
"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       str           =   SwinvaConfig()
							SCREAMING_SNAKE_CASE__						:       str           =   swinva_name.split("""_"""						)

							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   name_split[1]
							if "to" in name_split[3]:
														SCREAMING_SNAKE_CASE__						:       Dict           =   int(name_split[3][-3:]						)
							else:
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   int(name_split[3]						)
							if "to" in name_split[2]:
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   int(name_split[2][-2:]						)
							else:
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   int(name_split[2][6:]						)

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

							if "to" in swinva_name:
														SCREAMING_SNAKE_CASE__						:       str           =   (12, 12, 12, 6)

							if ("22k" in swinva_name) and ("to" not in swinva_name):
														SCREAMING_SNAKE_CASE__						:       Dict           =   21_841
														SCREAMING_SNAKE_CASE__						:       str           =   """huggingface/label-files"""
														SCREAMING_SNAKE_CASE__						:       Any           =   """imagenet-22k-id2label.json"""
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   json.load(open(hf_hub_download(_snake_case   ,_snake_case   ,repo_type="""dataset"""						)   ,"""r"""						)						)
														SCREAMING_SNAKE_CASE__						:       List[str]           =   {int(_snake_case						): v for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE__						:       Any           =   idalabel
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   {v: k for k, v in idalabel.items()}

							else:
														SCREAMING_SNAKE_CASE__						:       Dict           =   1_000
														SCREAMING_SNAKE_CASE__						:       Any           =   """huggingface/label-files"""
														SCREAMING_SNAKE_CASE__						:       int           =   """imagenet-1k-id2label.json"""
														SCREAMING_SNAKE_CASE__						:       str           =   json.load(open(hf_hub_download(_snake_case   ,_snake_case   ,repo_type="""dataset"""						)   ,"""r"""						)						)
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {int(_snake_case						): v for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   idalabel
														SCREAMING_SNAKE_CASE__						:       List[str]           =   {v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE__						:       List[str]           =   img_size
							SCREAMING_SNAKE_CASE__						:       Dict           =   num_classes
							SCREAMING_SNAKE_CASE__						:       Tuple           =   embed_dim
							SCREAMING_SNAKE_CASE__						:       str           =   depths
							SCREAMING_SNAKE_CASE__						:       str           =   num_heads
							SCREAMING_SNAKE_CASE__						:       List[str]           =   window_size

							return config

def lowercase_  (   _snake_case						):
							if "patch_embed.proj" in name:
														SCREAMING_SNAKE_CASE__						:       List[str]           =   name.replace("""patch_embed.proj"""   ,"""embeddings.patch_embeddings.projection"""						)
							if "patch_embed.norm" in name:
														SCREAMING_SNAKE_CASE__						:       str           =   name.replace("""patch_embed.norm"""   ,"""embeddings.norm"""						)
							if "layers" in name:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   """encoder.""" + name
							if "attn.proj" in name:
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   name.replace("""attn.proj"""   ,"""attention.output.dense"""						)
							if "attn" in name:
														SCREAMING_SNAKE_CASE__						:       str           =   name.replace("""attn"""   ,"""attention.self"""						)
							if "norm1" in name:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   name.replace("""norm1"""   ,"""layernorm_before"""						)
							if "norm2" in name:
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   name.replace("""norm2"""   ,"""layernorm_after"""						)
							if "mlp.fc1" in name:
														SCREAMING_SNAKE_CASE__						:       Dict           =   name.replace("""mlp.fc1"""   ,"""intermediate.dense"""						)
							if "mlp.fc2" in name:
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   name.replace("""mlp.fc2"""   ,"""output.dense"""						)
							if "q_bias" in name:
														SCREAMING_SNAKE_CASE__						:       int           =   name.replace("""q_bias"""   ,"""query.bias"""						)
							if "k_bias" in name:
														SCREAMING_SNAKE_CASE__						:       Dict           =   name.replace("""k_bias"""   ,"""key.bias"""						)
							if "v_bias" in name:
														SCREAMING_SNAKE_CASE__						:       str           =   name.replace("""v_bias"""   ,"""value.bias"""						)
							if "cpb_mlp" in name:
														SCREAMING_SNAKE_CASE__						:       Dict           =   name.replace("""cpb_mlp"""   ,"""continuous_position_bias_mlp"""						)
							if name == "norm.weight":
														SCREAMING_SNAKE_CASE__						:       Tuple           =   """layernorm.weight"""
							if name == "norm.bias":
														SCREAMING_SNAKE_CASE__						:       Dict           =   """layernorm.bias"""

							if "head" in name:
														SCREAMING_SNAKE_CASE__						:       Any           =   name.replace("""head"""   ,"""classifier"""						)
							else:
														SCREAMING_SNAKE_CASE__						:       str           =   """swinv2.""" + name

							return name

def lowercase_  (   _snake_case   ,_snake_case						):
							for key in orig_state_dict.copy().keys():
														SCREAMING_SNAKE_CASE__						:       Any           =   orig_state_dict.pop(_snake_case						)

