




"""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   _snake_case    (       lowercase__	):
    _lowerCamelCase  :    Dict					=					SwinvaConfig()
    _lowerCamelCase  :    Union[str, Any]					=					swinva_name.split('_'	)

    _lowerCamelCase  :    Union[str, Any]					=					name_split[1]
    if "to" in name_split[3]:
        _lowerCamelCase  :    List[str]					=					int(name_split[3][-3:]	)
    else:
        _lowerCamelCase  :    Optional[int]					=					int(name_split[3]	)
    if "to" in name_split[2]:
        _lowerCamelCase  :    Dict					=					int(name_split[2][-2:]	)
    else:
        _lowerCamelCase  :    str					=					int(name_split[2][6:]	)

    if model_size == "tiny":
        _lowerCamelCase  :    Any					=					96
        _lowerCamelCase  :    List[str]					=					(2, 2, 6, 2)
        _lowerCamelCase  :    Dict					=					(3, 6, 12, 24)
    elif model_size == "small":
        _lowerCamelCase  :    int					=					96
        _lowerCamelCase  :    int					=					(2, 2, 18, 2)
        _lowerCamelCase  :    Optional[int]					=					(3, 6, 12, 24)
    elif model_size == "base":
        _lowerCamelCase  :    Optional[int]					=					128
        _lowerCamelCase  :    Dict					=					(2, 2, 18, 2)
        _lowerCamelCase  :    str					=					(4, 8, 16, 32)
    else:
        _lowerCamelCase  :    Any					=					192
        _lowerCamelCase  :    List[str]					=					(2, 2, 18, 2)
        _lowerCamelCase  :    Optional[int]					=					(6, 12, 24, 48)

    if "to" in swinva_name:
        _lowerCamelCase  :    Optional[int]					=					(12, 12, 12, 6)

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        _lowerCamelCase  :    Any					=					21841
        _lowerCamelCase  :    Any					=					'huggingface/label-files'
        _lowerCamelCase  :    Tuple					=					'imagenet-22k-id2label.json'
        _lowerCamelCase  :    Union[str, Any]					=					json.load(open(hf_hub_download(lowercase__							,					lowercase__							,					repo_type='dataset'	)							,					'r'	)	)
        _lowerCamelCase  :    Optional[int]					=					{int(lowercase__	): v for k, v in idalabel.items()}
        _lowerCamelCase  :    Union[str, Any]					=					idalabel
        _lowerCamelCase  :    str					=					{v: k for k, v in idalabel.items()}

    else:
        _lowerCamelCase  :    Optional[Any]					=					1000
        _lowerCamelCase  :    Union[str, Any]					=					'huggingface/label-files'
        _lowerCamelCase  :    str					=					'imagenet-1k-id2label.json'
        _lowerCamelCase  :    Optional[Any]					=					json.load(open(hf_hub_download(lowercase__							,					lowercase__							,					repo_type='dataset'	)							,					'r'	)	)
        _lowerCamelCase  :    str					=					{int(lowercase__	): v for k, v in idalabel.items()}
        _lowerCamelCase  :    str					=					idalabel
        _lowerCamelCase  :    str					=					{v: k for k, v in idalabel.items()}

    _lowerCamelCase  :    str					=					img_size
    _lowerCamelCase  :    Union[str, Any]					=					num_classes
    _lowerCamelCase  :    int					=					embed_dim
    _lowerCamelCase  :    str					=					depths
    _lowerCamelCase  :    Any					=					num_heads
    _lowerCamelCase  :    List[str]					=					window_size

