


import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__  :Optional[int]             =							logging.get_logger(__name__)

lowerCAmelCase__  :Optional[Any]             =							torch.device('''cpu''')

def 	lowerCAmelCase__			(      )  ->		Any:


							'''simple docstring'''





							_UpperCAmelCase					=      'http://images.cocodataset.org/val2017/000000039769.jpg'
							_UpperCAmelCase					=      Image.open(requests.get(a__      ,  stream=a__	).raw	)
							return im

def 	lowerCAmelCase__			(      a__:		List[Any]	)  ->		Tuple:


							'''simple docstring'''





							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01]	)

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01]	)

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02]	)

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02]	)

def 	lowerCAmelCase__			(      a__:		int      ,  a__:		Union[str, Any]      ,  a__:		Optional[Any]	)  ->		Dict:


							'''simple docstring'''





							_UpperCAmelCase					=      dct.pop(a__	)
							_UpperCAmelCase					=      val

def 	lowerCAmelCase__			(      a__:		Union[str, Any]	)  ->		List[str]:


							'''simple docstring'''





							_UpperCAmelCase					=      []
							for k in state_dict.keys():
														_UpperCAmelCase					=      k
														if ".pwconv" in k:
																					_UpperCAmelCase					=      k_new.replace('.pwconv'      ,  '.point_wise_conv'	)
														if ".dwconv" in k:
																					_UpperCAmelCase					=      k_new.replace('.dwconv'      ,  '.depth_wise_conv'	)
														if ".Proj." in k:
																					_UpperCAmelCase					=      k_new.replace('.Proj.'      ,  '.proj.'	)
														if "patch_embed" in k_new:
																					_UpperCAmelCase					=      k_new.replace('patch_embed'      ,  'swiftformer.patch_embed.patch_embedding'	)
														if "network" in k_new:
																					_UpperCAmelCase					=      k_new.split('.'	)
																					if ls[2].isdigit():
																												_UpperCAmelCase					=      'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]	)
																					else:
																												_UpperCAmelCase					=      k_new.replace('network'      ,  'swiftformer.encoder.network'	)
														rename_keys.append((k, k_new)	)
							return rename_keys

@torch.no_grad()
def 	lowerCAmelCase__			(      a__:		Optional[int]      ,  a__:		str      ,  a__:		Dict	)  ->		Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase					=      SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							_UpperCAmelCase					=      1_0_0_0
							_UpperCAmelCase					=      'huggingface/label-files'
							_UpperCAmelCase					=      'imagenet-1k-id2label.json'
							_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()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														_UpperCAmelCase					=      [3, 3, 6, 4]
														_UpperCAmelCase					=      [4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														_UpperCAmelCase					=      [3, 3, 9, 6]
														_UpperCAmelCase					=      [4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														_UpperCAmelCase					=      [4, 3, 1_0, 5]
														_UpperCAmelCase					=      [4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														_UpperCAmelCase					=      [4, 4, 1_2, 6]
														_UpperCAmelCase					=      [6_4, 1_2_8, 3_2_0, 5_1_2]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith('https'	):
																					_UpperCAmelCase					=      torch.hub.load_state_dict_from_url(a__      ,  map_location='cpu'      ,  check_hash=a__	)
														else:
																					_UpperCAmelCase					=      torch.load(a__      ,  map_location='cpu'	)
							_UpperCAmelCase					=      checkpoint

							_UpperCAmelCase					=      create_rename_keys(a__	)
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(a__      ,  a__      ,  a__	)

							# load HuggingFace model
							_UpperCAmelCase					=      SwiftFormerForImageClassification(a__	).eval()
							hf_model.load_state_dict(a__	)

							# prepare test inputs
							_UpperCAmelCase					=      prepare_img()
							_UpperCAmelCase					=      ViTImageProcessor.from_pretrained('preprocessor_config'	)
							_UpperCAmelCase					=      processor(images=a__      ,  return_tensors='pt'	)

							# compare outputs from both models
							_UpperCAmelCase					=      get_expected_output(a__	)
							_UpperCAmelCase					=      hf_model(inputs['pixel_values']	).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]	)
							assert torch.allclose(hf_logits[0, 0:5]      ,  a__      ,  atol=1e-3	)

