
   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Dict:

       with ContextManagers([]      ):
           print('Transformers are awesome!'      )
       # The print statement adds a new line at the end of the output
       self.assertEqual(mock_stdout.getvalue() ,				'Transformers are awesome!\n'      )

   @unittest.mock.patch('sys.stdout' ,				new_callable=io.StringIO      )
   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       str:

       with ContextManagers([context_en()]      ):
           print('Transformers are awesome!'      )
       # The output should be wrapped with an English welcome and goodbye
       self.assertEqual(mock_stdout.getvalue() ,				'Welcome!\nTransformers are awesome!\nBye!\n'      )

   @unittest.mock.patch('sys.stdout' ,				new_callable=io.StringIO      )
   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       str:

       with ContextManagers([context_fr(), context_en()]      ):
           print('Transformers are awesome!'      )
       # The output should be wrapped with an English and French welcome and goodbye
       self.assertEqual(mock_stdout.getvalue() ,				'Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n'      )

   @require_torch
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       self.assertEqual(find_labels(snake_case_      ) ,				['labels']      )
       self.assertEqual(find_labels(snake_case_      ) ,				['labels', 'next_sentence_label']      )
       self.assertEqual(find_labels(snake_case_      ) ,				['start_positions', 'end_positions']      )
       class    __lowerCamelCase    (  _a  ):
          pass







       self.assertEqual(find_labels(snake_case_      ) ,				['labels']      )

   @require_tf
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       self.assertEqual(find_labels(snake_case_      ) ,				['labels']      )
       self.assertEqual(find_labels(snake_case_      ) ,				['labels', 'next_sentence_label']      )
       self.assertEqual(find_labels(snake_case_      ) ,				['start_positions', 'end_positions']      )
       class    __lowerCamelCase    (  _a  ):
          pass







       self.assertEqual(find_labels(snake_case_      ) ,				['labels']      )







   @require_flax
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       self.assertEqual(find_labels(snake_case_      ) ,				[]      )
       self.assertEqual(find_labels(snake_case_      ) ,				[]      )
       self.assertEqual(find_labels(snake_case_      ) ,				[]      )
       class    __lowerCamelCase    (  _a  ):
          pass







       self.assertEqual(find_labels(snake_case_      ) ,				[]      )












"""simple docstring"""





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_( SCREAMING_SNAKE_CASE					)					->	Tuple:
    """simple docstring"""

    UpperCamelCase__         =    SwinConfig()
    UpperCamelCase__         =    swin_name.split('_'					)

    UpperCamelCase__         =    name_split[1]
    UpperCamelCase__         =    int(name_split[4]					)
    UpperCamelCase__         =    int(name_split[3][-1]					)

    if model_size == "tiny":
        UpperCamelCase__         =    96
        UpperCamelCase__         =    (2, 2, 6, 2)
        UpperCamelCase__         =    (3, 6, 12, 24)
    elif model_size == "small":
        UpperCamelCase__         =    96
        UpperCamelCase__         =    (2, 2, 18, 2)
        UpperCamelCase__         =    (3, 6, 12, 24)
    elif model_size == "base":
        UpperCamelCase__         =    1_28
        UpperCamelCase__         =    (2, 2, 18, 2)
        UpperCamelCase__         =    (4, 8, 16, 32)
    else:
        UpperCamelCase__         =    1_92
        UpperCamelCase__         =    (2, 2, 18, 2)
        UpperCamelCase__         =    (6, 12, 24, 48)

    if "in22k" in swin_name:
        UpperCamelCase__         =    2_18_41
    else:
        UpperCamelCase__         =    10_00
        UpperCamelCase__         =    'huggingface/label-files'
        UpperCamelCase__         =    'imagenet-1k-id2label.json'
        UpperCamelCase__         =    json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	repo_type='dataset'					) ,	'r'					)					)
        UpperCamelCase__         =    {int(SCREAMING_SNAKE_CASE					): v for k, v in idalabel.items()}
        UpperCamelCase__         =    idalabel
        UpperCamelCase__         =    {v: k for k, v in idalabel.items()}

    UpperCamelCase__         =    img_size
    UpperCamelCase__         =    num_classes
    UpperCamelCase__         =    embed_dim
    UpperCamelCase__         =    depths
    UpperCamelCase__         =    num_heads
    UpperCamelCase__         =    window_size

    return config







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

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

    if name == "norm.weight":
        UpperCamelCase__         =    'layernorm.weight'
    if name == "norm.bias":
        UpperCamelCase__         =    'layernorm.bias'

    if "head" in name:
        UpperCamelCase__         =    name.replace('head' ,	'classifier'					)
    else:
        UpperCamelCase__         =    'swin.' + name

    return name







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	str:
    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        UpperCamelCase__         =    orig_state_dict.pop(SCREAMING_SNAKE_CASE					)

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

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

    return orig_state_dict







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    timm.create_model(SCREAMING_SNAKE_CASE ,	pretrained=SCREAMING_SNAKE_CASE					)
    timm_model.eval()

    UpperCamelCase__         =    get_swin_config(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    SwinForImageClassification(SCREAMING_SNAKE_CASE					)
    model.eval()

    UpperCamelCase__         =    convert_state_dict(timm_model.state_dict() ,	SCREAMING_SNAKE_CASE					)
    model.load_state_dict(SCREAMING_SNAKE_CASE					)

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

    UpperCamelCase__         =    AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_' ,	'-'					)					)					)
    UpperCamelCase__         =    Image.open(requests.get(SCREAMING_SNAKE_CASE ,	stream=SCREAMING_SNAKE_CASE					).raw					)
    UpperCamelCase__         =    image_processor(images=SCREAMING_SNAKE_CASE ,	return_tensors='pt'					)

    UpperCamelCase__         =    timm_model(inputs['pixel_values']					)
    UpperCamelCase__         =    model(**SCREAMING_SNAKE_CASE					).logits

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

    print(F'Saving model {swin_name} to {pytorch_dump_folder_path}'					)
    model.save_pretrained(SCREAMING_SNAKE_CASE					)

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


if __name__ == "__main__":
  A__					: Optional[Any]=	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__					: Tuple=	parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




