





'''simple docstring'''


import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification


def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[Any]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      SwinvaConfig()
    _lowerCAmelCase				=      swinva_name.split("_"    )

    _lowerCAmelCase				=      name_split[1]
    if "to" in name_split[3]:
        _lowerCAmelCase				=      int(name_split[3][-3:]    )
    else:
        _lowerCAmelCase				=      int(name_split[3]    )
    if "to" in name_split[2]:
        _lowerCAmelCase				=      int(name_split[2][-2:]    )
    else:
        _lowerCAmelCase				=      int(name_split[2][6:]    )

    if model_size == "tiny":
        _lowerCAmelCase				=      96
        _lowerCAmelCase				=      (2, 2, 6, 2)
        _lowerCAmelCase				=      (3, 6, 12, 24)
    elif model_size == "small":
        _lowerCAmelCase				=      96
        _lowerCAmelCase				=      (2, 2, 18, 2)
        _lowerCAmelCase				=      (3, 6, 12, 24)
    elif model_size == "base":
        _lowerCAmelCase				=      128
        _lowerCAmelCase				=      (2, 2, 18, 2)
        _lowerCAmelCase				=      (4, 8, 16, 32)
    else:
        _lowerCAmelCase				=      192
        _lowerCAmelCase				=      (2, 2, 18, 2)
        _lowerCAmelCase				=      (6, 12, 24, 48)

    if "to" in swinva_name:
        _lowerCAmelCase				=      (12, 12, 12, 6)

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        _lowerCAmelCase				=      21841
        _lowerCAmelCase				=      "huggingface/label-files"
        _lowerCAmelCase				=      "imagenet-22k-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()}

    else:
        _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()}

    _lowerCAmelCase				=      img_size
    _lowerCAmelCase				=      num_classes
    _lowerCAmelCase				=      embed_dim
    _lowerCAmelCase				=      depths
    _lowerCAmelCase				=      num_heads
    _lowerCAmelCase				=      window_size

    return config


def 	__a(SCREAMING_SNAKE_CASE_  :					Tuple    ):



    '''simple docstring'''





    if "patch_embed.proj" in name:
        _lowerCAmelCase				=      name.replace("patch_embed.proj"		,       "embeddings.patch_embeddings.projection"    )
    if "patch_embed.norm" in name:
        _lowerCAmelCase				=      name.replace("patch_embed.norm"		,       "embeddings.norm"    )
    if "layers" in name:
        _lowerCAmelCase				=      "encoder." + name
    if "attn.proj" in name:
        _lowerCAmelCase				=      name.replace("attn.proj"		,       "attention.output.dense"    )
    if "attn" in name:
        _lowerCAmelCase				=      name.replace("attn"		,       "attention.self"    )
    if "norm1" in name:
        _lowerCAmelCase				=      name.replace("norm1"		,       "layernorm_before"    )
    if "norm2" in name:
        _lowerCAmelCase				=      name.replace("norm2"		,       "layernorm_after"    )
    if "mlp.fc1" in name:
        _lowerCAmelCase				=      name.replace("mlp.fc1"		,       "intermediate.dense"    )
    if "mlp.fc2" in name:
        _lowerCAmelCase				=      name.replace("mlp.fc2"		,       "output.dense"    )
    if "q_bias" in name:
        _lowerCAmelCase				=      name.replace("q_bias"		,       "query.bias"    )
    if "k_bias" in name:
        _lowerCAmelCase				=      name.replace("k_bias"		,       "key.bias"    )
    if "v_bias" in name:
        _lowerCAmelCase				=      name.replace("v_bias"		,       "value.bias"    )
    if "cpb_mlp" in name:
        _lowerCAmelCase				=      name.replace("cpb_mlp"		,       "continuous_position_bias_mlp"    )
    if name == "norm.weight":
        _lowerCAmelCase				=      "layernorm.weight"
    if name == "norm.bias":
        _lowerCAmelCase				=      "layernorm.bias"

    if "head" in name:
        _lowerCAmelCase				=      name.replace("head"		,       "classifier"    )
    else:
        _lowerCAmelCase				=      "swinv2." + name

    return name


def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[Any]		,       SCREAMING_SNAKE_CASE_  :					List[str]    ):



    '''simple docstring'''





    for key in orig_state_dict.copy().keys():
        _lowerCAmelCase				=      orig_state_dict.pop(SCREAMING_SNAKE_CASE_    )

