

"""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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase   :	List[str]												=						logging.get_logger(__name__)





def       lowercase__						(       snake_case_   :List[Any]			,		snake_case_   :List[str]=False    ):
	__UpperCAmelCase      		= []

	# fmt: off
	# stem:
	rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''')    )
	rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''')    )

	rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''')    )
	rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''')    )

	# backbone
	rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''')    )
	rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''')    )
	rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''')    )

	for stage_idx in range(len(config.backbone_config.depths    )    ):
		for layer_idx in range(config.backbone_config.depths[stage_idx]    ):
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''')    )
			rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''')    )

		rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''')    )
		rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''')    )
		rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''')    )

	# transformer encoder
	for i in range(config.num_hidden_layers    ):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')    )
		rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')    )
		rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')    )
		rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')    )
		rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')    )
		rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')    )
		rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')    )
		rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')    )
		rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')    )
		rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')    )

	if base_model:
		# layernorm + pooler
		rename_keys.extend(
		    [
		        ('''norm.weight''', '''layernorm.weight'''),
		        ('''norm.bias''', '''layernorm.bias'''),
		        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
		        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
		    ]    )

		# if just the base model, we should remove "vit" from all keys that start with "vit"
		__UpperCAmelCase      		= [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''    ) else pair for pair in rename_keys]
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ('''norm.weight''', '''vit.layernorm.weight'''),
		        ('''norm.bias''', '''vit.layernorm.bias'''),
		        ('''head.weight''', '''classifier.weight'''),
		        ('''head.bias''', '''classifier.bias'''),
		    ]    )
	# fmt: on

	return rename_keys





def       lowercase__						(       snake_case_   :List[str]			,		snake_case_   :List[str]			,		snake_case_   :str=False    ):
	for i in range(config.num_hidden_layers    ):
		if base_model:
			__UpperCAmelCase      		= ''''''
		else:
			__UpperCAmelCase      		= '''vit.'''
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		__UpperCAmelCase      		= state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''    )
		__UpperCAmelCase      		= state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''    )
		# next, add query, keys and values (in that order) to the state dict
		__UpperCAmelCase      		= in_proj_weight[
		    : config.hidden_size, :
		]
		__UpperCAmelCase      		= in_proj_bias[: config.hidden_size]
		__UpperCAmelCase      		= in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		__UpperCAmelCase      		= in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		__UpperCAmelCase      		= in_proj_weight[
		    -config.hidden_size :, :
		]
		__UpperCAmelCase      		= in_proj_bias[-config.hidden_size :]





def       lowercase__						(       snake_case_   :str    ):
	__UpperCAmelCase      		= ['''head.weight''', '''head.bias''']
	for k in ignore_keys:
		state_dict.pop(snake_case_			,		snake_case_    )





def       lowercase__						(       snake_case_   :Optional[int]			,		snake_case_   :int			,		snake_case_   :str    ):
	__UpperCAmelCase      		= dct.pop(snake_case_    )
	__UpperCAmelCase      		= val





def       lowercase__						(       ):
	__UpperCAmelCase      		= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
	__UpperCAmelCase      		= Image.open(requests.get(snake_case_			,		stream=snake_case_    ).raw    )
	return im





@torch.no_grad()
def       lowercase__						(       snake_case_   :Union[str, Any]			,		snake_case_   :Any			,		snake_case_   :List[str]=False    ):
	__UpperCAmelCase      		= BitConfig(
	    global_padding='''same'''			,		layer_type='''bottleneck'''			,		depths=(3, 4, 9)			,		out_features=['''stage3''']			,		embedding_dynamic_padding=snake_case_			,		)
	__UpperCAmelCase      		= ViTHybridConfig(backbone_config=snake_case_			,		image_size=384			,		num_labels=1_000    )
	__UpperCAmelCase      		= False

	# load original model from timm
	__UpperCAmelCase      		= timm.create_model(snake_case_			,		pretrained=snake_case_    )
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	__UpperCAmelCase      		= timm_model.state_dict()
	if base_model:
		remove_classification_head_(snake_case_    )
	__UpperCAmelCase      		= create_rename_keys(snake_case_			,		snake_case_    )
	for src, dest in rename_keys:
		rename_key(snake_case_			,		snake_case_			,		snake_case_    )
	read_in_q_k_v(snake_case_			,		snake_case_			,		snake_case_    )

