





'''simple docstring'''






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()
snake_case_     =	logging.get_logger(__name__)

snake_case_     =	torch.device("""cpu""")
def 							_lowerCamelCase(  )		->  Union[str, Any]:
    A       :       Union[str, Any]							=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A       :       int							=						Image.open(requests.get(UpperCamelCase__    ,       stream=UpperCamelCase__		).raw		)
    return im
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]		)		->  str:
    if swiftformer_name == "swiftformer_xs":
        return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]		)

    elif swiftformer_name == "swiftformer_s":
        return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]		)

    elif swiftformer_name == "swiftformer_l1":
        return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]		)

    elif swiftformer_name == "swiftformer_l3":
        return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]		)
def 							_lowerCamelCase(  UpperCamelCase__    : Dict    ,       UpperCamelCase__    : List[Any]    ,       UpperCamelCase__    : str		)		->  int:
    A       :       Any							=						dct.pop(UpperCamelCase__		)
    A       :       Optional[int]							=						val
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]		)		->  str:
    A       :       Optional[Any]							=						[]
    for k in state_dict.keys():
        A       :       str							=						k
        if ".pwconv" in k:
            A       :       Optional[Any]							=						k_new.replace('''.pwconv'''    ,       '''.point_wise_conv'''		)
        if ".dwconv" in k:
            A       :       List[str]							=						k_new.replace('''.dwconv'''    ,       '''.depth_wise_conv'''		)
        if ".Proj." in k:
            A       :       Dict							=						k_new.replace('''.Proj.'''    ,       '''.proj.'''		)
        if "patch_embed" in k_new:
            A       :       List[str]							=						k_new.replace('''patch_embed'''    ,       '''swiftformer.patch_embed.patch_embedding'''		)
        if "network" in k_new:
            A       :       int							=						k_new.split('''.'''		)
            if ls[2].isdigit():
                A       :       Optional[int]							=						'''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]		)
            else:
                A       :       Optional[int]							=						k_new.replace('''network'''    ,       '''swiftformer.encoder.network'''		)
        rename_keys.append((k, k_new)		)
    return rename_keys
@torch.no_grad()
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[Any]    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : Dict		)		->  Dict:
    A       :       int							=						SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    A       :       Any							=						1_000
    A       :       int							=						'''huggingface/label-files'''
    A       :       Tuple							=						'''imagenet-1k-id2label.json'''
    A       :       Tuple							=						json.load(open(hf_hub_download(UpperCamelCase__    ,       UpperCamelCase__    ,       repo_type='''dataset'''		)    ,       '''r'''		)		)
    A       :       List[Any]							=						{int(UpperCamelCase__		): v for k, v in idalabel.items()}
    A       :       Optional[Any]							=						idalabel
    A       :       Optional[Any]							=						{v: k for k, v in idalabel.items()}

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        A       :       Optional[Any]							=						[3, 3, 6, 4]
        A       :       List[str]							=						[48, 56, 112, 220]

    elif swiftformer_name == "swiftformer_s":
        A       :       Optional[int]							=						[3, 3, 9, 6]
        A       :       Any							=						[48, 64, 168, 224]

    elif swiftformer_name == "swiftformer_l1":
        A       :       str							=						[4, 3, 10, 5]
        A       :       Optional[Any]							=						[48, 96, 192, 384]

    elif swiftformer_name == "swiftformer_l3":
        A       :       Tuple							=						[4, 4, 12, 6]
        A       :       Dict							=						[64, 128, 320, 512]

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith('''https'''		):
            A       :       Tuple							=						torch.hub.load_state_dict_from_url(UpperCamelCase__    ,       map_location='''cpu'''    ,       check_hash=UpperCamelCase__		)
        else:
            A       :       Dict							=						torch.load(UpperCamelCase__    ,       map_location='''cpu'''		)
    A       :       Optional[int]							=						checkpoint

    A       :       str							=						create_rename_keys(UpperCamelCase__		)
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)

    # load HuggingFace model
    A       :       Any							=						SwiftFormerForImageClassification(UpperCamelCase__		).eval()
    hf_model.load_state_dict(UpperCamelCase__		)

    # prepare test inputs
    A       :       str							=						prepare_img()
    A       :       Optional[int]							=						ViTImageProcessor.from_pretrained('''preprocessor_config'''		)
    A       :       Optional[int]							=						processor(images=UpperCamelCase__    ,       return_tensors='''pt'''		)

    # compare outputs from both models
    A       :       Tuple							=						get_expected_output(UpperCamelCase__		)
    A       :       Dict							=						hf_model(inputs['''pixel_values''']		).logits

    assert hf_logits.shape == torch.Size([1, 1_000]		)
    assert torch.allclose(hf_logits[0, 0:5]    ,       UpperCamelCase__    ,       atol=1e-3		)

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


if __name__ == "__main__":
     snake_case_     =	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.""")

     snake_case_     =	parser.parse_args()
     convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







'''simple docstring'''






def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  None:
    A       :       List[Any]							=						generate_pascal_triangle(UpperCamelCase__		)
    for row_idx in range(UpperCamelCase__		):
        # 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(  UpperCamelCase__    : int		)		->  list[list[int]]:

    if not isinstance(UpperCamelCase__    ,       UpperCamelCase__		):
        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'''		)

    A       :       list[list[int]]							=						[]
    for current_row_idx in range(UpperCamelCase__		):
        A       :       Optional[int]							=						populate_current_row(UpperCamelCase__    ,       UpperCamelCase__		)
        triangle.append(UpperCamelCase__		)
    return triangle
def 							_lowerCamelCase(  UpperCamelCase__    : list[list[int]]    ,       UpperCamelCase__    : int		)		->  list[int]:
    A       :       Dict							=						[-1] * (current_row_idx + 1)
    # first and last elements of current row are equal to 1
    A,	A       :       Union[str, Any]							=						1, 1
    for current_col_idx in range(1    ,       UpperCamelCase__		):
        calculate_current_element(
            UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
    return current_row
def 							_lowerCamelCase(  UpperCamelCase__    : list[list[int]]    ,       UpperCamelCase__    : list[int]    ,       UpperCamelCase__    : int    ,       UpperCamelCase__    : int    ,       )		->  None:
    A       :       Dict							=						triangle[current_row_idx - 1][current_col_idx - 1]
    A       :       Any							=						triangle[current_row_idx - 1][current_col_idx]
    A       :       Optional[int]							=						above_to_left_elt + above_to_right_elt
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  list[list[int]]:

    if not isinstance(UpperCamelCase__    ,       UpperCamelCase__		):
        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'''		)

    A       :       list[list[int]]							=						[[1]]

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

    return result
def 							_lowerCamelCase(  )		->  None:
    from collections.abc import Callable
    from timeit import timeit

    def benchmark_a_function(UpperCamelCase__    : Callable    ,       UpperCamelCase__    : int		) -> None:
        A       :       Dict							=						F'''{func.__name__}({value})'''
        A       :       List[Any]							=						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(15		):  # (1, 7, 14):
        for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
            benchmark_a_function(UpperCamelCase__    ,       UpperCamelCase__		)
        print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()