							Path(a__	).mkdir(exist_ok=a__	)
							print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''	)
							hf_model.save_pretrained(a__	)


if __name__ == "__main__":
		lowerCAmelCase__  :Optional[Any]             =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--swiftformer_name''',
		    default='''swiftformer_xs''',
		    choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
		    type=str,
		    help='''Name of the SwiftFormer model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default='''./converted_outputs/''',
		    type=str,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

		lowerCAmelCase__  :Optional[int]             =							parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)




def 	lowerCAmelCase__			(      a__:		int	)  ->		None:


							'''simple docstring'''





							_UpperCAmelCase					=      generate_pascal_triangle(a__	)
							for row_idx in range(a__	):
														# Print left spaces
														for _ in range(num_rows - row_idx - 1	):
																					print(end=' '	)
														# Print row values
														for col_idx in range(row_idx + 1	):
																					if col_idx != row_idx:
																												print(triangle[row_idx][col_idx]      ,  end=' '	)
																					else:
																												print(triangle[row_idx][col_idx]      ,  end=''	)
														print()

def 	lowerCAmelCase__			(      a__:		int	)  ->		list[list[int]]:


							'''simple docstring'''





							if not isinstance(a__      ,  a__	):
														raise TypeError('The input value of \'num_rows\' should be \'int\''	)

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'	)

							_UpperCAmelCase					=      []
							for current_row_idx in range(a__	):
														_UpperCAmelCase					=      populate_current_row(a__      ,  a__	)
														triangle.append(a__	)
							return triangle

def 	lowerCAmelCase__			(      a__:		list[list[int]]      ,  a__:		int	)  ->		list[int]:


							'''simple docstring'''





							_UpperCAmelCase					=      [-1] * (current_row_idx + 1)
							# first and last elements of current row are equal to 1
							_UpperCAmelCase		,	_UpperCAmelCase					=      1, 1
							for current_col_idx in range(1      ,  a__	):
														calculate_current_element(
														    a__      ,  a__      ,  a__      ,  a__	)
							return current_row

def 	lowerCAmelCase__			(      a__:		list[list[int]]      ,  a__:		list[int]      ,  a__:		int      ,  a__:		int      ,  )  ->		None:


							'''simple docstring'''





							_UpperCAmelCase					=      triangle[current_row_idx - 1][current_col_idx - 1]
							_UpperCAmelCase					=      triangle[current_row_idx - 1][current_col_idx]
							_UpperCAmelCase					=      above_to_left_elt + above_to_right_elt

def 	lowerCAmelCase__			(      a__:		int	)  ->		list[list[int]]:


							'''simple docstring'''





							if not isinstance(a__      ,  a__	):
														raise TypeError('The input value of \'num_rows\' should be \'int\''	)

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'	)

							_UpperCAmelCase					=      [[1]]

							for row_index in range(1      ,  a__	):
														_UpperCAmelCase					=      [0] + result[-1] + [0]
														_UpperCAmelCase					=      row_index + 1
														# Calculate the number of distinct elements in a row
														_UpperCAmelCase					=      sum(divmod(a__      ,  2	)	)
														_UpperCAmelCase					=      [
														    temp_row[i - 1] + temp_row[i] for i in range(1      ,  distinct_elements + 1	)
														]
														_UpperCAmelCase					=      row_first_half[: (row_index + 1) // 2]
														row_second_half.reverse()
														_UpperCAmelCase					=      row_first_half + row_second_half
														result.append(a__	)

							return result

def 	lowerCAmelCase__			(      )  ->		None:


							'''simple docstring'''





							from collections.abc import Callable
							from timeit import timeit

							def benchmark_a_function(a__:		Callable      ,  a__:		int	) -> None:
														_UpperCAmelCase					=      F'''{func.__name__}({value})'''
														_UpperCAmelCase					=      timeit(F'''__main__.{call}'''      ,  setup='import __main__'	)
														# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
														print(F'''{call:38} -- {timing:.4f} seconds'''	)

							for value in range(1_5	):  # (1, 7, 14):
														for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
																					benchmark_a_function(a__      ,  a__	)
														print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()