	__UpperCAmelCase      		= '''huggingface/label-files'''
	__UpperCAmelCase      		= '''imagenet-1k-id2label.json'''
	__UpperCAmelCase      		= json.load(open(hf_hub_download(snake_case_			,		snake_case_			,		repo_type='''dataset'''    )			,		'''r'''    )    )
	__UpperCAmelCase      		= {int(snake_case_    ): v for k, v in idalabel.items()}
	__UpperCAmelCase      		= idalabel
	__UpperCAmelCase      		= {v: k for k, v in idalabel.items()}

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		__UpperCAmelCase      		= ViTHybridModel(snake_case_    ).eval()
	else:
		__UpperCAmelCase      		= ViTHybridForImageClassification(snake_case_    ).eval()
	model.load_state_dict(snake_case_    )

	# create image processor
	__UpperCAmelCase      		= create_transform(**resolve_data_config({}			,		model=snake_case_    )    )
	__UpperCAmelCase      		= transform.transforms

	__UpperCAmelCase      		= {
	    '''bilinear''': PILImageResampling.BILINEAR,
	    '''bicubic''': PILImageResampling.BICUBIC,
	    '''nearest''': PILImageResampling.NEAREST,
	}

	__UpperCAmelCase      		= ViTHybridImageProcessor(
	    do_resize=snake_case_			,		size={'''shortest_edge''': timm_transforms[0].size}			,		resample=pillow_resamplings[timm_transforms[0].interpolation.value]			,		do_center_crop=snake_case_			,		crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}			,		do_normalize=snake_case_			,		image_mean=timm_transforms[-1].mean.tolist()			,		image_std=timm_transforms[-1].std.tolist()			,		)

	__UpperCAmelCase      		= prepare_img()
	__UpperCAmelCase      		= transform(snake_case_    ).unsqueeze(0    )
	__UpperCAmelCase      		= processor(snake_case_			,		return_tensors='''pt'''    ).pixel_values

	# verify pixel values
	assert torch.allclose(snake_case_			,		snake_case_    )

	# verify logits
	with torch.no_grad():
		__UpperCAmelCase      		= model(snake_case_    )
		__UpperCAmelCase      		= outputs.logits

	print('''Predicted class:'''			,		logits.argmax(-1    ).item()    )
	if base_model:
		__UpperCAmelCase      		= timm_model.forward_features(snake_case_    )
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(snake_case_			,		outputs.pooler_output			,		atol=1E-3    )
	else:
		__UpperCAmelCase      		= timm_model(snake_case_    )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(snake_case_			,		outputs.logits			,		atol=1E-3    )
	print('''Looks ok!'''    )

	if pytorch_dump_folder_path is not None:
		Path(snake_case_    ).mkdir(exist_ok=snake_case_    )
		print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''    )
		model.save_pretrained(snake_case_    )
		print(F'''Saving processor to {pytorch_dump_folder_path}'''    )
		processor.save_pretrained(snake_case_    )

	if push_to_hub:
		print(F'''Pushing model and processor to the hub {vit_name}'''    )
		model.push_to_hub(F'''ybelkada/{vit_name}'''    )
		processor.push_to_hub(F'''ybelkada/{vit_name}'''    )


if __name__ == "__main__":
				_lowercase   :	Dict												=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--vit_name',
				    default='vit_base_r50_s16_384',
				    type=str,
				    help='Name of the hybrid ViT 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.'
				)
				parser.add_argument(
				    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
				)

				_lowercase   :	List[str]												=						parser.parse_args()
				convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



SCREAMING_SNAKE_CASE		:Dict						=     [
    999,
    800,
    799,
    600,
    599,
    500,
    400,
    399,
    377,
    355,
    333,
    311,
    288,
    266,
    244,
    222,
    200,
    199,
    177,
    155,
    133,
    111,
    88,
    66,
    44,
    22,
    0,
]

SCREAMING_SNAKE_CASE		:Dict						=     [
    999,
    976,
    952,
    928,
    905,
    882,
    858,
    857,
    810,
    762,
    715,
    714,
    572,
    429,
    428,
    286,
    285,
    238,
    190,
    143,
    142,
    118,
    95,
    71,
    47,
    24,
    0,
]

SCREAMING_SNAKE_CASE		:int						=     [
    999,
    988,
    977,
    966,
    955,
    944,
    933,
    922,
    911,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    350,
    300,
    299,
    266,
    233,
    200,
    199,
    179,
    159,
    140,
    120,
    100,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

