





'''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()
_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				torch.device("cpu")


def 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   "http://images.cocodataset.org/val2017/000000039769.jpg"
    _lowerCAmelCase						      =   Image.open(requests.get(SCREAMING_SNAKE_CASE_		,							stream=SCREAMING_SNAKE_CASE_     ).raw     )
    return im


def 						__a(SCREAMING_SNAKE_CASE_	:       Any     ):



    '''simple docstring'''





    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 						__a(SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Tuple		,							SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   dct.pop(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   val


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[int]     ):



    '''simple docstring'''





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


@torch.no_grad()
def 						__a(SCREAMING_SNAKE_CASE_	:       List[Any]		,							SCREAMING_SNAKE_CASE_	:       List[Any]		,							SCREAMING_SNAKE_CASE_	:       Any     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    _lowerCAmelCase						      =   1000
    _lowerCAmelCase						      =   "huggingface/label-files"
    _lowerCAmelCase						      =   "imagenet-1k-id2label.json"
    _lowerCAmelCase						      =   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							repo_type="dataset"     )		,							"r"     )     )
    _lowerCAmelCase						      =   {int(SCREAMING_SNAKE_CASE_     ): v for k, v in idalabel.items()}
    _lowerCAmelCase						      =   idalabel
    _lowerCAmelCase						      =   {v: k for k, v in idalabel.items()}

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        _lowerCAmelCase						      =   [3, 3, 6, 4]
        _lowerCAmelCase						      =   [48, 56, 112, 220]

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

    elif swiftformer_name == "swiftformer_l1":
        _lowerCAmelCase						      =   [4, 3, 10, 5]
        _lowerCAmelCase						      =   [48, 96, 192, 384]

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

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

    _lowerCAmelCase						      =   create_rename_keys(SCREAMING_SNAKE_CASE_     )
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )

    # load HuggingFace model
    _lowerCAmelCase						      =   SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_     ).eval()
    hf_model.load_state_dict(SCREAMING_SNAKE_CASE_     )

    # prepare test inputs
    _lowerCAmelCase						      =   prepare_img()
    _lowerCAmelCase						      =   ViTImageProcessor.from_pretrained("preprocessor_config"     )
    _lowerCAmelCase						      =   processor(images=SCREAMING_SNAKE_CASE_		,							return_tensors="pt"     )

    # compare outputs from both models
    _lowerCAmelCase						      =   get_expected_output(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   hf_model(inputs["pixel_values"]     ).logits

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

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


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

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









'''simple docstring'''


import math

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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[Any]								=       "data2vec-audio"




 def __init__(						self      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=768      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=3072      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512)      ,  _lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2)      ,  _lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2)      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=19      ,  _lowerCAmelCase=5      ,  _lowerCAmelCase=0.05      ,  _lowerCAmelCase=10      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=10      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase="sum"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=256      ,  _lowerCAmelCase=(512, 512, 512, 512, 1500)      ,  _lowerCAmelCase=(5, 3, 3, 1, 1)      ,  _lowerCAmelCase=(1, 2, 3, 1, 1)      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=None      ,  **_lowerCAmelCase      ,  )     ->				Dict:
     super().__init__(**_lowerCAmelCase      ,  pad_token_id=_lowerCAmelCase      ,  bos_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase		)
     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   feat_extract_activation
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   conv_bias
     _lowerCAmelCase						      =   num_conv_pos_embeddings
     _lowerCAmelCase						      =   num_conv_pos_embedding_groups
     _lowerCAmelCase						      =   conv_pos_kernel_size
     _lowerCAmelCase						      =   len(self.conv_dim		)
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   hidden_dropout
     _lowerCAmelCase						      =   attention_dropout
     _lowerCAmelCase						      =   activation_dropout
     _lowerCAmelCase						      =   feat_proj_dropout
     _lowerCAmelCase						      =   final_dropout
     _lowerCAmelCase						      =   layerdrop
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   use_weighted_layer_sum

     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)`, but is `len(config.conv_dim) ="
             f''' {len(self.conv_dim		)}`, `len(config.conv_stride) = {len(self.conv_stride		)}`,'''
             f''' `len(config.conv_kernel) = {len(self.conv_kernel		)}`.'''		)

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     _lowerCAmelCase						      =   mask_time_prob
     _lowerCAmelCase						      =   mask_time_length
     _lowerCAmelCase						      =   mask_time_min_masks
     _lowerCAmelCase						      =   mask_feature_prob
     _lowerCAmelCase						      =   mask_feature_length
     _lowerCAmelCase						      =   mask_feature_min_masks

     # ctc loss
     _lowerCAmelCase						      =   ctc_loss_reduction
     _lowerCAmelCase						      =   ctc_zero_infinity

     # adapter
     _lowerCAmelCase						      =   add_adapter
     _lowerCAmelCase						      =   adapter_kernel_size
     _lowerCAmelCase						      =   adapter_stride
     _lowerCAmelCase						      =   num_adapter_layers
     _lowerCAmelCase						      =   output_hidden_size or hidden_size

     # SequenceClassification-specific parameter. Feel free to ignore for other classes.
     _lowerCAmelCase						      =   classifier_proj_size

     # XVector-specific parameters. Feel free to ignore for other classes.
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   xvector_output_dim





 @property
 def 				_snake_case (						self		)     ->				str:
     return math.prod(self.conv_stride		)


