
import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel


def 				A	( lowercase			)       ->  List[str]:
		'''simple docstring'''

		if "img_encoder.pos_embed" in name:
				UpperCamelCase          =					name.replace('img_encoder.pos_embed'   ,	'vision_model.embeddings.position_embeddings'			)
		if "img_encoder.patch_embed.proj" in name:
				UpperCamelCase          =					name.replace('img_encoder.patch_embed.proj'   ,	'vision_model.embeddings.patch_embeddings.projection'			)
		if "img_encoder.patch_embed.norm" in name:
				UpperCamelCase          =					name.replace('img_encoder.patch_embed.norm'   ,	'vision_model.embeddings.layernorm'			)
		if "img_encoder.layers" in name:
				UpperCamelCase          =					name.replace('img_encoder.layers'   ,	'vision_model.encoder.stages'			)
		if "blocks" in name and "res" not in name:
				UpperCamelCase          =					name.replace('blocks'   ,	'layers'			)
		if "attn" in name and "pre_assign" not in name:
				UpperCamelCase          =					name.replace('attn'   ,	'self_attn'			)
		if "proj" in name and "self_attn" in name and "text" not in name:
				UpperCamelCase          =					name.replace('proj'   ,	'out_proj'			)
		if "pre_assign_attn.attn.proj" in name:
				UpperCamelCase          =					name.replace('pre_assign_attn.attn.proj'   ,	'pre_assign_attn.attn.out_proj'			)
		if "norm1" in name:
				UpperCamelCase          =					name.replace('norm1'   ,	'layer_norm1'			)
		if "norm2" in name and "pre_assign" not in name:
				UpperCamelCase          =					name.replace('norm2'   ,	'layer_norm2'			)
		if "img_encoder.norm" in name:
				UpperCamelCase          =					name.replace('img_encoder.norm'   ,	'vision_model.layernorm'			)
		# text encoder
		if "text_encoder.token_embedding" in name:
				UpperCamelCase          =					name.replace('text_encoder.token_embedding'   ,	'text_model.embeddings.token_embedding'			)
		if "text_encoder.positional_embedding" in name:
				UpperCamelCase          =					name.replace('text_encoder.positional_embedding'   ,	'text_model.embeddings.position_embedding.weight'			)
		if "text_encoder.transformer.resblocks." in name:
				UpperCamelCase          =					name.replace('text_encoder.transformer.resblocks.'   ,	'text_model.encoder.layers.'			)
		if "ln_1" in name:
				UpperCamelCase          =					name.replace('ln_1'   ,	'layer_norm1'			)
		if "ln_2" in name:
				UpperCamelCase          =					name.replace('ln_2'   ,	'layer_norm2'			)
		if "c_fc" in name:
				UpperCamelCase          =					name.replace('c_fc'   ,	'fc1'			)
		if "c_proj" in name:
				UpperCamelCase          =					name.replace('c_proj'   ,	'fc2'			)
		if "text_encoder" in name:
				UpperCamelCase          =					name.replace('text_encoder'   ,	'text_model'			)
		if "ln_final" in name:
				UpperCamelCase          =					name.replace('ln_final'   ,	'final_layer_norm'			)
		# projection layers
		if "img_projector.linear_hidden." in name:
				UpperCamelCase          =					name.replace('img_projector.linear_hidden.'   ,	'visual_projection.'			)
		if "img_projector.linear_out." in name:
				UpperCamelCase          =					name.replace('img_projector.linear_out.'   ,	'visual_projection.3.'			)
		if "text_projector.linear_hidden" in name:
				UpperCamelCase          =					name.replace('text_projector.linear_hidden'   ,	'text_projection'			)
		if "text_projector.linear_out" in name:
				UpperCamelCase          =					name.replace('text_projector.linear_out'   ,	'text_projection.3'			)

		return name


def 				A	( lowercase   ,	lowercase			)       ->  str:
		'''simple docstring'''

		for key in orig_state_dict.copy().keys():
				UpperCamelCase          =					orig_state_dict.pop(lowercase			)

