


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,
)


A				:       int      =  {
    '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 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Any:


						'''simple docstring'''






						__snake_case				=		{}
						state_dict.pop("pixel_mean"   ,						_lowerCAmelCase			)
						state_dict.pop("pixel_std"   ,						_lowerCAmelCase			)

						__snake_case				=		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:
																								__snake_case				=		key.replace(_lowerCAmelCase   ,						_lowerCAmelCase			)

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

												__snake_case				=		value

						__snake_case				=		model_state_dict[
						    "prompt_encoder.shared_embedding.positional_embedding"
						]

						return model_state_dict





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase="ybelkada/segment-anything"			)    ->						Any:


						'''simple docstring'''






						__snake_case				=		hf_hub_download(_lowerCAmelCase   ,						F'''checkpoints/{model_name}.pth'''			)

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

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

												__snake_case				=		SamConfig(
												    vision_config=_lowerCAmelCase   ,						)

						__snake_case				=		torch.load(_lowerCAmelCase   ,						map_location="cpu"			)
						__snake_case				=		replace_keys(_lowerCAmelCase			)

						__snake_case				=		SamImageProcessor()

						__snake_case				=		SamProcessor(image_processor=_lowerCAmelCase			)
						__snake_case				=		SamModel(_lowerCAmelCase			)

						hf_model.load_state_dict(_lowerCAmelCase			)
						__snake_case				=		hf_model.to("cuda"			)

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

						__snake_case				=		[[[400, 650]]]
						__snake_case				=		[[1]]

						__snake_case				=		processor(images=np.array(_lowerCAmelCase			)   ,						return_tensors="pt"			).to("cuda"			)

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

						if model_name == "sam_vit_h_4b8939":
												assert scores[-1].item() == 0.5_79_89_02_51_15_96_68

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

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

												assert scores[-1].item() == 0.97_12_60_30_92_19_36_04

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

												__snake_case				=		processor(images=np.array(_lowerCAmelCase			)   ,						input_boxes=_lowerCAmelCase   ,						return_tensors="pt"			).to("cuda"			)

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

												assert scores[-1].item() == 0.86_86_01_56_05_92_65_14

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

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

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

												assert scores[-1].item() == 0.99_36_04_77_92_43_46_92


if __name__ == "__main__":
	A				:       Any      =  argparse.ArgumentParser()
	A				:       Union[str, Any]      =  ['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',
	)

	A				:       List[Any]      =  parser.parse_args()

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A				:       List[str]      =  {
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A				:       str      =  [
	    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TimeSeriesTransformerForPrediction',
	    'TimeSeriesTransformerModel',
	    'TimeSeriesTransformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	A				:       str      =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





