import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


_lowerCAmelCase       =		{
    "iou_prediction_head.layers.0": "iou_prediction_head.proj_in",
    "iou_prediction_head.layers.1": "iou_prediction_head.layers.0",
    "iou_prediction_head.layers.2": "iou_prediction_head.proj_out",
    "mask_decoder.output_upscaling.0": "mask_decoder.upscale_conv1",
    "mask_decoder.output_upscaling.1": "mask_decoder.upscale_layer_norm",
    "mask_decoder.output_upscaling.3": "mask_decoder.upscale_conv2",
    "mask_downscaling.0": "mask_embed.conv1",
    "mask_downscaling.1": "mask_embed.layer_norm1",
    "mask_downscaling.3": "mask_embed.conv2",
    "mask_downscaling.4": "mask_embed.layer_norm2",
    "mask_downscaling.6": "mask_embed.conv3",
    "point_embeddings": "point_embed",
    "pe_layer.positional_encoding_gaussian_matrix": "shared_embedding.positional_embedding",
    "image_encoder": "vision_encoder",
    "neck.0": "neck.conv1",
    "neck.1": "neck.layer_norm1",
    "neck.2": "neck.conv2",
    "neck.3": "neck.layer_norm2",
    "patch_embed.proj": "patch_embed.projection",
    ".norm": ".layer_norm",
    "blocks": "layers",
}
def 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     {}
					state_dict.pop('''pixel_mean'''  ,     __snake_case						)
					state_dict.pop('''pixel_std'''  ,     __snake_case						)

					_UpperCamelCase			     =     R'''.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'''

					for key, value in state_dict.items():
										for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
															if key_to_modify in key:
																				_UpperCamelCase			     =     key.replace(__snake_case  ,     __snake_case						)

										if re.match(__snake_case  ,     __snake_case						):
															_UpperCamelCase			     =     int(re.match(__snake_case  ,     __snake_case						).group(2						)						)
															if layer_nb == 0:
																				_UpperCamelCase			     =     key.replace('''layers.0'''  ,     '''proj_in'''						)
															elif layer_nb == 1:
																				_UpperCamelCase			     =     key.replace('''layers.1'''  ,     '''layers.0'''						)
															elif layer_nb == 2:
																				_UpperCamelCase			     =     key.replace('''layers.2'''  ,     '''proj_out'''						)

										_UpperCamelCase			     =     value

					_UpperCamelCase			     =     model_state_dict[
					    '''prompt_encoder.shared_embedding.positional_embedding'''
					]

					return model_state_dict
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case="ybelkada/segment-anything"						):
					_UpperCamelCase			     =     hf_hub_download(__snake_case  ,     f"""checkpoints/{model_name}.pth"""						)

					if "sam_vit_b" in model_name:
										_UpperCamelCase			     =     SamConfig()
					elif "sam_vit_l" in model_name:
										_UpperCamelCase			     =     SamVisionConfig(
										    hidden_size=1024  ,     num_hidden_layers=24  ,     num_attention_heads=16  ,     global_attn_indexes=[5, 11, 17, 23]  ,     )

										_UpperCamelCase			     =     SamConfig(
										    vision_config=__snake_case  ,     )
					elif "sam_vit_h" in model_name:
										_UpperCamelCase			     =     SamVisionConfig(
										    hidden_size=1280  ,     num_hidden_layers=32  ,     num_attention_heads=16  ,     global_attn_indexes=[7, 15, 23, 31]  ,     )

										_UpperCamelCase			     =     SamConfig(
										    vision_config=__snake_case  ,     )

					_UpperCamelCase			     =     torch.load(__snake_case  ,     map_location='''cpu'''						)
					_UpperCamelCase			     =     replace_keys(__snake_case						)

					_UpperCamelCase			     =     SamImageProcessor()

					_UpperCamelCase			     =     SamProcessor(image_processor=__snake_case						)
					_UpperCamelCase			     =     SamModel(__snake_case						)

					hf_model.load_state_dict(__snake_case						)
					_UpperCamelCase			     =     hf_model.to('''cuda'''						)

					_UpperCamelCase			     =     '''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
					_UpperCamelCase			     =     Image.open(requests.get(__snake_case  ,     stream=__snake_case						).raw						).convert('''RGB'''						)

					_UpperCamelCase			     =     [[[400, 650]]]
					_UpperCamelCase			     =     [[1]]

					_UpperCamelCase			     =     processor(images=np.array(__snake_case						)  ,     return_tensors='''pt'''						).to('''cuda'''						)

					with torch.no_grad():
										_UpperCamelCase			     =     hf_model(**__snake_case						)
					_UpperCamelCase			     =     output.iou_scores.squeeze()

					if model_name == "sam_vit_h_4b8939":
										assert scores[-1].item() == 0.579890251159668