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

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

    return orig_state_dict


def 	__a(SCREAMING_SNAKE_CASE_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					List[Any]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      timm.create_model(SCREAMING_SNAKE_CASE_		,       pretrained=SCREAMING_SNAKE_CASE_    )
    timm_model.eval()

    _lowerCAmelCase				=      get_swinva_config(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      SwinvaForImageClassification(SCREAMING_SNAKE_CASE_    )
    model.eval()

    _lowerCAmelCase				=      convert_state_dict(timm_model.state_dict()		,       SCREAMING_SNAKE_CASE_    )
    model.load_state_dict(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      "http://images.cocodataset.org/val2017/000000039769.jpg"

    _lowerCAmelCase				=      AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"		,       "-"    )    )    )
    _lowerCAmelCase				=      Image.open(requests.get(SCREAMING_SNAKE_CASE_		,       stream=SCREAMING_SNAKE_CASE_    ).raw    )
    _lowerCAmelCase				=      image_processor(images=SCREAMING_SNAKE_CASE_		,       return_tensors="pt"    )

    _lowerCAmelCase				=      timm_model(inputs["pixel_values"]    )
    _lowerCAmelCase				=      model(**SCREAMING_SNAKE_CASE_    ).logits

    assert torch.allclose(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       atol=1e-3    )

    print(F'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''    )
    model.save_pretrained(SCREAMING_SNAKE_CASE_    )

    print(F'''Saving image processor to {pytorch_dump_folder_path}'''    )
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE_    )

    model.push_to_hub(
        repo_path_or_name=Path(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )		,       organization="nandwalritik"		,       commit_message="Add model"		,       )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE	 =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--swinv2_name",
           default="swinv2_tiny_patch4_window8_256",
           type=str,
           help="Name of the Swinv2 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."
       )

       _SCREAMING_SNAKE_CASE	 =							parser.parse_args()
       convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)









'''simple docstring'''


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
       from transformers import TvltImageProcessor

if is_speech_available():
       from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class    lowerCAmelCase_		(		unittest.TestCase		):




 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      "ZinengTang/tvlt-base"
     _lowerCAmelCase				=      tempfile.mkdtemp()




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Any:
     return TvltImageProcessor.from_pretrained(self.checkpoint			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							str:
     return TvltFeatureExtractor.from_pretrained(self.checkpoint			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     shutil.rmtree(self.tmpdirname		)




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)
     processor.save_pretrained(self.tmpdirname		)
     _lowerCAmelCase				=      TvltProcessor.from_pretrained(self.tmpdirname		)

     self.assertIsInstance(processor.feature_extractor			,     _lowerCAmelCase		)
     self.assertIsInstance(processor.image_processor			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      np.ones([12000]		)

     _lowerCAmelCase				=      feature_extractor(_lowerCAmelCase			,     return_tensors="np"		)
     _lowerCAmelCase				=      processor(audio=_lowerCAmelCase			,     return_tensors="np"		)

     for key in audio_dict.keys():
         self.assertAlmostEqual(audio_dict[key].sum()			,     input_processor[key].sum()			,     delta=1E-2		)




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      np.ones([3, 224, 224]		)

     _lowerCAmelCase				=      image_processor(_lowerCAmelCase			,     return_tensors="np"		)
     _lowerCAmelCase				=      processor(images=_lowerCAmelCase			,     return_tensors="np"		)

     for key in image_dict.keys():
         self.assertAlmostEqual(image_dict[key].sum()			,     input_processor[key].sum()			,     delta=1E-2		)




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     _lowerCAmelCase				=      np.ones([12000]		)
     _lowerCAmelCase				=      np.ones([3, 224, 224]		)

     _lowerCAmelCase				=      processor(audio=_lowerCAmelCase			,     images=_lowerCAmelCase		)

     self.assertListEqual(list(inputs.keys()		)			,     ["audio_values", "audio_mask", "pixel_values", "pixel_mask"]		)

     # test if it raises when no input is passed
     with pytest.raises(_lowerCAmelCase		):
         processor()





 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      self.get_image_processor()
     _lowerCAmelCase				=      self.get_feature_extractor()

     _lowerCAmelCase				=      TvltProcessor(image_processor=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase		)

     self.assertListEqual(
         processor.model_input_names			,     image_processor.model_input_names + feature_extractor.model_input_names			,     msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"			,     )


