
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase					:    Tuple           = logging.get_logger(__name__)







def 		A_(     A						:		Union[str, Any]		,     A						:		Dict		,     A						:		Dict		,     A						:		Any):
							UpperCamelCase   			=     original_name.split('.')[0]
							UpperCamelCase   			=     key.split('.')
							UpperCamelCase   			=     int(key_list[key_list.index(A) - 2])
							UpperCamelCase   			=     int(key_list[key_list.index(A) - 1])
							UpperCamelCase   			=     orig_block_num - offset

							UpperCamelCase   			=     key.replace(f'''{orig_block_num}.{layer_num}.{original_name}'''		,     f'''block.{new_block_num}.{layer_num}.{new_name}''')
							return key







def 		A_(     A						:		Union[str, Any]):
							UpperCamelCase   			=     OrderedDict()
							UpperCamelCase			,	UpperCamelCase   			=     0, 0
							for key, value in state_dict.items():
														if key.startswith('network'):
																					UpperCamelCase   			=     key.replace('network'		,     'poolformer.encoder')
														if "proj" in key:
																					# Works for the first embedding as well as the internal embedding layers
																					if key.endswith('bias') and "patch_embed" not in key:
																												patch_emb_offset += 1
																					UpperCamelCase   			=     key[: key.find('proj')]
																					UpperCamelCase   			=     key.replace(A		,     f'''patch_embeddings.{total_embed_found}.''')
																					UpperCamelCase   			=     key.replace('proj'		,     'projection')
																					if key.endswith('bias'):
																												total_embed_found += 1
														if "patch_embeddings" in key:
																					UpperCamelCase   			=     'poolformer.encoder.' + key
														if "mlp.fc1" in key:
																					UpperCamelCase   			=     replace_key_with_offset(A		,     A		,     'mlp.fc1'		,     'output.conv1')
														if "mlp.fc2" in key:
																					UpperCamelCase   			=     replace_key_with_offset(A		,     A		,     'mlp.fc2'		,     'output.conv2')
														if "norm1" in key:
																					UpperCamelCase   			=     replace_key_with_offset(A		,     A		,     'norm1'		,     'before_norm')
														if "norm2" in key:
																					UpperCamelCase   			=     replace_key_with_offset(A		,     A		,     'norm2'		,     'after_norm')
														if "layer_scale_1" in key:
																					UpperCamelCase   			=     replace_key_with_offset(A		,     A		,     'layer_scale_1'		,     'layer_scale_1')
														if "layer_scale_2" in key:
																					UpperCamelCase   			=     replace_key_with_offset(A		,     A		,     'layer_scale_2'		,     'layer_scale_2')
														if "head" in key:
																					UpperCamelCase   			=     key.replace('head'		,     'classifier')
														UpperCamelCase   			=     value
							return new_state_dict







def 		A_(     ):
							UpperCamelCase   			=     'http://images.cocodataset.org/val2017/000000039769.jpg'
							UpperCamelCase   			=     Image.open(requests.get(A		,     stream=A).raw)

							return image







@torch.no_grad()
def 		A_(     A						:		Union[str, Any]		,     A						:		List[Any]		,     A						:		List[Any]):
							UpperCamelCase   			=     PoolFormerConfig()

							# set attributes based on model_name
							UpperCamelCase   			=     'huggingface/label-files'
							UpperCamelCase   			=     model_name[-3:]
							UpperCamelCase   			=     1000
							UpperCamelCase   			=     'imagenet-1k-id2label.json'
							UpperCamelCase   			=     (1, 1000)