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

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

							return orig_state_dict

def lowercase_  (   _snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   timm.create_model(_snake_case   ,pretrained=_snake_case						)
							timm_model.eval()

							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   get_swinva_config(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Dict           =   SwinvaForImageClassification(_snake_case						)
							model.eval()

							SCREAMING_SNAKE_CASE__						:       str           =   convert_state_dict(timm_model.state_dict()   ,_snake_case						)
							model.load_state_dict(_snake_case						)

							SCREAMING_SNAKE_CASE__						:       str           =   """http://images.cocodataset.org/val2017/000000039769.jpg"""

							SCREAMING_SNAKE_CASE__						:       Tuple           =   AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""   ,"""-"""						)						)						)
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   Image.open(requests.get(_snake_case   ,stream=_snake_case						).raw						)
							SCREAMING_SNAKE_CASE__						:       Dict           =   image_processor(images=_snake_case   ,return_tensors="""pt"""						)

							SCREAMING_SNAKE_CASE__						:       int           =   timm_model(inputs["""pixel_values"""]						)
							SCREAMING_SNAKE_CASE__						:       Dict           =   model(**_snake_case						).logits

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

							print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''						)
							model.save_pretrained(_snake_case						)

							print(f'''Saving image processor to {pytorch_dump_folder_path}'''						)
							image_processor.save_pretrained(_snake_case						)

							model.push_to_hub(
							    repo_path_or_name=Path(_snake_case   ,_snake_case						)   ,organization="""nandwalritik"""   ,commit_message="""Add model"""   ,)


if __name__ == "__main__":
					UpperCAmelCase__      :    List[str]    							= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--swinv2_name',
					    default='swinv2_tiny_patch4_window8_256',
					    type=str,
					    help='Name of the Swinv2 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__      :    int    							= parser.parse_args()
					convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			lowercase       =    mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		else:
			lowercase       =    max(
			    mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							) ,							mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							j - wt[i - 1]							) + val[i - 1] ,							)
		lowercase       =    val
	return f[i][j]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    [[0] * (w + 1) for _ in range(n + 1							)]

	for i in range(1 ,							n + 1							):
		for w_ in range(1 ,							w + 1							):
			if wt[i - 1] <= w_:
				lowercase       =    max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,							dp[i - 1][w_]							)
			else:
				lowercase       =    dp[i - 1][w_]

	return dp[n][w_], dp






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	if not (isinstance(__SCREAMING_SNAKE_CASE ,							(list, tuple)							) and isinstance(__SCREAMING_SNAKE_CASE ,							(list, tuple)							)):
		raise ValueError(
		    'Both the weights and values vectors must be either lists or tuples'							)

	lowercase       =    len(__SCREAMING_SNAKE_CASE							)
	if num_items != len(__SCREAMING_SNAKE_CASE							):
		lowercase       =    (
		    'The number of weights must be the same as the number of values.\n'
		    F'''But got {num_items} weights and {len(__SCREAMING_SNAKE_CASE							)} values'''
		)
		raise ValueError(__SCREAMING_SNAKE_CASE							)
	for i in range(__SCREAMING_SNAKE_CASE							):
		if not isinstance(wt[i] ,							__SCREAMING_SNAKE_CASE							):
			lowercase       =    (
			    'All weights must be integers but got weight of '
			    F'''type {type(wt[i]							)} at index {i}'''
			)
			raise TypeError(__SCREAMING_SNAKE_CASE							)

	lowercase  ,					lowercase       =    knapsack(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	lowercase       =    set()
	_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

	return optimal_val, example_optional_set






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	# for the current item i at a maximum weight j to be part of an optimal subset,
	# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
	# where i - 1 means considering only the previous items at the given maximum weight
	if i > 0 and j > 0:
		if dp[i - 1][j] == dp[i][j]:
			_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		else:
			optimal_set.add(__SCREAMING_SNAKE_CASE							)
			_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							i - 1 ,							j - wt[i - 1] ,							__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
	UpperCAmelCase	      = [3, 2, 4, 4]
	UpperCAmelCase	      = [4, 3, 2, 3]
	UpperCAmelCase	      = 4
	UpperCAmelCase	      = 6
	UpperCAmelCase	      = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
	UpperCAmelCase      , UpperCAmelCase	      = knapsack(w, wt, val, n)
	print(optimal_solution)
	print(mf_knapsack(n, wt, val, w))  # switched the n and w

	# testing the dynamic programming problem with example
	# the optimal subset for the above example are items 3 and 4
	UpperCAmelCase      , UpperCAmelCase	      = knapsack_with_example_solution(w, wt, val)
	assert optimal_solution == 8
	assert optimal_subset == {3, 4}
	print('''optimal_value = ''', optimal_solution)
	print('''An optimal subset corresponding to the optimal value''', optimal_subset)