				if "qkv" in key:
						# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
						# we need to split them up into separate matrices/vectors
						UpperCamelCase          =					key.split('.'			)
						UpperCamelCase     ,  UpperCamelCase          =					int(key_split[2]			), int(key_split[4]			)
						UpperCamelCase          =					config.vision_config.hidden_size
						if "weight" in key:
								UpperCamelCase          =					val[:dim, :]
								UpperCamelCase          =					val[dim : dim * 2, :]
								UpperCamelCase          =					val[-dim:, :]
						else:
								UpperCamelCase          =					val[:dim]
								UpperCamelCase          =					val[dim : dim * 2]
								UpperCamelCase          =					val[-dim:]
				elif "in_proj" in key:
						# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
						# we need to split them up into separate matrices/vectors
						UpperCamelCase          =					key.split('.'			)
						UpperCamelCase          =					int(key_split[3]			)
						UpperCamelCase          =					config.text_config.hidden_size
						if "weight" in key:
								UpperCamelCase          =					val[:dim, :]
								UpperCamelCase          =					val[
								    dim : dim * 2, :
								]
								UpperCamelCase          =					val[-dim:, :]
						else:
								UpperCamelCase          =					val[:dim]
								UpperCamelCase          =					val[dim : dim * 2]
								UpperCamelCase          =					val[-dim:]
				else:
						UpperCamelCase          =					rename_key(lowercase			)
						# squeeze if necessary
						if (
						    "text_projection.0" in new_name
						    or "text_projection.3" in new_name
						    or "visual_projection.0" in new_name
						    or "visual_projection.3" in new_name
						):
								UpperCamelCase          =					val.squeeze_()
						else:
								UpperCamelCase          =					val

		return orig_state_dict


def 				A	( )       ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase          =					'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCamelCase          =					Image.open(requests.get(lowercase   ,	stream=lowercase			).raw			)
		return im


@torch.no_grad()
def 				A	( lowercase   ,	lowercase   ,	lowercase="groupvit-gcc-yfcc"   ,	lowercase=False			)       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					GroupViTConfig()
		UpperCamelCase          =					GroupViTModel(lowercase			).eval()

		UpperCamelCase          =					torch.load(lowercase   ,	map_location='cpu'			)['model']
		UpperCamelCase          =					convert_state_dict(lowercase   ,	lowercase			)
		UpperCamelCase     ,  UpperCamelCase          =					model.load_state_dict(lowercase   ,	strict=lowercase			)
		assert missing_keys == ["text_model.embeddings.position_ids"]
		assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase			) == 0)

		# verify result
		UpperCamelCase          =					CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'			)
		UpperCamelCase          =					prepare_img()
		UpperCamelCase          =					processor(text=['a photo of a cat', 'a photo of a dog']   ,	images=lowercase   ,	padding=lowercase   ,	return_tensors='pt'			)

		with torch.no_grad():
				UpperCamelCase          =					model(**lowercase			)

		if model_name == "groupvit-gcc-yfcc":
				UpperCamelCase          =					torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]			)
		elif model_name == "groupvit-gcc-redcaps":
				UpperCamelCase          =					torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]			)
		else:
				raise ValueError(f'''Model name {model_name} not supported.'''			)
		assert torch.allclose(outputs.logits_per_image   ,	lowercase   ,	atol=1e-3			)

		processor.save_pretrained(lowercase			)
		model.save_pretrained(lowercase			)
		print('Successfully saved processor and model to'   ,	lowercase			)

		if push_to_hub:
				print('Pushing to the hub...'			)
				processor.push_to_hub(lowercase   ,	organization='nielsr'			)
				model.push_to_hub(lowercase   ,	organization='nielsr'			)


if __name__ == "__main__":
					_UpperCAmelCase :			List[str]              =	argparse.ArgumentParser()
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
					)
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
					parser.add_argument(
					    "--model_name",
					    default="groupvit-gccy-fcc",
					    type=str,
					    help="Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'",
					)
					parser.add_argument(
					    "--push_to_hub",
					    action="store_true",
					    help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
					)
					_UpperCAmelCase :			Any              =	parser.parse_args()

					convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)


_UpperCAmelCase :			str              =	[
    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,
]

_UpperCAmelCase :			Any              =	[
    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,
]

_UpperCAmelCase :			List[Any]              =	[
    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,
]

_UpperCAmelCase :			Tuple              =	[
    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,
]

_UpperCAmelCase :			Union[str, Any]              =	[
    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,
]

_UpperCAmelCase :			List[str]              =	[
    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,
]

_UpperCAmelCase :			Tuple              =	[
    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,
]

_UpperCAmelCase :			Dict              =	[
    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,
    471,
    457,
    442,
    428,
    414,
    400,
    399,
    379,
    359,
    340,
    320,
    300,
    299,
    279,
    259,
    240,
    220,
    200,
    199,
    166,
    133,
    100,
    99,
    66,
    33,
    0,
]
