





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      SwinConfig(image_size=1_92						)

		if "base" in model_name:
				lowerCamelCase_	:     Dict 						=      6
				lowerCamelCase_	:     Any 						=      1_28
				lowerCamelCase_	:     List[Any] 						=      (2, 2, 18, 2)
				lowerCamelCase_	:     Dict 						=      (4, 8, 16, 32)
		elif "large" in model_name:
				lowerCamelCase_	:     List[Any] 						=      12
				lowerCamelCase_	:     List[Any] 						=      1_92
				lowerCamelCase_	:     Optional[Any] 						=      (2, 2, 18, 2)
				lowerCamelCase_	:     List[Any] 						=      (6, 12, 24, 48)
		else:
				raise ValueError('Model not supported, only supports base and large variants'						)

		lowerCamelCase_	:     str 						=      window_size
		lowerCamelCase_	:     Dict 						=      embed_dim
		lowerCamelCase_	:     List[str] 						=      depths
		lowerCamelCase_	:     Tuple 						=      num_heads

		return config


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

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

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

		return name


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		for key in orig_state_dict.copy().keys():
				lowerCamelCase_	:     Tuple 						=      orig_state_dict.pop(lowerCAmelCase__						)

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

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

		return orig_state_dict


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     Optional[Any] 						=      torch.load(lowerCAmelCase__							,map_location='cpu'						)['model']

		lowerCamelCase_	:     Tuple 						=      get_swin_config(lowerCAmelCase__						)
		lowerCamelCase_	:     Optional[Any] 						=      SwinForMaskedImageModeling(lowerCAmelCase__						)
		model.eval()

		lowerCamelCase_	:     int 						=      convert_state_dict(lowerCAmelCase__							,lowerCAmelCase__						)
		model.load_state_dict(lowerCAmelCase__						)

		lowerCamelCase_	:     Union[str, Any] 						=      'http://images.cocodataset.org/val2017/000000039769.jpg'

		lowerCamelCase_	:     Any 						=      ViTImageProcessor(size={'height': 1_92, 'width': 1_92}						)
		lowerCamelCase_	:     int 						=      Image.open(requests.get(lowerCAmelCase__							,stream=lowerCAmelCase__						).raw						)
		lowerCamelCase_	:     Optional[int] 						=      image_processor(images=lowerCAmelCase__							,return_tensors='pt'						)

		with torch.no_grad():
				lowerCamelCase_	:     List[Any] 						=      model(**lowerCAmelCase__						).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(lowerCAmelCase__						)

				print(F"Saving image processor to {pytorch_dump_folder_path}"						)
				image_processor.save_pretrained(lowerCAmelCase__						)

		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__":
		_lowercase		:  Optional[int]			       =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."""
		)

		_lowercase		:  int			       =parser.parse_args()
		convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowercase		:  List[str]			       =5_0000
_lowercase		:  str			       =5000

_lowercase       , _lowercase		:  List[str]			       =os.path.split(__file__)
_lowercase		:  Union[str, Any]			       =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		for i in range(0							,len(lowerCAmelCase__						)							,lowerCAmelCase__						):
				lowerCamelCase_	:     Tuple 						=      dataset[i : i + batch_size]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(lowerCAmelCase__						):
						lowerCamelCase_	:     Tuple 						=      dataset[i]


@get_duration
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		with dataset.formatted_as(type=lowerCAmelCase__						):
				for i in range(0							,lowerCAmelCase__							,lowerCAmelCase__						):
						lowerCamelCase_	:     Optional[int] 						=      dataset[i : i + batch_size]


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Any 						=      {'num examples': SPEED_TEST_N_EXAMPLES}
		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
		    (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]

		lowerCamelCase_	:     Optional[Any] 						=      [
		    (read, {'length': SMALL_TEST}),
		    (read, {'length': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
		    (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
		    (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
		    (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
		]
		with tempfile.TemporaryDirectory() as tmp_dir:
				print('generating dataset'						)
				lowerCamelCase_	:     List[Any] 						=      datasets.Features(
				    {'list': datasets.Sequence(datasets.Value('float32'						)						), 'numbers': datasets.Value('float32'						)}						)
				lowerCamelCase_	:     Union[str, Any] 						=      generate_example_dataset(
				    os.path.join(lowerCAmelCase__							,'dataset.arrow'						)							,lowerCAmelCase__							,num_examples=lowerCAmelCase__							,seq_shapes={'list': (1_00,)}							,)
				print('first set of iterations'						)
				for func, kwargs in functions:
						print(func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[int] 						=      func(lowerCAmelCase__							,**lowerCAmelCase__						)

				print('shuffling dataset'						)
				lowerCamelCase_	:     int 						=      dataset.shuffle()
				print('Second set of iterations (after shuffling'						)
				for func, kwargs in functions_shuffled:
						print('shuffled '							,func.__name__							,str(lowerCAmelCase__						)						)
						lowerCamelCase_	:     Optional[Any] 						=      func(
						    lowerCAmelCase__							,**lowerCAmelCase__						)

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


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