SCREAMING_SNAKE_CASE		:Optional[Any]						=     [
    999,
    995,
    992,
    989,
    985,
    981,
    978,
    975,
    971,
    967,
    964,
    961,
    957,
    956,
    951,
    947,
    942,
    937,
    933,
    928,
    923,
    919,
    914,
    913,
    908,
    903,
    897,
    892,
    887,
    881,
    876,
    871,
    870,
    864,
    858,
    852,
    846,
    840,
    834,
    828,
    827,
    820,
    813,
    806,
    799,
    792,
    785,
    784,
    777,
    770,
    763,
    756,
    749,
    742,
    741,
    733,
    724,
    716,
    707,
    699,
    698,
    688,
    677,
    666,
    656,
    655,
    645,
    634,
    623,
    613,
    612,
    598,
    584,
    570,
    569,
    555,
    541,
    527,
    526,
    505,
    484,
    483,
    462,
    440,
    439,
    396,
    395,
    352,
    351,
    308,
    307,
    264,
    263,
    220,
    219,
    176,
    132,
    88,
    44,
    0,
]

SCREAMING_SNAKE_CASE		:int						=     [
    999,
    997,
    995,
    992,
    990,
    988,
    986,
    984,
    981,
    979,
    977,
    975,
    972,
    970,
    968,
    966,
    964,
    961,
    959,
    957,
    956,
    954,
    951,
    949,
    946,
    944,
    941,
    939,
    936,
    934,
    931,
    929,
    926,
    924,
    921,
    919,
    916,
    914,
    913,
    910,
    907,
    905,
    902,
    899,
    896,
    893,
    891,
    888,
    885,
    882,
    879,
    877,
    874,
    871,
    870,
    867,
    864,
    861,
    858,
    855,
    852,
    849,
    846,
    843,
    840,
    837,
    834,
    831,
    828,
    827,
    824,
    821,
    817,
    814,
    811,
    808,
    804,
    801,
    798,
    795,
    791,
    788,
    785,
    784,
    780,
    777,
    774,
    770,
    766,
    763,
    760,
    756,
    752,
    749,
    746,
    742,
    741,
    737,
    733,
    730,
    726,
    722,
    718,
    714,
    710,
    707,
    703,
    699,
    698,
    694,
    690,
    685,
    681,
    677,
    673,
    669,
    664,
    660,
    656,
    655,
    650,
    646,
    641,
    636,
    632,
    627,
    622,
    618,
    613,
    612,
    607,
    602,
    596,
    591,
    586,
    580,
    575,
    570,
    569,
    563,
    557,
    551,
    545,
    539,
    533,
    527,
    526,
    519,
    512,
    505,
    498,
    491,
    484,
    483,
    474,
    466,
    457,
    449,
    440,
    439,
    428,
    418,
    407,
    396,
    395,
    381,
    366,
    352,
    351,
    330,
    308,
    307,
    286,
    264,
    263,
    242,
    220,
    219,
    176,
    175,
    132,
    131,
    88,
    44,
    0,
]

SCREAMING_SNAKE_CASE		:Optional[int]						=     [
    999,
    991,
    982,
    974,
    966,
    958,
    950,
    941,
    933,
    925,
    916,
    908,
    900,
    899,
    874,
    850,
    825,
    800,
    799,
    700,
    600,
    500,
    400,
    300,
    200,
    100,
    0,
]

SCREAMING_SNAKE_CASE		:Dict						=     [
    999,
    992,
    985,
    978,
    971,
    964,
    957,
    949,
    942,
    935,
    928,
    921,
    914,
    907,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    300,
    299,
    200,
    199,
    100,
    99,
    0,
]

SCREAMING_SNAKE_CASE		:int						=     [
    999,
    996,
    992,
    989,
    985,
    982,
    979,
    975,
    972,
    968,
    965,
    961,
    958,
    955,
    951,
    948,
    944,
    941,
    938,
    934,
    931,
    927,
    924,
    920,
    917,
    914,
    910,
    907,
    903,
    900,
    899,
    891,
    884,
    876,
    869,
    861,
    853,
    846,
    838,
    830,
    823,
    815,
    808,
    800,
    799,
    788,
    777,
    766,
    755,
    744,
    733,
    722,
    711,
    700,
    699,
    688,
    677,
    666,
    655,
    644,
    633,
    622,
    611,
    600,
    599,
    585,
    571,
    557,
    542,
    528,
    514,
    500,
    499,
    485,
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]
