



'''simple docstring'''





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,
)


snake_case__					:     Tuple    		=   {
    """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_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			{}
	state_dict.pop("pixel_mean"  ,	_lowerCamelCase   )
	state_dict.pop("pixel_std"  ,	_lowerCamelCase   )

	__lowercase		       =			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:
				__lowercase		       =			key.replace(_lowerCamelCase  ,	_lowerCamelCase   )

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

		__lowercase		       =			value

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

	return model_state_dict



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE="ybelkada/segment-anything"   ):
	__lowercase		       =			hf_hub_download(_lowerCamelCase  ,	F"""checkpoints/{model_name}.pth"""   )

	if "sam_vit_b" in model_name:
		__lowercase		       =			SamConfig()
	elif "sam_vit_l" in model_name:
		__lowercase		       =			SamVisionConfig(
		    hidden_size=1_0_2_4  ,	num_hidden_layers=2_4  ,	num_attention_heads=1_6  ,	global_attn_indexes=[5, 1_1, 1_7, 2_3]  ,	)

		__lowercase		       =			SamConfig(
		    vision_config=_lowerCamelCase  ,	)
	elif "sam_vit_h" in model_name:
		__lowercase		       =			SamVisionConfig(
		    hidden_size=1_2_8_0  ,	num_hidden_layers=3_2  ,	num_attention_heads=1_6  ,	global_attn_indexes=[7, 1_5, 2_3, 3_1]  ,	)

		__lowercase		       =			SamConfig(
		    vision_config=_lowerCamelCase  ,	)

	__lowercase		       =			torch.load(_lowerCamelCase  ,	map_location="cpu"   )
	__lowercase		       =			replace_keys(_lowerCamelCase   )

	__lowercase		       =			SamImageProcessor()

	__lowercase		       =			SamProcessor(image_processor=_lowerCamelCase   )
	__lowercase		       =			SamModel(_lowerCamelCase   )

	hf_model.load_state_dict(_lowerCamelCase   )
	__lowercase		       =			hf_model.to("cuda"   )

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

	__lowercase		       =			[[[4_0_0, 6_5_0]]]
	__lowercase		       =			[[1]]

	__lowercase		       =			processor(images=np.array(_lowerCamelCase   )  ,	return_tensors="pt"   ).to("cuda"   )

	with torch.no_grad():
		__lowercase		       =			hf_model(**_lowerCamelCase   )
	__lowercase		       =			output.iou_scores.squeeze()

	if model_name == "sam_vit_h_4b8939":
		assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

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

		with torch.no_grad():
			__lowercase		       =			hf_model(**_lowerCamelCase   )
		__lowercase		       =			output.iou_scores.squeeze()

		assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

		__lowercase		       =			((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

		__lowercase		       =			processor(images=np.array(_lowerCamelCase   )  ,	input_boxes=_lowerCamelCase  ,	return_tensors="pt"   ).to("cuda"   )

		with torch.no_grad():
			__lowercase		       =			hf_model(**_lowerCamelCase   )
		__lowercase		       =			output.iou_scores.squeeze()

		assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

		# Test with 2 points and 1 image.
		__lowercase		       =			[[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
		__lowercase		       =			[[1, 1]]

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

		with torch.no_grad():
			__lowercase		       =			hf_model(**_lowerCamelCase   )
		__lowercase		       =			output.iou_scores.squeeze()

		assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


if __name__ == "__main__":
	snake_case__					:     int    		=   argparse.ArgumentParser()
	snake_case__					:     List[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""",
	)

	snake_case__					:     Optional[int]    		=   parser.parse_args()

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








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__					:     List[str]    		=   {
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case__					:     Dict    		=   [
	    """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MgpstrModel""",
	    """MgpstrPreTrainedModel""",
	    """MgpstrForSceneTextRecognition""",
	]

if TYPE_CHECKING:
	from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
	from .processing_mgp_str import MgpstrProcessor
	from .tokenization_mgp_str import MgpstrTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mgp_str import (
		    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MgpstrForSceneTextRecognition,
		    MgpstrModel,
		    MgpstrPreTrainedModel,
		)
else:
	import sys

	snake_case__					:     Union[str, Any]    		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