										_UpperCamelCase			     =     processor(
										    images=np.array(__snake_case						)  ,     input_points=__snake_case  ,     input_labels=__snake_case  ,     return_tensors='''pt'''						).to('''cuda'''						)

										with torch.no_grad():
															_UpperCamelCase			     =     hf_model(**__snake_case						)
										_UpperCamelCase			     =     output.iou_scores.squeeze()

										assert scores[-1].item() == 0.9712603092193604

										_UpperCamelCase			     =     ((75, 275, 1725, 850),)

										_UpperCamelCase			     =     processor(images=np.array(__snake_case						)  ,     input_boxes=__snake_case  ,     return_tensors='''pt'''						).to('''cuda'''						)

										with torch.no_grad():
															_UpperCamelCase			     =     hf_model(**__snake_case						)
										_UpperCamelCase			     =     output.iou_scores.squeeze()

										assert scores[-1].item() == 0.8686015605926514

										# Test with 2 points and 1 image.
										_UpperCamelCase			     =     [[[400, 650], [800, 650]]]
										_UpperCamelCase			     =     [[1, 1]]

										_UpperCamelCase			     =     processor(
										    images=np.array(__snake_case						)  ,     input_points=__snake_case  ,     input_labels=__snake_case  ,     return_tensors='''pt'''						).to('''cuda'''						)

										with torch.no_grad():
															_UpperCamelCase			     =     hf_model(**__snake_case						)
										_UpperCamelCase			     =     output.iou_scores.squeeze()

										assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
					_lowerCAmelCase       =		argparse.ArgumentParser()
					_lowerCAmelCase       =		["sam_vit_b_01ec64", "sam_vit_h_4b8939", "sam_vit_l_0b3195"]
					parser.add_argument(
					    "--model_name",
					    default="sam_vit_h_4b8939",
					    choices=choices,
					    type=str,
					    help="Path to hf config.json of model to convert",
					)
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument(
					    "--push_to_hub",
					    action="store_true",
					    help="Whether to push the model and processor to the hub after converting",
					)
					parser.add_argument(
					    "--model_hub_id",
					    default="ybelkada/segment-anything",
					    choices=choices,
					    type=str,
					    help="Path to hf config.json of model to convert",
					)

					_lowerCAmelCase       =		parser.parse_args()

					convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)

import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_lowerCAmelCase       =		HfApi()

_lowerCAmelCase       =		{}
# fmt: off
_lowerCAmelCase       =		torch.tensor([
    -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
    1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
    -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
    0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
_lowerCAmelCase       =		torch.tensor([
    -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
    1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
    -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
    2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
_lowerCAmelCase       =		torch.tensor([
    -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
    -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
    -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
    0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
_lowerCAmelCase       =		torch.tensor([
    0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
    -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
    0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
    -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
_lowerCAmelCase       =		torch.tensor([
    0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
    -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
    0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
    -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
_lowerCAmelCase       =		torch.tensor([
    0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
    -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
    0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
    -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
_lowerCAmelCase       =		torch.tensor([
    0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
    -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
    0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
    -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
_lowerCAmelCase       =		torch.tensor([
    0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
    -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
    0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
    -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
_lowerCAmelCase       =		torch.tensor([
    -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
    1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
    -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
    1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
_lowerCAmelCase       =		torch.tensor([
    -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
    0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
    -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
    1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
_lowerCAmelCase       =		torch.tensor([
    -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
    0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
    -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
    1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
_lowerCAmelCase       =		torch.tensor([
    -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
    1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
    -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
    3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
_lowerCAmelCase       =		torch.tensor([
    -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
    1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
    -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
    2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
_lowerCAmelCase       =		torch.tensor([
    -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
    1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
    -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
    3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
_lowerCAmelCase       =		torch.tensor([
    -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
    1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
    -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
    1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on

_lowerCAmelCase       =		api.list_models(filter="diffusers")
for mod in models:
					if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
										_lowerCAmelCase       =		"/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]

										print(f'Started running {mod.modelId}!!!')

										if mod.modelId.startswith("CompVis"):
															_lowerCAmelCase       =		UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
										else:
															_lowerCAmelCase       =		UNetaDModel.from_pretrained(local_checkpoint)

										torch.manual_seed(0)
										random.seed(0)

										_lowerCAmelCase       =		torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
										_lowerCAmelCase       =		torch.tensor([10] * noise.shape[0])
										with torch.no_grad():
															_lowerCAmelCase       =		model(noise, time_step).sample

										assert torch.allclose(
										    logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
										)
										print(f'{mod.modelId} has passed successfully!!!')
