


'''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 			UpperCAmelCase__					(	UpperCAmelCase_    :				Any							)     ->		List[Any]:
     __lowerCamelCase   :	List[str]								=		SwinConfig()
     __lowerCamelCase   :	List[str]								=		swin_name.split('_'							)

     __lowerCamelCase   :	Tuple								=		name_split[1]
     __lowerCamelCase   :	Union[str, Any]								=		int(name_split[4]							)
     __lowerCamelCase   :	Union[str, Any]								=		int(name_split[3][-1]							)

     if model_size == "tiny":
          __lowerCamelCase   :	int								=		96
          __lowerCamelCase   :	List[str]								=		(2, 2, 6, 2)
          __lowerCamelCase   :	List[str]								=		(3, 6, 12, 24)
     elif model_size == "small":
          __lowerCamelCase   :	Optional[Any]								=		96
          __lowerCamelCase   :	List[str]								=		(2, 2, 18, 2)
          __lowerCamelCase   :	Optional[Any]								=		(3, 6, 12, 24)
     elif model_size == "base":
          __lowerCamelCase   :	Any								=		1_28
          __lowerCamelCase   :	Optional[Any]								=		(2, 2, 18, 2)
          __lowerCamelCase   :	Optional[Any]								=		(4, 8, 16, 32)
     else:
          __lowerCamelCase   :	List[Any]								=		1_92
          __lowerCamelCase   :	Any								=		(2, 2, 18, 2)
          __lowerCamelCase   :	Optional[Any]								=		(6, 12, 24, 48)

     if "in22k" in swin_name:
          __lowerCamelCase   :	int								=		2_18_41
     else:
          __lowerCamelCase   :	str								=		10_00
          __lowerCamelCase   :	int								=		"""huggingface/label-files"""
          __lowerCamelCase   :	Dict								=		"""imagenet-1k-id2label.json"""
          __lowerCamelCase   :	List[Any]								=		json.load(open(hf_hub_download(UpperCAmelCase_      ,    UpperCAmelCase_      ,    repo_type='dataset'							)      ,    'r'							)							)
          __lowerCamelCase   :	Optional[int]								=		{int(UpperCAmelCase_							): v for k, v in idalabel.items()}
          __lowerCamelCase   :	Tuple								=		idalabel
          __lowerCamelCase   :	List[Any]								=		{v: k for k, v in idalabel.items()}

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

     return config



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

     if name == "norm.weight":
          __lowerCamelCase   :	str								=		"""layernorm.weight"""
     if name == "norm.bias":
          __lowerCamelCase   :	List[Any]								=		"""layernorm.bias"""

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

     return name



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				List[Any]							)     ->		Dict:
     for key in orig_state_dict.copy().keys():
          __lowerCamelCase   :	List[Any]								=		orig_state_dict.pop(UpperCAmelCase_							)

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

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

     return orig_state_dict



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[str]      ,    UpperCAmelCase_    :				str							)     ->		int:
     __lowerCamelCase   :	Optional[Any]								=		timm.create_model(UpperCAmelCase_      ,    pretrained=UpperCAmelCase_							)
     timm_model.eval()

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

     __lowerCamelCase   :	Optional[Any]								=		convert_state_dict(timm_model.state_dict()      ,    UpperCAmelCase_							)
     model.load_state_dict(UpperCAmelCase_							)

     __lowerCamelCase   :	List[Any]								=		"""http://images.cocodataset.org/val2017/000000039769.jpg"""

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

     __lowerCamelCase   :	Optional[int]								=		timm_model(inputs['pixel_values']							)
     __lowerCamelCase   :	Union[str, Any]								=		model(**UpperCAmelCase_							).logits

     assert torch.allclose(UpperCAmelCase_      ,    UpperCAmelCase_      ,    atol=1e-3							)

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

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


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__		:					int												=  parser.parse_args()
      convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)








import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase					   =5_0_0_0_0_0

_lowerCamelCase   ,  _lowerCamelCase					   =os.path.split(__file__)
_lowerCamelCase					   =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def     _a					(						lowerCamelCase,	**lowerCamelCase							):
 lowerCamelCase		:	Optional[Any]							=      dataset.map(**lowerCamelCase							)





@get_duration
def     _a					(						lowerCamelCase,	**lowerCamelCase							):
 lowerCamelCase		:	Optional[Any]							=      dataset.filter(**lowerCamelCase							)





def     _a					(						):
 lowerCamelCase		:	Optional[Any]							=      {"""num examples""": SPEED_TEST_N_EXAMPLES}
 with tempfile.TemporaryDirectory() as tmp_dir:
  lowerCamelCase		:	Any							=      datasets.Features({"""text""": datasets.Value("""string"""							), """numbers""": datasets.Value("""float32"""							)}							)
  lowerCamelCase		:	Tuple							=      generate_example_dataset(
      os.path.join(lowerCamelCase,	"""dataset.arrow"""							),	lowerCamelCase,	num_examples=lowerCamelCase							)

  lowerCamelCase		:	Tuple							=      transformers.AutoTokenizer.from_pretrained("""bert-base-cased""",	use_fast=lowerCamelCase							)

  def tokenize(lowerCamelCase							):
   return tokenizer(examples["""text"""]							)

  lowerCamelCase		:	List[str]							=      map(lowerCamelCase							)

  lowerCamelCase		:	int							=      map(lowerCamelCase,	batched=lowerCamelCase							)

  lowerCamelCase		:	int							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  with dataset.formatted_as(type="""numpy"""							):
   lowerCamelCase		:	Optional[int]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  with dataset.formatted_as(type="""pandas"""							):
   lowerCamelCase		:	List[str]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  with dataset.formatted_as(type="""torch""",	columns="""numbers"""							):
   lowerCamelCase		:	List[str]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  with dataset.formatted_as(type="""tensorflow""",	columns="""numbers"""							):
   lowerCamelCase		:	Optional[int]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  lowerCamelCase		:	int							=      map(lowerCamelCase,	function=lowerCamelCase,	batched=lowerCamelCase							)

  lowerCamelCase		:	Union[str, Any]							=      filter(lowerCamelCase							)

  # Activate later when tokenizer support batched inputs
  # with dataset.formatted_as(type='numpy'):
  #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

 with open(lowerCamelCase,	"""wb"""							) as f:
  f.write(json.dumps(lowerCamelCase							).encode("""utf-8"""							)							)


if __name__ == "__main__":  # useful to run the profiler
      benchmark_map_filter()




