



'''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  snake_case_     (					_lowerCAmelCase  :						Dict  )      ->   Union[str, Any]:
							UpperCAmelCase :       Optional[int] 							=		SwinConfig()
							UpperCAmelCase :       Tuple 							=		swin_name.split('''_'''  )

							UpperCAmelCase :       str 							=		name_split[1]
							UpperCAmelCase :       Optional[int] 							=		int(name_split[4]  )
							UpperCAmelCase :       str 							=		int(name_split[3][-1]  )

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

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

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

							return config


def  snake_case_     (					_lowerCAmelCase  :						int  )      ->   Dict:


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

							if name == "norm.weight":
														UpperCAmelCase :       Dict 							=		'''layernorm.weight'''
							if name == "norm.bias":
														UpperCAmelCase :       Optional[Any] 							=		'''layernorm.bias'''

							if "head" in name:
														UpperCAmelCase :       Optional[int] 							=		name.replace('''head'''   ,      '''classifier'''  )
							else:
														UpperCAmelCase :       Optional[int] 							=		'''swin.''' + name

							return name


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Optional[Any]  )      ->   Any:


							for key in orig_state_dict.copy().keys():
														UpperCAmelCase :       List[str] 							=		orig_state_dict.pop(_SCREAMING_SNAKE_CASE  )

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

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

							return orig_state_dict


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						int  )      ->   List[str]:
							UpperCAmelCase :       Union[str, Any] 							=		timm.create_model(_SCREAMING_SNAKE_CASE   ,      pretrained=_SCREAMING_SNAKE_CASE  )
							timm_model.eval()

							UpperCAmelCase :       Tuple 							=		get_swin_config(_SCREAMING_SNAKE_CASE  )
							UpperCAmelCase :       Optional[int] 							=		SwinForImageClassification(_SCREAMING_SNAKE_CASE  )
							model.eval()

							UpperCAmelCase :       Optional[int] 							=		convert_state_dict(timm_model.state_dict()   ,      _SCREAMING_SNAKE_CASE  )
							model.load_state_dict(_SCREAMING_SNAKE_CASE  )

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

							UpperCAmelCase :       Tuple 							=		AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''   ,      '''-'''  )  )  )
							UpperCAmelCase :       int 							=		Image.open(requests.get(_SCREAMING_SNAKE_CASE   ,      stream=_SCREAMING_SNAKE_CASE  ).raw  )
							UpperCAmelCase :       Dict 							=		image_processor(images=_SCREAMING_SNAKE_CASE   ,      return_tensors='''pt'''  )

							UpperCAmelCase :       Optional[Any] 							=		timm_model(inputs['''pixel_values''']  )
							UpperCAmelCase :       Optional[int] 							=		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__":
						UpperCamelCase__:   List[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."
						)

						UpperCamelCase__:   Optional[int]								=			parser.parse_args()
						convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)












import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)



def    __lowercase   (      _SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=None	) ->   List[str]:


      '''simple docstring'''


      return field(default_factory=lambda: default ,						metadata=_SCREAMING_SNAKE_CASE	)



@dataclass
class 		UpperCamelCase__ :



     '''simple docstring'''

     __snake_case    :	List[str]			=			list_field(
         default=[]							,      metadata={
             "help": (
                 "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
                 " of all available models"
             )
         }							,      )

     __snake_case    :	List[int]			=			list_field(
         default=[8]							,      metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}   )

     __snake_case    :	List[int]			=			list_field(
         default=[8, 32, 128, 512]							,      metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}							,      )

     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Use FP16 to accelerate inference."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Benchmark training of model"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Verbose memory tracing"}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
         }							,      )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Trace memory line by line"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Save result to a CSV file"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Save all print statements in a log file"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Whether to print environment information"}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": (
                 "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
                 " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
                 " for debugging / testing and on TPU."
             )
         }							,      )
     __snake_case    :	str			=			field(
         default=F"inference_time_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving time results to csv."}							,      )
     __snake_case    :	str			=			field(
         default=F"inference_memory_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving memory results to csv."}							,      )
     __snake_case    :	str			=			field(
         default=F"train_time_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving time results to csv for training."}							,      )
     __snake_case    :	str			=			field(
         default=F"train_memory_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving memory results to csv for training."}							,      )
     __snake_case    :	str			=			field(
         default=F"env_info_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving environment information."}							,      )
     __snake_case    :	str			=			field(
         default=F"log_{round(time()   )}.csv"							,      metadata={"help": "Log filename used if print statements are saved in log."}							,      )
     __snake_case    :	int			=			field(default=3							,      metadata={"help": "Times an experiment will be run."}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": (
                 "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
                 " model weights."
             )
         }							,      )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Dict:




           '''simple docstring'''


           warnings.warn(
               F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
               """ are deprecated in general and it is advised to use external Benchmarking libraries """
               """ to benchmark Transformer models."""						,lowerCamelCase__						,)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Any:




           '''simple docstring'''


           return json.dumps(dataclasses.asdict(self       )						,indent=2       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[str]:




           '''simple docstring'''


           if len(self.models       ) <= 0:
                 raise ValueError(
                     """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
                     """ bert-base-cased` or `args.models = ['bert-base-cased']."""       )
           return self.models






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		int:




           '''simple docstring'''


           if not self.multi_process:
                 return False
           elif self.is_tpu:
                 logger.info("""Multiprocessing is currently not possible on TPU."""       )
                 return False
           else:
                 return True


