






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      :    List[Any]   		=   {
    """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   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[Any]     ):
	snake_case__				:   Union[str, Any]     =						{}
	state_dict.pop("pixel_mean"  ,						lowerCamelCase_     )
	state_dict.pop("pixel_std"  ,						lowerCamelCase_     )

	snake_case__				:   str     =						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__				:   int     =						key.replace(lowerCamelCase_  ,						lowerCamelCase_     )

		if re.match(lowerCamelCase_  ,						lowerCamelCase_     ):
			snake_case__				:   int     =						int(re.match(lowerCamelCase_  ,						lowerCamelCase_     ).group(2     )     )
			if layer_nb == 0:
				snake_case__				:   Optional[Any]     =						key.replace("layers.0"  ,						"proj_in"     )
			elif layer_nb == 1:
				snake_case__				:   List[str]     =						key.replace("layers.1"  ,						"layers.0"     )
			elif layer_nb == 2:
				snake_case__				:   List[Any]     =						key.replace("layers.2"  ,						"proj_out"     )

		snake_case__				:   Any     =						value

	snake_case__				:   int     =						model_state_dict[
	    'prompt_encoder.shared_embedding.positional_embedding'
	]

	return model_state_dict







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[int]  ,						snake_case_      :							str  ,						snake_case_      :							Dict  ,						snake_case_      :							List[str]="ybelkada/segment-anything"     ):
	snake_case__				:   Dict     =						hf_hub_download(lowerCamelCase_  ,						F'''checkpoints/{model_name}.pth'''     )

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

		snake_case__				:   Tuple     =						SamConfig(
		    vision_config=lowerCamelCase_  ,						)
	elif "sam_vit_h" in model_name:
		snake_case__				:   Optional[Any]     =						SamVisionConfig(
		    hidden_size=1280  ,						num_hidden_layers=32  ,						num_attention_heads=16  ,						global_attn_indexes=[7, 15, 23, 31]  ,						)

		snake_case__				:   str     =						SamConfig(
		    vision_config=lowerCamelCase_  ,						)

	snake_case__				:   Optional[int]     =						torch.load(lowerCamelCase_  ,						map_location="cpu"     )
	snake_case__				:   Union[str, Any]     =						replace_keys(lowerCamelCase_     )

	snake_case__				:   int     =						SamImageProcessor()

	snake_case__				:   Dict     =						SamProcessor(image_processor=lowerCamelCase_     )
	snake_case__				:   Union[str, Any]     =						SamModel(lowerCamelCase_     )

	hf_model.load_state_dict(lowerCamelCase_     )
	snake_case__				:   Dict     =						hf_model.to("cuda"     )

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

	snake_case__				:   Dict     =						[[[400, 650]]]
	snake_case__				:   List[str]     =						[[1]]

	snake_case__				:   Any     =						processor(images=np.array(lowerCamelCase_     )  ,						return_tensors="pt"     ).to("cuda"     )

	with torch.no_grad():
		snake_case__				:   Tuple     =						hf_model(**lowerCamelCase_     )
	snake_case__				:   str     =						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__				:   Union[str, Any]     =						processor(
		    images=np.array(lowerCamelCase_     )  ,						input_points=lowerCamelCase_  ,						input_labels=lowerCamelCase_  ,						return_tensors="pt"     ).to("cuda"     )

		with torch.no_grad():
			snake_case__				:   List[str]     =						hf_model(**lowerCamelCase_     )
		snake_case__				:   List[Any]     =						output.iou_scores.squeeze()

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

		snake_case__				:   Tuple     =						((75, 275, 1725, 850),)

		snake_case__				:   str     =						processor(images=np.array(lowerCamelCase_     )  ,						input_boxes=lowerCamelCase_  ,						return_tensors="pt"     ).to("cuda"     )

		with torch.no_grad():
			snake_case__				:   Tuple     =						hf_model(**lowerCamelCase_     )
		snake_case__				:   Union[str, Any]     =						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__				:   str     =						[[[400, 650], [800, 650]]]
		snake_case__				:   Optional[int]     =						[[1, 1]]

		snake_case__				:   Any     =						processor(
		    images=np.array(lowerCamelCase_     )  ,						input_points=lowerCamelCase_  ,						input_labels=lowerCamelCase_  ,						return_tensors="pt"     ).to("cuda"     )

		with torch.no_grad():
			snake_case__				:   List[str]     =						hf_model(**lowerCamelCase_     )
		snake_case__				:   Optional[int]     =						output.iou_scores.squeeze()

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


if __name__ == "__main__":
	__lowerCamelCase      :    Dict   		=   argparse.ArgumentParser()
	__lowerCamelCase      :    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""",
	)

	__lowerCamelCase      :    List[str]   		=   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_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase      :    Tuple   		=   {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Tuple   		=   [
	    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RobertaPreLayerNormForCausalLM""",
	    """RobertaPreLayerNormForMaskedLM""",
	    """RobertaPreLayerNormForMultipleChoice""",
	    """RobertaPreLayerNormForQuestionAnswering""",
	    """RobertaPreLayerNormForSequenceClassification""",
	    """RobertaPreLayerNormForTokenClassification""",
	    """RobertaPreLayerNormModel""",
	    """RobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Union[str, Any]   		=   [
	    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRobertaPreLayerNormForCausalLM""",
	    """TFRobertaPreLayerNormForMaskedLM""",
	    """TFRobertaPreLayerNormForMultipleChoice""",
	    """TFRobertaPreLayerNormForQuestionAnswering""",
	    """TFRobertaPreLayerNormForSequenceClassification""",
	    """TFRobertaPreLayerNormForTokenClassification""",
	    """TFRobertaPreLayerNormMainLayer""",
	    """TFRobertaPreLayerNormModel""",
	    """TFRobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    List[Any]   		=   [
	    """FlaxRobertaPreLayerNormForCausalLM""",
	    """FlaxRobertaPreLayerNormForMaskedLM""",
	    """FlaxRobertaPreLayerNormForMultipleChoice""",
	    """FlaxRobertaPreLayerNormForQuestionAnswering""",
	    """FlaxRobertaPreLayerNormForSequenceClassification""",
	    """FlaxRobertaPreLayerNormForTokenClassification""",
	    """FlaxRobertaPreLayerNormModel""",
	    """FlaxRobertaPreLayerNormPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase      :    Dict   		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



