


"""simple docstring"""







import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def _A						(						lowercase		):







    """simple docstring"""
    a 		=SwinConfig()
    a 		=swin_name.split('''_'''		)

    a 		=name_split[1]
    a 		=int(name_split[4]		)
    a 		=int(name_split[3][-1]		)

    if model_size == "tiny":
        a 		=96
        a 		=(2, 2, 6, 2)
        a 		=(3, 6, 12, 24)
    elif model_size == "small":
        a 		=96
        a 		=(2, 2, 18, 2)
        a 		=(3, 6, 12, 24)
    elif model_size == "base":
        a 		=1_28
        a 		=(2, 2, 18, 2)
        a 		=(4, 8, 16, 32)
    else:
        a 		=1_92
        a 		=(2, 2, 18, 2)
        a 		=(6, 12, 24, 48)

    if "in22k" in swin_name:
        a 		=2_18_41
    else:
        a 		=10_00
        a 		='''huggingface/label-files'''
        a 		='''imagenet-1k-id2label.json'''
        a 		=json.load(open(hf_hub_download(lowercase       ,    lowercase       ,    repo_type='''dataset'''		)       ,    '''r'''		)		)
        a 		={int(lowercase		): v for k, v in idalabel.items()}
        a 		=idalabel
        a 		={v: k for k, v in idalabel.items()}

    a 		=img_size
    a 		=num_classes
    a 		=embed_dim
    a 		=depths
    a 		=num_heads
    a 		=window_size

    return config
def _A						(						lowercase		):







    """simple docstring"""
    if "patch_embed.proj" in name:
        a 		=name.replace('''patch_embed.proj'''       ,    '''embeddings.patch_embeddings.projection'''		)
    if "patch_embed.norm" in name:
        a 		=name.replace('''patch_embed.norm'''       ,    '''embeddings.norm'''		)
    if "layers" in name:
        a 		='''encoder.''' + name
    if "attn.proj" in name:
        a 		=name.replace('''attn.proj'''       ,    '''attention.output.dense'''		)
    if "attn" in name:
        a 		=name.replace('''attn'''       ,    '''attention.self'''		)
    if "norm1" in name:
        a 		=name.replace('''norm1'''       ,    '''layernorm_before'''		)
    if "norm2" in name:
        a 		=name.replace('''norm2'''       ,    '''layernorm_after'''		)
    if "mlp.fc1" in name:
        a 		=name.replace('''mlp.fc1'''       ,    '''intermediate.dense'''		)
    if "mlp.fc2" in name:
        a 		=name.replace('''mlp.fc2'''       ,    '''output.dense'''		)

    if name == "norm.weight":
        a 		='''layernorm.weight'''
    if name == "norm.bias":
        a 		='''layernorm.bias'''

    if "head" in name:
        a 		=name.replace('''head'''       ,    '''classifier'''		)
    else:
        a 		='''swin.''' + name

    return name
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    for key in orig_state_dict.copy().keys():
        a 		=orig_state_dict.pop(lowercase		)

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

            if "weight" in key:
                a 		=val[:dim, :]
                a 		=val[
                    dim : dim * 2, :
                ]
                a 		=val[-dim:, :]
            else:
                a 		=val[
                    :dim
                ]
                a 		=val[
                    dim : dim * 2
                ]
                a 		=val[
                    -dim:
                ]
        else:
            a 		=val

    return orig_state_dict
def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=timm.create_model(lowercase       ,    pretrained=lowercase		)
    timm_model.eval()

    a 		=get_swin_config(lowercase		)
    a 		=SwinForImageClassification(lowercase		)
    model.eval()

    a 		=convert_state_dict(timm_model.state_dict()       ,    lowercase		)
    model.load_state_dict(lowercase		)

    a 		='''http://images.cocodataset.org/val2017/000000039769.jpg'''

    a 		=AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''       ,    '''-'''		)		)		)
    a 		=Image.open(requests.get(lowercase       ,    stream=lowercase		).raw		)
    a 		=image_processor(images=lowercase       ,    return_tensors='''pt'''		)

    a 		=timm_model(inputs['''pixel_values''']		)
    a 		=model(**lowercase		).logits

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

    print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''		)
    model.save_pretrained(lowercase		)

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


if __name__ == "__main__":
       lowerCamelCase_   :   Tuple									=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--swin_name""",
           default="""swin_tiny_patch4_window7_224""",
           type=str,
           help="""Name of the Swin 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."""
       )

       lowerCamelCase_   :   str									=  parser.parse_args()
       convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
'''simple docstring'''




from collections import defaultdict




def 							lowerCamelCase	(      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					str )			->      bool:
							lowercase_					:			Tuple										=       first_str.lower().strip()
							lowercase_					:			List[Any]										=       second_str.lower().strip()

							# Remove whitespace
							lowercase_					:			Dict										=       first_str.replace(""" """      ,      """""" )
							lowercase_					:			Tuple										=       second_str.replace(""" """      ,      """""" )

							# Strings of different lengths are not anagrams
							if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
														return False

							# Default values for count should be 0
							lowercase_					:			defaultdict[str, int]										=       defaultdict(UpperCAmelCase__ )

							# For each character in input strings,
							# increment count in the corresponding
							for i in range(len(UpperCAmelCase__ ) ):
														count[first_str[i]] += 1
														count[second_str[i]] -= 1

							return all(_count == 0 for _count in count.values() )


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						_lowercase						: int						=						input("Enter the first string ").strip()
						_lowercase						: Tuple						=						input("Enter the second string ").strip()

						_lowercase						: Union[str, Any]						=						check_anagrams(input_a, input_b)
						print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")





