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 lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Dict			  =		SwinvaConfig()
    __UpperCamelCase     :Optional[int]			  =		swinva_name.split('''_'''	)

    __UpperCamelCase     :Optional[Any]			  =		name_split[1]
    if "to" in name_split[3]:
        __UpperCamelCase     :Any			  =		int(name_split[3][-3:]	)
    else:
        __UpperCamelCase     :Tuple			  =		int(name_split[3]	)
    if "to" in name_split[2]:
        __UpperCamelCase     :Optional[int]			  =		int(name_split[2][-2:]	)
    else:
        __UpperCamelCase     :Optional[Any]			  =		int(name_split[2][6:]	)

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

    if "to" in swinva_name:
        __UpperCamelCase     :Optional[Any]			  =		(12, 12, 12, 6)

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        __UpperCamelCase     :Union[str, Any]			  =		21_841
        __UpperCamelCase     :Union[str, Any]			  =		'''huggingface/label-files'''
        __UpperCamelCase     :List[str]			  =		'''imagenet-22k-id2label.json'''
        __UpperCamelCase     :Union[str, Any]			  =		json.load(open(hf_hub_download(a_  ,			a_  ,			repo_type='''dataset'''	)  ,			'''r'''	)	)
        __UpperCamelCase     :Optional[int]			  =		{int(a_	): v for k, v in idalabel.items()}
        __UpperCamelCase     :List[Any]			  =		idalabel
        __UpperCamelCase     :int			  =		{v: k for k, v in idalabel.items()}

    else:
        __UpperCamelCase     :str			  =		1_000
        __UpperCamelCase     :List[Any]			  =		'''huggingface/label-files'''
        __UpperCamelCase     :Dict			  =		'''imagenet-1k-id2label.json'''
        __UpperCamelCase     :Union[str, Any]			  =		json.load(open(hf_hub_download(a_  ,			a_  ,			repo_type='''dataset'''	)  ,			'''r'''	)	)
        __UpperCamelCase     :int			  =		{int(a_	): v for k, v in idalabel.items()}
        __UpperCamelCase     :Dict			  =		idalabel
        __UpperCamelCase     :List[Any]			  =		{v: k for k, v in idalabel.items()}

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

    return config



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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

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

    return name



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    for key in orig_state_dict.copy().keys():
        __UpperCamelCase     :Dict			  =		orig_state_dict.pop(a_	)

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

            if "weight" in key:
                __UpperCamelCase     :Optional[int]			  =		val[:dim, :]
                __UpperCamelCase     :Any			  =		val[dim : dim * 2, :]
                __UpperCamelCase     :Union[str, Any]			  =		val[-dim:, :]
            else:
                __UpperCamelCase     :Any			  =		val[:dim]
                __UpperCamelCase     :str			  =		val[
                    dim : dim * 2
                ]
                __UpperCamelCase     :Dict			  =		val[-dim:]
        else:
            __UpperCamelCase     :Tuple			  =		val

    return orig_state_dict



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :str			  =		timm.create_model(a_  ,			pretrained=a_	)
    timm_model.eval()

    __UpperCamelCase     :int			  =		get_swinva_config(a_	)
    __UpperCamelCase     :Any			  =		SwinvaForImageClassification(a_	)
    model.eval()

    __UpperCamelCase     :int			  =		convert_state_dict(timm_model.state_dict()  ,			a_	)
    model.load_state_dict(a_	)

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

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

    __UpperCamelCase     :Dict			  =		timm_model(inputs['''pixel_values''']	)
    __UpperCamelCase     :Dict			  =		model(**a_	).logits

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

    print(f"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""	)
    model.save_pretrained(a_	)

    print(f"""Saving image processor to {pytorch_dump_folder_path}"""	)
    image_processor.save_pretrained(a_	)

    model.push_to_hub(
        repo_path_or_name=Path(a_  ,			a_	)  ,			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)







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				False
SCREAMING_SNAKE_CASE							:Any							 =				True
SCREAMING_SNAKE_CASE							:Tuple							 =				False


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple							 =				argparse.ArgumentParser()

					parser.add_argument(
					    '--repo_path',
					    default=None,
					    type=str,
					    required=True,
					    help='The config json file corresponding to the architecture.',
					)

					parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				parser.parse_args()

					SCREAMING_SNAKE_CASE							:Dict							 =				{
					    'image_size': 'sample_size',
					    'num_res_blocks': 'layers_per_block',
					    'block_channels': 'block_out_channels',
					    'down_blocks': 'down_block_types',
					    'up_blocks': 'up_block_types',
					    'downscale_freq_shift': 'freq_shift',
					    'resnet_num_groups': 'norm_num_groups',
					    'resnet_act_fn': 'act_fn',
					    'resnet_eps': 'norm_eps',
					    'num_head_channels': 'attention_head_dim',
					}

					SCREAMING_SNAKE_CASE							:Optional[int]							 =				{
					    'time_steps': 'time_proj',
					    'mid': 'mid_block',
					    'downsample_blocks': 'down_blocks',
					    'upsample_blocks': 'up_blocks',
					}

					SCREAMING_SNAKE_CASE							:int							 =				'' if has_file(args.repo_path, 'config.json') else 'unet'

					with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
										SCREAMING_SNAKE_CASE							:Dict							 =				reader.read()
										SCREAMING_SNAKE_CASE							:List[str]							 =				json.loads(text)

					if do_only_config:
										for key in config_parameters_to_change.keys():
															config.pop(key, None)

					if has_file(args.repo_path, 'config.json'):
										SCREAMING_SNAKE_CASE							:Optional[int]							 =				UNetaDModel(**config)
					else:
										SCREAMING_SNAKE_CASE							:Optional[Any]							 =				UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
										SCREAMING_SNAKE_CASE							:List[str]							 =				class_name(**config)

					if do_only_config:
										model.save_config(os.path.join(args.repo_path, subfolder))

					SCREAMING_SNAKE_CASE							:List[str]							 =				dict(model.config)

					if do_only_renaming:
										for key, value in config_parameters_to_change.items():
															if key in config:
																				SCREAMING_SNAKE_CASE							:Optional[Any]							 =				config[key]
																				del config[key]

										SCREAMING_SNAKE_CASE							:Optional[Any]							 =				[k.replace('UNetRes', '') for k in config['down_block_types']]
										SCREAMING_SNAKE_CASE							:List[Any]							 =				[k.replace('UNetRes', '') for k in config['up_block_types']]

					if do_only_weights:
										SCREAMING_SNAKE_CASE							:Tuple							 =				torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

										SCREAMING_SNAKE_CASE							:Any							 =				{}
										for param_key, param_value in state_dict.items():
															if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
																				continue
															SCREAMING_SNAKE_CASE							:List[str]							 =				False
															for key, new_key in key_parameters_to_change.items():
																				if not has_changed and param_key.split('.')[0] == key:
																									SCREAMING_SNAKE_CASE							:List[Any]							 =				param_value
																									SCREAMING_SNAKE_CASE							:str							 =				True
															if not has_changed:
																				SCREAMING_SNAKE_CASE							:List[str]							 =				param_value

										model.load_state_dict(new_state_dict)
										model.save_pretrained(os.path.join(args.repo_path, subfolder))
