





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()
a_			=      logging.get_logger(__name__)

a_			=      torch.device("""cpu""")







def 		__lowercase					(       )       ->int:



    '''simple docstring'''







    __A   :						str									=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __A   :						Optional[int]									=					Image.open(requests.get(snake_case_    ,stream=snake_case_						).raw						)
    return im







def 		__lowercase					(       snake_case_  :  Optional[int]						)       ->List[Any]:



    '''simple docstring'''







    if swiftformer_name == "swiftformer_xs":
        return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]						)

    elif swiftformer_name == "swiftformer_s":
        return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]						)

    elif swiftformer_name == "swiftformer_l1":
        return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]						)

    elif swiftformer_name == "swiftformer_l3":
        return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]						)







def 		__lowercase					(       snake_case_  :  Dict    ,snake_case_  :  int    ,snake_case_  :  Tuple						)       ->Union[str, Any]:



    '''simple docstring'''







    __A   :						Any									=					dct.pop(snake_case_						)
    __A   :						Optional[int]									=					val







def 		__lowercase					(       snake_case_  :  Dict						)       ->Dict:



    '''simple docstring'''







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







@torch.no_grad()
def 		__lowercase					(       snake_case_  :  Union[str, Any]    ,snake_case_  :  str    ,snake_case_  :  Union[str, Any]						)       ->Optional[int]:



    '''simple docstring'''







    __A   :						str									=					SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    __A   :						Union[str, Any]									=					1000
    __A   :						Optional[Any]									=					'''huggingface/label-files'''
    __A   :						List[Any]									=					'''imagenet-1k-id2label.json'''
    __A   :						List[str]									=					json.load(open(hf_hub_download(snake_case_    ,snake_case_    ,repo_type='''dataset'''						)    ,'''r'''						)						)
    __A   :						Tuple									=					{int(snake_case_						): v for k, v in idalabel.items()}
    __A   :						List[str]									=					idalabel
    __A   :						Dict									=					{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   :						str									=					[48, 56, 112, 220]

    elif swiftformer_name == "swiftformer_s":
        __A   :						str									=					[3, 3, 9, 6]
        __A   :						List[str]									=					[48, 64, 168, 224]

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

    elif swiftformer_name == "swiftformer_l3":
        __A   :						int									=					[4, 4, 12, 6]
        __A   :						Optional[int]									=					[64, 128, 320, 512]

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith('''https'''						):
            __A   :						List[Any]									=					torch.hub.load_state_dict_from_url(snake_case_    ,map_location='''cpu'''    ,check_hash=snake_case_						)
        else:
            __A   :						Any									=					torch.load(snake_case_    ,map_location='''cpu'''						)
    __A   :						Optional[Any]									=					checkpoint

    __A   :						Union[str, Any]									=					create_rename_keys(snake_case_						)
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(snake_case_    ,snake_case_    ,snake_case_						)

    # load HuggingFace model
    __A   :						Dict									=					SwiftFormerForImageClassification(snake_case_						).eval()
    hf_model.load_state_dict(snake_case_						)

    # prepare test inputs
    __A   :						str									=					prepare_img()
    __A   :						Tuple									=					ViTImageProcessor.from_pretrained('''preprocessor_config'''						)
    __A   :						Optional[Any]									=					processor(images=snake_case_    ,return_tensors='''pt'''						)

    # compare outputs from both models
    __A   :						Dict									=					get_expected_output(snake_case_						)
    __A   :						List[str]									=					hf_model(inputs['''pixel_values''']						).logits

    assert hf_logits.shape == torch.Size([1, 1000]						)
    assert torch.allclose(hf_logits[0, 0:5]    ,snake_case_    ,atol=1e-3						)

