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)





"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. 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.
import re

from ..utils import cached_file


# docstyle-ignore
A		=						'\nHuman: <<task>>\n\nAssistant: '


A		=						'huggingface-tools/default-prompts'
A		=						{'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							str     ,		lowerCamelCase_:							Tuple="run" ):
						"""simple docstring"""






						if prompt_or_repo_id is None:
												snake_case   :			Any						    = DEFAULT_PROMPTS_REPO

						# prompt is considered a repo ID when it does not contain any kind of space
						if re.search("\\s"     ,		lowerCamelCase_ ) is not None:
												return prompt_or_repo_id

						snake_case   :			Optional[int]						    = cached_file(
						    lowerCamelCase_     ,		PROMPT_FILES[mode]     ,		repo_type="dataset"     ,		user_agent={"agent": agent_name} )
						with open(lowerCamelCase_     ,		"r"     ,		encoding="utf-8" ) as f:
												return f.read()