							# set config attributes
							UpperCamelCase   			=     json.load(open(hf_hub_download(A		,     A		,     repo_type='dataset')		,     'r'))
							UpperCamelCase   			=     {int(A): v for k, v in idalabel.items()}
							UpperCamelCase   			=     idalabel
							UpperCamelCase   			=     {v: k for k, v in idalabel.items()}
							if size == "s12":
														UpperCamelCase   			=     [2, 2, 6, 2]
														UpperCamelCase   			=     [64, 128, 320, 512]
														UpperCamelCase   			=     4.0
														UpperCamelCase   			=     0.9
							elif size == "s24":
														UpperCamelCase   			=     [4, 4, 12, 4]
														UpperCamelCase   			=     [64, 128, 320, 512]
														UpperCamelCase   			=     4.0
														UpperCamelCase   			=     0.9
							elif size == "s36":
														UpperCamelCase   			=     [6, 6, 18, 6]
														UpperCamelCase   			=     [64, 128, 320, 512]
														UpperCamelCase   			=     4.0
														UpperCamelCase   			=     1E-6
														UpperCamelCase   			=     0.9
							elif size == "m36":
														UpperCamelCase   			=     [6, 6, 18, 6]
														UpperCamelCase   			=     [96, 192, 384, 768]
														UpperCamelCase   			=     4.0
														UpperCamelCase   			=     1E-6
														UpperCamelCase   			=     0.95
							elif size == "m48":
														UpperCamelCase   			=     [8, 8, 24, 8]
														UpperCamelCase   			=     [96, 192, 384, 768]
														UpperCamelCase   			=     4.0
														UpperCamelCase   			=     1E-6
														UpperCamelCase   			=     0.95
							else:
														raise ValueError(f'''Size {size} not supported''')

							# load image processor
							UpperCamelCase   			=     PoolFormerImageProcessor(crop_pct=A)

							# Prepare image
							UpperCamelCase   			=     prepare_img()
							UpperCamelCase   			=     image_processor(images=A		,     return_tensors='pt').pixel_values

							logger.info(f'''Converting model {model_name}...''')

							# load original state dict
							UpperCamelCase   			=     torch.load(A		,     map_location=torch.device('cpu'))

							# rename keys
							UpperCamelCase   			=     rename_keys(A)

							# create HuggingFace model and load state dict
							UpperCamelCase   			=     PoolFormerForImageClassification(A)
							model.load_state_dict(A)
							model.eval()

							# Define image processor
							UpperCamelCase   			=     PoolFormerImageProcessor(crop_pct=A)
							UpperCamelCase   			=     image_processor(images=prepare_img()		,     return_tensors='pt').pixel_values

							# forward pass
							UpperCamelCase   			=     model(A)
							UpperCamelCase   			=     outputs.logits

							# define expected logit slices for different models
							if size == "s12":
														UpperCamelCase   			=     torch.tensor([-0.3_045, -0.6_758, -0.4_869])
							elif size == "s24":
														UpperCamelCase   			=     torch.tensor([0.4_402, -0.1_374, -0.8_045])
							elif size == "s36":
														UpperCamelCase   			=     torch.tensor([-0.6_080, -0.5_133, -0.5_898])
							elif size == "m36":
														UpperCamelCase   			=     torch.tensor([0.3_952, 0.2_263, -1.2_668])
							elif size == "m48":
														UpperCamelCase   			=     torch.tensor([0.1_167, -0.0_656, -0.3_423])
							else:
														raise ValueError(f'''Size {size} not supported''')

							# verify logits
							assert logits.shape == expected_shape
							assert torch.allclose(logits[0, :3]		,     A		,     atol=1E-2)

							# finally, save model and image processor
							logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''')
							Path(A).mkdir(exist_ok=A)
							model.save_pretrained(A)
							print(f'''Saving image processor to {pytorch_dump_folder_path}''')
							image_processor.save_pretrained(A)


if __name__ == "__main__":
						lowerCAmelCase					:    int           = argparse.ArgumentParser()

						parser.add_argument(
						    '--model_name',
						    default='poolformer_s12',
						    type=str,
						    help='Name of the model you\'d like to convert.',
						)
						parser.add_argument(
						    '--checkpoint_path', default=None, 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 folder to output PyTorch model.'
						)
						lowerCAmelCase					:    Union[str, Any]           = parser.parse_args()
						convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)







def 		UpperCamelCase   (lowercase_:					str   )	-> Optional[Any]:
	A__				:       Optional[int]	  =						[]
	A__				:       Optional[int]	  =						set({"""(""", """[""", """{"""}   )
	A__				:       str	  =						set({""")""", """]""", """}"""}   )
	A__				:       Optional[Any]	  =						{"""{""": """}""", """[""": """]""", """(""": """)"""}

	for i in range(len(lowercase_   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(lowercase_   ) == 0 or (len(lowercase_   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(lowercase_   ) == 0

def 		UpperCamelCase   ()	-> List[str]:
	A__				:       Optional[Any]	  =						input("""Enter sequence of brackets: """   )
	if is_balanced(lowercase_   ):
		print(lowercase_       , """is balanced"""   )
	else:
		print(lowercase_       , """is not balanced"""   )


if __name__ == "__main__":
	main()