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


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

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











"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_			=      logging.get_logger(__name__)

a_			=      {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					"""sew-d"""

 def __init__(   self ,						__lowerCamelCase=32 ,						__lowerCamelCase=768 ,						__lowerCamelCase=12 ,						__lowerCamelCase=12 ,						__lowerCamelCase=3072 ,						__lowerCamelCase=2 ,						__lowerCamelCase=512 ,						__lowerCamelCase=256 ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=("p2c", "c2p") ,						__lowerCamelCase="layer_norm" ,						__lowerCamelCase="gelu_python" ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-7 ,						__lowerCamelCase=1e-5 ,						__lowerCamelCase="group" ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,						__lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,						__lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,						__lowerCamelCase=False ,						__lowerCamelCase=128 ,						__lowerCamelCase=16 ,						__lowerCamelCase=True ,						__lowerCamelCase=0.0_5 ,						__lowerCamelCase=10 ,						__lowerCamelCase=2 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=10 ,						__lowerCamelCase=0 ,						__lowerCamelCase="mean" ,						__lowerCamelCase=False ,						__lowerCamelCase=False ,						__lowerCamelCase=256 ,						__lowerCamelCase=0 ,						__lowerCamelCase=1 ,						__lowerCamelCase=2 ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase ,						pad_token_id=__lowerCamelCase ,						bos_token_id=__lowerCamelCase ,						eos_token_id=__lowerCamelCase  )
     __A   :						str									=					hidden_size
     __A   :						List[Any]									=					feat_extract_norm
     __A   :						Tuple									=					feat_extract_activation
     __A   :						Dict									=					list(__lowerCamelCase  )
     __A   :						int									=					list(__lowerCamelCase  )
     __A   :						List[Any]									=					list(__lowerCamelCase  )
     __A   :						Any									=					conv_bias
     __A   :						List[Any]									=					num_conv_pos_embeddings
     __A   :						Any									=					num_conv_pos_embedding_groups
     __A   :						Optional[Any]									=					len(self.conv_dim  )
     __A   :						int									=					num_hidden_layers
     __A   :						Union[str, Any]									=					intermediate_size
     __A   :						Union[str, Any]									=					squeeze_factor
     __A   :						int									=					max_position_embeddings
     __A   :						Tuple									=					position_buckets
     __A   :						Tuple									=					share_att_key
     __A   :						List[str]									=					relative_attention
     __A   :						Optional[Any]									=					norm_rel_ebd
     __A   :						Dict									=					list(__lowerCamelCase  )
     __A   :						str									=					hidden_act
     __A   :						List[str]									=					num_attention_heads
     __A   :						Union[str, Any]									=					hidden_dropout
     __A   :						Optional[int]									=					attention_dropout
     __A   :						Optional[Any]									=					activation_dropout
     __A   :						List[str]									=					feat_proj_dropout
     __A   :						str									=					final_dropout
     __A   :						Tuple									=					layer_norm_eps
     __A   :						int									=					feature_layer_norm_eps
     __A   :						Optional[int]									=					initializer_range
     __A   :						Dict									=					vocab_size

     if (
         (len(self.conv_stride  ) != self.num_feat_extract_layers)
         or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
         or (len(self.conv_dim  ) != self.num_feat_extract_layers)
     ):
         raise ValueError(
             '''Configuration for convolutional layers is incorrect.'''
             '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
             F"""but is `len(config.conv_dim) = {len(self.conv_dim  )}`, `len(config.conv_stride)"""
             F"""= {len(self.conv_stride  )}`, `len(config.conv_kernel) = {len(self.conv_kernel  )}`."""  )

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     __A   :						int									=					apply_spec_augment
     __A   :						Any									=					mask_time_prob
     __A   :						Optional[int]									=					mask_time_length
     __A   :						Any									=					mask_time_min_masks
     __A   :						int									=					mask_feature_prob
     __A   :						Tuple									=					mask_feature_length
     __A   :						Dict									=					mask_feature_min_masks

     # ctc loss
     __A   :						Tuple									=					ctc_loss_reduction
     __A   :						Union[str, Any]									=					ctc_zero_infinity

     # sequence classification
     __A   :						Tuple									=					use_weighted_layer_sum
     __A   :						List[str]									=					classifier_proj_size







 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return functools.reduce(operator.mul ,						self.conv_stride ,						1  )