    return config







def   _snake_case    (       lowercase__	):
    if "patch_embed.proj" in name:
        _lowerCamelCase  :    List[Any]					=					name.replace('patch_embed.proj'							,					'embeddings.patch_embeddings.projection'	)
    if "patch_embed.norm" in name:
        _lowerCamelCase  :    Dict					=					name.replace('patch_embed.norm'							,					'embeddings.norm'	)
    if "layers" in name:
        _lowerCamelCase  :    Optional[Any]					=					'encoder.' + name
    if "attn.proj" in name:
        _lowerCamelCase  :    Tuple					=					name.replace('attn.proj'							,					'attention.output.dense'	)
    if "attn" in name:
        _lowerCamelCase  :    Any					=					name.replace('attn'							,					'attention.self'	)
    if "norm1" in name:
        _lowerCamelCase  :    List[str]					=					name.replace('norm1'							,					'layernorm_before'	)
    if "norm2" in name:
        _lowerCamelCase  :    List[Any]					=					name.replace('norm2'							,					'layernorm_after'	)
    if "mlp.fc1" in name:
        _lowerCamelCase  :    Tuple					=					name.replace('mlp.fc1'							,					'intermediate.dense'	)
    if "mlp.fc2" in name:
        _lowerCamelCase  :    Tuple					=					name.replace('mlp.fc2'							,					'output.dense'	)
    if "q_bias" in name:
        _lowerCamelCase  :    List[str]					=					name.replace('q_bias'							,					'query.bias'	)
    if "k_bias" in name:
        _lowerCamelCase  :    Optional[Any]					=					name.replace('k_bias'							,					'key.bias'	)
    if "v_bias" in name:
        _lowerCamelCase  :    Optional[Any]					=					name.replace('v_bias'							,					'value.bias'	)
    if "cpb_mlp" in name:
        _lowerCamelCase  :    Tuple					=					name.replace('cpb_mlp'							,					'continuous_position_bias_mlp'	)
    if name == "norm.weight":
        _lowerCamelCase  :    Tuple					=					'layernorm.weight'
    if name == "norm.bias":
        _lowerCamelCase  :    int					=					'layernorm.bias'

    if "head" in name:
        _lowerCamelCase  :    int					=					name.replace('head'							,					'classifier'	)
    else:
        _lowerCamelCase  :    str					=					'swinv2.' + name

    return name







def   _snake_case    (       lowercase__							,					lowercase__	):
    for key in orig_state_dict.copy().keys():
        _lowerCamelCase  :    int					=					orig_state_dict.pop(lowercase__	)

        if "mask" in key:
            continue
        elif "qkv" in key:
            _lowerCamelCase  :    List[Any]					=					key.split('.'	)
            _lowerCamelCase  :    Union[str, Any]					=					int(key_split[1]	)
            _lowerCamelCase  :    Optional[Any]					=					int(key_split[3]	)
            _lowerCamelCase  :    str					=					model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                _lowerCamelCase  :    Any					=					val[:dim, :]
                _lowerCamelCase  :    Optional[Any]					=					val[dim : dim * 2, :]
                _lowerCamelCase  :    List[str]					=					val[-dim:, :]
            else:
                _lowerCamelCase  :    List[str]					=					val[:dim]
                _lowerCamelCase  :    str					=					val[
                    dim : dim * 2
                ]
                _lowerCamelCase  :    List[str]					=					val[-dim:]
        else:
            _lowerCamelCase  :    Dict					=					val

    return orig_state_dict







def   _snake_case    (       lowercase__							,					lowercase__	):
    _lowerCamelCase  :    Any					=					timm.create_model(lowercase__							,					pretrained=lowercase__	)
    timm_model.eval()

    _lowerCamelCase  :    List[Any]					=					get_swinva_config(lowercase__	)
    _lowerCamelCase  :    Optional[Any]					=					SwinvaForImageClassification(lowercase__	)
    model.eval()

    _lowerCamelCase  :    int					=					convert_state_dict(timm_model.state_dict()							,					lowercase__	)
    model.load_state_dict(lowercase__	)

    _lowerCamelCase  :    List[str]					=					'http://images.cocodataset.org/val2017/000000039769.jpg'

    _lowerCamelCase  :    Optional[Any]					=					AutoImageProcessor.from_pretrained('microsoft/{}'.format(swinva_name.replace('_'							,					'-'	)	)	)
    _lowerCamelCase  :    Dict					=					Image.open(requests.get(lowercase__							,					stream=lowercase__	).raw	)
    _lowerCamelCase  :    str					=					image_processor(images=lowercase__							,					return_tensors='pt'	)

    _lowerCamelCase  :    Union[str, Any]					=					timm_model(inputs['pixel_values']	)
    _lowerCamelCase  :    List[Any]					=					model(**lowercase__	).logits

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

    print(f'''Saving model {swinva_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__	)

    model.push_to_hub(
        repo_path_or_name=Path(lowercase__							,					lowercase__	)							,					organization='nandwalritik'							,					commit_message='Add model'							,					)


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

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





"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available






@dataclass
class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    42


try:
			if not (is_transformers_available() and is_torch_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
			from .pipeline_text_to_video_synth import TextToVideoSDPipeline
			from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
			from .pipeline_text_to_video_zero import TextToVideoZeroPipeline